Changes in Vegetation Diversity and Plant Response in Nanda Devi Biosphere Reserve over the Last Two Decades

Thesis Submitted

to

Kumaun University, Nainital

For the degree of Doctor of Philosophy

In Botany (2013)

by

Balwant Rawat

G.B. Pant Institute of Himalayan Environment and Development Kosi-Katarmal, Almora-263 643 (Uttarakhand), India

DEDICATEDDEDICATEDDEDICATEDDEDICATED

TOTOTOTO

MOTHER NATURE

& MY BELOVED FAMILY

TABLE OF CONTENTS

Page Number

ACKNOWLEDGEMENTS……………………………………………………..... i

ABBREVIATIONS…………………………………………………………………. iii

Chapter 1: Introduction 1

1.1Background.………………………………………………………………….. 1

1.2 Objectives…………………………………………………………………….. 8

1.3 Importance of the work…..……………………………………………. 8

Chapter 2: Review of Literature 11

Chapter 3: Materials and Methods 21

3.1 Study area…………………………………………………………………… 21

3.2 Target sites…………………………………………………………………. 25

3.3 Vegetation sampling….…………...……………………………………. 26

3.4 Vegetation analysis……………………………………………………… 30

3.5 Statistical analysis…………………………..…………………………… 35

Chapter 4: Results 41

4.1 Compositional diversity….………………………………………….. 41

4.2 Integrity and Uniqueness of communities…..……………. 48

4.3 Demographic pattern and population structure……… 52

4.4 Vegetation ordination………………………….…………………….. 54

4.5 Clustering of communities……….…………………………………. 70

4.6 Altitudinal patterns of species richness and rate of change 75

4.7 Diversity in soil parameters………………………………………. 79

4.8 Temporal changes in vegetation………………………………… 81

4.9 Spatial pattern of Landuse/Landcover………………………. 102

4.10 Identification of rich and sensitive areas…………………. 107

Chapter 5: Discussion 111

5.1 Compositional diversity- current state 112

5.2 Current demographic profiles 114

5.3 Representativeness and uniqueness of the communities 116

5.4 Communities across environmental gradient 118

5.5 Patterns of species richness and rate of change 120

5.6 Temporal vegetation change 122

5.7 Evidences from Lu-Lc changes 124

5.8 Change sensitivity and rich areas 125

5.9 Change scenarios and alternative strategies 129

Summary 135

References 143

Appendices 174

List of Publications 177

i

ACKNOWLEDGEMENTS

In the journey of my life, stay at G.B. Pant Institute of Himalayan

Environment and Development (GBPIHED) forms a major milestone. This

Institute provided me the opportunity to explore hidden potential that was not

realized by me earlier. The appreciation goes to various members of Institute

for their contribution in making this work a reality.

My profound gratitude goes to my supervisors, Dr. R.S. Rawal at

GBPIHED and Dr. Lalit Tiwari at Kumaun University, Nainital for their

guidance and inspirations to accomplish this task. I would like to extend

special thanks to Dr. R.S. Rawal for his critical scientific suggestions that

helped me to go beyond traditional thinking and write this work in a way to

propagate a new ecological understanding of regional forests. All through my

stay in G.B. Pant Institute he remained supportive and encouraged for doing

better.

My sincere thanks are due to Dr. L.M.S. Palni, Director, G.B. Pant

Institute of Himalayan Environment and Development (GBPIHED), Kosi-

Katarmal, Almora, for encouragement, constructive suggestions and

extending all available facilities. Dr. S.K. Nandi, Group Head (BCM and BTA

Group), Dr. Anita Pandey, Dr. R.C. Sundriyal, Dr. I.D. Bhatt and Dr. K.

Chandra Sekar at GBPIHED are gratefully acknowledged for their

encouragement and moral support. I wish to thank Dr. Subrat Sharma for

extending help in RS & GIS analysis.

The prevailing vibrant academic environment at BCM thematic group,

as created by my seniors Drs Sanjay Gairola, Arvind Bhatt, Saumikant Joshi,

Gayatri Mahar, Kailash Gaira, Harish Andola, Vandana Rawat and Kishor

Kothari, and subsequently maintained by my colleagues especially Mr. Arun

Jugran, Mr. Sandeep Rawat, Mr. Lalit Giri, Mr. Avinash Sharma and Mrs.

Manju Pandey and my younger colleagues Mr. Tarun, Ms. Bhawana, Mr.

Praveen and Mr. Amit provided cheerful laboratory atmosphere which helped

me in may ways. “Thank You” all for being there with me.

The help rendered by finance, administration and library staff in the

Institute is sincerely acknowledged. Further, I owe sincere thanks to my hostel

inmates for continued moral support during this work.

ii

iii

ABBREVIATIONS

asl above sea level

BRs Biosphere Reserves

CBH Circumference at Breast Height

CCA Canonical Correspondence Analysis

CCS Community Change Index

CI Community Integrity

CII Community Importance Index

CRI Community Richness Index

CRPI Community Representativeness Index

CTI Community Threat Index

CUI Community Uniqueness Index

CVI Change Value Index

GAM Generalized Additive Model

GCPs Ground Control Points

GIS Geographical Information System

GLM Generalized Linear Model

GPS Geographical Positioning System

ha Hectare

IHR Indian Himalayan Region

IPCC Intergovernmental Panel on Climate Change

IUCN International Union for Conservation of Nature

IVI Important Value Index

km Kilometer

LCC Lambert Conformal Conic

LTP Lata-Tolma-Phagti

m Meter

MAB Man and Biosphere Reserve

MBRs Mountain Biosphere Reserves

MCT Main Central Thrust

NDBR Nanda Devi Biosphere Reserve

NDVI Normalized Difference Vegetation Index

NPs National Parks

NRSC National Remote Sensing Centre

PAs Protected Areas

PSK Pindari-Sunderdhunga-Kafni

r Correlation Coefficient

r2 Correlation Determination

RD Relative Density

RF Relative Frequency

RS Remote Sensing

RTBA Relative Total Basal Area

SOI Survey of India

TBA Total Basal Area

UNESCO United Nations Educational, Scientific and Cultural

Organization

WNBR World Network of Biosphere Reserve

iv

1

CHAPTERCHAPTERCHAPTERCHAPTER 1111

GENERAL INTRODUCTION

1.1 BACKGROUND

The extent and diversity of Himalayan forests is well known (Singh and Singh, 1987;

1992) and evidences indicate these forests significantly differ from both tropical and

temperate forests of the world (Zobel and Singh, 1997). Further, the intensive

ecological researches on Himalayan forests have highlighted the significance of

available voluminous data sets at the global scale (Rawal et al., 2003). A few studies

have also indicated the response intensity of these forests towards human induced

changes, i.e., anthropogenic disturbances (Singh, 1998; Silori, 2001; Khera et al.,

2001; Rawal et al., 2012). In these forests, increasing level of disturbance has been

reported to adversely affect various structural and compositional features. Recent

investigation by Rawal et al. (2012) on representative oak forests of the region has

reported differences in sensitivity of diverse oak forests towards disturbance

intensities. Another study (Gairola et al., 2009) from sub alpine forests of west

Himalaya has provided evidences of changing patterns of litterfall and nutrient

cycling across levels of disturbances.

In spite of these realizations, understanding on the relationships between

disturbance and vegetation patterns and processes, which form an important basis for

theories predicting the status of species diversity and population dynamics in plant

communities (Connell, 1978; Clark, 1991), is least developed for Himalayan forests.

These forests have largely remained unexplored for understanding trends and intensity

of impacts of ongoing changes under continued anthropogenic pressure.

Moreover, in recent decades global climate change has emerged as a major

factor that is influencing the entire gamut of life on the earth (IPCC, 2007). Among

others, investigations on world forest vegetation have generated evidences of changes

in patterns and processes in response to changing climate (Notaro et al., 2012).

Evidences further indicate that the Global Warming can also modify abiotic

conditions that influence individual plants performances (Kari, 2005; Dai and Huang,

2006; Ise and Moorcroft, 2006; Fissore et al., 2008; Xu et al., 2010). Furthermore, it

2

has been established that climate change is driving latitudinal and altitudinal changes

in species distribution worldwide (Parmesan and Yohe, 2003; Rosenzweig et al.,

2007) that leads to new species assemblages (Wing et al., 2005; Williams and

Jackson, 2007). Alpine treeline and high altitude ecosystems and species exhibit

trends of upward migration. With the forecasted warming, this globally visible

phenomenon is expected to continue. As such, changes in global climate and

atmospheric composition are expected to have an impact on the natural and man-made

environment (IPCC, 2001). Climatically-determined ecotones, such as alpine treelines

(Körner, 1998; Körner and Paulsen, 2004) are considered particularly sensitive to

altered temperature regimes (Theurillat and Guisan, 2001). On a wider scale, effects

of such structural dynamics may be contradictory, as changes in high elevation forests

can directly provide feedback to global warming in different ways. While, increase in

growth as well as forest expansion (treeline advance) due to the warming would

enhance CO2 uptake (Körner, 2000), thereby acting as a negative feedback to the

atmospheric CO2 concentration and reducing global warming, replacement of

grasslands by evergreen conifers decreases albedo (reflectivity), especially in areas

with long snow-cover duration, and act as positive feedback (Foley et al., 1994, 2000;

Betts, 2000). In general, plant communities and species compositions are expected to

change (Keller et al., 2000, Pauli et al., 2001, Walther et al., 2005) both as an effect of

a possible climate change and altered competition followed by boundary shifts.

Unfortunately such evidences from Himalayan forests are not available to describe

their responses, particularly as a consequence of climate change.

1.1.1 Mountains as Change Sensitive Ecosystems

The mountains have been recognized amongst most change sensitive ecosystems of

the world. However, The structure and functioning of fragile mountain eco-systems is

threatened by an array of anthropogenic changes, ranging from land-use and land-

cover changes to over harvesting of natural resources, and increasing impacts of

climatic changes. Therefore, the cumulative impact of these two factors (i.e.,

anthropogenic and climate change) significantly alters the ability of mountain regions

to provide critical goods and services, both to mountain inhabitants and lowland

communities. Around twenty years ago in 1992, during Rio Earth Summit, Chapter 13

of Agenda 21, for the first time, brought mountains in global attention by way of

providing convincing evidences that global mountains were undergoing rapid

3

degradation. While significant advances in knowledge and awareness on global

change impacts in mountain regions have been achieved during the last two decades,

in several instances the detrimental trajectories in mountain environments are

continuing unabated. Realizing the above, mountains are now being considered

amongst the most fragile environments in the world (Diaz et al., 2003). The proven

sensitivity of mountain ecosystems to climate change and the ongoing anthropogenic

changes across the globe, make them ideal places for research towards understanding

impacts of such changes (Schaaf, 2009).

1.1.2 Himalaya as Vulnerable Ecosystem

Considering mountains as a special thrust, the Himalayan ranges being the youngest

and loftiest, represent a highly complex and diversified system both in terms of

biological and physical attributes. Their vulnerability towards natural and human

induced disturbances is well recognized. On account of richness, uniqueness and

change sensitivity of biological elements, the region has been recognized amongst the

34 global biodiversity hotspots (Anonymous, 2009). High degree of endemism in the

region implies occurrence of various critical habitats and ecoregions having global

importance (Samant et al., 1998a). The diversity of representative ecosystem elements

and their sensitivity to human and/or climate-induced perturbations, and more

importantly the socio-economic marginality and lack of livelihood opportunities,

makes the Himalayan Mountains an important candidate for immediate action with

respect to: (i) understanding its complexities, (ii) maintenance of its biological

diversity, and (iii) sustainable flow of benefits to human society within and outside

physical boundaries (Palni and Rawal., 2012).

The Indian Himalayan Region (IHR), with a geographical coverage of over

5.37 lakh Km2, constitutes a significantly large portion of the Himalayan Biodiversity

Hotspot. It covers 16.2 % of total geographical area of the country. The temporal and

spatial variations caused by diverse geological orogeny have resulted in marked

differences in its climate and physiography, thus contributing greatly to the richness

and representativeness of its biodiversity components at all levels (Anonymous,

2009). Vulnerability of IHR for various kinds of perturbations has been highlighted

(Singh et al., 2010; Anonymous, 2009).

4

1.1.3 Mountain Biosphere Reserves as Special Candidates

Towards addressing many complex but important issues of global changes in

mountain regions, several representative landscapes in mountains have been identified

as Biosphere Reserves (BRs). The significance of mountain regions within the

UNESCO’s World Network of Biosphere Reserves is clearly seen in terms that over

40% of total Biosphere Reserves of the world are situated in mountainous regions,

and these are widely distributed across forty countries in the world. The Mountain

Biosphere Reserves (MBRs) have particularly received global attention as candidate

areas to: (i) understand the response patterns, and (ii) implement multidisciplinary

approaches under changing environment. As such, various actions have been

contemplated to address the challenges of mountains ecosystems, and three agreed

upon aims of these actions include: (i) development of an integrative research strategy

for detecting signals of global environmental change, (ii) defining the impacts of these

changes on mountain regions as well as lowland areas, and (iii) facilitating the

development of sustainable resource management regimes for mountains (Price et al.,

2006). In this context, the MBRs that promote and demonstrate balanced relationship

between man and nature, and are considered ‘living laboratories’ for testing out and

demonstrating integrated management of land, water and biodiversity; have emerged

as global priority sites (UNESCO-MAB, 2004) and are being used as ‘early warning’

systems (http://www.Unesco.org/mab/exosyst/mountain/gcmbr/html).

The strong altitudinal gradients within MBRs have been recognized to provide

excellent opportunities to detect and analyze global change processes and

phenomenon from both socio-economic and scientific perspective (Korner, 2000b).

Among various MBRs, selected for case studies across the globe, the Nanda Devi

Biosphere Reserve (NDBR) in the Indian Himalayan Region (IHR) has been

identified as potential site from Asia-Pacific Region (Reasoner et al., 2003;

UNESCO-MAB, 2004). Realizing this importance of NDBR, present study has

considered this BR as the target study site.

In recognition of its uniqueness, NDBR has been included in World Network

of Biosphere Reserves (WNBR) by UNESCO since 2004. Also, the Nanda Devi and

the Valley of Flowers National Parks, forming core zone of the reserve, have been

inscribed on the World Heritage List by UNESCO under Natural Criteria vii and x.

The reserve (30005’- 31

002’N Latitude, 79

012’-80

019’E Longitude) is located in the

5

northern part of west Himalaya and includes parts of Chamoli district in Garhwal; and

Bageshwar and Pithoragarh districts in Kumaun region of the Uttarakhand State. It

falls in West Himalayan Biogeographic province of zone Himalaya. The Reserve has

a wide altitudinal range (1800-7817m asl) and covers an area of 6,407.03 km2.

Besides, the reserve represents unique landscapes with outstanding naturalness values

(Rawal and Rawat, 2012).

Since its inception (1988) the diverse ecosystems and their components in

NDBR have remained attraction of researches. The representative ecosystems and

their components in the reserve have shown evidences of change with time and space.

In particular, the variations and changes in plant communities have been reported

highly dependent on geographical, environmental and anthropogenic factors. Besides,

differences in soil parameters, fire intensity, over harvesting and other kind of

disturbances contribute to the variation in vegetation from one stand to other or even

within a community. Therefore, the reserve management, most often, looks for

authentic and precise information on structure and composition of vegetation so as to

address diverse issues of conservation and management at different levels ranging

from species and community to landscape level.

1.1.4 Forest Vegetation as Specific Target

Realizing the overarching values of forests and considering their depletion at

unprecedented rate, conservation of forests has emerged as the prime objective. As

such, it is globally accepted that the depletion of forests has many ecological, social

and economic consequences; one among these is loss of biodiversity (Jha et al., 2000).

Forests form the renewable natural resource on earth and occupy very unique position

among the various natural resources through supporting life on earth in several ways

and providing services that cannot be substituted by any other means.

The forests in NDBR not only form diverse representative ecosystems but also

are the home for many rare and endangered species. While the core zone of reserve

consists of 10% forests, the buffer zone has nearly 27% area under forests. These

frosts help in maintaining rich floral (angiosperms-699, gymnosperms-11,

pteredophytes-137, bryophytes-146, lichens-77 and fungi-128 spp) and faunal

(mammals-29, birds-243, insects-229, molluscs-14, amphibian-8, annelids-6, reptiles-

3 and pisces-1) diversity in the reserve (Rawal and Rawat, 2012).

6

Among other aspects of investigation, study of natural recruitment of forests

holds great significance. In fact demographic profiles, including recruitment patterns,

act as indicators to know the past, the present and the future of any forest community.

The diversity and abundance of tree species at recruitment stage provides a broader

idea about the future composition of forests. Further, this life stage of tree species

being highly sensitive to the biotic and abiotic factors, understanding of recruitment

dynamics becomes an essential component of forest studies. Such kinds of studies, in

particular, assume greater importance in mountain forests where lack of adequate

regeneration is frequently reported as a major problem (Krauchi et al., 2000). Korner

(1999) reported that the most sensitive state in a plant’s life cycle at high altitudes is

the emergence and establishment of seedlings. This depends on various factors

impacting directly or indirectly. For example, seed germination depends strongly on

the quality and thickness of forest floor litter and quality of light (Shrestha, 2003).

Thick litter generally reduces the rate of germination and seedling establishment.

However, other studies (Tripathi and Khan, 1990; Dzwonko and Gawronski, 2002)

have reported herbaceous cover, rather than litter, has an even more adverse effect on

seedling emergence, survival and growth. Information on above aspects from high

altitude areas in the Himalaya is relatively meager (Rawal and Pangety, 1994a; Dhar

et al., 1997).

Very specifically, information regarding floristic diversity and forest structure

and composition from the region has been explored by various workers (Ghildyal,

1957; Rao, 1960, 1961; Shah, 1974; Pangety et al., 1982; Hajra, 1983; Balodi, 1993;

Samant, 1993, 1994, 1999; Samant et al., 2000; Joshi and Samant, 2004; Samant et

al., 2005, Gairola, 2005). The ecological exploration in the region began in late

eighties and included aspects of compositional patterns (Kalakoti et al., 1986;

Bankoti, 1990; Adhikari et al., 1991; Bankoti et al., 1992; Rawal et al., 1994b; Singh

et al., 1996; Rawal and Dhar, 1997; Rikhari et al., 1997; Kala, 1998; Prakash and

Uniyal, 1999; Rawat et al, 2001; Uniyal, 2001; Joshi, 2002; Joshi and Samant., 2004;

Kala, 2004, 2005; Samant et al., 2005), biomass production and nutrient cycling

(Adhikari, 1992; Garkoti, 1992, 1995, 1996, 1999; Garkoti and Singh, 1992, 1994,

1995, 1997, 1999), phonological aspects (Pangety et al, 1990; Bankoti, 1990; Rawal

et al., 1991) and studies on impacts of disturbance intensities on patterns and

processed of forests (Gairola, 2005; Gairola et al., 2009; Rawal et al., 2012).

7

1.1.5 RS & GIS as Monitoring Tools

As elsewhere in the world, as a complex response to several human induced and

natural changes in environment, forest biodiversity is changing at an unprecedented

rate in the Himalaya. This understanding calls for reliable information and data sets at

a range of spatial scales (Alexander and Millington, 2000). Therefore, optimization of

the use of effective monitoring methods is highly desired. Among others, the Remote

Sensing (RS) data sets, which provide quick and objectively available information for

decision-makers and managers (Howard, 1991; Millette et al, 1995), have emerged as

an important and timely technological tool (Semwal and Saradhi, 2004).

In India, the natural resource mapping and monitoring, using space

technology, has gained greater impetus with the launch of IRS-1A in 1988 followed

by IRS-1B, IRS-1C and IRS-1D, which helped in providing satellite images of the

entire country with enhanced capability to monitor and manage resources (Dutt et al.,

1994; Gopalan, 1998; Udaya and Dutt, 1998). Moreover, remote sensing technique

has also gained worldwide popularity in last few decades particularly w.r.t.

conservation and management planning at species and community level. Particularly

in mountain regions, due to the difficult access to most of these areas, remote sensing

often remains the only way to investigate large section of these mountains (Kaab,

2005). In case of NDBR, few studies have been conducted on mapping of NDBR

resources using RS technology (Sahai and Kimothi, 1994; Kimothi et al., 2002a;

Maikhuri et al., 2003; Nainwal et al., 2008; Bharti et al., 2011; Bali et al., 2011 a &

b). However, there is a clear gap of integration between the traditional field generated

data sets and the RS based spatial data. In this context, while considering global

literature, there are studies that include direct long-term observations and

measurements of changes (Hemond, 1983; Bell, 1997; Sykora et al., 2002; Bush and

Richter, 2006; Notaro et al., 2012; Rinella et al., 2012). However, this approach

requires a lot of efforts, manpower and time. Whereas, the repetitive satellite remote

sensing, over various spatial and temporal scales, offers economic means of assessing

the environmental parameters including forest cover, vegetation type and land use

changes. As such, this importance of remote sensing and GIS has been recognized in

the country for resource mapping (Lele et al., 1998; Amarnath et al., 2003; Goparaju

et al., 2005), change detection/ spatial analysis (Jha et al., 2000; Jayakumar et al.,

2002) and decision-making (Prasad, 1998; Prasad et al., 1998; Lakshmi et al., 1998).

As such, detection of changes through remote sensing has now a wide acceptability.

8

Comparison of time series vegetation maps is considered a useful tool for

analyzing vegetation changes (Kuchler, 1988; Zonneveld, 1988; Wittig and

Alberternst, 1999; Bernhardt-Romermann et al., 2007; Pancer-Koteja et al., 2009;

Waite et al., 2009; Farooq and Rashid, 2010; Li et al., 2010). In the context of NDBR,

a very few studies (Sahai and Kimothi, 1994; Bisht, 2000; Nautiyal et al., 2005; Bali

and Ali, 2010; Bali et al., 2011a; Bharti et al., 2011) have provided information on

changes in landuse/landcover.

1.2 OBJECTIVES

Realizing the importance and biodiversity representativeness of NDBR, and

recognizing that traditional field generated data sets and modern RS/GIS techniques

together can provide better understanding on changing patterns of forests, this study

was undertaken to address following objectives:

(i) Assessment of diversity in vegetation and other land use patterns in NDBR

using standard phytosociological approaches and application of RS/GIS.

(ii) Change detection (temporal/spatial) of vegetation at community and

species level (dominant/co-dominant).

(iii) Identification of sensitive/vulnerable areas and communities considering

patterns of natural recruitment.

(iv) Development of future scenarios and prediction maps to propose long-term

alternative management plans for NDBR.

1.3 IMPORTANCE OF THE WORK

Broadly, the study carries following major benefits:

• Comparison of two time data sets from representative watersheds of the reserve

was considered for depicting the actual ground level changes in diverse forest

communities and analyzing these patterns to predict long-term future scenario

of studied forest communities in the reserve.

• The approach, followed in the study, for developing alternative scenarios of

changes in vegetation diversity patterns is first of its kind in the Indian

Himalaya. As established through this study, the approach has replication value

for entire region and similar areas elsewhere.

9

• Synthesis of data sets generated under objective 1-4 has formed the basis for

generating prediction maps on GIS domain for NDBR. The focus was to make

predictions for: (i) future patterns of vegetation cover and land use; (ii) patterns

of dominant and co-dominants; (iii) distribution of sensitive elements

(threatened and endemic), in NDBR. Based on above following targets have

been achieved:

(a) The study has helped in understanding the vegetation cover and land use

change patterns in representative watersheds of Nanda Devi Biosphere

Reserve.

(b) Predictions of future changes have been made w.r.t. community diversity

along with likely consequences of such changes.

(c) Potential loss/changes in unique vegetation elements in Nanda Devi

Biosphere Reserve have been assessed.

(d) Various scenarios for addressing different conservation and management

goals have been suggested based on diverse compositional attributes.

(e) Integration of phytosociological data with landscape analysis has resulted in

identification of sensitive/vulnerable areas and communities so as to help in

developing appropriate planning for conservation in the reserves.

10

11

CHAPTERCHAPTERCHAPTERCHAPTER 2222

REVIEW OF LITERATURE

The Himalayan Biodiversity, due to its representativeness, richness and uniqueness,

has attracted several persons from different parts of the globe. Among others, it has

always remained a major source of attraction for taxonomists, naturalists and

ecologists largely on account of its uniqueness in complex biogeographic formations,

varied range of habitat types, and consequent diversity in biological assemblages and

ecosystem components. While considering the available literature on the subject, an

attempt has been made to review and analyse the work on different aspects of

biodiversity, especially the work on plant diversity that has appeared in scientific

publications during past few decades in Indian Himalayan Region (IHR). The review

broadly considers focusing on Himalayan BRs, vegetational studies,

landuse/landcover change assessment and sensitivity assessment, etc., as follows:

2.1 REVIEW ON HIMALAYAN BIOSPHERE RESERVES

The information under this category was mostly obtained from the bibliographic data

base on Himalayan BRs available with Lead Biosphere Reserve Center, GBPIHED,

Almora. The analysis of compiled information revealed that a total of 676

publications have appeared between 1990-2010 on Himalayan BRs, of which

considerably large number pertain to Nanda Devi Biosphere Reserve (283; 43%). The

detailed studies published in Himalayan BRs, in particular from NDBR, during last

two decades have been depicted (Box 2.1). While considering studies pertaining to

NDBR, it was revealing that maximum studies (34%) deal with floristic and

vegetation analysis, followed by management and development related studies (17%)

and faunal studies (11%). A considerably large proportion (18%) of studies in NDBR

falls in general category that describes various socio-cultural and bio-physical features

of the reserve.

12

The review of information on different aspects of biodiversity in terms of

vegetation composition, spatial distribution and sensitivity analysis, etc., can be the

The review of information on different aspects of biodiversity in terms of

vegetation composition, spatial distribution and senstivity analyasis, etc., can be

summarized as follows:

2.2 VEGETATION STUDIES

2.2.1 Global Information

Plant communities have been a major attraction for investigation since time

immemorial. However, the focus of studies changes following the most pressing

global needs. For instance, in recent decades, under the rapidly changing climate and

socio-economic scenarios, the focus of vegetation studies has shifted towards

analyzing impacts of such changes on vegetational composition and ecosystem

processes. The ecological investigations since early 20th

centaury till few decades

back targeted exploring ecological concepts and highlighting the importance of such

concepts across the world. These studies included concept of communities,

boundaries and ecotones (Clements, 1916; Shipley and Keddy, 1987, Wilson and

Agnew, 1992), population and soil structures and plant distribution (Braun-Blanquet,

1932; Westhoff and van der Maarel, 1978), vegetation and species patterns (Curtis,

1959; Muller-Dombois and Ellenberg, 1974; Whittaker, 1978), sampling of ecosystem

(Mueller-Dombois and Ellenberg, 1974; Allen and Hoekstra, 1992), characteristics of

vegetation stands (Westhoff and Maarel, 1978). While analyzing the abundance

a b

0

100

200

300

NDBR

MBR

KBR

DSBR

DDBR

CDBR

43%

17%

7%

6%

15%

12%

0

50

100

Floral

Faunal

Ethnobotanical

SocioeconomicGeophysical

Management &

Development

Miscellaneous

34%

11%

7%

6%5%

17%

18%

Box 2.1

Analysis of available publications across Himalayan Biosphere Reserves. a)

Proportion of information in different BRs; and b) Publications under different work

category in NDBR over the last two decades [CDBR- Cold Desert Biosphere Reserve, NDBR- Nanda Devi Biosphere Reserve, KBR- Khanchendzonga

Biosphere Reserve, MBR- Manas Biosphere Reserve, DSBR- Dibru Saikhowa Biosphere Reserve, DDBR-

Dehang Debang Biosphere Reserve]

13

distribution patterns of vegetation, Wilson et al. (1998) realized that the relative

abundance distributions are an important feature of community structure. Further,

Wilson (1999) discussed different types of assembly rule for plant communities.

Relevance of constraints in the representation of traits in relation to environmental

variation were highlighted by Weiher and Keddy (1999) and they suggested that traits

related to the availability of mineral resources, such as maximum biomass and leaf

shape, are more tightly constrained as soil fertility increases. Mechanisms of plant

community assembly were described by Grime (2001). He proved dominance-

diversity relations while dividing the participating species into dominant, subordinate

and transient categories, and taking into account the different plant functional types

that play a part.

In recent decades, most important ecological researches, that target field

investigations, attempted to answer a few widely accepted questions: How much

carbon dioxide do the plants take up from the atmosphere? Does the number of plant

species in a community affects productivity? Why do some plant communities, such

as tropical forests, have so many species, whereas others like salt water marshes and

mountain forests have so few? What effects are imposed by non-native species on

native plant communities? What is the impact of long-term changes on plant

communities? etc.

Some of these investigations include studies on vegetation responses towards:

climate change (Paster and Post, 1988; Payette et al., 1989; Lenihan et al., 2003;

Neilson et al., 2005; Parmesan, 2006); human interference (Ganeshaiah et al., 1998;

Guo, 2004; Moinde-Fockler et al., 2007) and environmental factors (Pysek, 1993;

Kneeshaw and Bergeron, 1996; Kleijn and Verbeek, 2000; He et al., 2007) and

studies on change assessment (Bell, 1997; Hemond et al., 1983; Sykora et al., 2002;

Bush and Richter, 2006; Notaro et al., 2012; Rinella et al., 2012).

2.2.2 Himalayan Information

Credit for initiating of the floristic studies in India and adjacent countries goes to

Thomas Hardwicke who collected plants for the first time from Northwest Himalaya

in 1798. This was followed by several workers. For example, David Don (1825)

developed a list of flowering plants and ferns from Nepal, Royle (1839-1840)

contributed on floristic knowledge of the Himalaya, Thomson (1851, 1852) studied

climate and vegetation, Sir J.D. Hooker (1872-1897) wrote Flora of British India, that

14

included Himalayan Plants, Strachey and Winterbottom (1882) prepared a Catalogue

of Plants of Kumaun and Adjacent Areas of Garhwal and Tibet. The first known

contribution on ecology of Himalayan vegetation, however, goes to Smith (1913) who

described the ecology of alpine and sub-alpine vegetation of Sikkim. Later, Kanoyer

(1921) explained the succession and forest communities in Kumaun Himalaya.

Osmaston (1922) continued with this by providing description of forest communities

of Garhwal Himalaya. Champion (1923) investigated the distribution of forest types

in relation to biotic factors in Kumaun Himalaya. Besides, Kashyap (1932), Gorrie

(1933), Mohan (1933), Schmid (1938), Puri and Gupta (1951), Mohan and Puri

(1955), Champion and Seth (1968), Mani (1974), Singh and Singh (1987, 1992)

explored and described various aspects of vegetation ecology in the region.

Series of publications during last two decades have provided information on

floristic diversity of the region (Bankoti et al., 1992; Rawal and Pangety, 1994b;

Joshi and Samant, 2004; Samant et al., 2005), conservation priorities (Uniyal et al.,

2002; Kala, 2005; Samant et al., 2005), habitat specificity (Dhar et al., 1996; Kala,

2004) and floristic integrity (Samant, 1998; Samant et al., 1998a and b; Joshi and

Samant, 2004), etc.

Various studies have considered structure and composition of forests and

alpine vegetation as major focus. For instance, in Pindari region (Clarke, 1979;

Kalakoti et al., 1986; Bankoti, 1990; Adhikari et al., 1991; Singh, 1991; Adhikari,

1992; Bankoti et al., 1992; Rawal and Pangtey, 1993, 1994a and 1994b; Rawal et al.,

1994; Singh et al., 1992, 1994, 1995, 1996; Garkoti and Singh, 1997; Rawal and

Dhar, 1997; Rikhari et al., 1997; Samant et al., 2000), and in Valley of Flowers (Kala,

1998; Kala et al., 1998; Prakash and Uniyal, 1999; Rawat et al., 1999, 2001; Samant

et al., 2001). Likewise, several other studies have targeted productivity and nutrient

cycling (Garkoti, 1992, 1995, 1996, 1999; Garkoti and Singh, 1992, 1994, 1995,

1997; Gairola, 2005; Gairola et al., 2009). Information pertaining to phenology is also

available from the region (Ram et al., 1989; Pangtey et al., 1990; Rawal et al., 1991;

Negi et al., 1992; Ram, 1992), floristic surveys (Joshi et al., 2000; Samant et al.,

2000; Rawat et al., 2001; Samant and Joshi., 2003; Kala, 2004; Kala et al., 2004;

Samant et al., 2005), etc.

15

2.2.3 Sensitivity Assessment

In more recent times, sensitivity of vegetation towards various kinds of factors is

being investigated across the globe. Among these factors, climate change sensitivity

has gained maximum attention. Studies in this regard have provided evidences

suggesting that many plant species have changed their related activities when spring

and autumn arrives in order to adapt to longer growing seasons caused by global

warming (Keeling et al., 1996). Also, vegetation activities have shown greater

amplitude during seasonal changes (Fang and Yu, 2002). The investigations, however,

focus more on reconstruction of tree-line vegetation response to long-term climate

change (Payette et al., 1989), response of forest vegetation to increased warming

(Paster and Post, 1988), effect of climate change on structural and functional

attributes of forests (Lenihan et al., 2003). Further, species distribution and upward

shifting on mountain slopes as a consequence of climate change has remained a major

area of investigation both at global and regional level (Parmesan and Yohe, 2003;

Neilson et al., 2005; Kelly and Goulden, 2008). Empirical evidences have also been

generated on declining trends of abundance that raises risk of extinction (Pounds et

al., 1999; Thomas et al., 2004), phenological change in relation to warming surface

temperature (Bradley et al., 1999; Fitter and Fitter, 2002; Root et al., 2003; Cleland et

al., 2006; Bowers, 2007; Miller-Rushing and Primack, 2008).

In addition, sensitivity of vegetation towards human and other kinds of

environmental factors have been considered for response studies (Ganeshaiah et al.,

1998; Guo, 2004; Moinde-Fockler et al., 2007). Among others, it has been reported

that settlement size is responsible for diversity of flora (Pysek, 1993), fire promotes

the regeneration of plant species (Kneeshaw and Bergeron, 1996), extraction of non-

timber forest products and important medicinal plants reduce the quantum of

vegetation (Ganeshaiah et al., 1998; Amri and Kisangau, 2012), prolonged human

disturbance results into slow recovery of perennial plants (Guo, 2004), elevation and

edaphic factors determine the vegetation composition (He et al., 2007). In the context

of Himalaya, studies are available that indicate sensitivity of vegetation towards

human induced factors, i.e., Kala and Shrivastava, 2004; Gairola, 2005; Kala, 2005;

Nautiyal et al., 2005; Khumbongmayum et al. 2006; Singh et al., 2008; Srivastava et

al., 2010; Gairola et al., 2008, 2009; Rawal et al., 2012).

16

2.3 LANDUSE/LANDCOVER ASSESSMENT

The studies that use RS/GIS as analysis tool, to cover various aspects of biodiversity

in relation to diverse environmental and human factors, have increased rapidly in

recent years. In this context, the recent studies on remote sensing have focused on:

investigating soil complexes through vegetation mapping (Azzali and Menenti, 2000),

global landcover classification (Hansen et al., 2000), importance of multi-temporal

satellite data in estimation of crop yield (Li et al., 2007), the use of satellite-sensed

vegetation index for investigating the patterns of global vegetation change and

assessment of variations in vegetation activity (Zhou et al., 2001; Li et al., 2010), and

changes in vegetation cover (Pancer-Koteja et al., 2009; Songer et al., 2009; Kim and

Daigle 2010). The forests, with diverse forms and utility, have always been

considered as prime natural resource in a landscape and their ecological and

biological significance is globally recognized. Information available through remote

sensing suggests the natural forests across the world are shrinking (Sharma and Roy,

2007), which is represented by an annual loss of 12 million ha (Braatz, 1997).

In the Himalaya, human population is settled across the range. Therefore,

degradation of Himalayan forests is a major environmental issue of global

significance (Singh et al., 1984; Ives and Messerli, 1990). In this context, the

landscape level attributes of forest distribution, especially in the conservation areas

like NDBR, using satellite remote sensing has assumed greater importance. Like

many other conservation sites in the region, NDBR is steadily being linked with

issues related with people’s concern on traditional knowledge, access to genetic

resources, sharing of benefit, policy conflicts and overall sustainable development

(Rawal and Dhar, 2001; Palni and Rawal, 2012). Landuse and landcover of the

reserve (i.e., NDBR) has been investigated (Sahai and Kimothi, 1994; Maikhuri et al.,

2002), along with, history of landmasses (Nainwal et al., 2007), priorities for

conservation (Negi et al., 1998), natural hazard management (Kimothi et al., 2002b)

and management strategies (Nautiyal and Kaechele, 2007), etc. A detailed

comparative analysis of satellite imagery of different time periods (Sahai and

Kimothi, 1994; Nautiyal et al., 2005) revealed that after notification of NDBR,

protection of the forest resources in the reserve has improved. A few studies using

remote sensing techniques have been carried out to determine spatial distribution and

health of forests and grasslands. Sahai and Kimothi (1994) suggested that

combination of remotely sensed data with ground based information would help in

17

planning the conservation measures. As such, the rate and intensity of landuse and

landcover change is very high as a result assessment of cause and consequences

would forms the first step towards developing a successful conservation and

management strategy (Brandt and Townsend, 2006). In this context, more recently,

Bharti et al. (2011) have assessed changes in timberline ecotones of Nanda Devi

National Park in the reserve.

Other studies, pertaining to various aspects of landuse and landcover, in the

reserve include Rastogi (1993), Sahai and Kimothi, (1994), Bali et al. (2011a), etc.

2.4 CHANGE ASSESSMENT

Under changing climate and socio-economic scenarios, the biodiversity of the world

is changing at an unprecedented rate and the Himalaya is no exception. In this

context, the long-term study of vegetation dynamics is of utmost importance.

However, realizing that the change in forest composition takes decades, very few

long-term direct studies of vegetation change are available across the world (Hemond

et al., 1983; Bell, 1997; Sykora et al., 2002; Bush and Richter, 2006; Notaro et al.,

2012; Rinella et al., 2012). Sykora et al. (2002) have shown that the environmental

factors are responsible for the changes in vegetation. Bush and Richter (2006)

exhibited that the changes in the density and basal area in forest stands was dependent

on age of the forest. Rinella et al. (2012) have laid emphasis on the long-term

persistence of seeded plants in invaded grasslands, while Notaro et al. (2012)

projected the future vegetation scenario in relation to climate change through different

kinds of ecological modeling.

The landuse dynamics and the patterns of landscape change, using RS and GIS

tools, were analysed in mountainous watershed of Nepal (Gautam et al., 2003).

Further, towards obtaining a greater understanding of global change and

corresponding vegetation responses, investigations on the spatial-temporal patterns of

vegetation change over time have gained momentum. In last few decades, changes in

vegetation are increasingly being assessed through RS/GIS techniques. A number of

research studies have explored the spatial correlation between NDVI and climate

variables (Kawabata and Yamaguchi, 2001; Sarkar and Kafatos, 2004; Nezlin et al.,

2005) but diverse vegetation types exhibit different responses to climate change (Piao

et al., 2003). Normally, vegetation activity is closely correlated to surface temperature

(Notaro, 2008). However, the relationship between vegetation growth and temperature

18

in low latitudes is relatively complex, where temperature does not inhibit vegetation

growth (Bajgiran et al., 2008). Precipitation and carbon dioxide concentrations also

affect vegetation growth (Nemani et al., 2002; Lotsch et al., 2003). Study also

suggested that there is a time lag for vegetation growth in response to precipitation

(Shigehara, 1991). This lag causes significant variation in regional vegetation’s

sensitivity to precipitation thus contributing to heterogeneity and complexity in the

response of vegetation growth to global change. In addition, the spatio-temporal

pattern of global vegetation growth is also influenced by large-scale regional climate

oscillations (Ottersen et al., 2001; Poveda and Salazar, 2004). For example, regions

with obvious NDVI dynamic change were found to have certain spatial connections to

regions where vegetation is sensitive to the impact of North Atlantic Oscillation (Li et

al., 2010).

Besides climate change, land cover change and land use conversion have been

projected to cause change in the local or sub-regional NDVI. For instance, the tropical

forest degradation caused by logging and burning leads to an obvious decreasing trend

in temporal NDVI (Souza et al., 2005). The logging in the mature forests of North

America also caused a NDVI decrease in the 1990s (Potter et al., 2005). Globally, the

rate of deforestation has accelerated from the 1980s onwards, especially in tropical

Asia and Latin America (Hansen and DeFries, 2004). It is reported that forestation

and regeneration could facilitate an increasing NDVI trend. For example, the - Grain

for Green� Program launched in 1999 resulted in a significant increase of the forest

cover in western China by planting trees and sowing grasses on steep slope

agricultural cropland (Chen et al., 2009; Zhou et al., 2009). Changes in vegetation

cover by way of integration in management policy have been investigated by Kim and

Daigle (2010). Likewise, the deforestation dynamics in protected areas were focused

recently (Pancer-Koteja et al., 2009; Songer et al., 2009).

A few attempts have also been made in Indian context. Jha et al. (2000)

explored the rate of deforestation and landuse changes in Western Ghats. Jayakumar

et al. (2002) defined the conservation strategies for the forests in Eastern Ghats. More

recently, Waite et al. (2009) predicted degradation in protected area through remotely

sensed change signals in Rajasthan. Panigrahy et al. (2010) have come up with forest

cover change detection of Western Ghats using RS based visual interpretation

technique. In the context of Himalaya, study by Sahai and Kimothi (1994) has

depicted changes in landuse and landcover in NDBR. Mountain watershed in Nepal

19

has been investigated for landuse dynamics and landscape change patterns (Gautam et

al., 2003). Prioritization for sustainable forest management in the Sikkim Himalaya

has been assessed recently (Tambe et al., 2011). Farooq and Rashid (2010) worked on

change analysis of forest density in Jammu and Kashmir. Temporal change in

landscape features over nearly two decades were analysed in representative

watersheds of west Himalaya (Tiwari and Khanduri, 2011). Similarly, study carried

out by Bharti et al. (2011) revealed changes in timberline vegetation of Nanda Devi

National Park. More recently, Munsi et al. (2012) have provided insight on spatio-

temporal change patterns of forest cover in Himalayan foothills. The, landscape

dynamics in western Himalaya has been assessed by Ramchandra et al. (2012).

Further, the changes in forest ecosystems in the Himalaya under climate change

scenario have been reviewed by Negi et al. (2012).

2.5 SENSITIVITY ASSESSMENT

Conservation of threatened bioresources and their representative habitats/ sources has

gained attention under global change scenario. At global level, attempts have been

made to identify sensitivity of biogeographic ranges and habitats using various

attribute such as rarity of species, population size and use values (Ayensu, 1981;

Rabinowitz, 1981; Beloussova and Denissova, 1981; Arevalo et al., 2005).

In the Indian Himalayan Region (IHR), including NDBR, attempts have been

made to identify threatened plants that ultimately help in identifying sensitive and rich

areas (Jain and Rao, 1983; Goel and Bhattacharya, 1983; Hajra, 1983; Pangtey and

Samant, 1988; Samant and Pangtey, 1993; Samant et al., 1993; 1996 a & b, 1998 a &

b, 2000, 2001; Samant, 1994, 1999; Rawal and Dhar, 1997; Kala et al., 1998; Rikhari

et al., 1998; Dhar, 2002; Kala, 2005; Khuroo et al., 2010; Khuroo et al., 2011; Giri,

2012). Among others, Rawal and Dhar (1997) used multiple attributes to define

floristic sensitivity of timberline vegetation in west Himalaya. Subsequently, Dhar

(2002) used diverse attributes to define sensitivity of endemic plants in the Himalaya.

Mahar et al. (2009), based on four different indices (i.e., species richness, weighted

endemism, 1-4 cell endemism and corrected weighted endemism) succeeded in

identification of potential areas for conservation and prioritization of endemic rich

areas in Indian Himalayan Region.

The review of literature, particularly pertaining to the IHR, indicates a clear

gap with respect to use of multiple time ground data for elaborating on the temporal

20

change patterns of vegetation composition. Also, there have been no attempts to use

multiple compositional integrity attributes to define various possible scenarios for

addressing conservation and management needs in the region in general and

conservation reserves in particular.

21

CHAPTERCHAPTERCHAPTERCHAPTER 3333

MATERIALS AND METHODS

3.1 STUDY AREA

3.1.1 Background

The Nanda Devi Biosphere Reserve (NDBR), which forms the extensive study area,

was designated as Biosphere Reserve by Government of India on 18th

January, 1988.

The reserve has a unique combination of diverse ecosystems including traditional

agro ecosystems, various types of temperate forests, alpine meadows, and glaciers,

etc. It represents the west Himalayan highland (2b) province of the biogeographic

zone-Himalaya and lies between 30006’ and 31

004’ North latitude and 79

013’ and

80017’ East longitudes (Figure 3.1), and covers a total of 6,407.03 km

2 (Core zone

712.12 km2; Buffer zone 5,148.57 km

2, Transition zone 546.34 km

2).

Two representative sites (i.e., Pindari-Sunderdhunga-Kafni: PSK in Kumaun

and Lata-Tolma-Phagti: LTP in Garhwal region) in the buffer zone of NDBR formed

the intensive study sites. The study mostly targeted revisiting the previously

investigated tracts/plots of these sites. Extensive surveys were conducted during

2008-2012 in these sites using the earlier reference points of studies (i.e., Bankoti,

1990 for PSK; Joshi, 2002 for LTP).

3.1.2 Geology and Climate

Geologically the study area falls within the Greater Himalaya or Himadri System in

the Zanskar range (Wadia, 1966; Joshi, 2002). The parent material includes crystalline

rocks that include garentiferous mica schists, garnet mica quartzite, mica quartzite,

etc, that are highly metamorphosed crystalline of Vaikrita group and lowest part of the

Tethys sediments (Valdiya, 1979; Yugi, 1979). The study area lies north of the Main

Central Thrust (MCT) and includes the high altitude zone with a large proportion of

land under perpetual snow (Wadia, 1931).

In general, NDBR represents an area of very high altitude peaks, rocky cliffs,

narrow precipitous valleys and massive glaciers. Considering, climatic parameters, the

22

year can be divided into three seasons, rainy (mid June- mid September), prolonged

winter (late September-April) and short summer (May-mid June). Average annual

rainfall in the reserve ranges from 1500-2000 mm, of which more than half occurs

during rainy season (Adhikari et al., 1991; Rawal and Pangtey, 1994a) suggesting a

strong monsoonic influence. September – November months are the driest of all. The

mean monthly temperature ranges between 1.20 C-31.7

0 C (Rikhari et al., 1997;

Badola, 1998; Kala et al., 1998).

The Rishiganga valley (core zone) and the areas north of it do no get much

rainfall due to a high rim of mountains in the south (Nanda Devi – Nanda Khat –

Mrigthuni – Trishul). The Rishi valley, the upper Dhauli valley, the Girthi valley and

the upper Goriganga valley are very dry and cold. These areas are more similar to

trans-Himalayan (i.e., Cold desert) conditions. The entire area of the reserve gets

snow during winters in varying quantities. The higher reaches (higher than 4500 m)

mostly remain snow bound all through the year.

3.1.3 Biodiversity

The wide altitudinal range of the reserve has contributed to existence of a number of

habitat types and consequent richness of biodiversity. Along the entire altitudinal

range, the reserve supports temperate, sub-alpine forest vegetation. The temperate

forests (2000-2800 m) are characterized by three physiognomic types, i.e., broadleaf

evergreen (e.g., Quercus floribunda, Q. semecarpifolia, etc.), mixed broadleaf

deciduous (e.g., Alnus nepalensis, Aesculus indica, Acer ceasium, A. cappadocicum,

Hippophae salicifolia) and Needle leaf evergreen conifer (e.g., Cedrus deodara, Pinus

wallichiana, Cupressus torulosa, Abies pindrow, etc.). The sub-alpine (2800-3900 m)

forests are characterized by dominance of deciduous broadleaf and evergreen

coniferous species, i.e., A. acuminatum, Betula utilis, Prunus cornuta, Abies

spectabilis, A. pindrow and Taxus baccata, etc. (Bankoti, 1990; Rawal and Pangtey,

1994b; Joshi, 2002, Gairola, 2005). However, broadleaf evergreen Q. semecarpifolia

aong with patches of Rhododendron spp., are also common at sub-alpine zone of the

reserve.

The reserve supports over 1000 plant species which include: angiosperms -699

species; gymnosperms -11, pteridophytes -137, bryophytes -148; lichens -77, and

fungi -128. The known faunal diversity includes mammals -29; birds -243; insects -

229; mulluscs -14; amphibians -8; annelids -6; reptiles -3, and pisces -1 (Joshi, 2002;

23

Rawal and Rawat, 2012). The reserve is a repository of a large number of plants and

animals of economic value. However, due to excessive exploitation and habitat

degradation, a number of plants and animal species now fall in threatened category.

Some plant rarities in the reserve include: Allium stracheyi, Aconitum balfori, A.

heterophyllum, Angelica glauca, Arnebia benthamii, Dactylorhiza hatagirea,

Hedychium spicatum, Paeonia emodi, Picrorhiza kurrooa, Podophyllum hexandrum,

Saussura costus, S. obvallata, Taxus baccata, etc. Notable faunal rarities in NDBR

are: Panthera uncia, Moschus chrysogaster, Selenarctos thibetanus, Pseudois nayaur,

Lophophorous impejanus, Pucrasia macrolopha, Tragopan melanocephalus,

Tetragallus himalyensis, Gyps bengalensis, Sarcogyps calvus, Gypaetus barbatus,

Catreus wallichii, etc. (Rawal and Rawat, 2012). Some of the floral and faunal rarities

in the reserve are presented (Plate 3.1 and 3.2).

3.1.4 Socio-cultural diversity

A total of 47 villages of indigenous communities fall within buffer zone of the

reserve. The inhabitants mainly belong to the Indo-Mongoloid (Bhotiya tribes) and

Indo-Aryans Groups. The transition zone is inhabited by over 55 villages. The most of

the settlement are permanent except the settlements located in Chamoli and

Pithoragarh districts, which are summer settlements. Inhabitants are dependent on the

reserve’s resources for use as medicine, food, fodder, fuel, timber, fiber, agricultural

tools, etc. The Bhotiya tribal community of the reserve; traditionally the trans-border

traders who traded in agro-products from Indian side and woolen and livestock from

Tibet prior to 1962, have more recently got involved in various activities such as eco-

restoration, cultivation of medicinal plants, apiculture, animal husbandry, ecotourism,

land stabilization, etc., have been implemented by Biosphere Reserve Authority

(Rawal and Rawat, 2012).

The native communities of the reserve are socially, culturally and emotionally

attached to the area. Religious and cultural significance of the reserve is very high.

Among others, two famous pilgrimage sites (e.g., the Hindu religious shrine –

Badrinath, and the Sikh religious shrine –Shri Hemkind Saheb) are the major

settlements with noticeable number of followers visiting the reserve, especially during

summer season.

24

Plate 3.2 Faunal rarities in NDBRa) Pseudois nayaur (Bharal); b) Moschus chrysogaster (Musk deer); c) Panthera uncia ( Snow leopard); d) Pucrasia macrocephala

(Koklas pheasant); e) Tragopan melanocephalus (Western tragopan); f) Gyps bengalensis (Slender-billed vulture).

a

d

b c

e f

Plate 3.1 Floral rarities in NDBRa) Saussurea obvallata (Brahmkamal); b) Hedychium spicatum (Van Haldi); c) Picrorhiza kurooa (Kutki); d) Arnebia

benthamii (Baalchhadi); e) Dctylorhiza hatagirea (Hathhajadi); f) Aconitum heterophyllum (Atis).

a

d

b c

e f

[Representative faunal photographs not necessarily from NDBR]

25

3.1.5 Conservation Significance

The identified core zones (Nanda Deiv and Valley of Flowers NPs) of the reserve

represent unique landscapes with outstanding naturalness value. The Nanda Devi

National Park (NDNP), the catchment of basin of the Rishi Ganga, an Eastern

tributary of the Dhauli Ganga which feeds as a major tributary of the Ganges –the

Alaknanda, represents a vast glacial basin. The basin displays rugged topography and

is one amongst the deepest gorges of the world. The Valley of Flower National Park

(VoFNP) has been acknowledged by renowned mountaineers and botanists over the

century for its exquisite floral diversity and natural beauty and exclusively has been

designated for conservation of Himalayan flora. Realizing these values, both of the

NPs have been inscribed in the list of World Heritage Sites (e.g., NDNP in 1988;

VoFNP in 2005). Ecological integrity and authenticity of the core areas has been

maintained thoroughly ever since they have been declared as National Parks. The

reserve, besides fulfilling the livelihood needs of indigenous communities, is as

important for its unparallel ecological services. For example, the glaciers in the

reserve contribute significantly to the mighty Ganges River.

As indicated above, NDBR in the Indian Himalaya sets a case of becoming a

potential mountain Biosphere Reserve to fulfill all the functions as conceptualized for

BRs. While representing a classical case for absolute conservation of core zone, the

participatory eco-development activities in the buffer zone have resulted into

increased co-operation between inhabitants and management. Unique bio-physical

values of the reserve and its sensitivity towards changing climate and human

interventions, however, call for improved attention form different stakeholders.

3.2 TARGET SITES

As indicated, the identified intensive study sites cover Pinder valley in Kumaun (i.e.,

Pindari - Sunderdhunga - Kafni site – PSK) and Dhauliganga valley in Garhwal

regions (i.e., Lata – Tolma – Phagti site – LTP) of West Himalaya. Three altitudinal

belt transects were considered along river valley in each site, i.e., Pindari (Khati to

Phurkia, 2200-3300 m), Kafni (Dwali to Khatiya, 2700-3300 m) and Sundardhunga

(Retung to Kathliya, 2050-3350 m) in PSK site and Lata (Lata village to Lata Kharak,

2350-3900 m), Tolma (Suraithoda to Jhandidhar, 2400-3850 m), Phagti (Pagrasu to

Dhunadhar, 2350-3900 m) in LTP site. The PSK site has been earlier investigated

during 1988-90 (Bankoti, 1990), and LTP site was explored during 2000-2002 (Joshi,

26

2002). While study stands in PSK site were identified in 11 representative forest

communities, for LTP site 8 major forest communities were considered for locating

study stands (Box 3.1). Alnus nepalensis (Alder), Mixed Oak deciduous, Hippophae

salicifolia (Chuck), Quercus floribunda (Telonj-Oak), Q. semecarpifolia (Kharsu-

Oak), Mixed deciduous, Mixed Silver fir-Oak, Mixed Silver fir-Rhododendron-

Maple, Abies pindrow (Silver fir), Mixed Birch-Silver fir and Betula utilis (Birch)

were representative communities in PSK site and Cedrus deodara (Deodar), Mixed

Juglans regia-Prunus cornuta, Mixed Acer caesium-Prunus cornuta, Pinus

wallichiana (Kelu), A. spectabilis (Fir), Mixed Taxus baccata-Abies pindrow, A.

pindrow (Silver fir) and B. utilis (Birch) in LTP site.

3.3 VEGETATION SAMPLING

Extensive field surveys were conducted during May 2008 to August 2011. In general,

months of July and August (rainy season) were used for collection of data on

vegetation so as to ensure maximum coverage of herbaceous species. Further, winter

(November-December) and summer (May-June) seasons were included for collection

of data sets on regeneration from the permanent plots. These plots/communities were

revisited seasonally for two years to see the patterns of regeneration seasonally and

spatially.

27

PINDARI-SUNDERDHUNGA-KAFNI (PSK) LATA-TOLMA-PHAGTI (LTP)

Figure 3.1 Location map of study area –NDBR and target sites

28

Box 3.1

Representative forest communities in target sites; a) Pindari-Sunderdhunga-Kafni (PSK) site,

and b) Lata-Tolma-Phagti (LTP) site

a

b

Communities Code Stands

(1 ha)

Altitude

(m)

Betula utilis BU 023343

Mixed Birch- Silver fir MBA02

3238

Abies pindrow AP 02 2970

Mixed Silver fir-

Rhododendron-Maple

MARM03 2860

Mixed Silver fir-Oak MAO 022855

Mixed deciduous MD 032733

Quercus semecarpifolia QS 04 2669

Hippophae salicifolia HS 032504

Quercus floribunda QF 04 2452

Mixed-Oak deciduous MOD 03 2217

Alnus nepalensis AN 022025

29

3.3.1 Sampling and Data Collection

A reconnaissance survey of the study sites was carried out during March to May

2008. Considering the available information (Bankoti, 1990; Joshi, 2002) on altitude,

aspects, slopes and forest composition, 32 forest stands (plots) in PSK site and 30

stands in LTP site were identified. Attempt was made to reach maximum possibly

approachable stands. Specific details of locations (altitude, latitude and longitude)

were recorded using hand-held Global Positioning System [GPS (Garmin make-12)].

Details of representative altitude transects in target site are given under (3.2 Target

site) and distribution of stands across forest communities is included (Box 3.1).

Towards maintaining the compatibility of data sets with earlier studies, the approach

and methodology followed in present study was kept similar with the approach of

previous studies (Bankoti, 1990 for PSK; Joshi, 2002 for LTP). The major

components of sampling were as follows.

PSK site: In each identified forest stand, ten (10x10 m) quadrats were laid randomly

for investigation of tree and saplings. For enumeration of shrubs and seedlings, within

each 10x10 m quadrat, five (2x2 m) sub quadrats and for herbs ten (1x1) sub-quadrats

were laid randomly (Bankoti, 1990).

LTP site: Tree, sapling and seedlings were enumerated in randomly placed ten (10x10

m) quadrats. Whereas, enumeration considered ten (5x5 m) quadrats for shrubs and

twenty (1x1 m) quadrats for herbs in each stand (Joshi, 2002).

Standard phytosociological methods were followed to obtain quadrat data

(Grieg-Smith, 1957; Misra, 1968; Kershaw, 1973; Muller-Dombois and Ellenberg,

1974; Dhar et al., 1997). In general, from each quadrat circumference at breast height

(CBH at 1.37 m from the ground) of all tree individual was recorded. Based on this

information, individuals were considered as tree >31cm; sapling 11-30 cm; seedling

<11 cm CBH. Shrubs were considered as the woody species having branching from

the base (Saxena and Singh, 1982). Among herbs, angiosperms and pteridophytes

were enumerated. In case of clumped shrub and herb species, each individual of the

clump was counted as 1 individual.

30

3.3.2 Taxonomic Identity, Nativity and Endemism

The plant specimens were collected and preserved following the standard herbarium

methods (Jain and Rao, 1977). Herbarium specimens were identified with the help of

regional and national flora (Hooker, 1872-1897; Bor, 1960; Naithani, 1984; Deva and

Naithani, 1986; Sharma et al., 1993; Sharma and Balakrishnan, 1993; Sharma and

Sanjappa, 1993; Hajra et al., 1995; Hajra and Balodi 1995; Kumar and Panigrahi,

1995; Hajra et al., 1997; Gaur, 1999), some monograph/revision studies (Mukherjee

and Constance, 1993; Dikshit and Panigrahi, 1998) and checklists (Uniyal et al.,

2007) were also used. Other specimens, which could not be identified, were matched

with regional herbarium at Botany Department, Kumaun and Garhwal University, for

proper identification.

The nativity of the species was assigned following Anonymous (1833-1970),

Samant et al. (1998a), Samant (1999), Samant et al. (2000). Broadly, the species

having their origin in Himalayan region and distribution in the region and neighboring

countries/states were considered as natives. Endemism of the species was determined

based on the extent of geographical distribution (Dhar and Samant, 1993; Dhar et al.,

1996; Samant et al., 1996 a, 1998 a, 2000; Samant and Dhar, 1997; and Dhar, 2002).

The species restricted to Indian Himalayan Region (IHR) were considered as endemic

whereas those with feebly extended distribution in neighboring countries were

considered as near endemics (Dhar and Samant, 1993).

3.4 VEGETATION ANALYSIS

3.4.1 Community Structure and Demographic Patterns

The quadrat information was pooled for calculating density, frequency, total basal area

and their relative values (Misra, 1968; Muller-Dombois and Ellenberg, 1974). The sum

of relative values of density (RD), frequency (RF) and total basal area (RTBA) was

considered as Important Value Index - IVI (Curtis, 1959). Following, Whittaker (1975)

and Pielou (1975) species richness was considered simply as the number of species per

unit area. Species diversity index was computed using Shannon-Wiener information

function (Shannon and Weiner, 1963). Canopy cover was determined following

Johnson (1990). The density distribution (d-d) in tree size (CBH) classes was employed

to develop the population structures of tree species. The individuals of tree species

were grouped into six arbitrary CBH classes (A: <10; B: 11-30; C: 31-60; D: 61-120;

E: >120 cm). The total number of individuals, belonging to each of the above classes,

31

Density

Relative Density (RD)

Relative Frequency (RF)

Frequency

Mean Basal Area

Total Basal Area (TBA)

Relative Total Basal Area

(RTBA)

Importance Value Index

(IVI)

Diversity Index

Total number of individuals of a species in all the quadrats

Total number of quadrats studied

Total number of individuals of a species in all the quadrats

Total number of individuals of all the species in all the quadrats X 100

Total number of quadrats in which the species occurs

Total number of quadrats studied X 100

Number of occurrence of the species

Number of occurrence of all the species X 100

C2 / 4∏

[C = mean circumference (at 1.37 m) of species

Total Basal Area of the species

Total Basal Area of all the species X 100

Mean Basal Area of species x Density of the species

RF + RD + RTBA

H’ = ∑(Ni/N) log2 (Ni/N)

Ni = The total density value for species i, and N is the

total density value for all the species in a stand

Box 3.2

Summary of the formulae used for calculation of different ecological parameters

:

:

:

:

:

:

:

:

was calculated for each species in individual stand and stand information was pooled to

represent community. Size class A and B represented seedlings and saplings,

respectively. Other classes (C-E) represented tree classes. Relatively density of species

in a particular size class was calculated as a percentage of total number of individuals

in all size classes. Box 3.2 summarizes the formula used for calculations of diverse

ecological parameters.

3.4.2 Soil Sampling and Analysis

As for the vegetation analysis, the methods adopted for soil sampling followed earlier

works of Bankoti (1990) and Joshi (2002) for PSK and LTP sites respectively. From

each stand, five samples, preferably one from centre and four from corners were

32

collected. Twenty centimeter depth was used to core up soil and mixed to make a

composite sample. The homogenized composite soil samples were brought to the

laboratory in air-tight polythene bags. The fresh soil was used for pH measurement.

Rest of samples were shade dried and passed through 2 mm sieve. Dried samples

were analysed for organic carbon, organic matter and total nitrogen.

Soil pH was determined by pH meter (LI-127, ELICO) and organic carbon

and organic matter by rapid titration method (Walkley and Black, 1934). Total

nitrogen was calculated by sample digestion following Kjeldhal technique

(Novosamsky et al., 1983) and distillation in Kjeltec unit (Teator, Kjeltec Auto 1030

analyser).

3.4.3 Spatial Database

Distribution maps of different forest communities at different altitudinal zones were

developed using Remote Sensing (RS) and Geographical Information System (GIS).

The main focus was to highlight the major physiognomic types present in the study

area. Satellite data was used for delineation of forest cover, and topo-maps were used

for topographic features. The digital data of Landsat TM with spatial resolution of 28

m was acquired from National Remote Sensing Centre (NRSC) and

www.landcover.org.T (free image downloader). The topo maps (1:50,000) of the

study area were procured form Survey of India (SOI) (Topo sheet numbers; 53N-

01,05,06,09,10,11,13,14,15,16 and 62B-01,02,03,04,06,07).

3.4.3.1 Satellite image selection and pre-processing

Landsat TM-1990 (path 145-row 39) and Landsat TM-2005 (path 145-row 39)

standard false colour composite imagery were procured for the month of November;

years 1990 and 2005. The datasets could not be procured for year 2010 due to

unavailability of clear images for the same season. The November imagery was

chosen because of lowest azimuth, sun angle, and cloud so that the shadow influence

from landform was efficiently avoided.

3.4.3.2 Software’s used

ERDAS software version 9.1 (Earth Resource data Analysis System Inc Atlanta, Ga)

available with GIS laboratory of the Institute, was used for digital image processing,

georefrencing and digital classification of satellite image. Arc View 3.2 (ESRI, 1996)

33

was used for plotting GPS points on the image. Whereas, Arc GIS 9.2 (ISRI, 1999)

was used for spatial analysis.

3.4.3.3 Radiometric Corrections

Unwanted artifacts like additive effects due to atmospheric scattering were removed

through set of pre- processing or cleaning up routines. First-order radiometric

corrections were applied using dark pixel subtraction technique (Lillesand and Kiefer,

1994). This technique assumes that there is a high probability that at least a few pixels

within an image should be black (0% reflectance). However, because of atmospheric

scattering, the imaging system records a non-zero Digital Number (DN) value at the

supposedly dark- shadowed pixel location. Therefore, the DN value was subtracted

from the data to remove the first- order scattering component.

3.4.3.4 Geometric Corrections

Images were registered geometrically and uniformly distributed Ground Control

Points (GCPs) were marked with root mean square error of less than one pixel. The

image was resampled by nearest neighborhood method. All the scenes were mosaiced

and the study area was extracted using boundary map. The Ortho-rectified Satellite

data (Landsat TM) with UTM Projection were re-projected in to Lambert Conformal

Conic (LCC) Projection and used as a master. Each image was independently geo-

rectified using geo-referenced and Ortho-rectified cloud-free Landsat TM imagery

(path 145 – row 39), obtained from the Global Land cover Facility

(www.landcover.org).

3.4.3.5 Image Interpretation and classification accuracy

Visual interpretation scheme was used for extraction of land cover and forest

vegetation classes. The land cover features were visually digitized using ERDAS

software. The accuracy of landcover and vegetation maps was evaluated by redundant

training areas and field sample points based on the commission and omission error

matrix and hence over all classification accuracy was calculated.

34

3.4.4 Ground Truthing

Field surveys conducted during April 2008 to December 2011 provided the fair idea

of broad vegetation types in the study area. Information was collected in the same

season as the remotely sensed data, so as to achieve best possible matching of

information. Various observations were noted on vegetation and physiognomy and

strata. Further information was also acquired from local forest officials, and other

relevant departments and other stakeholders by way of interaction/formal interviews,

regarding previous/past status of forests, fauna and its distribution, land use/land

cover.

3.4.5 Change Detection

On the ground changes were obtained through comparison of two time (i.e., previous

and present) data sets for both the target sites. Comparison of two time datasets were

recorded in tabular form for the density, IVI and species diversity, and also presented

in graphical form. Table information was analysed for species (i) not recorded in

present investigations and assumed as locally disappeared, and (ii) not recorded in

previous investigation and reported as new arrivals. Also, specific attention was paid

for those which have decreased or remarkably increased in current study. The changes

in dominations of species were also assessed and represented through bar diagrams.

However, on a GIS domain, the changes were detected using the following approach.

3.4.5.1 Post Classification

Thirteen land cover classes were determined for each image: Glacier, Snow and

Moraine, Alpine meadow, Alpine scrub, Open rocks, Conifer dominated forest,

Evergreen broadleaf forest, Mixed forest, Forest blanks, River, High altitude lake,

Low land grassland and Cropland. Comparison of maps for landcover changes

followed post classification technique and the identity of vegetation unit at each point

for the year 1990 and 2005 was determined. Statistical measures, describing the

landscape configuration and composition, were used for quantification of landscape

structure and its changes. Towards achieving better clarity in comparison, forest

communities were pooled into bigger units of physiognomy. The forest cover density

maps, thus generated, were subjected to geospatial change analysis to synthesize the

resultant change maps depicting the changes in forest cover over a period of 15 years

from 1990 to 2005.

35

Box 3.3

Paradigm for change detection and assessment of rich and sensitive areas

LANDSAT-TM

2005

LANDSAT-TM

1990

Radiometric and Geometric

Corrections of Datasets

Toposheet

Mosaicing

Mosaice FCC

generation

Subset generation

On-screen visual

interpretation

Forest/community

type map

Res

erve

bou

ndar

y

Bas

e m

ap

Buffer generation

Maps of sensitive

forest physiognomy

Field Survey

Environment Vegetation

ClassificationClimate

Soil

Ordination

CCA

Model generation

Regression

Analysis

Constraints

Community types

Field check and

correction

Land cover maps

1990 and 2005

Accuracy

assessmentChange detection

Canopy Map 1990

and 2005

3.4.5.2 Prediction maps

The whole approach using RS/GIS application in the study for change detection and

identification of sensitive areas in the reserve has been depicted (Box 3.3).

The lower resolution of imagery did not allow to reach the individually marked forest

stand. However, to overcome this problem and to highlight sensitive and rich areas

broad physiognomic types have been used (see 3.3.5.1).

3.5 STATISTICAL ANALYSIS

3.5.1 Simple correlation

Statistical analysis (t-test and Correlation coefficient (r) and coefficient determination

(r2) were calculated using SPSS version 16 to determine the relationship between

different phytosociological parameters and environmental factors.

36

3.5.2 Canonical Correspondence Analysis (CCA)

The interrelationship between environmental variables and species distribution was

determined using the computer package Canonical Correspondence Analysis

(CANOCO; version 3.12, ter Braak 1991). This is an ordination technique that

correlates the species directly with environmental variables (ter Braak, 1986, 1987).

This technique works on two sets of variables; dependent variables (i.e., species

abundance, species richness, density, IVI, etc.) which are assessed in relation to

independent variables, i.e., environmental parameters (physical and chemical). The

analysis used 5 environmental variables [i.e., altitude (ALT), soil pH, soil total

organic carbon (OC), soil total nitrogen (N), slope of the stands (SLP)] and, 11 co-

variables [i.e., total canopy cover % (CNP), disturbance in terms of grazing/lopping %

(DST), total basal area of each stand (TBA), species density and richness in seedling

layer (DS, RS), species density and richness in sapling layer (DSP, RSP), species

density and richness in trees layer (DT, RT), species density and richness in herbs

layer (HB), species density and richness in shrubs layer (SH), species density and

richness of native species (NAT), species density and richness of non native species

(NN) and specie density and richness of endemic species (E)].

3.5.3 Generalized Additive Model (GAM)

GAM was applied to estimate the rate of changes in species richness and species

density. GAM has been observed appropriate for handling the: (i) complexity in data

structure, (ii) non- experimental data, and (iii) wide range of sampling data (Gaira et

al., 2011). Because, it is specified as a sum of smooth function of predictor variables

(Guisan et al., 2002) and suitable in two major respects: (i) the distribution of the

response variable can be (explicitly) non-normal, and (ii) the response variable values

are predicted from a linear combination of predictor variables, which are connected to

the response variable via a link functions.

To illustrate, in the general linear model (GLM) a response variable Y is linearly

associated with values on the X explanatory variables (e.g. year, elevation and

temperature) while the relationship on the GLM is assumed to be as:

( )mm XbXbbgY +++= .......110

37

Where g ( ) is a function and gi(.) is called the link function, so that:

( ) mmi XbXbbYg +++= .......110

Where, Y stands for the expected value of response variable. However, the

notion of additive models with GLMs (Generalized Linear Models) is derived as

GAMs

( ) ( )iiii XfSYg = .

Generally, GAMs allow for choosing a wide variety of distribution for the

response variable and linking functions to improve the effective quality of the

prediction. The GAMs fit model considered the estimation of the smoothing terms in

the additive model, general algorithm added in the model using any regression-type

smoothens as partial residuals (i.e., Rjth

set of partial residuals)

( )k

jh

kji XSSYR ∑≡≠

−−= 0

The partial residuals remove the effects of all the other variables from Y, therefore, Y

can be used to model the effects against Xj. Such foundation algorithm provides a way

for estimating each smoothing function Sj (.) given estimates ( )

≠= jiS i ,.^

for all.

3.6 SENSITIVITY ASSESSMENT AND IDENTIFICATION OF RICHN

AREAS

3.6.1 Change Sensitivity

The Community Change Sensitivity (CCS) of representative forest communities in

target sites (i.e., PSK and LRP) was analysed considering the changes in

compositional features that have occurred between two time intervals (i.e., 1988-1990

and 2008-2010 for PSK; 2000-2002 and 2008-2010 for LTP). Various compositional

features, such as, species richness, density, diversity, and TBA, were considered for

analysis. CCS was calculated as follows: ‘

(i) Absolute values of change w.r.t. each compositional feature for individual

community was calculated as CV= PV-EV; (CV- Change Value, PV- Present

38

Value, EV- Earlier Value). The absolute values of changes for various

compositional features (Richness-RI, Diversity-DI, Abundance-AB,

Dominance-DO) are presented (Appendix-1). While CVI (Change Value

Index) in RI, DI, AB was assessed across tree, sapling and seedling layer, the

DO was considered for tree layer only.

(ii) To remove the bias for any feature and community, values in each case were

assigned equal weight. An index value of 100 was assigned to the best score

for each compositional feature and corresponding index score (i.e., CVI) was

calculated for each of the community. CVI, thus calculated, are given

(Appendix-2).

(iii) The Community Change Sensitivity (CCS) for each of the community was

calculated as mean of all CVIs (i.e., CVI-Richness, CVI-Diversity, CVI-

Abundance, CVI-Dominance) for target community. Based on the CCS,

communities were ranked as 1-11 for PSK and 1-8 for LTP (1 representing

highest sensitivity and 11 the lowest).

3.6.2 Richness, Representativeness, Uniqueness and Community Integrity

(i) Richness value of the target communities was assessed as a cumulative value

of species richness and diversity in different tree strata (i.e., trees, saplings and

seedlings) along with abundance and dominance. As in case of CVI, the

existing absolute values under each richness feature were assigned equal

weight by assigning an index value of 100 to the best score in given category

and calculating corresponding Community Richness Index (CRI). CRI scores,

thus calculated, have been presented (Appendix-3). Based on CRI,

communities were scored with richness values 11-1 in PSK site and 8-1 in

LTP site (1 being least rich community).

(ii) Considering Himalayan natives as indicators of representativeness (Dhar et al.,

1997) Community Representativeness Index (CRPI) was calculated as a mean

of cumulative values of Native Richness (NR) and Native Abundance (ND).

Following the CRPI, communities were given representativeness scores as 11-

1 in PSK site and 8-1 in LTP site (value of 1 being least representative).

(iii) Further, realizing that endemics represent unique biodiversity elements (Dhar,

2002) the Community Uniqueness Index (CUI) was calculated to represent the

mean of the Richness and Abundance Index of endemics for representative

39

communities. Communities were assigned uniqueness scores as in earlier

cases (e.g., CRPI).

(iv) Finally Community Importance Index (CII) was calculated as sum of the

richness (CRI), representativeness (CRPI) and uniqueness (CUI) scores for

individual community in each target site. The communities with greater CII

value are considered ecologically more important.

3.6.3 Community Integrity

Considering that “Ecological Integrity” measures the composition, structure and

function of an ecosystem, as compared with its natural or historical range of variation

(Tierney et al., 2009), an attempt has been made to assess the community integrity for

study area. As the current data sets are limited to compositional features of forests,

therefore, the integrity of communities was evaluated using these features only. In

fact, these features have been used in defining various indices for the communities

(i.e., CCS, CRI, CRPI and CII) and these indices along with Community Threat Index

(CTI) have been used to determine the community integrity as follows:

(i) CII of the communities, which reflects richness, representativeness and

uniqueness status, was used as the reflection of current state of community

integrity. This means the communities with higher CII values are assumed at

greater level of integrity. In this way communities were assigned integrity

scores from 1-11 (one being at minimum level of integrity and 11 at highest)

for PSK site and 1-8 for LTP site.

(ii) Considering that the communities with higher level of CCS are likely to affect

the integrity of community composition in near future, the CCS scores of

communities were used in reverse direction (i.e., community with minimum

CCS score was assumed to retain maximum integrity and with highest CCS

score to retain minimum). The communities were thus given integrity scores

w.r.t. CCS from 11-1 (i.e., 11 being least affected and 1 being highest) for

PSK site and 1-8 for LTP site.

(iii) Further, assuming that level of existing threats (e.g., level of canopy

disturbance) and proportional representation of non-native species in the

community (i.e., richness and abundance) would also define the potential of

threat to the forest composition, the Community Threat Index (CTI) was

calculated. In this case, richness of non-natives and their proportional density

40

along with level of canopy disturbance was used to arrive at various weightage

values, as in case of other parameters (i.e., richness, representativeness,

uniqueness, etc.). Finally the CTI values were obtained as means of all

weightage values in each case. The CTI scores were assigned to indicate

integrity of each community as 11-1 (11 representing the minimum threat

hence highest integrity and 1 the maximum threat and minimum integrity) for

PSK site and 8-1 for LTP site.

(iv) Finally, the Community Integrity (CI) value for each community was

calculated as CI= CII Score + CCS Score + CTI Score. The communities were

then arranged in highest to lowest CI range. The higher CI values reflects the

integrity of community composition in target community and, therefore,

would remain intact for long.

41

Herbs

Shrubs

Trees

0

10

20

30

40

50

60

70

80

90

100

Total PSK LTP

Sp

ecie

s ri

chn

ess

(%)

451 spp 332 spp 248 spp

CHAPTERCHAPTERCHAPTERCHAPTER 4444

RESULTS

4.1 COMPOSITIONAL DIVERSITY

4.1.1 Representativeness and Floristic Diversity

The representative forest communities in PSK (11 communities) and LTP (8

communities) sites were distributed between 2000 to 3900 m asl. The community

types, site representation and important species (TBA & IVI based) in both the sites

have been presented (Table 4.1, 4.2). In general, 451 plant species (94 families) were

recorded from target sites in NDBR. Of these, greater proportion (70.51%; 318 spp.)

was of herbs. Shrubs constituted 17.71% (80 spp.), and trees 11.8% (53 spp.).

Figure 4.1 Floristic diversity and proportional distribution in different life forms

Proportional distribution of species in different life forms for entire study area

and representative sites is depicted (Figure 4.1). In general, PSK site represented

73.6% of the total plant species recorded in the study area (i.e., NDBR). Whereas,

LTP site represented only 54.9% of total species. Of the total species, herbs shared

greater proportion in both the sites (PSK- 70.8%; LTP- 72.6%) followed by shrubs

(PSK- 16.3%; LTP- 18.1%) and trees (PSK- 13.0%; LTP- 9.3%). Across life forms,

representation in the study sites as compared to the entire area varies as follows:

42

Trees- PSK (81.0%), LTP (43.4%); Shrubs- PSK (67.5%), LTP (56.3%); Herbs- PSK

(73.9%), LTP (56.6%). Considering various taxonomic groups, of the total 332

species in PSK site 88.9% were angiosperms, 1.2% gymnosperms, and 9.9%

pteridophytes. Whereas for LTP site, 87% representation was of angiosperms, 3.2%

of gymnosperms and 9.9% of pteridophytes.

Table 4.1 Representative dominant species across forest communities of PSK site

Community types Dominant species

(TBA m2 ha

-1, Relative proportion

%)

Important species

(IVI value)

Alnus nepalensis (Utis) Alnus Nepalensis (13.4; 42.9%)

Alnus nepalensis (128.2)

Ulmus wallichiana (32.9)

Mixed Oak- Deciduous Quercus floribunda (27.8; 45.3%)

Aesculus indica (9.9; 16.1%)

Quercus floribunda (79.8)

Aesculus indica (34.6)

Hippophae salicifolia

(Chuck)

Hippophae salicifolia (7.49; 84.6%)

Hippophae salicifolia (232.0)

Alnus nepalensis (32.6)

Quercus floribunda

(Tilonj Oak)

Quercus floribunda (46.14; 76.1%)

Quercus floribunda (127.1)

Rhododendron arboreum (42.7)

Quercus semecarpifolia

(Kharsu Oak)

Quercus semecarpifolia (36.0;

54.6%)

Rhododendron arboreum (10.4;

15.8%)

Quercus semecarpifolia (112.7)

Rhododendron arboreum (37.9)

Quercus floribunda (35.3)

Mixed-deciduous

Aesculus indica (9.4; 22.3%)

Acer cappadocicum (5.7; 13.5%)

Acer cappadocicum (49.4)

Ulmus wallichiana (28.1)

Rhododendron arboreum (26.9)

Mixed Silver fir-Oak Abies pindrow (14.6; 34.3%)

Aesculus indica (12.1; 28.4%)

Abies pindrow (68.5)

Quercus semecarpifolia (33.8)

Aesculus indica (30.4)

Mixed Silver fir-

Rhododendron-Maple

Abies pindrow (6.0; 8.3%)

Aesculus indica (6.0; 8.3%)

Rhododendron barbatum (74.0)

Abies pindrow (38.9)

Ilex dipyrena (27.5)

Abies pindrow (Silver fir) Abies pindrow (8.9; 41.8%)

Abies pindrow (99.9)

Rhododendron barbatum (47.6)

Betula utilis (44.0)

Mixed Birch-Silver fir Betula utilis (8.1; 49.5%)

Betula utilis (126.4)

Abies pindrow (67.9)

Taxus wallichiana (32.6)

Betula utilis (Birch) Betual utilis (9.5; 74.7%)

Betula utilis (183.4)

Euonymous fimbriatus (29.1)

Rhododendron campanulatum

(20.8)

43

Table 4.2 Representative dominant species in different forest communities of LTP

4.1.1 Compositional Patterns – PSK Site

Considering the forest composition, tree species richness was highest in Quercus

semecarpifolia and Mixed Silver fir-Oak communities (23 spp. each) and minimum in

Hippophae salisifolia community (4 spp.). Sapling species richness peaked in Q.

floribunda (18 spp.) followed by Mixed deciduous and Mixed Silver fir-Oak

communities (15 spp. each). Lowest richness was recorded in H. salicifolia

community (3 spp.). Species richness at seedling stage was maximum in Q.

floribunda (19 spp.) followed by Q. semecarpifolia and Mixed deciduous community

(16 spp. each) and lowest in H. salicifolia and Betula utilis communities (5 spp. each).

While shrub species richness was highest in Q. semecarpifolia community (23 spp.),

followed by Q. floribunda and B. utilis community (17 spp. each), herb species

richness peaked in Alnus nepalensis and Mixed Oak deciduous communities (96 spp.

each) followed by Mixed Silver fir-Rhododendron-Maple (91 spp.) and Mixed Silver

fir-Oak community (90 spp.). The details of species richness in different layers and

across communities are presented (Table 4.3).

The tree density ranged from 260 (Mixed Silver fir-Oak community) to 535

ind ha-1

in B. utilis community. In case of saplings, maximum density was recorded in

H. salicifolia community (633 ind ha-1

) and minimum in Mixed Silever fir-Oak

Community types Dominant species

(TBA m2 ha-1, Relative proportion

%)

Important species

(IVI value)

Cedrus deodara

(Deodar) Cedrus deodara (68.66; 85.4%)

Cedrus deodara (245.6)

Cupressus torulosa (24.6)

Mixed Juglans regia-

Prunus cornuta

Juglans regia (6.68; 44.2%)

Prunus cornuta (4.60; 30.5%)

Prunus cornuta (94.0)

Juglans regia (92.0)

Acer acuminatum (46.2)

Mixed Acer caesium-

Prunus cornuta Acer caesium (54.80; 76.7%)

Acer caesium (127.6)

Prunus cornuta (120.4)

Pinus wallichiana (Kail) Pinus wallichiana (31.46; 90.1%) Pinus wallichiana (235.0)

Abies spectabilis (Fir) Abies spectabilis (19.60; 65.2%) Abies spectabilis (158.6)

Betula utilis (68.8)

Mixed Taxus

wallichiana-Abies

pindrow

Abies pindrow (17.58; 45.1%)

Taxus wallichiana (15.86; 40.7%)

Taxus wallichiana (137.0)

Abies pindrow (112.0)

Abies pindrow (Silver

fir) Abies pindrow (42.60; 69.3%)

Abies pindrow (156.4)

Betula utilis (80.4)

Betula utilis (Birch) Betula utilis (48.60; 85.1%) Betula utilis (216.9)

Abies pindrow (34.0)

44

community (145 ind ha-1

). Seedling density, however, peaked in Mixed Oak

deciduous community (8170 ind ha-1

) followed by Q. floribunda (5650 ind ha-1

) and

A. nepalensis community (5325 ind ha-1

). The minimum seedling density was

recorded in B. utilis community (1450 ind ha-1

). Shrub density ranged between

maximum of 25575 ind ha-1

(Q. semecarpifolia) to minimum of 2150 ind ha-1

(H.

salicifolia). Highest herb density was recorded in Mixed Birch-Silver fir community

(93600 ind 100 m-2

) followed by B. utilis community (72400 ind 100-2

) and lowest in

H. salicifolia community (9100 ind 100 m-2

).

While considering the diversity index, highest value for tree layer was in case

of Mixed Oak deciduous community (3.37) followed by Mixed deciduous (3.31) and

Mixed Silver fir-Rhododendron-Maple community (3.22). Whereas, Mixed Silver fir-

Rhododendron-Maple community (2.67) followed by Mixed Oak deciduous (2.66)

and Mixed Birch-Silver fir community (2.64) showed highest values in sapling layer.

Mixed Silver fir-Rhododendron-Maple community (2.51) also peaked for seedling

diversity followed by Mixed Birch-Silver fir (2.49) and Q. floribunda community

(2.46). H. salicifolia community invariably had lowest diversity values across three

tree strata (Tree - 0.73, Sapling - 0.64, Seedling - 0.70). For shrub layer, diversity

index peaked in B. utilis (2.77) followed by Mixed Birch-Silver fir (2.34) and H.

salicifolia community (1.92). However, A. nepalensis community (1.00) exhibited

minimum diversity. In case of herb layer, B. utilis (3.21) followed by Mixed Birch-

Silver fir (3.12) and Mixed Silver fir-Rhododenron-Maple community (3.10) showed

higher diversity. Herb diversity index was minimum for H. salicifolia community

(2.02). The details of diversity indices across communities and forest strata are given

(Table 4.5).

As shown in Table 4.1, Mixed Silver fir-Rhododendron-Maple community

(72.03 m2 ha

-1) followed by Q. semecarpifolia (65.88 m

2 ha

-1) and Mixed Oak

deciduous community (61.31) exhibited higher TBA values, whereas H. salicifolia

(8.85 m2 ha

-1) has minimum TBA. Details of IVI for most important species in

different communities are depicted in Table 4.1.

45

Table 4.3 Richness of species in different forest strata and across communities in

PSK site

Table 4.4 Patterns of density in different forest strata and across communities in PSK

site

Species richness Community types

Seedlings Saplings Trees Shrubs Herbs

Alnus nepalensis 9 9 14 12 96

Mixed Oak deciduous 13 12 21 14 96

Hippophae salicifolia 5 3 4 8 74

Quercus floribunda 19 18 20 17 45

Quercus semecarpifolia 16 14 23 23 63

Mixed-deciduous 16 15 21 16 66

Mixed Silver fir-Oak 9 13 21 11 90

Mixed Silver fir-Rhododendron-Maple 14 15 23 16 91

Abies pindrow 9 10 17 15 58

Mixed Birch-Silver fir 9 11 10 15 84

Betula utilis 5 6 8 17 74

Species density (Ind ha-1) Community types

Seedlings Saplings Trees Shrubs Herbs (Ind 100

m-2

)

Alnus nepalensis 5325±878 345 ±53 460±49 25200 ±1470 40000 ±1580

Mixed Oak deciduous 8170±3012 233 ±33 480 ±50 14800 ±1660 37700 ±2680

Hippophae salicifolia 3167 ±609 633 ±179 423 ±174 2150 ±870 9100 ±3200

Quercus floribunda 5650 ±1613 170 ±27 378 ±40 19525 ±3020 36200 ±13010

Quercus semecarpifolia 4663 ±819 200 ±37 473 ±42 25575 ±9400 23500 ±9660

Mixed-deciduous 3117 ±348 197 ±41 407 ±73 18850 ±4430 66300 ±7920

Mixed Silver fir-Oak 3325 ±469 145 ±12 260 ±16 14475±430 20100 ±910

Mixed Silver fir-Rhododendron-

Maple 3583 ±866 200 ±26 367 ±39 22967 ±2350 31100 ±6280

Abies pindrow 2150 ±653 190 ±8 320 ±49 23975 ±6470 12800 ±1220

Mixed Birch-Silver fir 2600 ±694 210 ±0 355 ±102 10475 ±5410 93600 ±6160

Betula utilis 1450 ±531 235 ±20 535 ±116 8350 ±2570 72400 ±2760

46

Table 4.5 Patterns of diversity in different forest strata and across communities in

PSK site

Species diversity (H’) Community types

Seedlings Saplings Trees Shrubs Herbs

Alnus nepalensis 2.20 ± 0.25 2.38 ±0.32 2.38 ±0.14 1.00 ±0.09 2.76 ±0.09

Mixed Oak deciduous 2.28 ±0.15 2.66 ±0.11 3.37 ±0.03 1.51 ±0.12 2.82 ±0.26

Hippophae salicifolia 0.70 ±0.23 0.64 ±0.26 0.73 ±0.10 1.92 ±0.41 2.02 ±0.45

Quercus floribunda 2.46 ±0.41 2.48 ±0.10 2.84 ±0.16 1.55 ±0.18 2.38 ±0.16

Quercus semecarpifolia 2.27 ±0.29 2.10 ±0.30 2.73 ±0.36 1.42 ±0.29 2.12 ±0.47

Mixed-deciduous 2.34 ±0.33 2.47 ±0.21 3.31 ±0.06 1.57 ±0.25 2.32 ±0.28

Mixed Silver fir-Oak 2.27 ±0.01 2.39 ±0.01 3.20 ±0.03 1.56 ±0.02 2.88 ±0.08

Mixed Silver fir-Rhododendron-

Maple 2.51 ±0.08 2.67 ±0.30 3.22 ±0.28 1.64 ±0.47 3.10 ±0.17

Abies pindrow 2.08 ±0.41 2.55 ±0.07 2.71 ±0.14 1.79 ±0.01 2.22 ±0.03

Mixed Birch-Silver fir 2.49 ±0.29 2.64 ±0.10 2.02 ±0.03 2.34 ±0.26 3.12 ±0.28

Betula utilis 1.50 ±0.01 1.81 ±0.31 1.55 ±0.21 2.77 ±0.38 3.21 ±0.11

4.1.2 Compositional Patterns – LTP Site

Patterns of species richness across forest communities and different strata are

presented in Table 4.6. Richness of species varies across the communities with a

range of: 4-18 (trees), 10-29 (shrubs) and 18-107 species (herbs). Pinus wallichiana

community (18 spp.) followed by B. utilis (10 spp.) and Abies spectabilis and Abies

pindrow (9 spp. each) exhibited relatively higher tree species richness. Lowest tree

species richness was recorded in Mixed Taxus wallichiana-A. pindrow and Cedrus

deodara community (4 spp. each). Maximum species richness in sapling (11 spp.) and

seedling layers (16 spp.) was also recorded in case of P. wallichiana community. The

P. wallichiana community also had maximum richness of shrubs (29 spp.) and herbs

(107 spp.).

Considering the other compositional attributes, density values for tree layer

ranged between 599-1211 ind ha-1

, with maximum values in Mixed T. wallichiana-A.

pindrow community (1211 ind ha-1

) followed by A. caesium (960 ind ha-1

) and B.

utilis (856 ind ha-1

). In sapling layer, density ranged from 70 (Mixed J. regia-P.

cornuta community) to 951 ind ha-1

(A. pindrow community). The range of seedling

density was recorded between 470 ind ha-1

(C. deodara community) to 1665 ind ha-1

(A. pindrow community). In case of shrub layer, B. utilis community showed the

highest density (4025 ind ha-1

) and A. pindrow (1226 ind ha-1

) the minimum. B. utilis

community (26112 ind 100 m-2

) also exhibited highest herb density. The lowest herb

density was, however, recorded in Mixed T. wallichiana-A. pindrow community

47

(3980 ind 100 m-2

). Details of density in different communities across diverse strata

are presented (Table 4.7).

Species diversity index across different forest strata varied considerably. In

tree layer, diversity ranged between 0.63 (C. deodara community) to 1.61 (Mixed J.

regia-P. cornuta community). In case of saplings, A. spectabilis (1.36) followed by A.

pindrow (1.24) and P. wallichiana community (1.16) had relatively higher values,

while Mixed J. regia-P. cornuta and A. caesium-P. cornuta mixed community

showed lowest diversity (0.76 each). P. wallichiana community (1.79) had maximum

diversity in seedling layer followed by Mixed J. regia-P. cornuta and B. utilis

community (1.32 each). Lowest seedling diversity was recorded in Mixed A. caesium-

P. cornuta community (0.35). Highest shrub (2.73) and herb (3.61) diversity was

revealing in case of A. pindrow community. The details of diversity patterns across

communities across different strata are presented (Table 4.8).

As reflected in Table 4.2, maximum TBA was recorded in C. deodara

community (80.43 m2 ha

-1) followed by Mixed A. caesium-P. cornuta (71.44) and A.

pindrow community (61.43). Minimum TBA was, however, recorded in case of

Mixed J. regia-P. cornuta community (15.10). The IVI values of most important

species in identified communities are presented (Table 4.2).

Table 4.6 Richness of species in different forest strata and across communities in LTP

site

Species richness Community types

Seedlings Saplings Trees Shrubs Herbs

Cedrus deodara 4 6 4 14 68

Mixed Juglans regia-Prunus cornuta 6 6 7 15 18

Mixed Acer caesium-Prunus

cornuta 7 4 5 10 24

Pinus wallichiana 16 11 18 29 107

Abies spectabilis 11 6 9 12 72

Mixed Taxus wallichiana-Abies

pindrow 8 5 4 14 36

Abies pindrow 8 7 9 24 79

Betula utilis 9 7 10 21 86

48

Table 4.7 Patterns of density in different forest strata and across communities in LTP

site Species density (Ind ha-1) Community types

Seedlings Saplings Trees Shrubs Herbs (Ind

100 m-2

)

Cedrus deodara 470 ±16.52 404 ±18.98 612 ±7.51 1968 ±248 22570 ±1168

Mixed Juglans regia-Prunus cornuta 850 ±0.00 70 ±0.00 750 ±0.00 1460 ±0.00 5690 ±0.00

Mixed Acer caesium-Prunus cornuta 490 ±0.00 790 ±0.00 960 ±0.00 1910 ±0.00 6140 ±0.00

Pinus wallichiana 644 ±59.35 346 ±61.38 630 ±39.24 2830 ±189 17860 ±2741

Abies spectabilis 890 ±189 406 ±80.83 599 ±46.54 2321 ±364 24055 ±2062

Mixed Taxus wallichiana-Abies

pindrow 966 ±0.00 789 ±0.00 1211 ±0.00 3987 ±0.00 3980 ±0.00

Abies pindrow 1665±286 951 ±148 818 ±79.24 1226 ±235 10110 ±2843

Betula utilis 658 ±74.53 435 ±42.23 856 ±24.71 4025 ±186 26112 ±5567

Table 4.8 Patterns of diversity in different forest strata and across communities in

LTP site

Species diversity (H’) Community types

Seedlings Saplings Trees Shrubs Herbs

Cedrus deodara 0.96 ±0.12 0.86 ±0.06 0.63 ±0.16 1.64 ±0.11 2.98 ±0.23

Mixed Juglans regia-Prunus cornuta 1.32 ± 0.00 0.76 ±0.00 1.61 ±0.00 2.18 ±0.00 2.69 ±0.00

Mixed Acer caesium-Prunus cornuta 0.35 ±0.00 0.76 ±0.00 0.96 ±0.00 0.98 ±0.00 2.76 ±0.00

Pinus wallichiana 1.79 ±0.39 1.16 ±0.06 1.22 ±0.17 2.46 ±0.20 3.45 ±0.34

Abies spectabilis 0.58 ±0.10 1.36 ±0.26 1.52 ±0.38 1.20 ±0.20 2.83 ±0.94

Mixed Taxus wallichiana-Abies

pindrow 1.10 ±0.00 0.82 ±0.00 1.04 ±0.00 0.98 ±0.00 2.48 ±0.00

Abies pindrow 1.15 ±0.30 1.24 ±0.16 1.42 ±0.19 2.73 ±0.42 3.61 ±0.32

Betula utilis 1.32 ±0.20 0.86 ±0.12 1.21 ±0.13 1.57 ±0.19 2.82 ±0.27

4.2 INTEGRITY AND UNIQUENESS OF COMMUNITIES

Species distribution plays a significant role in defining the integrity and uniqueness of

plant communities, particularly by means of representation of nativity and endemism.

Nativity and endemism also define the uniqueness value of communities and,

therefore, play great role in determining conservation priorities. The analysis in this

respect reveals the following patterns.

4.2.1 Patterns of Nativity and Endemism in PSK Site

Out of total species in PSK site (332), native species contributed 59.6 % (198 spp.). In

general, 24.7% species in PSK site were Himalaya endemics. Among natives, 63.1%

were herbs, 19.7% shrubs and 17.2% trees species. The details of native and endemic

species distribution in PSK site are depicted (Figure 4.2).

49

Sp

ecie

s ri

chn

ess

198 spp 134 spp 82 spp

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Native Non -Natives Endemics

Trees

Shrubs

Herbs

Among the communities, maximum native species were recorded in Mixed

Abies-Rhododendron-Maple community (79 spp.) followed by Mixed Oak deciduous

(72 spp.) and Mixed Silver fir-Oak (71 spp.). Least number of native species was,

however, recorded in Hippophae salicifolia community (45 spp.). Maximum number

of endemics was recorded in Mixed Abies-Rhododendron-Maple (36 spp.) followed

by Mixed-deciduous (29 spp.) and Betula utilis community (29 spp.). Minimum

richness of endemics was recorded in Hippophae salicifolia community (18 spp.).

Proportional distribution of native and endemic species in different communities has

been depicted (Figure 4.3).

Figure 4.2 Distribution of native and endemics across different life forms in PSK site

50

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10 11

EndemismNativesNon-natives

Sp

eci

es

ric

hn

ess

Forest Communities

Figure 4.3 Proportional distribution of native, non-native and endemic species in

different plant communities in PSK site [1. Alnus nepalensis; 2. Mixed Oak-deciduous; 3.

Hippophae salicifolia; 4. Quercus floribunda; 5. Quercus semecarpifolia; 6. Mixed-deciduous; 7.

Mixed Silver fir-Oak; 8. Mixed Abies-Rhododendron-Maple; 9. Abies pindrow; 10. Mixed Birch-

Abies; 11. Betula utilis]

4.2.2 Patterns of Nativity and Endemism in LTP Site

In LTP site, 66.5% (165 spp.) species were Himalayan natives and 33.5% endemics.

Among natives, herb contribution was 67.8% followed by shrubs 21.2% and trees

11%. The details of native and endemic species distribution in LTP site is depicted

(Figure 4.4).

While considering communities, the maximum native and endemic species

were recorded in Pinus wallichiana (109 and 54 spp., respectively) followed by

Betual utilis (81 and 42 spp.) and Abies pindrow (75 and 36 spp.). Mixed Acer

caesium-Prunus cornuta community, however, showed minimum number of native

(21 spp.) and endemic species (8 spp.). The proportional representation of native and

endemic species across forest communities in LTP site is presented (Figure 4.5).

51

Trees

Shrubs

Herbs

Sp

ecie

s ri

chn

ess

165 spp 83 spp 83 spp

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Native Non-Natives Endemics

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8

EndemismNativesNon-natives

Forest Communities

Sp

ecie

s ri

chn

ess

Figure 4.4 Distribution of native and endemics across different life forms in LTP site

Figure 4.5 Proportional distribution of native, non-native and endemic species in

different plant communities in LTP site [1. Cedrus deodara; 2. Mixed Juglans regia-Prunus

cornuta; 3. Mixed Acer caesium-Prunus cornuta; 4. Pinus wallichiana; 5. Abies spectabilis; 6. Mixed

Taxus baccata-Abies pindrow; 7. Abies pindrow; 8. Betula utilis]

52

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Total PSK LTP

Seedlings

Saplings

Trees

Sp

ecie

s ri

chn

ess

4.3 DEMOGRAPHIC PATTERNS AND POPULATION STRUCTURE

4.3.1 Broad Profiles

Proportional distribution of individuals in three broad tree strata (tree, sapling, and

seedling) of study area and representative sites is depicted (Figure 4.6). In general,

when compared to the entire region, PSK site represented 81.13% (43 spp.) and Lata

site 43.4% (23 spp.) species of the total tree species (53 spp.). Of the total species in

sapling and seedling layers of both the sites, PSK site showed greater proportion

(saplings- 74.5%; seedlings- 72.7%) site as compared to LTP (saplings- 48.9%;

seedlings- 48.9%). Representation compared to total species across different strata

indicated: NDBR (saplings 88.7%; seedlings 83%), PSK (saplings 81.4%, seedlings

74.4%), and LTP (saplings 100%, seedlings 100%). The proportionate ratio of species

richness in different strata of NDBR and study sites is presented in Figure 4.6.

4.3.2 Site level Population Structure

The overall population structure for two sites and the entire reserve have been

presented (Figure 4.7). As reflected, PSK site has considerably larger number of

individuals in seedling stage which was much higher than the LTP site. On the

contrary, LTP site exhibited more number of individuals in sapling and tree classes as

compared to PSK site. Overall, PSK site has more gradual decline of individuals

towards higher tree size classes, whereas LTP site showed accumulation at mid level

tree size class (D).

Figure 4.6 Proportional distribution of species richness across different tree strata

53

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

5500

6000

6500

7000

7500

8000

8500

9000

A B C D E

Alnus nepalensis

Mixed Oak deciduous

Hippophae salicifolia

Quercus floribunda

Quercus semecarpifolia

Mixed-deciduous

Mixed Silver fir -Oak

Mixed Silver fir -Rhododendron-Maple

Abies pindrow

Mixed Birch-Silver fir

Betula utilis

Ind

ha-1

Girth classes

0

500

1000

1500

2000

2500

3000

3500

4000

4500

NDBR PSK LTP

A B C D E

Ind

ha-1

Figure 4.7 Population structures for entire reserve and representative sites (PSK and

LTP)

4.3.2.2Patterns at forest community level

The demographic patterns of studied forest communities in two sites are shown in

Figure 4.8 and 4.9. While the profile of demography for different forests in PSK site

exhibited more or less similar patterns (Figure 4.8), the patterns in LTP site varied

considerably amongst communities (Figure 4.9).

Figure 4.8 Population structure for different forest communities in PSK site

54

Ind

ha-1

Girth classes

0

200

400

600

800

1000

1200

1400

1600

1800

A B C D E

Cedrus deodara

Mixed Juglans regia-Prunus cornuta

Mixed Acer caesium-Prunus cornuta

Pinus wallichiana

Abies spectabilis

Mixed Taxus wallichiana-Abies

pindrow

Abies pindrow

Betula utilis

Figure 4.9 Population structure for different forest communities in LTP site

In Mixed Juglans regia-Prunus cornuta community, the individuals in sapling

stage were almost negligible, thereafter individuals exhibited accumulation at young

tree (31-60 cm cbh) class. Among others, Mixed Acar caesium-Prunus cornuta Mixed

community showed lesser number of individuals in seedling stage (A class) and

greater accumulation in sapling (B class) (Figure 4.9).

4.4 VEGETATION ORDINATION

In view of the large number of co-variables, ordination was performed separately for

density and richness co-variables for each study site. The first analysis followed

density parameters (density of seedlings, saplings, tree, herbs, shrubs, etc.) and the

second analysis considered richness parameters (species richness of seedlings,

saplings, tree, herbs, shrubs, etc.).

4.4.1 PSK Site

4.4.1.1 CCA using density as co-variable

All 16 variables showed high inter-set correlation in CCA (Table 4.9). Monte Carlo

test for species-environment correlation were 0.999, 0.982 and 0.888 for axis 1, 2 and

3 respectively, with significant p value (0.0020) (Table 4.10).

55

Table 4.9 Inter-set correlation for 16 variables in PSK site using density as co-

variable

Correlations

Variable Axis 1 Axis 2 Axis 3

1 ALT 0.123 -0.892 -0.249

2 SLP 0.775 -0.459 0.075

3 pH 0.607 0.149 -0.162

4 OC 0.529 -0.579 -0.022

5 N 0.446 -0.630 -0.188

6 DS 0.107 0.306 -0.324

7 DSP -0.875 0.105 0.048

8 DT -0.311 -0.247 0.111

9 HB 0.315 -0.523 0.296

10 SH 0.516 0.417 0.059

11 NAT 0.350 -0.548 0.189

12 NN 0.554 -0.240 0.330

13 E 0.473 -0.428 0.026

14 CNP -0.018 -0.079 0.263

15 DST 0.088 0.502 -0.094

16 TBA 0.481 0.496 -0.347

The scatter plots of tree species generated from PC-ORD through CCA are

depicted (Figure 4.10). All 16 variables showed high correlation with both the axes.

Altitude showed high negative correlation (-0.892) with axis 2. However, pH (0.607),

organic carbon (0.529) and soil nitrogen (0.446) showed good positive correlation

with axis 1. Of these, organic carbon and nitrogen had good negative correlation with

axis 2 (-0.579 and -0.630) as well. Density of seedlings showed good positive

correlation with axis 2 (0.306). Whereas, density of saplings showed good negative

correlation with axis 1 (-0.875). The tree density (DT) showed negative correlation

with axis 1 (-0.311) and axis 2 (-0.247). Details of various relationships in different

axis are depicted (Table 4.9).

56

Table 4.10 Monte Carlo test results for 16 species-environment correlations (obtained

after CCA) in PSK site of NDBR using density as co-variables

Randomized data

Real data Monte Carlo test, 499 runs

-------------------------------------------------------------------------------------

Axis Eigenvalue Spp-Envt Corr. Mean Minimum Maximum p

-------------------------------------------------------------------------------------------------------

--

1 0.862 0.990 0.890 0.781 0.981 0.0020

2 0.698 0.982 0.853 0.720 0.970

3 0.364 0.888 0.852 0.678 0.968

-------------------------------------------------------------------------------------------------------

------

p = proportion of randomized runs with species-environment correlation greater than or equal to the

observedspecies-environment correlation; i.e.,

p = (1 + no. permutations >= observed)/(1 + no. permutations)

p is not reported for axis 2 and 3 because using a simple randomization test for these axis may bias the

p value.

57

APAS

AAC

AC

ACP

AI

AN

BA

BU

CV

CA

CM

CJ

EF

EP

FM

HSID

JR

LO

MB

MD

NP

PC

PL

PV

QFQI

QSRA

RB

RC

RP

SD

SCSR

SE

TW

UW

VN

ALT

pH

OCN

DS

DSP

DT

HB

SH

NAT

NN

E SLP

CNP

DST TBA

0

0

40 80

40

80

Axis 1

Ax

is 2

Figure 4.10 CCA depicting environmental correlation of trees in PSK site using

density as co-variables

While considering the scatter plots of tree species (Figure 4.10), Quadrant I

(clockwise) represents an environment with high disturbance, low canopy cover, high

total basal area, high density of seedlings, relatively good proportion of non-natives,

high soil pH content and high shrub density on moderate slopes of lower altitudinal

zones. Quadrant II, however, represents higher slopes, greater proportion of natives,

relatively good proportion of non-natives, endemics and herbs towards higher

altitudes. This quadrant also represents higher content of soil organic carbon and

nitrogen, and relatively good canopy cover. Quadrant III represents low disturbance

areas with high tree density and good canopy cover in higher altitudes. Quadrant IV

represents low to moderate altitude areas with high sapling density, and low soil

58

components (soil pH, soil organic carbon, soil nitrogen). Species distributed in

different quadrant as per their ecological behavior are presented in Table 4.11.

Table 4.11 Distribution of tree species in different quadrants analyzed in CCA using

density as co-variables

Quadrants Code Species Code Specis

EP Elaeagnus parvifolia RA Rhododendron arboreum

MB Mahonia borealis QS Quercus semecarpifolia

PL Pyrus lanata LO Lyonia ovalifolia

FM Fraxinus micarantha UW Ulmus wallichiana

CA Celtis australis VN Viburnum nervosum

NP Neolitsea pallens JR Juglans regia

RP Rhus punjabensis ID Ilex dipyrena

QI Quercus incana CV Carpinus viminea

QF Quercus floribunda PD Persea duthiei

AI Aesculus indica MD Meliosma dilleniaefolia

SC Symplocos chinensis ACP Acer cappadocicum

I. High disturbance,

high TBA, high

density of seedlings,

and shrubs on

moderate slopes and

low canopy cover

SR Symplocos ramosissima

CM Cornus macrophylla AC Acer caesium

AAC Acer acuminatum EF Euonymus fimbriatus

RB Rhododendron barbatum SE Syringa emodi

CJ Corylus jacquemontii TW Taxus wallichiana

RC Rhododendron campanulatum BU Betula utilis

II. High density of

natives, non-natives,

endemics and herbs

under good canopy

cover in higher

slopes towards higher

altitudes

PV Pyrus vestita AP Abies pindrow

AS Abies spectabilis

SD Salix daphnoides

III. Low disturbance

areas with high tree

density and good

canopy cover in

higher altitudes with

low ground cover

PC Prunus cornuta

HS Hippophae salicifolia BA Betula alnoides IV. Low to moderate

altitude areas with

high sapling density,

low soil nutrients and

low ground cover

AN Alnus nepalensis

59

4.4.1.2 CCA using species richness as co-variables

With high inter-set correlation for 16 used variables in CCA (Table 4.12), Monte

Carlo test correlation for species-environment were 0.978, 0.982 and 0.902 for axis 1,

2 and 3 respectively, with significant p value (0.0020) (Table 4.13).

Table 4.12 Inter-set correlation for 16 variables in PSK site using species richness as

co-variables

Correlations

Variable Axis 1 Axis 2 Axis 3

1 ALT 0.062 -0.882 -0.339

2 SLP 0.728 -0.541 0.114

3 pH 0.622 0.088 -0.108

4 OC 0.478 -0.624 -0.064

5 N 0.395 -0.670 -0.155

6 RS 0.687 0.326 0.015

7 RSP 0.678 0.138 -0.024

8 RT 0.775 0.336 -0.113

9 HB -0.077 -0.137 0.391

10 SH 0.408 -0.441 0.103

11 NAT 0.346 -0.355 0.167

12 NN -0.106 0.217 0.381

13 E 0.135 -0.579 0.217

14 CNP -0.036 -0.077 0.163

15 DST 0.131 0.471 0.133

16 TBA 0.541 0.446 -0.190

Figure 4.11 shows the scatter plots of tree species generated from PC-ORD

through CCA. Among five environmental variables, altitude (ALT) showed good

negative correlation (-0.882) with axis 2, slope with axis 1 (0.728), pH with axis 1

(0.662), and both organic carbon and nitrogen with axis 1 (0.478 and 0.395) and good

negative correlation with axis 2 (-0.624 and -0.670). Richness of seedlings, saplings

and tree species exhibited good positive correlation with axis 1 (0.687, 678 and 0.775

respectively). The remaining parameters are presented (Table 4.12).

Quadrant I (clockwise) represents an environment with greater disturbance

and moderate slope in lower altitudinal zones with high pH content, high total basal

area and high species richness in seedling, sapling and tree layers. Quadrant II

represents higher slopes with high number of natives, endemics and shrubs in higher

60

altitudes. This quadrant is also delineated with high content of soil organic carbon and

nitrogen and good canopy cover. Quadrant III represents low disturbance areas with

good canopy cover in higher altitudes having high number of herb species. Quadrant

IV represents low to moderate altitude areas with high non-native richness and low

soil nutrients. Species distribution in different quadrants, as per their ecological

behavior, is presented (Table 4.14).

Table 4.13 Monte Carlo test results for 16 species-environment correlations (obtained

after CCA) in PSK site using richness as co-variables

Randomized data

Real data Monte Carlo test, 499 runs

-------------------------------------------------------------------------------------

Axis Eigenvalue Spp-Envt Corr. Mean Minimum Maximum p

-------------------------------------------------------------------------------------------------------

--

1 0.838 0.978 0.891 0.768 0.971 0.0020

2 0.713 0.988 0.849 0.744 0.960

3 0.384 0.902 0.856 0.675 0.963

-------------------------------------------------------------------------------------------------------

p = proportion of randomized runs with species-environment correlation greater than or equal to the

observedspecies-environment correlation; i.e.,

p = (1 + no. permutations >= observed)/(1 + no. permutations)

p is not reported for axis 2 and 3 because using a simple randomization test for these axis may bias the

p value.

61

APAS

AAC

AC

ACP

AI

AN

BA

BU

CV

CA

CMCJ

EF

EP

FM

HS

IDJRLO

MB

MD

NP

PC

PL

PV

QFQI

QSRA

RB

RC

RP

SD

SCSR

SETW

UW

VN

ALT

pH

OCN

RS

RSP

RT

HB

SHNAT

NN

E SLP

CNP

DST TBA

0

0

40 80

40

80

Axis 1

Ax

is 2

Figure 4.11 CCA depicting environmental correlation of trees in PSK site using

species richness as co-variable s

62

Table 4.14 Distribution of tree species in different quadrants analyzed in CCA using

species richness as co-variables

Quadrants Code Species Code Specis

MB Mahonia borealis UW Ulmus wallichiana

FM Fraxinus micarantha VN Viburnum nervosum

CA Celtis australis JR Juglans regia

NP Neolitsea pallens ID Ilex dipyrena

RP Rhus punjabensis CV Carpinus viminea

QI Quercus incana PD Persea duthiei

QF Quercus floribunda MD Meliosma dilleniaefolia

AI Aesculus indica ACP Acer cappadocicum

SC Symplocos chinensis CM Cornus macrophylla

SR Symplocos ramosissima AAC Acer acuminatum

RA Rhododendron arboreum CJ Corylus jacquemontii

QS Quercus semecarpifolia AC Acer caesium

I. High disturbance,

high TBA, high

species richness in

seedlings, saplings,

and tree layers,

moderate to low

canopy cover

LO Lyonia ovalifolia

RB Rhododendron barbatum AP Abies pindrow

RC Rhododendron campanulatum

PV Pyrus vestita

EF Euonymus fimbriatus

SE Syringa emodi

II. High richness of

native, endemic and

shrubs, good canopy

cover, higher slopes,

and altitude, rich in

soil nutrients TW Taxus wallichiana

AS Abies spectabilis BU Betula utilis III. Low disturbance

areas with high herb

species richness and

good canopy cover in

higher altitudes

SD Salix daphnoides PC Prunus cornuta

HS Hippophae salicifolia BA Betula alnoides

AN Alnus nepalensis EP Elaeagnus parvifolia

IV. Low to moderate

altitude areas with

high non-native

species richness, low

soil nutrients and low

ground cover

PL Pyrus lanata

63

4.4.2 LTP Site

4.4.2.1 CCA using density as co-variable

Inter-set correlation of 16 variables in CCA is presented (Table 4.15). Monte Carlo

test for species-environment correlation were: axis 1- 0.967, axis 2- 0.972 and axis 3-

0.967; and p value (0.0100) was significant (Table 4.16).

Table 4.15 Inter-set correlation for 16 variables in LTP site using density as co-

variables

Correlations

Variable Axis 1 Axis 2 Axis 3

1 ALT -0.608 0.244 -0.337

2 SLP -0.472 -0.058 -0.504

3 pH 0.558 -0.333 0.261

4 OC -0.216 -0.217 -0.713

5 N -0.542 0.507 -0.275

6 DS -0.502 0.295 -0.269

7 DSP -0.346 0.447 -0.190

8 DT -0.294 0.133 0.112

9 HB 0.292 -0.004 -0.168

10 SH -0.053 -0.151 -0.129

11 NAT 0.162 -0.142 -0.382

12 NN 0.456 0.142 -0.155

13 E -0.190 0.170 -0.221

14 CNP 0.039 0.128 0.211

15 DST 0.348 -0.307 0.056

16 TBA 0.160 0.722 -0.007

The scatter plots of tree species generated from PC-ORD through CCA using

density value in vegetation parameters is depicted (Figure 4.12). Among

environmental variables, altitude had good negative correlation (-0.608) with axis 1,

soil nitrogen showed good correlation with axis 1 and 2 (-0.542, 0.507), pH (0.558)

and slope (-0.472) with axis 1. With respect to density, seedlings showed good

negative correlation with axis 1 (-0.502), saplings with both the axes 1 and 2 (-0.346,

0.447). Density of non-native species showed good positive correlation with axis 1

(0.456) and trees with axis 1 (-0.294). The disturbance exhibited good correlation

with both axes 1 and 2 (0.348, -0.307). However, TBA showed high positive

correlation with axis 2 (0.722).

64

Considering the scatter plot, Quadrant I (clockwise) represents an environment

with high disturbance, moderate canopy and high TBA in lower altitudinal zones with

high density of herbs, natives and non-native species. Quadrant II represents moderate

disturbance, moderate canopy cover, low TBA, moderate density of native, non-native

and shrub species in lower altitude areas. Quadrant III represents middle altitudinal

zones with moderate slopes, higher organic carbon, low TBA and with moderate

density of shrub species. Quadrant IV represents low disturbance areas with moderate

canopy cover in higher altitude areas with high density of tree, seedling and sapling

species, and moderate density of endemics and shrub species. This quadrant is rich in

soil components. Species distributed in different quadrants, as per their ecological

behavior, are presented (Table 4.17).

Table 4.16 Monte Carlo test results for 16 species-environment correlations (obtained

after CCA) in LTP site using density as co-variables

Randomized data

Real data Monte Carlo test, 499 runs

-------------------------------------------------------------------------------------

Axis Eigenvalue Spp-Envt Corr. Mean Minimum Maximum p

-------------------------------------------------------------------------------------------------------

--

1 0.840 0.967 0.883 0.755 0.983 0.0100

2 0.719 0.972 0.812 0.646 0.965

3 0.671 0.967 0.750 0.569 0.934

-------------------------------------------------------------------------------------------------------

p = proportion of randomized runs with species-environment correlation greater than or equal to the

observedspecies-environment correlation; i.e.,

p = (1 + no. permutations >= observed)/(1 + no. permutations)

p is not reported for axis 2 and 3 because using a simple randomization test for these axis may bias the

p value.

65

AP

AS

AAC

AC

BU

CD

CM

CJ

CT

EF

EP

JR

MB

PS

PW

PCI

PC

PP

PPS

RA

SD

SDI

TW

ALT

pHOC

N

DS

DSP

DT

HB

SH NAT

NNE

SLP

CNP

DST

TBA

0

0

40 80

40

80

Axis 1

Ax

is 2

Figure 4.12 CCA depicting environmental correlation of trees species in LPT site

using density as co-variables

66

Table 4.17 Distribution of tree species in different quadrants analyzed in CCA using

density as co-variables Quadrant Code Species Code Specis

CT Cupressus torulosa

PP Prunus persica

I. Low altitude,

moderate canopy,

High density of non-

natives and herbs, high

TBA and disturbance

CD Cedrus deodara

CJ Corylus jacquemontii PS Picea smithiana

AS Abies spectabilis MB Malus baccata

SDI Salix disperma PCI Populus ciliata

II. Low altitude,

moderate disturbance,

moderate density of

native, shrub and high

pH

PW Pinus wallichiana PPS Pyrus pashia

AC Acer caesium

PC Prunus cornuta

JR Juglans regia

CM Cornus macrophylla

III. Mid altitude,

moderate slopes,

moderate shrub

density, good amount

of organic carbon EF Euonymus fimbriatus

BU Betula utilis RA Rhododendron arboreum

SD Salix daphnoides AP Abies pindrow

AAC Acer acuminatum TW Taxus wallichiana

IV. Higher altitude, high

slopes, good soil

nitrogen, high density

of seedlings, saplings,

trees, and endemics

EP Euonymus pendulus

4.4.2.2 CCA using species richness as co-variable

Inter-set correlation in CCA for 16 variables is shown (Table 4.18), and Monte Carlo

test for species-environment correlation exhibited: axis 1- 0.957, axis 2- 0.968 and

axis- 0.910 with significant p value (0.0100) (Table 4.19).

67

Table 4.18 Inter-set correlation for 16 variables in LTP site using species richness as

co-variables

Table 4.19 Monte Carlo test results for 16 species-environment correlations (obtained

after CCA) in LTP site using species richness as co-variables

Randomized data

Real data Monte Carlo test, 499 runs

-------------------------------------------------------------------------------------

Axis Eigenvalue Spp-Envt Corr. Mean Minimum Maximum p

-------------------------------------------------------------------------------------------------------

--

1 0.748 0.957 0.880 0.706 0.967 0.0100

2 0.732 0.968 0.813 0.640 0.966

3 0.613 0.910 0.747 0.560 0.952

------------------------------------------------------------------------------------------------------- p = proportion of randomized runs with species-environment correlation greater than or equal to the

observed species-environment correlation; i.e.,

p = (1 + no. permutations >= observed)/(1 + no. permutations)

p is not reported for axis 2 and 3 because using a simple randomization test for these axis may bias the

p value.

Correlations

Variable Axis 1 Axis 2 Axis 3

1 ALT -0.698 -0.333 0.110

2 SLP -0.410 -0.566 0.160

3 pH 0.694 0.235 -0.065

4 OC -0.118 -0.620 0.411

5 N -0.778 -0.061 0.225

6 RS 0.546 -0.689 -0.134

7 RSP 0.425 -0.468 0.118

8 RT 0.470 -0.684 -0.037

9 HB 0.330 -0.616 0.369

10 SH 0.192 -0.674 0.168

11 NAT 0.325 -0.728 0.230

12 NN 0.254 -0.435 0.355

13 E 0.073 -0.506 0.283

14 CNP -0.059 0.202 -0.06

15 DST 0.498 0.057 0.037

16 TBA -0.264 0.578 0.416

68

AP

AS

AAC

AC

BU

CD

CM

CJ

CT

EF

EP

JR

MBPS

PW

PCI

PC

PP

PPS

RASD

SDI

TW

ALT

pH

OC

N

RS

RSP

RTHBSH NAT

NNESLP

CNPDST

TBA

0

0

40 8040

80

Axis 1A

xis

2

Figure 4.13 CCA depicting environmental correlation of tree species in LTP site

using species richness as co-variables

Figure 4.13 represents the scatter plot of tree species generated from PC-ORD

through CCA using species richness as co-variable. Of the five environmental

variables, altitude had good correlation (-0.805) with axis 1, pH with axis 1 (0.694),

nitrogen and organic carbon with axis 1 and 2 (-0.778 and -0.620) respectively.

Whereas, slope showed good negative correlation with both the axis (-0.410, -0.566).

Species richness in seedling layer (0.546, -0.689), sapling layer (0.425, -0.468) and

tree layer (0.470, -0.684) exhibited good correlations with both the axes. Richness of

herbs (-0.616), shrubs (-0.674), natives (-0.728), non-natives (-0.435) and endemics (-

0.506) showed good negative correlation with axis 2. While, disturbance had good

positive correlation with axis 1 (0.498), TBA showed good positive correlation with

axis 2 (0.578).

69

Quadrant I (clockwise) represents an environment with high disturbance and

moderate slope in lower altitudinal zones with moderate canopy cover, high total

basal area, and average richness of tree, saplings, seedlings, shrubs, herbs and non-

native species. Quadrant II represents high disturbance areas with low canopy cover

and high total basal area in lower altitudinal areas with high richness of tree, saplings,

seedlings, shrubs, herbs and non-native species. Quadrant III depicts low disturbance

areas in higher altitudes with nutrient rich soils, low species richness and high slopes.

Quadrant IV represents low disturbance areas with average canopy cover in higher

altitude areas having high TBA and low species richness. Species distributed in

different quadrants, as per their ecological behavior, are presented (Table 4.20).

Table 4.20 Distribution of tree species in different quadrants analyzed in CCA using

species richness as co-variables

Quadrant Code Species Code Specis

AC Acer caesium EF Euonymus fimbriatus

PC Prunus cornuta CT Cupressus torulosa

JR Juglans regia PP Prunus persica

CM Cornus macrophylla CD Cedrus deodara

I. High disturbance

lower altitudinal

zones, moderate

canopy cover, high

TBA, moderate

richness of tree,

seedling, sapling,

herb, shrub, native

and non-native

species

CJ Corylus jacquemontii PS Picea smithiana

SDI Salix disperma MB Malus baccata

PW Pinus wallichiana PCI Populus ciliata

PPS Pyrus pashia

II. High species richness

in seedling, sapling,

tree, herb, shrub

layers and species

richness of native,

non-native in low

altitude areas with

high disturbance

AS Abies spectabilis SD Salix daphnoides

BU Betula utilis RA Rhododendron arboreum

III. Low disturbance

areas in high altitude

zones, rich in soil

nutrients, low species

richness in high

slopes, high species

richness of endemics

AAC Acer acuminatum AP Abies pindrow

EP Euonymus pendulus TW Taxus wallichiana

. IV. Low disturbance

areas with good

canopy cover, high

altitude areas, low

species richness,

high TBA

70

4.5 CLUSTERING OF COMMUNITIES (Squared Euclidean Distance)

Forest communities were assessed for their similarity using SPSS version 16, and

considering two vegetation parameters (i.e., species richness and density). The

clusters, thus generated, were divided in major groups. The major groups, wherever

possible, were further separated into sub-groups of communities.

4.5.1 PSK Site

The identified eleven forest communities when considered for their similarity showed

different groupings. For instance, in case of tree layer, the species richness parameter

made two clear groups (i.e., i & ii) whereas using density parameter three major

groups were discernible (i.e., i, ii & iii). Further, subgroups were also possible. For

example, Mixed-deciduous and Mixed Abies-Rhododendron-Maple communities

showed closeness when focusing on richness values (Figure 4.14 a1), whereas Mixed

Silver fir-Oak, Mixed Abies-Rhododendron-Maple, Abies pindrow communities

showed higher similarity considering species density parameter (Figure 4.14 b1). In

general, higher elevational communities formed a different group, except for

Hippophae salicifolia community. Considering the sapling layer, again two major

groups, for each parameter, were apparent. Further analysis on species richness values

showed closeness of Mixed Birch-Abies, Abies pindrow and Betula utilis, and Alnus

nepalensis and Mixed Oak-deciduous communities (Figure 4.14 a2). In case of

saplings, the high elevational communities made one major group including H.

salicifolia community. While considering similarity in terms of density almost all

communities, except for H. salicifolia, showed broad similarity (Figure 4.14 b2). Two

major groups were apparent for both vegetation parameters in case of seedling layer.

Alnus nepalensis and Mixed Silver fir-Oak communities showed highest similarity in

terms of seedlings (Figure 4.14 a3). Mixed Abies-Rhododendron-Maple, Abies

pindrow and Mixed Birch-Abies communities showed maximum similarity in terms

of seedling species density (Figure 4.14 b3). In case of the shrub layer, cluster for

species richness formed two and for species density three major groups. Mixed Oak-

deciduous and Mixed Silver fir-Oak communities shared maximum similarity both in

case of species richness and density (Figure 4.14 a4, b4). For shrub species richness

the high elevational communities, including H. salicifolia, made one major group.

While considering density, Mixed deciduous forests behaved more similar to high

altitude communities. In this case H. salicifolia community tended to be separate from

71

this group. In case of herb layer, 4 clusters each were apparent for both species

richness and density. While considering species richness, the cluster analysis depicted

Mixed deciduous and Mixed Abies-Rhododendron-Maple communities, and Quercus

semecarpifolia and Mixed silver fir-oak communities shared greater similarity (Figure

4.14 a5). Whereas, in case of density, Mixed Abies-Rhododendron Maple, Mixed

Silver fir-Oak, Abies pindrow and Mixed Birch-Abies communities formed one group

of most similar communities. Betula utilis and Q. floribunda tended to be dissimilar

from other communities w.r.t. density of herb species.

4.5.2 LTP Site

The clustering of eight identified forest communities is depicted (Figure 4.15). In case

of tree layer, three clusters of forest communities were apparent (i.e., Figure 4.15 i, ii

& iii) for both species richness and species density parameters. Abies spectabilis and

Betula utilis communities exhibited maximum closeness while considering species

richness values (Figure 4.15 a1). The higher elevational communities tended to make

one major group followed by another group consisting of low altitude communities.

However, P. wallichiana in the mid altitude zone, remained separated from other two

groups. While considering species density, Mixed Juglans regia-Prunus cornuta and

Mixed Acer caesium-Prunus cornuta communities shared higher similarity (Figure

4.15 b1). Taxus baccata-Abies pindrow community remained most distant from other

community clusters. In sapling layer, community cluster was divisible into two major

groups for each vegetation parameter. Abies spectabilis and Betula utilis communities

were closest when species richness was taken into accounts (Figure 4.15 a2).

However, C. deodara community remained separate from all other groups. Mixed

Juglans regia-Prunus cornuta, Pinus wallichiana and Abies spectabilis communities

were similar to each other when using density. In this regard, Mixed Acer caesium-

Prunus cornuta community remained most dissimilar (Figure 4.15 b2). When

considering seedling layer, the two major groups were formed. Abies spectabilis and

Abies pindrow communities were closest in case of species richness (Figure 4.15 a3).

Most of the high elevational communities tended to form one major group. For

density parameter, Pinus wallichiana and Betula utilis, and Mixed Juglans regia-

Prunus cornuta and Mixed Acer caesium-Prunus cornuta communities showed higher

similarity (Figure 4.15 b3). In case of shrub layer, two major groups were apparent.

Cedrus deodara and Mixed Juglans regia-Prunus cornuta communities exhibited

72

maximum similarity w.r.t. shrub species richness (Figure 4.15 a4). Considering

density values, Abies spectabilis and B. utilis communities were found closet (Figure

4.15 b4). While considering herb layer, cluster was divided into two major groups

wherein C. deodara alone forms a group when species richness was taken into

account. While using species richness, the cluster depicts closeness of Abies

spectabilis and Betula utilis, and Mixed Acer caesium-Prunus cornuta and Mixed

Taxus baccata-Abies pindrow communities (Figure 4.15 a5). In case of herb density,

Abies spectabilis and Betula utilis communities were most similar and formed one

distinct group (Figure 4.15 b5).

73

i

ii

iii

i

iiiii

i

iii

i

ii

i

iiiiiiv

i

ii

i

ii

i

ii

i

ii

i

iiiiv

ii

H I E R A R C H I C A L C L U S T E R A N A L Y S I S

Dendrogram using Average Linkage (Between Groups)

a1

a2

a3

b1

b2

b3

a4

a5

b4

b5

Figure 4.14 Clustering of forest communities in PSK site w.r.t. species richness a1-a5

and density b1-b5 [species richness and density of tree (a1, b1), saplings (a2, b2),

seedlings (a3, b3), shrubs (a4, b4) and herbs species (a5, b5)]; 1. Alnus nepalensis; 2.

Mixed Oak-deciduous; 3. Hippophae salicifolia; 4. Quercus floribunda; 5. Quercus semecarpifolia; 6.

Mixed-deciduous; 7. Mixed Silver fir-Oak; 8. Mixed Abies-Rhododendron-Maple; 9. Abies pindrow;

10. Mixed Birch-Abies; 11. Betula utilis

74

ii

ii

iii

i

ii

i

ii

i

ii

i

ii

H I E R A R C H I C A L C L U S T E R A N A L Y S I S

Dendrogram using Average Linkage (Between Groups)

b1

b2

b3

i

ii

i

ii

i

ii

i

ii

b5

b4

a1

a2

a3

a4

a5

Figure 4.15 Clustering of forest communities in LTP site w.r.t. species richness a1-a5

and density b1-b5 [species richness and density of tree (a1, b1), saplings (a2, b2),

seedlings (a3, b3), shrubs (a4, b4) and herbs species (a5, b5)]; 1. Cedrus deodara; 2.

Mixed Juglans regia-Prunus cornuta; 3. Mixed Acer caesium-Prunus cornuta; 4. Pinus wallichiana; 5.

Abies spectabilis; 6. Mixed Taxus baccata-Abies pindrow; 7. Abies pindrow; 8. Betula utilis

75

4.6 ALTITUDINAL PATTERNS OF SPECIES RICHNESS AND RATE OF

CHANGE

In view of the non-normality of data GAM was found best fit model (95% confidence

level) to predict rate of change in species richness and density at a particular change

point. Amongst different GAM based results, poisson based GAM indicated the

significant patterns for maximum species richness and density, and that may be

considered for detecting change point. In this context, the poisson (linear) function

was considered for the species richness and density patterns of different elevation

zones as a smoothened trend.

4.6.1 Patterns of Change – PSK Site

Across all tree strata, for species richness and density, largely the declining trends

across elevational gradients were revealing (Figure 4.16 a1-d1). While considering

total species richness of seedlings, the change point was revealing at 2800 m asl

(Figure 4.16 a1). Whereas, in case of trees and saplings species richness the change

point was observed around 3000 m (Figure 4.16 b1 and c1). Considering the overall

species richness, significant change point exists at 2700-2800 m asl (Figure 4.16 d1).

The model for estimating rate of change in density in different categories suggested

that the change point falls at 2400 m asl in case of seedling and saplings; 2600 m asl

for tree species. Interestingly, at 3000-3200 m asl another change point was seen

suggesting increasing trend. However, no distinct change point was observed while

taking total density into consideration (Figure 4.16 a2-d2).

Table 4.21 depicts overall rate of change values across elevational gradients.

The model estimated decrease in species richness (but non significant) and density

(significant; p<0.001) along elevation in all categories [i.e., total species richness (1-3

species), tree richness (2-4 species), sapling richness (0-1), seedling richness (0-1

species) per 1000 m elevation]. There is a more frequent decrease in density values

across classes, [i.e., total density (319-355 ind ha-1

), tree density (3-5 ind ha-1

), sapling

density (9-10 ind ha-1

) and seedling density (363-364 ind ha-1

) per 100 m elevation

(Table 4.21).

76

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rtia

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Figure 4.16 Spatial rate of change models w.r.t. species richness a1-d1 and density a2-d2 [a1, a2 seedling species richness and density; b1, b2-sapling species richness and density; c1, c2- tree species richness and density; d1, d2- total species richness and density] GAM estimates partial residual (species richness and density) (i.e., relationship between the predictor with the adjusted dependent variable values) in PSK site.

77

Category Predicted models

Tree richness Y = 33.33 - 0.003X; SE = 0.001; R2 = 21.76; n = 30; (p = ns)

Tree density Y = 1298.77 - 0.040X; SE = 0.010; R2 = 14.70; n = 30; (p < 0.001)

Sap richness Y = 20.59 - 0.00003X; SE = 0.001; R2 = 17.16; n = 30; (p = ns)

Sapling density Y = 1869.57 - 0.097X; SE = 0.008; R2 = 15.74; n = 30; (p < 0.0001)

Seedling richness Y = 27.28 - 0.0015X; SE = 0.001; R2 = 9.22; n = 30; (p = ns)

Seedling density Y = 33227.49 - 3.66X; SE = 0.029; R2 = 36.89; n = 30; (p < 0.0001)

Total richness Y = 28.00 – 0.002X; SE = 0.001; R2 = 23.78; n = 30; (p = ns)

Total density Y = 39025.12 – 3.23X; SE = 0.032; R2 = 21.91; n = 30; (p < 0.0001)

Table 4.21 Elevational change modeling for plant species richness and density across

diverse tree strata in PSK site

4.6.2 Patterns of Change – LTP Site

All the three tree strata, for species richness, depicted declining trend and, thus,

showed the change point at 2800 m asl except for seedling richness that showed

increasing trend and change point at 3300 m asl (Figure 4.17 a1-d1). ). The overall

rate of change in richness was observed at 2800 m asl (Figure 4.17 d1). In case of

density the change points varied for different strata. The rate of change in density of

trees was observed at 3000 m asl, which remained higher for seedlings (3400 m asl)

and for saplings (2900 m asl) (Figure a2-d2). Considering the overall density rate of

change was observed at 2900 m asl (Figure 4.17 d2).

Table 4.22 depicts overall rate of change values across elevational gradient.

The modal estimated that the species richness showed decreasing trends for tree

richness [(1-3 species), saplings (1-3 species), total species richness (0-2 species) and

increasing trend for seedling species (0-2 species) per 1000 m elevation]. The density

was observed increasing along elevation [i.e., total species density (53-56 ind ha-1

),

trees (7-10 ind ha-1

), saplings (15-17 ind ha-1

) and seedlings (22-24 ind ha-1

) per 100

m elevation (Table 4.22).

78

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Figure 4.17 Spatial rate of change models w.r.t. species richness a1-d1 and density

a2-d2 [a1, a2 seedling species richness and density; b1, b2-sapling species richness

and density; c1, c2- tree species richness and density; d1, d2- total species richness

and density] GAM estimates partial residual (species richness and density) (i.e.,

relationship between the predictor with the adjusted dependent variable values) in

LTP site

Pa

rtia

l re

sid

ua

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79

Category Predicted models

Tree richness Y = 47.18 - 0.002X; SE = 0.001; R2 = 43.76; n = 30; (p < 0.001)

Tree density Y = 1617.68 + 0.090X; SE = 0.012; R2 = 16.06; n = 30; (p < 0.001)

Sap richness Y = 22.17 - 0.002X; SE = 0.001; R2 = 33.81; n = 30; (p = ns)

Sapling density Y = 2795.47 + 0.163X; SE = 0.009; R2 = 30.52; n = 30; (p < 0.001)

Seedling richness Y = 43.12 + 0.0009X; SE = 0.001; R2 = 41.30; n = 30; (p < 0.001)

Seedling density Y = 4275.20 + 0.232X; SE = 0.011; R2 = 40.52; n = 30; (p < 0.001)

Total richness Y = 34.14 - 0.001X; SE = 0.001; R2 = 50.44; n = 30; (p < 0.001)

Total density Y = 4927.33 + 0.549X; SE = 0.019; R2 = 36.95; n = 30; (p < 0.001)

Table 4.22 Elevational change modeling for plant species richness and density across

diverse tree species strata in LTP site

.

4.7 DIVERSITY IN SOIL PARAMETERS

Means of various soil parameters [pH, total organic carbon (%), total organic matter

(%), total nitrogen (%) and C/N ratio] were used for describing diversity of soil in

studied communities. In general, across studied communities of the reserve, pH

ranged from 4.79 to 7.04, organic carbon from 1.21 to 6.90%, organic matter from

2.09 to 11.89 %, total nitrogen from 0.22 to 1.10 % and C/N ratio from 5.15 to 10.25.

4.7.1 Soil Parameters - PSK Site

The soil pH varied from 6.57 (Hippophae salicifolia) to 7.04 (Mixed Silver fir-

Rhododendron-Maple). Total soil organic carbon ranged from 3.38 (Alnus nepalensis)

to 4.69 % (Betula utilis). Soil organic matter, however, varied between 5.83 to 8.08 %

and followed the same trend as for organic carbon. The total nitrogen was minimum

in H. salicifolia community (0.40 %) and maximum in B. utilis community (0.91 %).

The C/N ratio for communities ranged from 5.15 to 8.68 %., with highest value for H.

salicifolia followed by Q. floribunda and Mixed Oak deciduous communities (Table

4.23).

80

Table 4.23 Variations in soil parameters across communities in PSK site

Soil parameters Community types

pH OC OM N C/N

Alnus nepalensis 6.78±0.01 3.38±0.64 5.83±0.64 0.63 ±0.30 5.39±2.13

Mixed Oak deciduous 6.93±0.02 4.14±0.31 7.15±0.31 0.68 ±0.13 6.12±2.39

Hippophae salicifolia 6.57±0.05 3.47±0.19 5.99±0.19 0.40 ±0.13 8.68±1.45

Quercus floribunda 6.91±0.21 4.06±0.86 6.99±0.86 0.62 ±0.18 6.55±4.83

Quercus semecarpifolia 6.86±0.09 3.90±0.15 6.73±0.15 0.72 ±0.07 5.45±2.02

Mixed-deciduous 6.86±0.17 4.22±0.07 7.28±0.07 0.74 ±0.07 5.73±1.06

Mixed Silver fir-Oak 6.84±0.06 4.67±0.12 8.04±0.12 0.86 ±0.01 5.42±1.12

Mixed Silver fir-Rhododendron-Maple 7.04±0.04 4.23±0.02 7.29±0.02 0.76 ±0.03 5.54±0.75

Abies pindrow 6.77±0.08 4.50±0.74 7.76±0.74 0.84 ±0.20 5.35±3.71

Mixed Birch-Silver fir 6.81±0.02 4.63±0.02 7.97±0.02 0.86 ±0.06 5.41±0.26

Betula utilis 6.75±0.01 4.69±0.09 8.08±0.09 0.91 ±0.06 5.15±1.37

4.7.2 Soil Parameters - LTP Site

The pH values across communities ranged from 4.79 to 6.32 with maximum pH in

Cedrus deodara community followed by Acer caesium-Prunus cornuta mixed and

Juglans regia-Prunus cornuta mixed communities. Total organic carbon ranged from

1.21 (Acer caesium-Prunus cornuta) to 6.90% (Betula utilis). Organic matter ranged

from 2.09 to 11.89% and followed the same trends as for organic carbon. The total

nitrogen ranged from 0.22 (A. caesium-P. cornuta) to 1.10% (B. utilis). The C/N ratio

ranged from 5.50 to 10.25%, with highest values in case of Cedrus deodara followed

by Abies spectabilis and Pinus wallichiana communities (Table 4.24).

Table 4.24 Variations in soil parameters across communities in LTP site

Soil parameters Community types

pH OC OM N C/N

Cedrus deodara 6.32±0.03 4.03±0.11 6.94±0.11 0.39±0.05 10.25±2.12

Juglans regia-Prunus cornuta mixed 6.11±0.00 4.06±0.00 7.00±0.00 0.5±0.00 8.12±0.00

Acer caesium-Prunus cornuta mixed 6.27±0.00 1.21±0.00 2.09±0.00 0.22±0.00 5.50±0.00

Pinus wallichiana 6.06±0.58 3.37±0.20 5.80±0.20 0.36±0.07 9.31±2.67

Abies spectabilis 4.79±0.10 3.32±0.01 5.73±0.01 0.34±0.09 9.67±0.12

Taxus wallichiana-Abies pindrow mixed 5.12±0.00 5.49±0.00 9.46±0.00 0.85±0.00 6.46±0.00

Abies pindrow 5.31±0.16 5.49±0.07 9.46±0.07 0.79±0.17 6.96±0.40

Betula utilis 5.34±0.44 6.90±0.14 11.89±0.14 1.10±0.04 6.26±3.06

81

4.8 TEMPORAL CHANGES IN VEGETATION

This section, by way of comparison of two time data sets, attempts to analyse

compositional changes in forest communities in represented sites of NDBR (i.e., PSK

and LTP). As described earlier, soil and vegetations parameters were re-examined in

present study for assessing the temporal change trends. The current data sets were

subsequntly compared with the available earlier datasets for representative sites (PSK-

Bankoti 1992 and LTP - Joshi 2002) to generate following patterns.

4.8.1 Responses at Site and Community Level

4.8.1.1 Species richness and diversity

Significant increase in species richness in seedling (paired mean 8.91 to 11.27;

p<0.01) and sapling layers (paired mean 8.91 to 11.45; p<0.05) over the last two

decades was revealing in PSK site. However, no significant change in tree layer

species richness was recorded (Table 4.25). At community level, maximum increase

in species richness in seedling layer was recorded in Quercus floribunda and Mixed-

deciduous communities (8 species each; p<0.05). In sapling layer, maximum increase

was recorded in Q. semecarpifolia community (6 species; p<0.05) followed by Q.

floribunda community (5 species; p<0.05). Some other communities, like Mixed-

deciduous, Alnus nepalensis and Mixed Silver fir-Rhododendron-Maple also showed

increase (p=ns) in species richness sapling layer during last two decades. None of the

communities, however, showed significant increase or reduction in tree layer richness

(Table 4.25).

Considering the diversity index values no significant changes were revealing

at any tree strata over the last two decades (Table 4.26). Increase (p=ns) in species

diversity at seedling (paired mean 2.07 to 2.10), sapling (paired mean 2.02 to 2.25)

was recorded. In case of tree layer the diversity has marginally decreased (paired

mean 2.60 to 2.55). At community level, Mixed Silver fir-Oak community, however,

showed significant increase in diversity index value of sapling layer (p<0.05).

82

Table 4.25 Temporal changes in species richness in tree, sapling and seedling layers

in PSK site

Species richness Community types

Seedlings

Saplings Trees

1988-90 2008-10 1988-90 2008-10 1988-90 2008-10

Alnus nepalensis 9 9 5 9 14 14

Mixed Oak deciduous 11 13 10 12 21 21

Hippophae salicifolia 4 5 4 3 5 4

Quercus floribunda 11* 19* 13* 18* 21 20

Quercus semecarpifolia 15 16 8* 14* 24 23

Mixed-deciduous 8* 16* 11 15 21 21

Mixed Silver fir-Oak 9 9 13 13 21 21

Mixed Silver fir-Rhododendron-Maple 11 14 11 15 23 23

Abies pindrow 9 9 7 10 15 17

Mixed Birch-Silver fir 6 9 11 11 10 10

Betula utilis 5 5 5 6 7 8

Paired mean 8.91 11.27 8.91 11.45 16.54 16.54

SE 0.95 1.39 0.98 1.30 2.03 1.99

T-test (p<0.05*; p<0.01**) 0.02* 0.004** 1.00

Table 4.26 Temporal changes in diversity (H’) values in tree, sapling and seedling

ayers in PSK site

Species diversity (H’) Community types

Seedlings

Saplings Trees

1988-90 2008-10 1988-90 2008-10 1988-90 2008-10

Alnus nepalensis 2.25 2.20 1.84 2.38 2.50 2.38

Mixed Oak deciduous 2.35 2.28 2.22 2.66 3.26 3.37

Hippophae salicifolia 0.70 0.70 0.72 0.64 1.16 0.73

Quercus floribunda 2.35 2.46 2.40 2.48 3.10 2.84

Quercus semecarpifolia 2.01 2.27 1.58 2.10 2.97 2.73

Mixed-deciduous 2.26 2.34 2.39 2.47 3.29 3.31

Mixed Silver fir-Oak 2.23 2.27 0.79* 2.39* 3.18 3.20

Mixed Silver fir-Rhododendron-Maple 2.51 2.51 2.83 2.67 3.01 3.22

Abies pindrow 2.36 2.08 2.93 2.55 2.49 2.71

Mixed Birch-Silver fir 2.32 2.49 2.41 2.64 2.25 2.02

Betula utilis 1.49 1.50 2.11 1.81 1.41 1.55

Paired mean 2.07 2.10 2.02 2.25 2.60 2.55

SE 0.159 0.163 0.221 0.179 0.222 0.250

T-test (p<0.05*; p<0.01**) 0.57 0.19 0.46

While considering LTP site, no significant change in species richness of any

tree strata was observed (Table 4.27). However, increasing trend in mean species

richness was observed at seedling (paired mean 7.87 to 8.62) and sapling (paired

mean 6.00 to 6.50) level. Amongst communities, Acer caesium-Prunus cornata mixed

community showed significant increase in seedling layer (7 species; p<0.01). Species

83

diversity showed similar patterns of non significant changes in seedling and sapling

layer. However, change for the tree layer was significant (p<0.05). At community

level, Acer caesium-Prunus cornata mixed (p<0.05) in seedling layer and Taxus

baccata-Abies pindrow mixed community in tree layer (p<0.05) showed significant

increase in diversity values.

Table 4.27 Temporal changes in species richness in tree, sapling and seedling layers

in LTP site

Species richness Community types

Seedlings Saplings Trees

1998-2000 2008-10 1998-2000 2008-10 1998-2000 2008-10

Cedrus deodara 3 4 5 6 4 4

Juglan regia-Prunus cornuta mixed 5 6 5 6 7 7

Acer caesium-Prunus cornata mixed 0 ** 7** 2 4 5 5

Pinus wallichiana 18 16 12 11 18 18

Abies spectabilis 12 11 6 6 9 9

Taxus baccata-Abies pindrow mixed 7 8 5 5 4 4

Abies pindrow 9 8 6 7 9 9

Betula utilis 9 9 7 7 10 10

Paired mean 7.87 8.62 6.00 6.50 8.25 8.25

SE 1.96 1.28 1.00 0.73 1.62 1.62

T-test (p<0.05*; p<0.01**) 0.47 0.17 -

Table 4.28 Temporal changes in diversity (H’) values in tree, sapling and seedling

layers in LTP site

Species diversity (H’) Community types

Seedlings Saplings Trees

1998-2000 2008-10 1998-2000 2008-10 1998-2000 2008-10

Cedrus deodara 0.75 0.96 0.77 0.86 0.59 0.63

Juglan regia-Prunus cornuta mixed 1.26 1.32 0.69 0.76 1.60 1.61

Acer caesium-Prunus cornata mixed 0.00* 0.35* 0.66 0.76 0.94 0.96

Pinus wallichiana 1.84 1.79 1.17 1.16 1.15 1.22

Abies spectabilis 0.78 0.58 1.44 1.36 1.47 1.52

Taxus baccata-Abies pindrow mixed 0.95 1.10 0.67 0.82 0.92* 1.04*

Abies pindrow 1.33 1.15 1.31 1.24 1.27 1.42

Betula utilis 1.28 1.32 0.86 0.86 1.26 1.21

Paired mean 1.02 1.07 0.95 0.98 1.15 1.20

SE 0.19 0.16 0.11 0.08 0.11 0.11

T-test (p<0.05*; p<0.01**) 0.50 0.32 0.05*

4.8.1.2 Species density and Total Basal Area (TBA)

In case of PSK site, patterns of density in seedling layer showed significant increase

in last two decades (paired mean 2885 to 3927; p<0.05). Sapling layer (paired mean

220 to 250) also exhibited increasing trend (p=ns). However, decreasing trend of

density in tree layer was revealing (paired mean 428 to 406; p=ns). Amongst

communities, maximum and significant increase in seedling density was recorded in

84

Mixed Oak deciduous community (p<0.05) followed by Q. floribunda and Q.

semecarpifolia communities (p=ns). In sapling layer, maximum increase was recorded

in Mixed Oak deciduous community followed by Alnus nepalensis and Abies pindrow

community (p=ns). While changes in the tree density of all communities were non-

significant, it was interesting to note certain communities especially in lower altitudes

showed decreasing trends of densities during last two decades (Table 4.29).

While using TBA no significant changes over the last two decades were

observed (Table 4.30). At community level, A. nepalensis, Mixed Silver fir-

Rhododendron-Maple, and Betula utilis communities showed considerable increase.

Whereas, Mixed Oak deciduous, Q. floribunda, Q. semecarpifolia showed noticeable

decrease in TBA. A general decrease in TBA in the lower altitude communities was

recorded in last two decades (Table 4.30).

Table 4.29 Temporal changes in density (ind ha-1

) of tree, sapling and seedling layers

in PSK site Species density (ind ha

-1) Community types

Seedlings

Saplings Trees

1988-90 2008-10 1988-90 2008-10 1988-90 2008-10

Alnus nepalensis 4500 5325 295 345 435 460

Mixed Oak deciduous 2870* 8170* 130 233 520 480

Hippophae salicifolia 2333 3166 757 633 703 423

Quercus floribunda 3250 5650 143 170 375 378

Quercus semecarpifolia 3750 4663 198 200 490 473

Mixed-deciduous 2400 3117 147 197 417 407

Mixed Silver fir-Oak 4000 3325 105 145 280 260

Mixed Silver fir-Rhododendron-Maple 3433 3583 160 200 327 360

Abies pindrow 2300 2150 105 190 305 320

Mixed Birch-Silver fir 1800 2600 175 210 360 370

Betula utilis 1100 1450 210 235 500 535

Paired mean 2885 3927 220 250 428 406

SE 305 573 56 41 36 23

T-test (p<0.05*; p<0.01**) 0.05* 0.12 0.42

85

Table 4. 30 Temporal changes in TBA (m2 ha

-1) of forest communities in PSK site

Community types TBA

1988-90 2008-10

Alnus nepalensis 22.06 31.23

Mixed Oak deciduous 80.31 61.31

Hippophae salicifolia 10.42 8.85

Quercus floribunda 74.24 60.60

Quercus semecarpifolia 71.4 65.88

Mixed-deciduous 44.41 42.13

Mixed Silver fir-Oak 42.9 42.61

Mixed Silver fir-Rhododendron-Maple 46.46 72.03

Abies pindrow 22.27 21.28

Mixed Birch-Silver fir 13.44 16.37

Betula utilis 6.90 12.72

Paired mean 39.52 39.54

SE 8.06 6.93

T-test (p<0.05*; p<0.01**) 0.99

For LTP site, seedling layer showed significant increase in species density

over the last one decade (paired mean 599 to 829; p<0.01). Sapling layers (paired

mean 538 to 523) and tree layer (paired mean 807 to 804) exhibited non-significant

decreasing trends. Amongst communities, significant increase in seedling density was

recorded in Abies spectabilis community (p<0.01), followed by Abies pindrow and

Pinus wallichiana communities (p<0.05). In sapling layer, maximum increase was

recorded in Juglans regia-Prunus cornuta mixed community (p<0.05) followed by

Pinus wallichiana and Acer caesium-Prunus cornata mixed community (p=ns). The

tree density has more or less remained unchanged during last one decade (Table 4.31).

Table 4.31 Temporal changes in density (ind ha-1

) of tree, sapling and seedling in

LTP site

Species density (ind ha-1

) Community types

Seedlings Saplings Trees

1998-2000 2008-10 1998-2000 2008-10 1998-2000 2008-10

Cedrus deodara 430 470 440 404 606 612

Juglans regia-Prunus cornuta mixed 690 850 40* 70* 760 750

Acer caesium-Prunus cornata mixed 440 490 760 790 940 960

Pinus wallichiana 371* 644* 300 346 632 630

Abies spectabilis 400** 890** 445 406 620 599

Taxus baccata-Abies pindrow mixed 840 966 810 789 1220 1211

Abies pindrow 1195* 1665* 1094 951 837 818

Betula utilis 430 658 416 435 848 856

Paired mean 599 829 538 523 807 804

SE 103 135 117 103 73 73

T-test (p<0.05*; p<0.01**) 0.007** 0.53 0.52

86

Comparison on TBA revealed no significant change over the last one decade

(Table 4.32). However, in many cases, increasing trend was apparent (paired mean

47.6 to 48.7). Amongst communities, Pinus wallichiana and Betula utilis

communities showed noticeable increase while A. spectabilis and A. pindrow showed

decrease in TBA.

Table 4.32 Temporal changes in TBA (m2 ha

-1) in tree layer of LTP site

Community types TBA

1998-2000 2008-10

Cedrus deodara 80.28 80.43

Juglans regia-Prunus cornuta mixed 14.68 15.10

Acer caesium-Prunus cornata mixed 72.06 71.44

Pinus wallichiana 29.13 34.97

Abies spectabilis 34.28 30.07

Taxus baccata-Abies pindrow mixed 35.78 38.96

Abies pindrow 64.92 61.43

Betula utilis 49.8 57.09

Paired mean 47.6 48.7

SE 8.14 7.92

T-test (p<0.05*; p<0.01**) 0.94

4.8.1.3 Changes in soil parameters

Significant changes in studied soil parameters were observed during last two decades

in PSK site [i.e., organic carbon (paired mean 4.60 to 4.17; p<0.05), organic matter

(paired mean 7.93 to 7.19; p<0.05) and C/N ratio (paired mean 7.18 to 5.94; p<0.01)

showed significant decrease and nitrogen (paired mean 0.65 to 0.73; p<0.01) showed

significant increase]. pH did not exhibit any significant change (paired mean 6.80 to

6.83; p=ns). However, no significant changes were seen in these parameters for

individual communities (Table 4.33).

In LTP site, significant increase in nitrogen (paired mean 0.51 to 0.57;

p<0.01) was revealing with a decline in C/N ratio (paired mean 8.90 to 7.82; p<0.01).

However, organic carbon (paired mean 4.27 to 4.23; p<ns) and organic matter (paired

mean 7.37 to 7.30; p<ns) showed non significant decrease. Likewise, pH showed non

significant decrease (paired mean 5.76 to 5.67; p=ns). Like PSK site, at individual

community level no significant change was observed in the studied soil parameters

(Table 4.34).

87

Table 4.33 Temporal changes in soil parameters of PSK site

Soil parameters Community types

pH OC OM N C/N

1988-90 2008-10 1988-90 2008-10 1988-90 2008-10 1988-90 2008-10 1988-90 2008-10

Alnus nepalensis 6.75 6.78 4.00 3.38 6.90 5.83 0.50 0.63 8.08 5.39

Mixed Oak deciduous 6.87 6.93 4.80 4.14 8.28 7.15 0.53 0.68 9.06 6.12

Hippophae salicifolia 6.80 6.91 3.37 4.06 5.80 6.99 0.51 0.40 6.64 10.19

Quercus floribunda 6.85 6.57 4.68 3.47 8.06 5.99 0.59 0.62 7.99 5.61

Quercus semecarpifolia 6.80 6.86 4.65 3.90 8.02 6.73 0.63 0.72 7.35 5.45

Mixed-deciduous 6.83 6.86 4.67 4.22 8.05 7.28 0.67 0.74 7.00 5.73

Mixed Silver fir-Oak 6.80 6.84 4.70 4.67 8.10 8.04 0.71 0.86 6.67 5.42

Mixed Silver fir-

Rhododendron-Maple 6.93 7.04 4.77 4.23 8.22 7.29 0.65 0.76 7.33 5.54

Abies pindrow 6.75 6.77 4.80 4.50 8.28 7.76 0.73 0.84 6.62 5.35

Mixed Birch-Silver fir 6.75 6.81 5.05 4.63 8.71 7.97 0.83 0.86 6.12 5.41

Betula utilis 6.70 6.75 5.10 4.69 8.79 8.08 0.84 0.91 6.11 5.15

Paired mean 6.80 6.83 4.60 4.17 7.93 7.19 0.65 0.73 7.18 5.94

SE 0.019 0.036 0.149 0.136 0.258 0.233 0.036 0.044 0.273 0.431

T-test (p<0.05*;

p<0.01**) 0.43 0.01* 0.01* 0.007** 0.04*

Table 4.34 Temporal changes in soil parameters of LTP site

Soil parameters Community types

pH OC OM N C/N

1998-02 2008-10 1998-02 2008-10 1998-02 2008-10 1998-02 2008-10 1998-02 2008-10

Cedrus deodara 6.36 6.32 4.15 4.03 7.16 6.94 0.39 0.39 10.64 10.25

Jugrlans regia-Prunus

cornuta mixed 6.2 6.11 4.13 4.06 7.12 7.00 0.48 0.5 8.60 8.12

Acer caesium-Prunus

cornata mixed 6.4 6.27 1.28 1.21 2.21 2.09 0.18 0.22 7.11 5.50

Pinus wallichiana 6.39 6.06 3.34 3.37 5.76 5.80 0.30 0.36 11.13 9.31

Abies spectabilis 4.75 4.79 3.28 3.32 5.65 5.73 0.28 0.34 11.71 9.67

Taxus baccata-Abies

pindrow mixed 5.2 5.12 5.53 5.49 9.53 9.46 0.81 0.85 6.83 6.46

Abies pindrow 5.21 5.31 5.48 5.49 9.44 9.46 0.67 0.79 8.18 6.96

Betula utilis 5.54 5.34 7.00 6.90 12.07 11.89 1.00 1.10 7.00 6.26

Paired mean 5.76 5.67 4.27 4.23 7.37 7.30 0.51 0.57 8.90 7.82

SE 0.23 0.20 0.62 0.61 1.60 1.60 0.10 0.11 0.70 0.63

T-test (p<0.05*;

p<0.01**) 0.09 0.10 0.10 0.006** 0.003**

88

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1988 -90 2008 -10

4.8.2 Responses at Species Level

4.8.2.1 Responses in PSK Site

Comparison of data sets for two times (1988-90 and 2008-10), across forest

communities, have yielded interesting trends of changes as follows:

In Alnus nepalensis community, A. nepalensis still continues to dominate (density

230 ind ha-1

; IVI 128.2), with increased dominance over the last two decades.

However, the co-dominant species Lyonia ovalifolia of 1988-90 seems to have lost

some of its dominance to other associates (Figure 4.18 a & b).

Figure 4.18 Changing trends of important species in Alnsus nepalensis

community of PSK site [a) tree IVI; b) tree density; c) sapling density; d) seedling

density]

Rhododendron arboreum exhibited considerable increase in sapling and

seedling layer. Some other species have shown increase in IVI (e.g., Neolitsea

pallens) and tree density (e.g., Betula alnoides, Acer cappadocicum). At sapling and

seedling level, the density of dominant A. nepalensis has declined. Some species

showed their presence for first time at sapling level (i.e., U. wallichiana, A.

cappadocicum, B. alnoiedes and Ilex dipyrena).

Major changes were apparent at dominant species level in Mixed Oak

deciduous community. At tree layer, Aesculus indica and Ulmus wallichiana

exhibited considerable decrease in density and IVI. Quercus floribunda with highest

89

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1988 -90 2008 -10

density and IVI (density 80 ind ha-1

, IVI 79.75) has enjoyed increased dominance in

last two decades. R. arboreum, under canopy dominant, also exhibited similar trends

of increase. Viburnum nervosum (at sapling layer) showed drastic increase in density.

Some other species, like A. cappadocicum and Q. semecarpifolia showed more recent

entry in sapling and seedling layers respectively. With considerable increase in

seedling density and the increasing trends of dominance in tree layer Q. floribunda

has emerged as new dominant along with R. arboreum appearing as important co-

dominant in this community (Figure 4.19 a-d).

Figure 4.19 Changing trends of important species in Mixed Oak deciduous

community of PSK site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

Hippophae salicifolia, the dominant species, continues to enjoy dominance in

Hippophae salicifoila community across all layers (tree, sapling and seedling). The

major co-dominant A. nepalensis exhibited decline in both tree density and IVI. In

case of sapling density R. arboreum seems to be a new entry to this community that

has established itself in each tree layer during last two decades (Figure 4.20 a-d).

90

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1988 -90 2008 -10

Figure 4.20 Changing trends of important species in Hippophae salicifolia

community of PSK site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

In case of Tilonj- Oak (Quercus floribunda) community, the dominant

species, Q. floribunda showed increased IVI and density in all three layers. This

species even showed significant presence in sapling layer where it was not recorded in

1988-90, and species now contributes maximum seedling density as well. Density of

R. arboreum, N. pallens, A. cappadocicum and A. acuminatum in sapling and seedling

layers exhibited increasing trends of their establishment (Figure 4.21 a-d).

The dominant Q. semecarpifolia in Kharsu Oak (Quercus semecarpifolia)

community along with co-dominant R. arboreum showed marginal increase in IVI

and tree density. The IVI values of major co-dominant Q. floribunda (IVI 35.27)

have declined which seems to have taken over by R. arboreum (IVI 37.96). The

emergence of some new species at sapling (A. cappoadocicum) and seedling (A.

pindrow) level are indicative of possible future changes in compositional diversity of

the community (Figure 4.22 a-d).

91

0.0020.0040.0060.0080.00

100.00120.00140.00

0.020.040.060.080.0

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a b

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1988 -90 2008 -10

0.00

30.00

60.00

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120.00

150.00

180.00

0.020.040.060.080.0

100.0120.0140.0

01020304050607080

0250500750

10001250150017502000

a b

c d

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Den

sit

y (

ind h

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Den

sit

y (

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Den

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1988 -90 2008 -10

Figure 4.21 Changing trends of important species in Quercus floribunda community

in PSK site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

Figure 4.22 Changing trends of important species in Quercus semecarpifolia

community in PSK site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

The density and IVI values of dominant species Acer cappadocicum showed

increasing trend in tree, sapling and seedling layers of Mixed deciduous community.

The major co-dominant species A. pindrow did not exhibit any change at tree layer

and had very low saplings and seedlings density. Some other species, which were

92

either not present before or had poor density at sapling and seedling level, presently

exhibited good presence in the community (i.e., Neolitsea pallens, Ilex dipyrena,

Alnus nepalensis, Acer caesium, etc.) (Figure 4.23 a-d).

010203040506070

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Den

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Den

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Den

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1988 -90 2008 -10

Figure 4.23 Changing trends of important species in Mixed deciduous community in

PSK site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

In Mixed Silver fir-Oak community, the former dominant species A. indica

seems to have lost its importance for A. pindrow and Q. semecarpifolia. Some

species, like R. arboreum, A. nepalensis and A. spectabilis have also emerged as

interesting species that can play major role in future compositional changes at tree

layer. Besides, A. pindrow, N. pallens, R. arboreum, Q. floribunda and Q.

semecarpifolia were amongst the species exhibiting major changes at sapling and

seedling layers (Figure 4.24 a-d).

93

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70

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1988 -90 2008 -10

Figure 4.24 Changing trends of important species in Mixed Silver fir-Oak community

in PSK site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

All three dominant and co-dominant species (R. barbatum, A. pindrow, A.

cappadocicum) showed slight decrease in IVI value in Mixed Silver fir-

Rhododendron-Maple community. Few other species have been identified that may

be held responsible for future compositional changes at tree layer (A. indica, L.

ovalfolia, S. emodi), sapling layer (A. caesium) and seedling layer (R. campanulatum,

A. cappadocicum, A. pindrow) (Figure 4.25 a-d).

Abies pindrow, with highest IVI and density values, continues to be the

dominant species in Abies pindrow community. Importance of earlier co-dominant

species B. utilis (44) seems to have taken over by R. barbatum (47.63). Interestingly,

Q. floribunda has emerged as an important new co-dominant species in this

community. M. dilleniaefolia, A. cappadocicum, A. acuminatum at sapling and A.

pindrow, A. cappadocicum and Q. floribunda at seedling layers have appeared either

as new entry to this community or exhibited increased density during the last two

decades (Figure 4.26 a-d).

94

0153045607590

105

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1988 -90 2008 -10

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1988 -90 2008 -10

Figure 4.25 Changing trends of important species in Mixed Silver fir-Rhododendron-

Maple community in PSK site [a) tree IVI; b) tree density; c) sapling density; d) seedling

density]

Figure 4.26 Changing trends of important species in Abies pindrow community in

PSK site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

95

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1988 -90 2008 -10

In Mixed Birch-Silver fir community, the earlier dominant and co-dominant

species continue to enjoy the same dominance w.r.t. IVI and tree density. R.

barbatum, A. pindrow and A. acuminatum have, however, shown considerable

increase in sapling and seedling layers (Figure 4.27 a-d).

Betula utilis remains the most dominating species (IVI 183.42 and tree density

375 ind ha-1

) in Betula utilis community. However, considerable decrease in IVI of

major co-dominant species A. pindrow was recorded. E. fimbriatus, R. campanulatum,

R. arboreum have shown trends of better establishment in recent times (Figure 4.28 a-

d).

Figure 4.27 Changing trends of important species in Mixed Birch-Silver fir

community in PSK site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

96

050

100150200250300350400450

0.025.050.075.0

100.0125.0150.0175.0200.0225.0

0255075

100125150175

0

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500

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700

a b

c d

IVI

Den

sit

y (

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a-1)

Den

sit

y (

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a-1)

Den

sit

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a-1)

1988 -90 2008 -10

Figure 4.28 Changing trends of important species in Betula utilis community in PSK

site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

4.8.2.2 Species response in LTP Site

The site did not exhibit major changes over the last one decade (2000-2010). Most of

the communities more or less followed the same trends as were recorded 10 years

back by Joshi (2002). However, in certain cases species showed trends of changes

especially at sapling and seedling layer. These changes have been described below:

In case of Cedrus deodara community no changes were observed for

dominant and co-dominant species. However, density of P. wallichiana has increased

noticeably in sapling and seedling layers, thereby suggesting possibilities of its

preponderance in future. Prunus persica, a co-dominant, seems to has lost its relative

dominance in seedling and sapling layers (Figure 4.29 a-d).

The dominant species Juglans regia in Juglans regia-Prunus cornuta mixed

community showed decrease in IVI, and earlier co-dominant P. cornuta (IVI 94) has

now emerged as most important species. J. regia and A. acuminatum were not

represented in sapling layer. At seedling layer, Acer acuminatum and A. caesium have

shown recent entry in the forest. Major changes at seedling (e.g., E. fimbriatus) and

sapling layer (e.g., A. caesium) were observed compared to earlier reported

composition in 2000-02 (Figure 4.30 a-d).

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)

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)

2000 -02 2008 -10

Figure 4.29 Changing trends of important species in Cedrus deodara community in

LTP site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

Figure 4.30 Changing trends of important species in Juglans regia-Prunus cornuta

community in LTP site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

98

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In Acer caesium-Prunus cornuta mixed community, the dominant species A.

caesium showed more or less similar dominance status as in 2000-02. The species still

has very low numbers of saplings and seedlings. The major co-dominant species P.

cornuta remains unchanged across tree layer. However, marginal decrease at sapling

stage and recent entry in seedling stage was revealing. The co-dominant, E.

fimbriatus, with improved status in all layers showed better prospects for future. At

sapling and seedling level, newly emergent species (i.e., A. caesium, R. arboreum and

P. cornuta) were observed (Figure 4.31 a-d).

Figure 4.31 Changing trends of important species in Acer caesium-Prunus cornuta

community in LTP site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

Both dominant and co-dominant species in Pinus wallichiana community

remained unchanged. P. wallichiana with stronger presence in sapling and seedling

stages, and slight improvement in tree layer as well, exhibited possibilities of further

strengthening. Healthy presence of A. pindrow, A. caesium at sapling and seedling

layers indicated their increasing importance in the community. A few new species

(i.e., R. arboreum and S. daphnoides) have also appeared in seedling layer (Figure

4.32 a-d).

Important species in Abies spectabilis community showed similar trends as for

Pinus wallichiana community. However, rapid increase in co-dominant B. utilis at

seedling layer accompanied by newly established seedlings of other species (i.e., A.

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2000 -02 2008 -10

acuminatum, R. arboreum and S. daphnoides) attracted the attention while

considering future compositional changes in the community (Figure 4.33 a-d).

Figure 4.32 Changing trends of important species in Pinus wallichiana community in

LTP site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

Figure 4.33 Changing trends of important species in Abies spectabilis community in

LTP site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

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2000 -02 2008 -10

Very small changes were visible at dominant and co-dominant species in Taxus

wallichiana-Abies pindrow mixed community. However, co-dominant B. utilis

showed its emergence in sapling and seedling (along with P. wallichiana) layer where

it was not present ten years back. While T. wallichiana has shown decrease in

seedling density. A. pindrow along with A. acuminatum has strengthened its presence

in seedling layer (Figure 4.34 a-d).

Figure 4.34 Changing trends of important species in Taxus wallichiana-Abies

pindrow mixed community in LTP site [a) tree IVI; b) tree density; c) sapling density; d)

seedling density]

Compositional patterns in Abies pindrow community showed that the

dominant and co-dominant species have remained more or less unchanged over the

last 10 years, and these species exhibit increasing trends in seeling layer. P.

wallichiana and S. daphnoides have appeared as new entry at sapling and seedling

layers (Figure 4.35 a-d).

Dominant species Betual utilis continues to enjoy the dominance in Betula

utilis community with improved density at seedling stage. Likewise, A. pindrow still

remains co-dominant in the community at every level. In last 10 years, R. arboreum

has gained good number of seedlings along with S. disperma (Figure 4.36 a-d).

101

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2000 -02 2008 -10

Figure 4.35 Changing trends of important species in Abies pindrow community in

LTP site [ a) tree IVI; b) tree density; c) sapling density; d) seedling density]

Figure 4.36 Changing trends of important species in Betula utilis community in LTP

site [a) tree IVI; b) tree density; c) sapling density; d) seedling density]

102

4.9 SPATIAL PATTERNS OF LANDUSE/LANDCOVER

The patterns of various landuse/landcover classes present in NDBR at different times

have been shown in Figure 4.37 (1990) and 4.38 (2005).

4.9.1 Patterns of Spatial Distribution of Landuse/Landcover Classes

Based on Landsat-TM (2005) data, the recent spatial distribution (2005) and area

statistics of different landuse/landcover classes of NDBR have been presented (Table

4.35 and Figure 4.37). This statistics showed that over 65% area of NDBR is

snowbound or covered by glaciers. The next dominant landcover is forest vegetation

(11.1%) including three physiognomic types Conifer dominated evergreen, Evergreen

broadleaf, and Mixed broadleaf forests. Alpine meadows (10.9%) and alpine scrubs

(6.4%) are other important landcover classes. The agriculture land (crop land)

occupies only 0.1% of the reserve which confines to buffer and transition zones only.

4.9.2 Temporal Changes in the Forest Vegetation

Temporal changes in various landuse/landcover categories, with a particular focus on

forest vegetation (Physiognomic types), have been investigated in the present study

using Landsat TM 1990 and 2005 data. The change statistics have been given (Table

4.36).

While considering the overall vegetation cover in the reserve, a total decline of

51.3 km2 (2.8%) was registered between 1990 and 2005. Most of the vegetation cover

classes exhibited negative change over 15 years time interval, with a maximum

decrease of 39.6 km2 (5.6%) in case of alpine meadows followed by alpine scrubs

(15.8 km2; 3.9%). Among delineated forest physiognomic types in the reserve,

Conifer dominated evergreen (8.1 Km2; 2.1%) and Mixed broadleaf forests (4.8 Km

2;

3.9%) showed negative changes. However, a noticeable positive change was revealing

for Evergreen broadleaf forest (17.4 km2; 10.6%). Temporal changes in different

forest physiognomic types have been presented (Figure 4.39). Zone based analysis

showed that the core zone has registered an increase of 13.2 km2 for Conifer

dominated forests since 1990 till 2005. However, maximum decrease has been

recorded in Conifer dominated forests in buffer and transition zones which was 19.12

km2 in last 15 years (Table 4.37).

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Table 4.35 Area statistics of different landuse/landcover classes of NDBR

Cover class 2005

Area (km2) Area (%)

Vegetation

Alpine Meadows 668.9 10.9

Alpine Scrubs 392.6 6.4

Conifer Dominated Forest 378.6 6.2

Evergreen Broadleaf Forest 180.9 3.0

Mixed Broadleaf Forest 117.8 1.9

Low land Grassland 25.9 0.4

Non-vegetation including croplands

Snow and Moraine 3715.9 60.7

Glacier 377.5 6.2

High Altitude Lake 0.2 0.0

River 17.4 0.3

Open Rocks 68.6 1.1

Forest Blanks 165.7 2.7

Crop Land 8.1 0.1

Total 6118.0 100

Table 4.36 Change in landuse/landcover of NDBR during the period 1990-2005

Cover class 1990 2005

Area (km2) Area (%) Area (km

2) Area (%)

Vegetation

Alpine Meadows 708.5 11.6 668.9 10.9

Alpine Scrubs 408.4 6.7 392.6 6.4

Conifer Dominated Forest 386.7 6.3 378.6 6.2

Evergreen Broadleaf

Forest

163.5 2.7 180.9 3.0

Mixed Broadleaf Forest 122.6 2.0 117.8 1.9

Low land Grassland 26.3 0.4 25.9 0.4

Non-vegetation including croplands

Snow and Moraine 3683.9 60.2 3715.9 60.7

Glacier 359.1 5.9 377.5 6.2

High Altitude Lake 0.3 0.0 0.2 0.0

River 11.3 0.2 17.4 0.3

Open Rocks 69.5 1.1 68.6 1.1

Forest Blanks 170.8 2.8 165.7 2.7

Crop Land 7.3 0.1 8.1 0.1

Total 6118.1 100.0 6118.0 100

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GIS lab, GBPIHED

Figure 4.37 Landuse and Landcover map of NDBR based on Landsat-TM 2005

105

Landuse & Landcover MAP

Year 1990

GIS lab, GBPIHED

Figure 4.38 Landuse and Landcover map of NDBR based on Landsat-TM 1990

106

Table 4.37 Change statistics for forest physiognomy types across different zones of

NDBR during the period 1990-2005

Physiognomy Core Buffer & Transition

1990 2005 1990 2005

Conifer dominated 13.2 24.22 373.5 354.38

Evergreen broadleaf - - 163.5 176.3

Mixed broadleaf

forests

- - 122.6 117.8

Figure 4.39 Changes in different forest physiognomic types in NDBR [ a-b) Conifer

dominated forests (1990&2005); c-d) Evergreen broadleaf forests (1990&2005) and; e-f) Mixed

broadleaf forests (1990&2005) ]

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4.10 IDENTIFICATION OF SENSITIVE AND RICH AREAS

The Community Change Index (CCS), the Community Integrity Index (CII), and the

Community Threat Index (CTI) helped in identification/prioritization of the important

communities/areas w.r.t diversity, richness, change potential and existing threats.

4.10.1 Change Sensitive Communities

Considering the Community Change Sensitivity, for PSK site the maximum CCS was

recorded for Q. floribunda (46) followed by A. pindrow (41) and Mixed deciduous

community (41) and minimum for Mixed Silver fir-Oak (17) community (Figure 4.40

a). One community H. salicifolia (41) also showed case of higher sensitivity but in

reverse direction by loosing quantum of compositional attributes.

In case of LTP site, maximum CCS was observed for A. pindrow (60)

followed by A. caesium-P. cornuta Mixed community (51) and A. spectabilis (44) and

minimum for J. regia-P. cornuta Mixed (24) community. However, A. pindrow and

A. spectabilis showed the changes in reverse direction by way of exhibiting loss of

quantum in different attributes (Figure 4.40 b).

4.10.2 Important Communities

The relative ecological importance of communities in PSK and LTP sites based on

various indices has been depicted as follows:

(i) CRI (Figure 4.41 & 4.42 a); CRPI ((Figure 4.41 & 4.42 b); and CUI (Figure 4.41

& 4.42 c). Finally, the CII ranking, derived on CRI, CRPI and CUI, of the

communities has been depicted (Figure 4.41 & 4.42 d).

4.10.2 Threatened Communities

Considering that the existing and potential threats on communities would define the

real management interventions, attempts were made to develop Community Threat

Index (CTI). This includes the proportionate contribution of non-native species in

community composition and the existing level of canopy disturbance. The

communities with higher CTI would depict the larger threat to overall compositional

integrity. Such communities would, therefore, require appropriate management

interventions.

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Figure 4.40 Identification of change sensitive communities in PSK (a) and LTP (b)

sites based on changes in phytosociological parameters during two time data sets.

[AN- A. nepalensis, MOD- Mixed Oak deciduous, HS- H. salicifolia, QF- Q. floribunda, QS- Q.

semecarpifolia, MD- Mixed deciduou, MSO- Mixed Silver fir-Oak, MSRM- Mixed Silver fir-

Rhododendron-Maple, AP- A. pindrow, MBS- Mixed Birch-Silver fir, BU- B. utilis, CD- C. deodara,

JPM- J. regia-P. cornuta mixed, APM- A. caesium-P. cornuta Mixed, PW- P. wallichiana, AS- A.

spectabilis, TAM- T. wallichiana-A. pindrow mixed]

-50 -40 -30 -20 -10 0 10 20 30 40 50

AN

MOD

HS

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ScoreCommunity Change Sensitivity

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In the above context, Mixed Oak deciduous (CTI-93), Q. floribunda (CTI-84),

Q. semecarpifolia (CTI-84), A. nepalensis (CTI-80) in PSK site were the communities

facing greater threat to their compositional integrity. A. pindrow (CTI-56) and Mixed

Silver fir-Rhododendron-Maple (CTI-57) were, however, least threatened. In LTP

site, C. deodara (CTI-95), A. caesium-P. cornuta Mixed (CTI-86) were identified as

most threatened communities. Whereas, A. spectabilis (CTI-69) and Taxus-Abies

Mixed (CTI-69) communities were currently under minimum threat.

Figure 4.41 Relative importance of communities in PSK site using various

compositional attributes; a) CRI= Community Richness Index; CRPI= Community

Representativeness Indix; CUI= Community Uniqueness Index; CII= Community

Importance Index. [AN- A. nepalensis, MOD- Mixed Oak deciduous, HS- H. salicifolia, QF- Q.

floribunda, QS- Q. semecarpifolia, MD- Mixed deciduou, MSO- Mixed Silver fir-Oak, MSRM- Mixed

Silver fir-Rhododendron-Maple, AP- A. pindrow, MBS- Mixed Birch-Silver fir, BU- B. utilis]

110

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Figure 4.42 Relative importance of communities in PSK site using various

compositional attributes; a) CRI= Community Richness Index; CRPI= Community

Representativeness Indix; CUI= Community Uniqueness Index; CII= Community

Importance Index. [CD- C. deodara, JPM- J. regia-P. cornuta mixed, APM- A. caesium-P. cornuta

Mixed, PW- P. wallichiana, AS- A. spectabilis, TAM- T. wallichiana-A. pindrow mixed, AP- A.

pindrow, BU- B. Utilis]

111

CHAPTERCHAPTERCHAPTERCHAPTER 5555

DISCUSSION

The in situ Conservation Sites, such as National Parks, Sanctuaries, and Biosphere

Reserves, etc., are repositories of biodiversity in given biogeographic unit. These

sites, most often, function as a refuge for native plants, animals and micro-organisms

and act as field laboratory (Brandt and Rickard, 1994). These representative

landscapes harbour rich diversity of both biological and physical features, therefore,

not only act as reservoir of much of the local biodiversity but also help in maintaining

the physical resources and natural beauty (Dhar et al., 1997, 1998). The biological and

physical resources, thus maintained, contribute goods and services for the welfare of

people and ensuring stability to the local environment (Khumbongmayum et al.,

2006). Among various forms of Conservation Sites, the Biosphere Reserves (BRs)

have gained global popularity on account of their significant contribution towards

building a harmonious balance between the human activities and ecosystem

conservation. The BRs are now being considered as important testing grounds for

linking conservation with sustainable livelihood needs of local communities. Among

others, towards infusing further dynamism, the BRs are being projected as most

suitable sites for establishing mechanisms for un-interrupted long term researches to

capture the changing dimensions of bio-physical values in the area so as to develop

scenarios and action plans to address issues associated with such changes (Palni et al.,

2012).

The mountain regions, globally recognized for their ecological and economic

values manifested by ecosystem integrity, adaptability, and services, are well known

for their vulnerability to anthropogenic and environmental changes that severely

affect overall integrity and life support values of these systems. The consequences of

these changes are far reaching that impact both upland and lowland communities

much beyond their physical boundaries. This realization has brought mountains on the

main agenda of global debate on Environment and Development. As a result, the

specific needs of Research and Development (R&D) in these regions have been

recognized as a global priority. In this context, while considering R&D needs to

112

understand change scenarios in natural systems and its implications for socio-

economic development, the Mountain Biosphere Reserves (MBRs) have emerged as

special candidates and are being considered ‘living laboratories’

(http://www.unesco.org/mab/ecosyst/mountains/gcmbr.shtml).

The Nanda Devi Biosphere Reserve (NDBR) is one amongst the identified

Mountain BRs from Asia Pacific regions for undertaking in depth research on

environmental change issues and likely implications. The present study, realizing this

potential of NDBR, considered investigating change patterns of representative

communities in two altitudinal transects in the buffer zone of the reserve. The study,

for the first time, based on comparison of two time field data sets (i.e., PSK site-1988-

1990 and 2008-2010; LTP site- 2000-2002 and 2010-2012) provided evidences on

patterns of compositional changes in representative forest communities. Various

aspects of studies are discussed as below:

5.1 COMPOSITIONAL DIVERSITY- CURRENT STATE

Considering the floristic richness of representative sites, the PSK site with over 73.6%

representation of total species of reserve emerged as more species rich and

representative as compared to LTP site. This can be attributed to the existence of

more diverse forest communities in PSK site (11 forest communities compared to 8 in

LTP). Representation of diverse types of forest communities along elevational

gradient is a consequence of change in climate parameters (i.e., temperature, rainfall,

snowfall and wind velocity) having has a direct bearing on the diversity of species and

habitats (Singh et al. 1996; Muller 1982). In this context, PSK site with greater

variations in community types and richness of species would suggest more

pronounced climatic fluctuations than the LTP site. In general, PSK site has 72%

coverage of broadleaf dominated forests and nearly 28% conifer dominated ones,

while in LTP site broadleaf forest constitute 63% and remaining 37% include conifer

dominated forests. These observations are comparable to previous study (Joshi 2002;

Joshi and Samant 2004). The explanations for this lies in the fact that PSK site

supports more mesic (moist) conditions and LTP site has more xeric (dry) climatic

conditions. Further, the very steep slopes in LTP site do not allow the water retention

by the soil, thereby giving rise to dry conditions that are more suitable for growth of

coniferous communities (Joshi, 2002). In general, coniferous communities are broadly

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reported to be species poor as compared to broadleaf communities (Singh and Singh,

1992)

The mean tree density (PSK: 260-535 ind ha-1

and LTP site: 599-1211 ind ha-

1) was comparable to the values (320-1670 ind ha

-1) reported in earlier studies

pertaining to low and high altitude forests of west Himalaya (Ralhan et al., 1982;

Saxena and Singh, 1982; Tewari, 1982; Kalakoti et al., 1986; Bankoti, 1990; Adhikari

et al., 1991; Rawal et al., 1991; Adhikari, 1992; Bankoti et al., 1992; Rawal et al.,

1994; Singh et al., 1996, Rikhari et al., 1997; Joshi, 2002; Joshi and Samant, 2004;

Gairola et al., 2008; Garkoti, 2008). The density values in PSK site closely

corresponded with the values (270-610 ind ha-1

) recorded from the same region two

decade back (Bankoti, 1990), however, these values fall in lower range of values

reported for the region (Zobel et al., 1994). The range of current Total Basal Area in

PSK (8.85-72.03 m2 ha

-1) and LTP (15.10-80.43 m

2 ha

-1) sites was comparable to

earlier reports (17.9-180.1 m2 ha

-1) from high altitude areas (Kalakoti et al., 1986;

Bankoti et al., 1992; Joshi, 2002; Joshi and Samant, 2004; Gairola et al., 2008;

Garkoti, 2008) and other warm temperate areas of Kumaun Himalaya (Ralhan et al.,

1982; Saxena and Singh, 1982; Tewari, 1982).

The mean values range for seedling density in PSK site (1450-8170 ind ha-1

)

was considerably higher as compared to earlier reports for high altitude forest in

similar areas (Bankoti, 1990; Joshi, 2002; Joshi and Samant, 2004). Whereas, the

range (470-1665 ind ha-1

) in LTP site was comparable to the earlier studies (Bankoti,

1990; Joshi, 2002; Joshi and Samant, 2004). The range of mean sapling density, both

in PSK (145-633 ind ha-1

) and LTP (40-951 ind ha-1

) sites falls within the lower range

(40-6667 ind ha-1

) reported earlier (Bankoti, 1990; Joshi, 2002; Joshi and Samant,

2004; Gairola et al., 2008).

Total shrub density in PSK (2150-25575 ind ha-1

) and LTP (1226-4025 ind ha-

1) sites was within the reported range (871-29114 ind ha

-1) for the region (Bankoti,

1990, Joshi, 2002; Joshi, and Samant, 2004; Gairola et al., 2008). Likewise, total herb

density (9100- 93600 ind 100 m-2

in PSK and 3980-26112 ind 100 m-2

in LTP sites)

was comparable to earlier reported range (4980-84480 ind 100 m-2

) (Joshi, 2002;

Gairola et al., 2008). However, the density values in LTP site approached lower part

of defined range in the region.

Current state of tree species richness (23-43 species) was comparable to earlier

reports (24-42 species) from high altitude forests of the region. Similarly, the diversity

114

values (0.63-3.37) are within the reported range (0.45-3.29) from the region (Bankoti,

1990; Adhikari et al., 1991; Joshi, 2002; Gairola, 2005; Gairola et al., 2008). The

species richness (45-54 species) and diversity (0.98-2.77) of shrubs fall within the

range reported earlier (species richness 10-54; diversity 0.51-3.55) from the area and

these values approached close to the reports from cool temperate and sub-alpine zone

of west Himalaya (Gairola et al., 2008; Singh, 2008). However, the diversity values

for tree and shrubs were higher than the values reported in some of studies in lower

altitude forests in western Himalaya (Saxena and Singh, 1982; Joshi and Tiwari,

1990; Singh et al., 1995; Rawat et al., 1999; Uniyal, 2001). The trends of species

richness and diversity would indicate forests in higher altitudes are more diverse w.r.t.

tree and shrub species, than the lower altitude forests in the west Himalaya. Herb

species richness (180-235 species) and diversity (2.02-3.45) values were close to

earlier reports from the region (Joshi, 2002 and Gairola et al., 2008). However, these

values are higher compared to Saxena and Singh (1982), Adhikari et al. (1991) and

Bankoti et al. (1992).

5.2 CURRENT DEMOGRAPHIC PROFILES

The size class distribution gives a demographic profile of the region which may

indicate future prospects of target communities (Gairola, 2005). In general, the forest

communities in PSK and LTP sites showed greater accumulation of individual in

seedlings and a sharp decline towards sapling and tree size classes. This structure

reveals that the conversion from seedling to sapling is not proportional. This can be

explained on account of greater mortality of seedlings due to severe winters. Similar

conclusions have been drawn by other workers elsewhere (Khumbongmayum et al.,

2006). Further, large scale extraction of biomass, particularly of selected species, has

also been reported to bring in structural changes in plant communities in the region

and elsewhere (Thadani and Ashton, 1995; Singh et al., 1997; Spurr and Barnes,

1980; Cairns and Moen 2004; Shrestha et al., 2007). As such, disturbances have been

observed to exert profound effect on forest development, since they alter vegetation

and release growing space, making it available for other species to occupy (Oliver and

Larson, 1990; Mishra et al., 2003; Mishra et al., 2004; Gairola et al., 2008).

While considering the demographic profiles of two sites unusually greater

accumulation of seedlings in PSK site can be attributed to occasional mast seedling

and recruitment for some dominant species (i.e., Q. floribunda, Q. semecarpifolia).

115

However, the long-term persistence of such recruits was later confirmed by two years

seasonal investigation on seedling survival patterns. The seasonal recruitment and

survival studies (unpublished report Project 8, GBPIHED) suggested that the

seedlings, thus emerged, have established. Therefore, if this trend continues, the forest

communities are likely to have increased dominance of such species.

Broadly the demographic profiles in both PSK and LTP sites exhibited

progressive structures suggesting long term persistence of the communities/species in

these sites. However, diverse trends of density and richness of recruits helps us to

depict the status of species in different forest communities (Khan et al., 1987;

Shankar, 2001; Bhuyan et al., 2003). In this respect following patterns across

recruitment layers are noticeable for different communities:

Sapling layer:

• Communities having high representation of dominant species: A. nepaensis, H.

salicifolia, Mixed deciduous, Mixed Silver fir-Rhododendron-Maple, B. utilis in

PSK site and C. deodara, P. wallichiana, A. spectabilis, Mixed Taxus

wallichiana-Abies pindrow, B. utilis in LTP site.

• Communities having high representation of co-dominant species: Mixed Birch-

Silver fir in PSK site and Mixed J. regia-P. cornurta, A. caesium-P. cornuta, A.

pindrow communities in LTP site.

• Communities having poor representation of dominant species but highest of co-

dominant species: Q. floribunda, Q. semecarpifolia in PSK site.

• Communities having poor representation of dominant and co-dominant species

and high representation of other species: Mixed Oak deciduous, Mixed Silver fir-

Oak in PSK site.

Seedling layer:

• Communities having high seedling representation of dominant species: Mixed

Oak deciduous, H. salicifolia, Q. floribunda, Q. semecarpifolia, Mixed Oak

deciduous, Mixed Silver fir-Oak, Mixed Silver fir-Rhododendron-Maple, A.

pindrow, B. utilis in PSK site and C. deodara, Mixed J. regia-P. cornurta, P.

wallichiana, A. spectabilis, Mixed Taxus wallichiana-Abies pindrow, B. utilis in

LTP site.

• Communities having sufficient representation of both dominant and co-dominant

species, and accompanied by high representation of other species as well: Mixed

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Birch-Silver fir in PSK site and A. caesium-P. cornuta, and A. pindrow in LTP

site.

Therefore, based on above trends of seedlings and saplings various combinations

and trends of communities can be drawn. For example, (i) the communities with

greater representation of dominant species in both seedling and sapling stage would

suggest further strengthening of dominant species; (ii) the communities with greater

representation of both dominant and co-dominant in sapling and seedling layers

would indicate the composition remains unchanged in future; (iii) the communities

having greater proportion of seedling and saplings of co-dominant would indicate

possible dominance of such species in future; (iv) the communities having greater

representation of seedlings and saplings of the species other than the dominants and

the co-dominants would indicate likely future changes in composition of target

communities.

The demographic profiles of some of the dominant and some relatively less

prominent tree species definitely require attention. For example, in case of tow

dominant species, Q. floribunda and Q. Semecarpifolia, in spite of their greater

seedling numbers both were less prominently represented in sapling layer of PSK site.

Certain relatively less prominent species like Celtis australis, Elaeagnus parvifolia,

Fraxinus micrantha, Prunus cornuta, Quercus incana, Rhus punjabensis were,

however, represented only in tree layer suggesting that these species are not properly

regenerating through near past and in present. Therefore, long-term persistence of

such species is in question. Besides, Abies spectabilis, Aesculus indica, Symplocos

ramosissima with representation only in tree and sapling class and Cornus

macrophylla, Juglans regia, Mahonia borealis, Viburnum nervosum in tree and

seedling class only would require attention. On the contrary, species like Buxus

wallichiana, Pyrus pahsia, Eurya acuminata having individuals in sapling and

seedling class only indicated their recent introduction in respective communities.

5.3 REPRESENTATIVENESS AND UNIQUENESS OF THE COMMUNITIES

It is well recognized that the non native species present a range of threats to native

ecosystems (Wilcove et al., 1998; Schlaepfer et al., 2011), and the nature and extent

of change in a native flora because of non-native introductions could lead to long term

changes in ecosystem processes (Vitousek, 1986; Ramakrishnan and Vitousek, 1989).

117

Presence of non-natives in Indian flora and that of the Himalaya is well known

(Maheshwari., 1962; Khuroo et al. 2010, 2011; Negi and Hajra, 2007), which is

mostly attributed to migrations over geological time period (Saxena, 1991). In this

context, the extent of native and non-native species availability in the target

communities was analyzed so as to assess their representativeness.

In general, LTP site with 66.5% representation of natives emerged as more

natural site compared to PSK site with only 59.6% natives. Greater influence of non-

natives in PSK site can be attributed to the frequent movement of tourists in the site.

Such possibilities of non-native introduction and establishment associated with the

human intervention have been reported in other parts of the region (Dhar et al., 1997).

Endemics, which define the uniqueness of an area or plant communities, also

were more abundant in LTP site (33.5%) as compared to PSK (24.7%). This would

suggest LTP site harbours greater number of unique plants and thus deserves greater

conservation attention.

It is reported that the degree of invasion by non-native species greatly depends

on vegetation types (Hobbs and Atkins, 1988). The results of present study also

indicate community dependent patterns of non-native proliferation. However,

significant decrease in non-native species representation with changing forest

composition along an elevation gradient in PSK site was revealing. Results of the

present study are in general agreement with the reports of increasing diversity and

abundance of natives towards higher elevations (Macdonald et al., 1989; 1995; Dhar

et al., 1997; Joshi, 2002). Communities like Mixed Silver fir-Rhododendron-Maple,

Abies pindrow and Mixed Birch-Silver fir in PSK, and Taxus wallichana- A. pindrow

mixed, and J. regia-P. cornuta mixed in LTP site had relatively more presence and

abundance of native species. Whereas, with respect to uniqueness (endemics) Q.

floribunda and Mixed-Silver fir-Rhododendron-Maple community in PSK and T.

wallichiana-A. pindrow mixed and A. spectabilis community in LTP site were most

endemic rich. While considering the conservation value communities with greater

number of endemics would deserve precedence over others.

The invasibility of natural systems is associated with disturbance (Hobbs and

Huenneke, 1992), which largely remains human-induced (Tyser and Worely, 1992).

The low elevation areas in our study sites, as in other parts of Kumaun (Rawal and

Pangtey, 1994a) and elsewhere (Poldini et al., 1991), are relatively densely populated.

Increased human interference in these areas, therefore, facilitates the introduction and

118

establishment of non-native species in respective forest communities. Often such

introductions are accidental. It is argued that the open spaces created by biomass

harvesting, that facilitates high light intensity, may be a major factor responsible for

plant invasion (Rejmanek and Randall, 1994). This fact has been found true with

many forests in other parts of the region (Dhar et al., 1997). In present study also the

greater density and richness of non-natives in areas near to human habitation and

communities having canopy disturbance supports the above arguments.

Although both the sites are away from vehicular road, yet these off-road areas

are experiencing introduction and proliferation of non native species. This may be

attributed to hiking trails or trekking corridors. These linear structures represent

human-disturbed corridors of possible importance to the occurrence and spread of non

native species (Tyser and Worley, 1992). Studies have further provided evidences that

these corridors provide long, linear stretches of habitat available for the colonization

and movement of non native species (Tyser and Worley, 1992; Hochrein, 2008).

Along such kind of constructions, non-native species usually find lower competition

relative to the dense native vegetation, and receive higher amount of light and water

to proliferate (Wester and Jurvik, 1983; Parendes and Jones, 2000).

Further, the nomadic lifestyle of the inhabitants in the study area also may

have contributed to the introduction, spread, and persistence of non-native species.

For centuries the nomads of this region have been rearing sheeps and goats. An

annual cycle of migration with scores of sheeps and goats from high alpine regions (in

summer) to the sub-tropical ranges of Terai and Bhabar (in winter) and vice-a-versa

characterizes this occupation (Bhandari, 1981). These migratory herds of animals

most often act as a vector for the dispersal of species from one area to another (Dhar

et al., 1997).

5.4 COMMUNITIES ACROSS ENVIRONMENTAL GRADIENT

Plant distribution and compositions is well known to be determined by the local

environmental features (Keenan and Kimmins, 1993). Among others, the soil

characteristics such as organic carbon, nitrogen and pH have been reported to strongly

correlate with vegetation (Wales, 1967; Pregitzer and Barnes, 1982; Pregitzer et al.,

1983). Further, species and community responses to the climatic gradients and

atmospheric gases are well established (Aguado-Santacruz and Garcia-Moya, 1998;

Vasseur and Potvin, 1998).

119

In present study, the cumulative percentage variance of the species

environment relationship for axes 1 and 2 was 98.7 (using density values as co-

variables) and 98.2 (using richness values) respectively, in PSK, and 96.9 (using

density values as co-variables) and 96.2 (using richness values) respectively, in LTP

site, suggested that most of the information regarding community distribution and

composition was concentrated on these two axes. Overall permutation tests, the first

axis eigen value (PSK site: λ1= 0.862, λ1= 0.838; LTP site: λ1= 0.840, λ1= 0.748) for

both analysis, and for each variable added to model, were all significant (PSK site: P=

0.002; LTP site: P= 0.010). In CCA, eigenvalue indicates how much variation in

community structure is explained by both species composition and species-

environment interaction (Vasseur and Potvin, 1998). It follows that the community

variation observed at the different communities/sites is significantly related to the

evaluated environmental variables. This further indicates that environmental factors

play an important role in the distribution and composition of communities (Aguado-

Santacruz and Garcia-Moya, 1998; Vasseur and Potvin, 1998).

In PSK site, CCA based relationship between environmental variables

suggested increasing elevation is associated with higher slopes, high organic carbon,

and nitrogen. As such, increase in soil organic carbon, organic matter and nitrogen

with increase in elevation has already been predicted (Sims and Nielsen, 1986; Garten

et al., 1999; Xu et al., 2010; Dai and Huang, 2006; Proll et al., 2011). Besides, high

altitude communities also supports good canopy cover, density of herbs, native and

endemic species, richness of native, endemic and shrub species. In case of elevational

gradients, studies have also demonstrated relationship between native species and

altitude (Khuroo et al., 2011). Low altitudinal zones in PSK support high density of

seedlings and shrubs; high richness of tree and seedlings and high TBA in moderately

disturbed areas with open canopy. Higher tree density in low disturbed areas with

good canopy cover suggests the effect of disturbance on vegetation composition. The

open canopy sites supported good density of recruitment and shrub species (Mishra et

al., 2003; Mishra et al., 2004).

CCA results for LTP site showed more or less similar behavior of

environmental variables. The high altitude communities supported high species

density but low species richness. Low elevational zones were rich in density of herbs,

native and non-native species. The disturbance and moderate to low canopy in lower

altitudinal areas was common in both the sites. This is due to the fact that most of the

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local villages are settled below 2800 m in NDBR (Nautiyal et al., 2005). The greater

density of herbaceous species and high level of disturbance upto middle altitudinal

ranges, as depicted in CCA, can be attributed to winter migration of local

communities having two types of settlements in LTP site.

The patterns of increased similarity in richness (i.e., maximum in lower

altitudinal areas) exhibited similar distribution pattern of species across the climatic

zones (i.e., cool temperate zone and sub-alpine zone) in PSK site. In case of species

occurring in both the zones, their broad climatic tolerance (Alexander et al. 2011) was

evident in the form of range occurrence from lower elevations, especially from point

of introduction, till higher ranges. The similarity pattern in case of species density

showed uneven patterns that suggested the dominance (high species density) of those

species which were not common in two closely related communities (in terms of

species richness). For example, in PSK site, Mixed deciduous and Mixed Silver fir-

Rhododendron-Maple community were most similar in common species cluster but

shifted a bit apart when density was considered. In case of saplings, some

communities like B. utilis and Mixed Birch-Silver fir exhibited closeness in the

cluster based on common species but shifted far apart following species density based

cluster. This kind of behaviour may be attributed to the higher density of some widely

distributed species like R. arboreum, R. barbatum, A. cappadocicum, A. caesium, I.

dipyrena, L. ovalifolia, M. dilleniaefolia, Q. floribunda, T. wallichiana, U.

wallichiana in PSK site and E. fimbriatus, P. wallichiana, P. cornuta, R. arboreum, T.

wallichiana in LTP site.

Besides, the distribution and composition of certain communities may be

explained based on their specific locations and/or habitat needs. For example, B. utilis

community is exclusively concentrated in sub-alpine zone and, therefore, has diverse

species as compared to other communities. Similarly, H. salicifolia community

occupies the river beds.

5.5 PATTERNS OF SPECIES RICHNESS AND RATE OF CHANGE

Studies of species diversity along altitudinal gradient have demonstrated relationship

between altitude and species richness (Grytnes and Vetaas, 2002; Vetaas and Grytnes,

2002; Grytnes and Gairola et al 2008; Khuroo et al., 2011). These patterns vary from

hump-shaped relationship with maximum species richness at mid-altitudes (Rahbek,

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1995; Vetaas and Grytnes, 2002; Arevalo et al., 2005; Carpenter, 2005; Nogues-

Bravo et al., 2008) to monotonically decreasing relationships (Korner, 2004; Mallen-

Cooper and Pickering, 2008). Species richness and diversity patterns in relation to

elevation range in Kumaun, west Himalaya, have been described (Singh at el., 1994).

In the present study, GAM provided the patterns of species richness and density

distribution along elevational range.

In PSK and LTP sites, species richness broadly suggested a transition for

species richness at elevational point 2700-2800 m asl, this can be explained on

account of separation of two broader bio-climatic zones at this point [i.e., cool

temperate zone (2000-2700 m) and sub-alpine zone (>2700 upto tree line)]. The

cluster based similarity analysis also confirmed this phenomenon as the clusters were

mostly divided in two major groups in case of species richness broadly representing

these two zones. However, non-existence of any clear change point in case of species

density suggested that density largely remains independent of elevational effect. The

formation of uneven groups in case of density based cluster analysis further supported

this hypothesis. In most of the cases, the CCA in both the sites revealed inversely

proportionate relationship between species density co-variables and richness with

elevation.

Overall, the decreasing species richness and density in all tree classes followed

by total species richness and total species density along elevational gradient of PSK

site may be attributed to the distinct bio-climatic zones (cool temperate and sub-

alpine) accompanied by the differences in habitat and micro-environmental factors.

Particularly in higher elevational areas (i.e., sub-alpine) harsh climatic conditions

usually act as a barrier for existence of lower altitudinal species by having adverse

effects of soil moisture stress and unfavourable temperature on survival of plant

species which ultimately become responsible for reduction in plant populations

(Perira and Kozlowski, 1977; Schulte and Marshall, 1983; Kumar et al., 1994;

Khumbongmayum et al., 2006). The different change points for density at different

strata corresponded with the broad density patterns described for elevational range in

the region (Singh et al., 1994)

In LTP site, species richness pattern was more or less similar to PSK site

except for species richness in seedling strata. Therefore possible factors and causes

would also remain similar. On the contrary, the increasing species density along

elevational gradients in LTP site calls for further explanation. One possible reason

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could be better enforcement of protected area regulations in this site as compared to

PSK. Also, further variations in community types (i.e. conifer dominance in LTP)

may be considered as yet another responsible factor.

5.6 TEMPORAL VEGETATION CHANGE

Vegetation scientists generally agree that plant communities are very dynamic.

However, it is more difficult to obtain a general consensus with respect to the real rate

and scope of vegetation changes. In general, broad-scale vegetation surveys are

seldom repeated; even if they are, the methods employed usually varies with time

thereby making any comparison difficult, and the results equivocal. On the other

hand, there are numerous small scale studies in permanent plots that provide very

convincing examples of vegetation dynamism. However, such examples are usually

strictly local and very hard to generalize. Therefore, quantification of vegetation

dynamics has emerged as a quite difficult task. Recently Dai et al. (2011) conducted a

study in Changbai Mountains of China and investigated changes in forest structure

and tree species composition from 1981 to 2010. As such, there are large number of

factors that could affect the vegetation structure and composition. For instance,

stability of the environment determines the climax vegetation whereas the edaphic

factors bring about local variation in vegetation types that are further attenuated by

biotic factors (Dudgeon and Kenoyer, 1925; Champion and Seth, 1968; Mishra et al.,

2003, 2004; Gairola et al., 2008). The current study, for the first time in the region,

used the earlier studied forest stands (10 to 20 year old) and followed similar

approach to generate data sets on current status of vegetation composition. Therefore,

the data sets are comparable and the changes thus depicted can be considered

adequately reliable.

The data sets generated on compositional features, pertaining to tree species,

revealed that the dominant tree species in 10 out of 11 communities of PSK site and 8

out of 8 communities in LTP site continued to exhibit maximum importance even

now. The composition of these forests have, therefore, changed very little in past (i.e.,

over the past two decades for PSK and one decade for LTP). Similar observations of

little changes in forest composition during three decades have been reported from

Changbai Mountains of Northeast China (Dai et al., 2011). However, the significant

increase in seedling and sapling species richness and diversity in both the study sites

of NDBR is indicative that forest composition in the area tends to change in future.

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This inference is further supported by invariably increasing trends of tree density in

most cases. Among others, the increasing species richness and density in seedling and

sapling layers may be explained as a likely consequence of climate change in the

region. In general, the increasing temperature is projected to enhance rate of

proliferation of species towards higher elevations in mountain ecosystems (Nogues-

Bravo et al., 2007; Singh et al., 2010; Notaro et al., 2012). However, this explanation

for increased recruitment warrants further investigation before arriving at any

generalization.

While considering the trends of compositional features at individual

community level, there were diverse indications of changes. For example, A.

nepalensis, Mixed Siilver fir-Rhododendron-Maple, Mixed Birch-Silver fir and B.

utilis communities in PSK site and C. deodara, P. wallichiana and B. utilis

communities of LTP site exhibited no change or marginal increase (although non

significant) in both density and TBA values, suggesting that the communities were

still maturing during the study period. Dai et al (2011) have reported similar trends for

broadleaved/Conifer mixed forest zone of Changbai Mountains. On the contrary, a

few other communities, Mixed Oak-Deciduous, H. salicifolia, Q. semecarpifolia,

Mixed deciduous in PSK site and A. spectabilis, A. pindrow communities in LTP site

exhibited decline in both density and basal area (although non significant), indicating

that the communities have declined in tree abundance over last few decades. Since,

the dominant and co-dominant tree species in these forests are still continuing to

dominate, and in terms of importance value no significant change has occurred for

such species, presumably the composition of these forests has changed very little over

the last two decades or so. However, trends of gain or loss of TBA or density would

need further observations.

The forest communities that approached sub-alpine conditions (i.e, Mixed

Silver fir-Rhododendron-Maple, A. pindrow, Mixed Birch- Silver fir and B. utilis) in

PSK site tended to exhibit increase in density and basal area along with increasing

trends of sapling and seedling density. These observations suggested that the forest

communities at their upper elevation limit in the region are exhibit progressive trends

of maturation and expansion. Recently, Rai et al. (2012), while working on

community structure along timber line ecotone in west Himalaya, had concluded that

the timber lines in the region would be showing shifts in elevation if anthropogenic

disturbances do not impact the regeneration provided the present climatic condition

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continues to prevail (they had also indicated the climatic condition in timber line zone

have become favourable for regeneration of forest in recent decades). Further, while

generalizing the ecosystem responses towards rapid worming in the Himalayan

region, Singh et al. (2010b) have reported that the woody species at higher elevation

zones have began to invade the alpine meadows in the region, and because of the

occurrence of divers plant functional types (e.g., both evergreen and deciduous

woody plants), the shifts in communities will result in a complex series of changes in

ecosystem process with a considerable involvement of the soil subsystem.

The comparison of two time soil data sets in present study has also established

significant changes in soil parameters over two decades in PSK site and one decade

LTP site. While total soil nitrogen values have shown significant increase, even in one

decade time interval (LTP site), soil carbon did show significant decrease only in two

decade time interval (PSK site). Thse observations call for long term investigations on

the trends of shift and possible consequence of changing climate as well as

anthropogenic disturbances on compositional patterns of communities and edaphic

features as well.

Apart from human disturbances, changes in vegetation structure have been

reported due to many reasons including effect of climate change, and changes in soil

composition, etc., (Jobbagy and Jackson, 2000; Dai and Huang, 2006; Ise and

Moorcroft, 2006; Fissore et al., 2008; Proll et al., 2011).The increased recruitment

density in many areas has been attributed to the increased rate of litter decomposition

(Jobbagy and Jackson, 2000; Ise and Moorcroft, 2006). Present study with significant

decrease in soil organic carbon and organic matter in last two decades supported this

fact. Increased rate of decomposition provides favourable conditions to plant species

by decomposition of soil organic carbon and release of nutrients to the soil (Xu et al.,

2010). Most often, the increased temperature is held responsible for improved

decomposition rate along elevational gradients in mountains (Xu et al., 2010; Ise and

Moorcroft, 2006). In this context, increase in temperature in the study region, over

last few decades, has already been established (Gaira et al., 2011).

5.7 EVIDENCES FROM LU-LC CHANGES

On a broader spatial scale, comparison of two time period data sets, using Landsat

TM (1990 and 2005) data, revealed an overall decline of 2.8% in forests in the

reserve. Most of the vegetation cover classes exhibited decline in cover over 15 years

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time period. This trend does not agree with indicated progression in forest density in

different classes from year 1986 to 1999 (Sahai and Kimothi, 1994). Considering the

forest physiognomic types, Conifer dominated forest (2.1%) and Mixed broadleaf

forests (3.9%) exhibited overall decline in cover. On the contrary an increase in

Evergreen Broadleaf Forests (10.6%) was noticed. The decline in forest cover in

Himalayan region is most often attributed to human disturbances (Singh and Singh,

1992; Gairola, 2005) whereas increase is a consequence of favourable climatic

conditions (Rai et al., 2012).

5.8 CHANGE SENSITIVITY AND RICH AREAS

5.8.1 Change Sensitivity

The observations of the present study and the interpretation of data sets in previous

sections have provided evidences of ongoing and potential compositional changes.

The indicators of change, generated through studies over two time periods,

established strong basis to explore the change sensitivity of communities in NDBR.

As the Community Change Sensitivity (CCS) concurrently considered various

compositional features (i.e., species richness, density, diversity and TBA), the

outcome (i.e., CCS based ranking) would provide a reasonably agreeable sensitivity

ranking for communities. The overall conservation and management prescriptions for

the reserve, therefore, would benefit from these findings.

In PSK site, considering changes during last two decades, Q. floribunda (CCS

- 46), followed by Mixed Oak-Deciduous, H. salicifolia and A. pindrow (CCS – 41

each) communities exhibited higher compositional change sensitivity. Of these,

except for H. salicifolia wherein changes (i.e., decline in values) in composition can

be attributed to natural hazards (i.e., uprooting of community due to river flooding),

other communities are showed changes due to various natural reasons. While larger

share of CCS in Mixed Oak-Deciduous and Q. floribunda community can be

attributed to decline in TBA and increase in seedling density along with species

richness. A. pindrow, however, owes greater sensitivity on account of increased

richness of species in saplings and tree layers, along with decline of density and

diversity in seedling layer. With lower values of CCS, Mixed Silver fir-Oak (CCS –

17) and B. utilis (CCS – 18), however, can be considered as resilient communities in

PSK site. Overall, the communities in lower elevation zone, with relatively higher

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CCS scores, appeared in more dynamic state of change compared to the ones at higher

elevations.

In case of LTP site, A. pindrow (CCS - 60) followed by A. caesium-P. cornuta

Mixed (CCS – 51) and A. spectabilis (CCS – 44) exhibited higher change sensitivity.

The greater proportion of this sensitivity was contributed by density changes in tree,

seedling and sapling layers of A. pindrow and A. spectabilis communities and

increased species richness in seedling and sapling layers of A. caesium-P. cornuta

Mixed community. As in case of PSK site, B. utilis community emerged amongst

most stable communities in LTP site.

Considering the CCS of communities, the reserve would require differential

management strategies to accommodate the pace and direction of changes for

maintaining the representativeness of compositional changes. These evidences can

also be utilized fro re-defining the successional status of various communities. For

example, riverine communities like A. nepalensis, H. salicifolia etc., are usually

considered comparable with ‘habitat pioneer’ communities of Ohsawa (1991).

However, in present study, A. nepaensis stand behaved more of a mid successional

community that tend to be stable (i.e., less sensitive to change) community. Likewise,

P. wallichiana community which is considered as an early successional community

currently did not exhibit indications of greater dynamism (change sensitivity) and

behaved more of a nearly stable community (CCS – 32). Similarly, successional stage

of two sub-alpine forests, A. pindrow and B. utilis, is considered to be climatic climax

for west Himalaya (Champion and Seth, 1968; Dhar et al., 1997). Of these two, B.

utilis with low CCS scores behaved more of a stable community but A. pindrow

exhibited greater dynamism of change. Oaks, that are considered as climatic climax

for the region (Champion and Seth, 1968; Singh and Singh, 1987), exhibited

relatively higher range of sensitivity in the study area (CCS: 46- Q. floribunda, 39 –

Q. semecarpifoila). This seems to be an interesting finding in the context of wider

generalizations. Theurillat and Guisan (2001) while reviewing potential impacts of

climate change on vegetation in European Alps have suggested differential responses

of climatic climax communities. It is indicated that for climatic climax communities,

new plant communities are likely to develop and, partially or totally, replace present

ones. However, the physiographic and edaphic factors will still play a detrimental role

(regionally and locally). Therefore, present observations of higher change sensitivity

of two climatic climax Oak forest, to some extent agrees with above generalization.

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However, more data sets from future long-term studies on patterns of composition as

well as edaphic features can provide more convincing explanations.

5.8.2 Rich, Representative and Unique Communities/Areas

The ecological systems are inherently complex, composed of many interacting

biological and physical components. Therefore, predicting the behaviuor of such

complex systems is difficult yet the management and policy decision require

information on status, condition and trends of ecosystems (Andreasen and Neill,

2001). In this context, in addition to trends of community composition generated

through change sensitivity, other possibilities to define relative conservation and

management values of the studied communities have been explored. Especially, these

considerations intend to provide logical inputs for determining the compositional

integrity of target communities.

The Community Richness Index (CRI), which considered both species

richness, diversity, and proportional density at different tree layers (i.e., seedling,

sapling and trees) as well as the TBA and canopy cover of the respective

communities, provided first level of ranking of communities on the basis of relative

value of maintaining the local and regional species pool. In this context, Mixed Oak

deciduous (CRI-85), Q. floribunda (CRI- 82) and Mixed Silver fir-Rhododendron-

Maple (CRI- 80) communities in PSK site and A. pindrow (CRI-75), P. wallichiana

(CRI -73) and B. utilis (CRI- 65) communities exhibited richness in LTP site.

Interestingly the richness index based ranking was not uniform for some of the similar

forests in two sites. For instance, high ranking B. utilis and A. pindrow communities

in of LTP site ranked low in CRI consideration for PSK site. This indicates that the

total species richness of forests is not uniformly determined. The prevailing local

conditions, which might include various types and level of disturbances, would have

contributed for differential total species pool of similar communities in two sites.

The Community Representative Index (CRPI) and Community Uniqueness

Index (CUI), however, provided more sensitive species targeted conservation and

management values of the communities. These indices have particular relevance

under the emerging principal of habitat management for biodiversity, which, among

others, suggests diversity per se is not necessarily the best criteria for building

management options. Sampson and Knopf (1982) argued that many local scale

management practices that encourage diversity within local communities ignore large

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scale problems of maintaining viable populations of endemic and native species

within the landscape. In this context, CRPI that demonstrates the contribution of

native species to the community composition, identified Mixed Silver fir–

Rhododendron-Maple (CRPI- 96), Mixed Birch-Silver fir (CRPI- 92) and A. pindrow

(CRPI-92) as most representative communities in PSK site. In general, the trend of

native species occurrence suggested higher representation of natives in communities

located towards upper reaches of elevation. These finding are in general agreement

with earlier reports of significant increase in nativity towards higher elevations in the

region (Dhar et.al., 1997). The LTP site, on the other hand, has greater accumulation

of natives in T. wallichiana-A. pindrow Mixed (CRPI-93) and J. regia-P. cornuta

Mixed (CRPI-92) communities. The results suggested that the two most diverse

communities (as indicated by CRI) are not the best representative communities both

in PSK and LTP sites.

The CUI further narrowed the conservation priority by way of defining the

endemic (unique species) support potential of communities. In this respect also the

communities differed considerably. Apparently the communities having best CUI

were not necessarily the best CRI and/or CRPI communities. Q. floribunda (CUI- 94),

Mixed Silver fir-Rhododendron-Maple (CUI- 93) in PSK and T. wallichiana-A.

pindrow Mixed (CUI-88), A. spectabilis (CUI- 84) in LTP site have emerged as the

best forest communities with highest contribution of unique species (endemics) in

composition. In this respect, endemics being the high priority biodiversity elements

(Dhar, 2002), communities with greater CUI in the reserve would deserve higher

attention.

As it is reflected from the foregoing discussion individually each of these

indices help in defining different priorities and ranking of communities along one

index does not always match with the ranking through other index. In other words,

this makes the situation very complex for a manger to decide upon a particular

value/ranking. Therefore, to avoid this confusion and to facilitate the management

with quick and more relevant decision, community importance index (CII) was

developed that takes note of all other indices (CRI, CRPI and CUI). The ranks of

communities, thus achieved, would reflect the cumulative contribution of all these

attributes. In this respect, the most important two communities that maintain

compositional integrity include Mixed Silver fir-Rhododendron-Maple (CII-90) and

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Q. floribunda (CII- 83) in PSK site, and P. wallichiana (CII-81) and T. wallichiana-A.

pindrow Mixed (CII- 80) in LTP site.

5.9 CHANGE SCENARIOS AND ALTERNATIVE STRATEGIES

Having defined the compositional integrity based Community Importance ranks for

existing forest communities in representative sites, it is important to further define the

scenarios under ongoing and potential changes that may affect the compositional

integrity of communities. Two Indices, the Community Change Sensitivity (which

defines the intensity of change dynamism in a given community) and the Community

Threat Index (CTI) that provides an indication of existing and looming threats on a

community (defined as an outcome of non native contribution and canopy

disturbance), were used to denote the intensity of potential threat to overall

compositional integrity. The Community Integrity (CI) values, thus obtained for each

community, provided a more refined and more realistic ranking of individual

community. The overall scenarios of communities considering various priorities have

been depicted (Table 5.1). It is clear that depending on the conservation and

management objective to be addressed, different scenarios can be used for building

strategies and management prescriptions.

Scenario I: When the objective is to ensure conservation of maximum plant

biodiversity of the reserve the community scoring based on the

Community Importance Index (CII) seems best fit. This not only takes

into account the entire species pool and their performance but also the

contribution of representative and unique elements.

Scenario II: If the conservation/management objective is to plan for accommodating

the potential changes in community structure (e.g., potential shifts in

vegetation/species boundaries and/or changes in dominance of forest

species, etc.), one may like to build on indications obtained through

Community Change Sensitivity (CSS).

Scenario III: In all such cases when the management objective is to address potential

threats of non-native proliferation in the reserve, the best option would

be to consider the community scoring based on Community Threat

Index (CTI).

Scenario IV: Finally if one looks into the problem on a more holistic manner, the

Community Integrity (CI) score that not only define the level of

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relative resistance of a community to environmental change, and

stresses but also exhibits its ability to recover to original state after

perturbation (Andreasen and Neill, 2001), emerge as the best way of

defining conservation and management priorities across forest

communities in the reserve.

There is a further, possibility of using the first III scenarios in different

combinations to address the issues of local and regional forest management as well as

conservation of optimal biodiversity pool under changing climate and human caused

perturbations.

Tale 5.1 Compositional Integrity of communities and scenarios for addressing diverse

management issues

Indices and Ranks Rank Site/Communities

CII Scenario-

I

CCS Scenario-

II

CTI Scenario-

III

CI

Scenario-

IV

PSK

AN 71 4 25 8 80 5 17 VII

MOD 80 9 41 3 93 1 13 IX

HS 56 1 41 4 81 4 9 XI

QF 83 10 46 1 84 2 13 VIII

QS 71 3 39 5 84 3 11 X

MD 80 8 27 7 66 8 23 II

MSO 73 6 17 11 77 6 23 III

MSRM 90 11 31 6 57 10 27 I

AP 79 7 41 2 56 11 20 V

MBS 73 5 22 9 61 9 23 IV

BU 70 2 18 10 66 7 19 VI

LTP

CD 66 2 27 6 95 1 9 VII

JPM 75 5 24 7 84 3 15 IV

APM 61 1 51 2 86 2 5 VIII

PW 81 8 32 5 82 5 18 I

AS 73 4 44 3 69 8 15 V

TAM 80 7 36 4 69 7 18 II

AP 77 6 60 1 83 4 11 VI

BU 71 3 24 8 79 6 17 III

[AN- A. nepalensis, MOD- Mixed Oak deciduous, HS- H. salicifolia, QF- Q. floribunda, QS- Q.

semecarpifolia, MD- Mixed deciduou, MSO- Mixed Silver fir-Oak, MSRM- Mixed Silver fir-

Rhododendron-Maple, AP- A. pindrow, MBS- Mixed Birch-Silver fir, BU- B. utilis, CD- C. deodara,

JPM- J. regia-P. cornuta mixed, APM- A. caesium-P. cornuta Mixed, PW- P. wallichiana, AS- A.

spectabilis, TAM- T. wallichiana-A. pindrow mixed; CII= Community Importance Index; CCS=

Community Change Sensitivity; CTI= Community Threat Index]

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Conclusions

• The review of available information suggests that NDBR since its inception

(1988) has attracted several researchers and nature lovers from all over the world,

as a result biodiversity of the reserve is well explored during last two decades or

so. However, critical review of information also suggests certain gap areas, such

as, ongoing and potential changes in the reserve’s biodiversity, sensitivity

indicator and related management, critical areas and habitat prioritization, use of

space technology in assessment and monitoring, ecosystem services, and

community compositional integrity, etc.

• The targeted sites in the study (i.e,. PSK and LTP) reflect richness,

representativeness, and uniqueness values of the reserve. The data sets available,

and generated through this study, provide enough basis for establishing these two

sites in NDBR as potential sites for long-term ecological monitoring under various

change scenarios.

• Comparatively, the PSK site of the reserve supported greater diversity of plant

communities and species. However, in the context of representativeness (nativity),

and uniqueness (endemism) the LTP site emerged as more important site (native –

66.5%; endemics – 33.5%).

• Communities in both the sites, broadly exhibited progressive demographic profiles

which suggested long-term persistence of communities. However, unusually

greater accumulation of seedling in PSK site with indications of successful

establishment was indicative of possible changes in composition of communities

in this site. Also, various community specific patterns of demography were

revealing.

• Comparison of two time data sets, generated through using similar approach,

provided evidences of change in community composition. The Community

Change Sensitivity (CCS) assessment helped in identification of most sensitive

communities in selected sites. For example, Q. floribunda, H. salicifolia and A.

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pindrow in PSK and A. pindrow, A. caesium-P. cornuta Mixed communities in

LTP site.

• Changes in representative physiognomic types, based on analysis of RS data sets

(1990-2005), indicated decline in total forest cover (2.8%). While Conifer

dominated (2.1%) and Mixed broadleaf (3.9%) showed characteristic decline,

Evergreen broadleaf exhibited an increase of 10.6% in the reserve.

• The Community Integrity (CI) scores, considering the richness,

representativeness, uniqueness and threat index, highlighted the relative stability

and resilience of the communities. In this respect, Mixed Silver fir-

Rhododendron-Maple and Mixed deciduous communities in PSK, and P.

wallichiana and T. wallichiana-A. pindrow Mixed communities in LTP site

exhibited maximum community integrity thereby suggesting more stability and

resilience of respective communities.

Recommendations

Considering the results and interpretations of present study following can be

recommended:

• Realizing the availability of ecological information base, specially two time

period compositional data sets (PSK 1990 and 2010; LTP 2002 and 2012), the

present study sites can be recommended as potential candidates for

establishing long-term ecological monitoring sites in west Himalaya to

understand intensity and patterns of changes.

• The Community Change Sensitivity (CCS) based ranking of communities

needs to be considered for prioritizing management intervention sites within

the reserve. Also, the potential changes, as evident through present study,

should be accommodated in the long-term perspective plans of the reserve.

• The four scenarios, as suggested through current study, may be considered for

addressing different management objectives in the reserve and elsewhere: (i)

the CCS based prioritization of communities is recommended for

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accommodating potential changes over short to long-term duration; (ii) the CII

(Community Importance Index) based ranking is recommended for ensuring

conservation of maximum plant diversity; (iii) the Community Threat Index

(CTI) of communities is recommended as a potential tool to address issues

pertaining to the threat of non-native proliferation and canopy disturbances;

and (iv) on a more holistic level, CI (Community Integrity) has been

recommended as a potential prioritization approach for assigning the score

based on relative resilience and stability of forest communities.

• The evidences generated through this study, more importantly the patterns

drawn from compositional features of the forests, are indicative of ongoing

and potential changes in forests of the region. This scenario, therefore, calls

for further long-term studies on various attributes highlighted in present study

and other attributes which have not been covered in this study, such as the

ecosystem processes.

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135

SUMMARY

Among global mountain systems, which have emerged as a special thrust under global

change scenario, the Himalaya being youngest and loftiest represents a highly

complex and diversified system both in terms of biological and physical attributes. On

account of its representativeness, richness and uniqueness, the Himalayan biodiversity

has attracted workers from different parts of the globe and the region is recognized

amongst 34 Global Biodiversity Hotspots. Among others, the biodiversity in the

region has remained a major source of attraction for taxonomists, naturalists and

ecologists. The complex biogeographic formations, varied range of habitat types, and

consequent diversity in biological assemblages and ecosystem components have

fascinated all of these. Moreover, the diversity of representative ecosystem elements

and their sensitivity to human and/or climate-induced perturbations, and more

importantly, the socio-economic marginality and lack of livelihood opportunities in

the region make it an important candidate for immediate action with respect to both

maintenance of biological diversity and sustainable flow of benefits to the society.

The action, thus desired, for maintenance of biodiversity needs strong research based

information backup. In this context, the Mountain Biosphere Reserves (MBRs) have

been identified among the most suitable areas for long-term Research & Development

activities across the world. The globally selected MBRs for case studies include the

Nanda Devi Biosphere Reserve (NDBR) in the Indian Himalayan Region (IHR) as a

potential site from Asia-Pacific Region. Considering this recognition of NDBR, the

present study identified this BR as the extensive study site.

The present investigation deals with the assessment of compositional changes

over the last two decades in representative forest communities in Nanda Devi

Biosphere Reserve. Attempt has been made to explore the compositional patterns

across altitudinal range covering temperate to sub-alpine forest communities. The

study particularly focused to address following objectives: (1) Assessment of diversity

in vegetation and other land use patterns in NDBR using standard phytosociological

approaches and application of RS/GIS; (2) Change detection (temporal/spatial) of

vegetation at community and species level (dominant/co-dominant); (3) Identification

136

of sensitive/vulnerable areas and communities considering patterns of natural

recruitment; and (4) Development of future scenarios and prediction maps to propose

long-term alternative management plans for NDBR.

Two representative sites [i.e., Pindari-Sunderdhunga-Kafni (PSK) in Kumaun

part and Lata-Tolma-Phagti (LTP) in Garhwal part] in buffer zone of NDBR formed

the intensive study sites. The study largely targeted revisiting the previously

investigated plots of these sites (PSK: 1990-1992; LTP: 2000-2002). Extensive

surveys were conducted during 2008-2012 in these sites using the earlier reference

points and approach of studies (i.e., Bankoti, 1990 for PSK; Joshi, 2002 for LTP).

The representative forest communities in PSK (11 communities) and LTP (8

communities) sites were distributed between an altitudinal range of 2000 to 3900 m

asl. All together, 451 plant species (94 families) were recorded from the study sites.

Of these, greater proportion (70.51%; 318 spp.) was of herbs. Shrubs constituted

17.71% (80 spp.), and trees 11.8% (53 spp.). Considering various taxonomic groups,

PSK site (total 332 species) consisted of 88.9% angiosperms, 1.2% gymnosperms,

and 9.9% pteridophytes. Whereas, for LTP site, 87% representation was of

angiosperms, 3.2% of gymnosperms, and 9.9% of pteridophytes.

In PSK site, native species contributed 59.6% (198 spp.) with 24.7%

endemics (82 spp.). Among natives, herbs constituted 63.1%, shrubs 19.7% and

trees17.2% species. In case of LTP site, out of total 248 species, 66.5% (165 spp.)

were native. Endemics in this site constituted 33.5 % of the total species. Among

natives, herb contribution was 67.8 % followed by shrubs 21.2 % and trees 11 %.

Across life forms, representation in study sites, in relation to entire area, was as

follows: Trees (PSK- 81.13%; 43 spp.; LTP- 43.4%; 23 spp.), shrubs (PSK- 67.5%;

LTP- 56.3%), herbs (PSK- 73.9%; LTP- 56.6%).

Considering the compositional features, the mean tree density (PSK: 260-535

ind ha-1

, LTP: 599-1211 ind ha-1

) was comparable with the reported values (320-1670

ind ha-1

) in low to high altitude forests of west Himalaya. The range of sapling density

(PSK: 145-633 ind ha-1

; LTP: 40-951 ind ha-1

) in study sites approached the lower

range of values (40-6667 ind ha-1

) reported in the region. However, the seedling

density range (PSK- 1450-8170 ind ha-1

; LTP- 470-1665 ind ha-1

) in present study

was considerably higher than the earlier reports for high altitude forests. Other forest

strata exhibited following trends: (i) shrub density (PSK- 2150-25575 ind ha-1

; LTP-

1226-4025 ind ha-1

) was within the range reported for the region (871-29114 ind ha-1

),

137

(ii) herb density (PSK- 9100-93600 ind 100 m-2

; LTP- 3980-26112 ind 100 m-2

) was

comparable with earlier reported range (4980-84480 ind ha-1

).

The Canonical Correspondence Analysis (CCA) distributed all the species in

different types of habitats (quadrants). It helped in depicting the relationship between

vegetation and environmental parameters. However, used co-variables (species

richness and density) behaved differently with environmental variables. The

cumulative percentage variance of the species environment relationship for axis 1 and

2 was 98.7 (using density as co-variable) and 98.2 (using richness co-variable)

respectively in PSK and 96.9 (density co-variable) and 96.2 (richness co-variable) for

LTP site indicated that most of the information regarding community distribution and

composition was concentrated on these two axes.

In view of the non-normality of data Generalized Additive Model (GAM) was

found best fit model (95% confidence level) to predict rate of change in species

richness and density along altitude range. All the tree strata in PSK site, both for

species richness and density, generally showed declining trends along altitude

gradient. While considering total species richness of seedlings, the change point was

revealing at 2800 m asl, whereas, in case of richness of species in trees and saplings

layers the change point was observed at 3000 m. Considering, total species richness,

significant change point appears at 2700-2800 m asl. However, the model for

estimating rate of change in density in different categories suggested that the change

point more or less falls at 2400 m asl in case of seedling and saplings; and at 2600 m

asl for trees.

In case of LTP site, while considering species richness, declining trend was

revealing with a major change point at 2800 m asl, except for seedling richness which

exhibited an increasing trend with a change point at 3300 m asl. Taking overall

richness in consideration, the rate of change in richness was observed at 2800 m asl.

In case of density the change points varied for different strata. The rate of change in

density of trees was observed at 3000 m asl, which was slightly higher for seedlings

(3400 m asl) and lower for saplings (2900 m asl). The overall density, however,

showed significant rate of change at 2900 m asl.

The soil parameters showed variations across sites and communities. The soil

pH in PSK site varied from 6.57 (Hippophae salicifolia) to 7.04 (Mixed Silver fir-

Rhododendron-Maple). Total soil organic carbon ranged from 3.38 (Alnus nepalensis)

to 4.69 % (Betula utilis). Soil organic matter, however, varied between 5.83 to 8.08 %

138

and followed the same trend as for organic carbon. The total nitrogen was minimum

in H. salicifolia community (0.40 %) and maximum in B. utilis community (0.91 %).

The C/N ratio for communities ranged from 5.15 to 8.68 %., with a highest value for

H. salicifolia followed by Q. floribunda and Mixed Oak deciduous communities. The

pH values across communities in LTP site ranged from 4.79 to 6.32 with maximum

pH in Cedrus deodara community. Total organic carbon ranged from 1.21 (Acer

caesium-Prunus cornuta) to 6.90 % (Betula utilis). Organic matter ranged from 2.09

to 11.89 % and followed the same trends as for organic carbon. The total nitrogen

ranged from 0.22 (A. caesium-P. cornuta) to 1.10 % (B. utilis). The C/N ratio ranged

from 5.50 to 10.25 %, with highest values in case of Cedrus deodara followed by

Abies spectabilis and Pinus wallichiana communities.

While considering the temporal changes in vegetation composition between

two time observations (i.e., 1992 and 2010 for PSK and 2002 and 2012 for LTP site),

the following patterns were revealing: (i) significant increase in species richness in

seedling (paired mean 8.91 to 11.27; p<0.01) and sapling layers (paired mean 8.91 to

11.45; p<0.05) over the last two decades were revealing in PSK site. But no

significant change in tree layer was recorded; (ii) at community level, maximum

increase in species richness in seedling layer was recorded in Quercus floribunda and

Mixed-deciduous communities (8 species in each; p<0.05). In sapling layer,

maximum increase was recorded in Q. semecarpifolia community (6 species; p<0.05)

followed by Q. floribunda community (5 species; (p<0.05). Some other communities

like Mixed-deciduous, Alnus nepalensis and Mixed Silver fir-Rhododendron-Maple

also showed good increase in species richness of sapling layer in last two decades;

(iii) no significant changes in diversity patterns were revealing at any tree strata over

the last two decades. In general, increase in species diversity of seedling (paired mean

2.07 to 2.10), and sapling (paired mean 2.02 to 2.25) was recorded. In case of tree

layer the diversity has slightly declined (paired mean 2.60 to 2.55). At community

level, Mixed Silver fir-Oak showed significant increase of diversity in sapling layer

(p<0.05).

In case of LTP site the change patterns can be summarized as: (i) no

significant change was observed in species richness of any tree strata. However, trend

of increase in species richness was apparent at seedling (paired mean 7.87 to 8.62)

and sapling (paired mean 6.00 to 6.50) level; (ii) amongst communities, Acer

caesium-Prunus cornata mixed community showed significant increase in seedling

139

layer (7 species; p<0.01); (iii) species diversity showed patterns of non significant

changes in seedling and sapling layer. However, change for the tree layer were

significant (p<0.05); (iv) Acer caesium-Prunus cornata mixed community (p<0.05) in

seedling layer and Taxus wallichiana-Abies pindrow mixed community in tree layer

showed significant (p<0.05) increase of species during last one decade.

In case of PSK site, density in seedling layer exhibited significant increase in

last two decades (paired mean 2885 to 3927; p<0.05). Sapling layer (paired mean 220

to 250) also exhibited increasing trends. However, decreasing trend in tree layer was

revealing (paired mean 428 to 406). Amongst communities, maximum and significant

increase in density of seedling layer was recorded in Mixed Oak deciduous

community (p<0.05). In sapling layer, maximum increase was recorded in Mixed Oak

deciduous community followed by Alnus nepalensis and Abies pindrow community.

However, these changes were non significant (p>0.05). Most of the lower altitude

communities in PSK site showed decline in tree densities during the last two decades.

While using TBA values, no significant changes over the last two decades were

observed. At community level, A. nepalensis, Mixed Silver fir-Rhododendron-Maple,

and Betula utilis communities showed noticeable increase. On the contrary, Mixed

Oak deciduous, Q. floribunda, Q. semecarpifolia exhibited noticeable decrease in

TBA. A general trend of decrease in TBA of the lower altitude communities was

apparent.

Considering the LTP site, significant increase in density of seedling layer over

the last one decade was revealing (paired mean 599 to 829; p<0.01). However,

sapling layer (paired mean 538 to 523) and tree layer (paired mean 807 to 804)

exhibited decreasing trends. Amongst communities, maximum increase in density of

seedling layer was recorded in Abies spectabilis community (p<0.01), followed by

Abies pindrow and Pinus wallichiana communities (p<0.05). In sapling layer,

maximum increase was recorded for Juglans regia-Prunus cornuta mixed community

(p<0.05) followed by Pinus wallichiana and Acer caesium-Prunus cornata mixed

community (p=ns). In case of tree layer no discernable change was observed.

Significant changes in studied soil parameters were also revealing during last

two decades in PSK site [i.e., organic carbon (paired mean 4.60 to 4.17; p<0.05),

organic matter (paired mean 7.93 to 7.19; p<0.05) and C/N ratio (paired mean 7.18 to

5.94; p<0.01) showed significant decrease; and total nitrogen (paired mean 0.65 to

0.73; p<0.01) showed significant increase]. Likewise, in LTP site significant increase

140

in nitrogen (paired mean 0.51 to 0.57; p<0.01) was revealing with a significant

decline in C/N ratio (paired mean 8.90 to 7.82; p<0.01). However, organic carbon

(paired mean 4.27 to 4.23; p=ns) and organic matter (paired mean 7.37 to 7.30; p=ns)

showed non significant decrease.

At individual community level, there were indications of change in certain

communities. For example, A. nepalensis, Mixed Silver fir-Rhododendron-Maple,

Mixed Birch-Silver fir and B. utilis communities in PSK site and C. deodara, P.

wallichiana and B. utilis communities of LTP site either exhibited no change or

increase (albeit non significant) in density and TBA values, thereby suggesting that

the communities were still maturing during the study period. On the contrary, a few

other communities, such as Mixed Oak-Deciduous, H. salicifolia, Q. semecarpifolia,

Mixed deciduous in PSK site and A. spectabilis, A. pindrow communities in LTP site

exhibited decline in both density and basal area (albeit non significant), indicating

that the communities have declined in abundance over last few decades. Since, the

dominant and co-dominant tree species in these forests still continue to dominate, and

in terms of importance value no significant changes have been recorded for such

species, the composition largely remained unchanged over the last two decades or so.

The results based on Landsat-TM (2005) data, showed that over 65% area of

NDBR is snowbound or covered by glaciers. The next dominant landcover is forest

vegetation (11.1%), including Conifer dominated needleleaf, Evergreen broadleaf and

Mixed broadleaf forests followed by alpine meadows (10.9%) and alpine scrubs

(6.4%). The agriculture land (crop land) occupies only 0.1% of the reserve. Temporal

changes in various landuse/landcover categories, with a particular focus on forest

vegetation (forest physiognomy) were investigated in the present study using Landsat

TM 1990 and 2005 data. While considering the overall vegetation cover in the

reserve, a total decline of 51.3 Km2 (2.8 %) was registered between 1990 and 2005.

Most of the vegetation cover classes revealed negative change over 15 years time

interval, with a maximum decrease of 39.6 Km2 (5.6 %) in case of alpine meadows

followed by alpine scrubs (15.8 Km2; 3.9 %). Among delineated three forest

physiognomic types in the reserve, Conifer dominated (8.1 Km2; 2.1 %) and Mixed

broadleaf forests (4.8 Km2; 3.9 %) showed decline. However, a noticeable increase

was revealing for Evergreen broadleaf forests (17.4 Km2; 10.6 %).

Considering the Community Change Sensitivity (CCS) in PSK site, the

maximum CCS was recorded for Q. floribunda (46) followed by A. pindrow (41) and

141

Mixed deciduous community (41) and minimum for Mixed Silver fir-Oak (17)

community. One more community, H. salicifolia (41), also exhibited case of higher

sensitivity but on the reverse direction by way of loosing quantum of compositional

attributes. In case of LTP site, maximum CCS was observed for A. pindrow (60)

followed by A. caesium-P. cornuta Mixed community (51) and A. spectabilis (44) and

minimum for J. regia-P. cornuta Mixed (24) community. However, A. pindrow and

A. spectabilis showed changes in reverse direction by way of exhibiting loss in

quantum of studied compositional attributes.

Realizing that the existing and potential threats on communities would define

the need for required management interventions, attempts were made to develop

Community Threat Index (CTI), that considered two broadly accepted indicators of

threat to composition; (i) the proportionate contribution of non-native species in

community composition, and (ii) the existing level of canopy disturbance. The

communities with higher CTI depicted the greater threat to overall compositional

integrity. Such communities would, therefore, require more rapid management

interventions.

On the basis of Community Threat Index (CTI), Mixed Oak deciduous (CTI-

93), Q. floribunda (CTI-84), Q. semecarpifolia (CTI-84), A. nepalensis (CTI-80) in

PSK site were the communities that are facing greater threat to their compositional

integrity. A. pindrow (CTI-56) and Mixed Silver fir-Rhododendron-Maple (CTI-57)

are, however, least threatened communities of this site. In case of LTP site, C.

deodara (CTI-95), A. caesium-P. cornuta Mixed (CTI-86) were the most threatened

communities, whereas, A. spectabilis (CTI-69) and Taxus-Abies Mixed (CTI-69)

communities currently face minimum threat for compositional integrity.

The Community Importance Index (CII) developed on the basis of Community

Representativeness Index (CRPI), Community Richness Index (CRI) and Community

Uniqueness Index (CUI) provided a relatively unbiased ranking tool. The ranking of

communities by way of using CII reflected cumulative contribution of all these

compositional attributes. In this respect, most important two communities that

maintain compositional integrity include Mixed Silver fir-Rhododendron-Maple (CII-

90) and Q. floribunda (CII- 83) in PSK site, and P. wallichiana (CII-81) and T.

wallichiana-A. pindrow Mixed (CII- 80) in LTP site.

The study further considered deriving the Community Integrity (CI) values so

as to provide a more refined and realistic ranking of individual community. The

142

overall scenarios of communities considering various priorities have been defined.

Analysis of priorities, thus defined, suggests that depending upon the management

objectives, different scenarios can be used for building strategies and management

prescriptions. The scenarios discussed in the thesis included: (i) ensuring conservation

of maximum plant diversity of the reserve by way of using CII; (ii) planning to

accommodate the potential changes in community structure (i.e., shift in

vegetation/species boundaries and/or change in dominance of forests, etc.) by build on

CCS ranking; (iii) addressing the threats of non-native proliferation through use of

CTI; and (iv) holistic way of defining community stability and resilience using

Community Integrity (CI) scores.

143

REFERENCES

Adhikari BS, Rikhari HC, Rawat YS, Singh SP (1991) High altitude forests:

composition diversity and profile structures in a part of Kumaun Himalaya.

Tropical Ecology 32:86-97.

Adhikari BS (1992) Biomass productivity and nutrient cycling of kharsu oak and

silver fir forests in Central Himalaya. Ph.D. Thesis (Botany), Kumaun

University, Nainital.

Aguado-Santacruz GA, Garcia-Moya E (1998) Environmental factors and community

dynamics at the southernmost part of the North American Graminetum. Plant

Ecology 135:13-29.

Alexander JM, Kueffer C, Daehler CC, Edwards PJ, Pauchard A, Seipel T, Miren C

(2011) Assembly of non-native floras along elevational gradients explained by

directional ecological filtering. Proceedings National Academy Sciences USA

108: 656-661.

Alexander RW, Millington AC (2000) Vegetation Mapping: from patch to planet.

John Wiley & Sons Ltd. USA.

Allen TFH, Hoekstra TW (1992) Toward a unified ecology. Columbia University

Press, New York.

Amarnath G, Murthy MSR, Britto SJ, Rajashekar G, Dutt CBS (2003) Diagnostic

analysis of conservation zones using remote sensing and GIS techniques in wet

evergreen forests of the Western Ghats – An ecological hotspots, Tamil Nadu,

India. Biodiversity and Conservation 12:2331-2359.

Amri E, Kisangau DP (2012) Ethnomedicinal study of plants used in villages around

Kimboza forest reserve in Morogoro, Tanzania. Journal of Ethnobiology and

Ethnomedicine 8(1): 1-10.

Andreasen JK, Neill RVO (2001) Consideration for the development of a terrestrial

index of ecological integrity. Ecological Indicators 1: 21-35.

Anonymous (1883-1970) Index Kewensis Plantarum Phanerogamarum Vol. 1-2

(1883-1885) and 15 Suppl. (1886-1970). Claredron Press Oxford.

Anonymous (2009) Governance for Sustaining Himalayan Ecosystem (G-SHE)-

Guidelines & best practices. MoEF & GBPIHED, pp. 1-51.

144

Arevalo JR, Delgado JD, Otto R, Naranjo A, Salas M, Fernandez-Palacios JM (2005)

Distribution of alien vs native plant species in roadside communities along an

altitudinal gradient in Tenerife and Gran Canaria (Canary Islands). Perspectives

in Plant Ecology, Evolution and Systematics 7: 185-202.

Ayensu ES (1981) Assessment of threatened plant specids in the United States. In

Synge H (eds.). The biological aspects of rare plants conservation. John Willy &

Sons Ltd. USA, pp. 19-58.

Azzali A, Menenti M (2000) Mapping vegetation- Soil complexes in southern Africa

suing temporal Fourier analysis of NOAA AVHRR NDVI data. International

Journal of Remote Sensing 21: 973-996.

Badola R (1998) Nanda Devi Biosphere Reserve: a study on socio-economic aspects

for the sustainable development of dependent population. Final Report, Wildlife

Institute of India, Dehradun, India.

Bajgiran PR, Darvishesefat AA, Khalili, A, Makhdoum MF (2008) Using AVHRR-

based vegetation indices for drought monitoring in the northwest of Iran. Journal

of Arid Environment 72: 1086-1096.

Bali R, Ali SN (2010) Dynamics of Pindari glacier during the last 600 years. Current

Science 99:1307.

Bali R, Agrawal KK, Patil SK et al. (2011a) Record of Neotectonic activity in the

Pindari Glacier valley: study based on Glacio-geomorphic and AMS Fabric

evidences. Earth Science India 4(1): 1-14.

Bali R, Agarwal KK, Ali SN, Rastogi SK, Krishna K (2011b) Drainage morphometry

of Himalayan Glacio-fluvial basin, India: hydrologic and neotectonic

implications. Environmental Earth Sciences. DOI 10.1007/s12665-011-1324-1.

Balodi B (1993) Expedition to Nanda Devi: Floristic analysis. In: Scientific and

Ecological Expedition to Nanda Devi. Report, Army Head Quarters, New Delhi,

pp 86-95.

Bankoti NS (1990) Woody vegetation along elevational gradient (2000-3600 m) of

upper Pindari catchment (Kumaun Himalaya). Ph.D. Thesis (Botany), Kumaun

Unversity, Nainital.

Bankoti NS, Rawal RS, Samant SS, Pangtey YPS (1992) Forest vegetation of inner

hill ranges in Kumaun, Central Himalaya. Tropical Ecology 33:41-53.

Bell DT (1997) Eighteen years of change in an Illinois streamside deciduous forest.

Journal of the Torrey Botanical Society 124: 174-188.

145

Beloussova L, Denissova L (1981) The USSR Red Data Book and its compilation. In

Synge H (eds.). The biological aspects of rare plants conservation. John Willy &

Sons Ltd. USA, pp. 93-99.

Bernhardt-Romermann M, Kudernatsch T, Pfadenhauer J (2007) Long-term effects of

nitrogen deposition on vegetation in a deciduous forest near Munich Germany.

Applied Vegetation Science 10:399-406.

Betts RA (2000) Offset of the potential carbon sink from boreal forestation by

decreases in surface albedo. Nature 408, 187-190.

Bhandari JS (1981) Structure and change among the borderland communities of the

Kumaun Himalaya. In: Lal JS (eds.). The Himalaya: aspects of changes. Oxford

University Press, New Delhi, pp. 204-216.

Bharti RR, Adhikari BS, Rawat GS (2011) Assessing vegetation changes in

timberline ecotone of Nanda Devi National Park, Uttarakhand. International

Journal of Applied Earth Observation and Geoinformation. DOI

10.1016/j.jag.2011.09.018.

Bhuyan P, Khan ML, Tripathi RS (2003) Tree diversity and population structure in

undisturbed and human-impacted stands of tropical wet evergreen forest in

Arunachal Pradesh, Eastern Himalayas, India. Biodiversity and Conservation

12, 1753-1773.

Bisht MS (2000) Monitoring of vegetation cover and land uses in Nanda Devi

Biosphere Reserve. Indian Forester 126:664-673.

Bor NL (1960) The grasses of Burma, Ceylon, India and Pakistan. Pergmon Press,

Oxford.

Bowers JE (2007) Has climatic warming altered spring flowering date of Sonoran

desert shrubs? Southwestern Naturalist 52:347-355.

Braatz SM (1997) State of the world’s forests. Nature and Resources 33(3/4): 18-25.

Bradley NL, Leopold AC, Ross J, Huffaker W (1999) Phenological change reflect

climate change in Wisconsin. Proceedings of National Academy of Sciences,

USA 96:9701-

9704.

Brandt JS, Townsend PA (2006) Land use-landcover conversion, regeneration and

degradation in the high elevation Bolvian Andes. Landscape Ecology 21:607–

623.

146

Braun-Blanquet J (1932) Plant Sociology. The Study of Plant Communities.

Authorized English translation of 'Pflanzensoziologie' by G.D. Fuller & H.S.

Conard. McGraw-Hill Book Company, New York.

Bush JK, Richter FA (2006) Two decades of vegetation change on terraces of a south

Texas river. Journal of the Torrey Botanical Society 133(2): 280-288.

Cairns DM, Moen J (2004) Herbivory influences tree lines. Journal of Ecology 92:

1019-1024.

Carpenter C (2005) The environmental control of plant species density on a

Himalayan elevation gradient. Journal of Biogeography 32: 999-1018.

Champion HF (1923) The interaction between Pinus longifolia and its habitat in

Kumaun Hills. Indian Forester 49: 7-8.

Champion HG, Seth SK (1968) A revised survey of forest types of India. Government

of India, Publication Division, New Delhi.

Chen XG, Zhang XQ, Zhang YP, Wan CB (2009) Carbon sequestration potential of

the stands under the grain for green program in Yunnan province, China. Forest

Ecology and Management 258: 199-206.

Clark DB (1991) Answers to Amazonian deforestation. In: Anderson AB (eds.).

Review of Alternatives to deforestation: step towards sustainable use of the

Amazon Rain Forest. Columbia University Press, New York, USA.

Clarke R (1979) Ecological impact of Nanda Devi area. The American Alpine Journal

53:281.

Clements FE (1916) Plant Succession. An Analysis of the Development of

Vegetation. Carnegie Institution, Washington.

Cleland EE, Chiariello NR, Loarie SR, Mooney HA,Field CB (2006) Diverse

responses of phenology to global changes in a grassland ecosystem. Proceedings

of National Academy of Sciences, USA 103:13740-13744.

Connell JH (1978) Diversity in tropical rain forest and coral reefs. Science 199: 1302-

1310.

Curtis JT (1959) The vegetation of Wisconsin. University of Wisconsin Press,

Madison.

Dai L, Qi L, Wang Q, Su D et al. (2011) Changes in forest structure and composition

on Changbai Mountain in Northeast China. Annals of Forest Science 1-7.

Dai W, Huang Y (2006) Relationship of soil organic matter concentration to climate

and altitude in zonal soils of China. Catena 65: 87-94.

147

Deva S, Naithani HB (1986) The orchid flora of north west Himalaya. Print and

Media associates, New Delhi, pp. 459.

Dhar U, Samant SS (1993) Endemic diversity of Indian Himalaya I: Rannunculaceae

and II: Paeoniaceae. Journal of Biogeography 20: 659-668.

Dhar U, Rawal RS, Samant SS (1996) Endemic plant diversity in Indian Himalaya III:

Brassicaceae. Biogeographica 72(1): 19-32.

Dhar U, Rawal RS, Samant SS (1997) Structural diversity and representativeness of

forest vegetation in a protected area of Kumaun Himalaya, India: implications

for conservation. Biodiversity and Conservation 6:1045-1062.

Dhar U, Rawal RS, Samant SS (1998) Wild plant resources and use pattern in Askot

Wildlife Sanctuary of Kumaun in the Indian Himalaya: Manifestations of

commonness and rarity. In: Kotwal PC, Banerjee S. (eds.). Biodiversity

Conservation in Managed Forest and Protected areas. Agro Botanica Bikaner,

pp. 128-142.

Dhar U (2002) Conservation implication of plant endemism in high-altitude

Himalaya. Current Science 82(2): 141-148.

Diaz HF, Grosjean M, Graumlich L (2003) Climate variability and change in high

elevation regions: past, present and future. Climate Change 59: 1-4.

Dikshit BK, Panigrahi G (1998) The Family Rosaceae in India - Revisionary studies

on Potentilla L., Sibbaldia L. & Brachycaulos. Bishen Singh Mahendra Pal

Singh, Dehradun, Vol 4, pp. 1-348.

Don D (1825) Prodomus Florae Nepalensis. Dehradun, India (Reprinted 1976).

Dudgeon W, Kanoyer LA (1925) The ecology of Tehri Garhwal. A contribution to the

ecology of western Himalaya. Journal of Indian Botanical Society 4:233-285.

Dutt CBS, Udaya LV, Sadasivaiah AS (1994) Proceeding of the 15th

ACRS,

Bangalore. 1(E) 2, pp. 1-27

Dzwonko Z, Gawronski S (2002) Influence of litter and weather on seedling

recruitment in a mixed oak pine woodland. Annals of Botany 90:245-251.

Fang XQ, Yu WH (2002) Progress in the studies on the phenological responding to

global warming. Advanced Earth Sciences 17, 714–719

Farooq M, Rashid H (2010) Spatio-temporal change analysis of forest density in

Doodhganga forest range, Jammu & Kashmir. International Journal of

Geomatics and Geosciences 1:132-140.

148

Fissore C, Giardina CP, Kolka RK et al. (2008) Temperature and vegetation effects on

soil organic carbon quality along a forested mean annual temperature gradient in

North America. Global Change Biology 14: 193-205.

Fitter AH, Fitter RSR (2002) Rapid changes in flowering time in British plants.

Science 296:1689-1691.

Foley JA, Kutzbach JE, Coe MT, Levis S (1994) Feedbacks between Climate and

Boreal forests during the Holocene Epoch. Nature 371:52-54.

Foley JA, Levis S, Costa MH, Cramer W, Pollard D (2000) Incorporating dynamic

vegetation cover within global climate models. Ecological Applications

10:1620-1632.

Gaira KS, Dhar U, Belwal OK (2011) Potential of herbarium records to sequence

phenological pattern: a case study of Aconitum heterophyllum in the Himalaya.

Biodiversity and Conservation 20(10): 2201-2210.

Gairola S (2005) Assessment of diversity pattern in sub-alpine forests of west

Himalaya: recruitment strategy, litterfall and nutrient return. Ph.D. Thesis

(Forestry), HNB University, Srinagar (Garhwal).

Gairola S, Rawal RS, Todaria NP (2008) Forest vegetation patterns along an

altitudinal gradient in sub-alpine zone of west Himalaya, India. African Journal

of Plant Science 2: 42-48.

Gairola S, Rawal RS, Dhar U (2009) Pattern of litterfall and return of nutrients across

anthropogenic disturbance gradients in three sub-alpine forests in west

Himalaya, India. Journal of Forestry Research 14: 73-80.

Ganeshaiah KN, Shaanker RU, Murali KS, Shankar U, Bawa KS (1998) Extraction of

non-timber forest products in the forests of Biligiri Rangan Hills, India. 5.

Influence of dispersal mode on species response to anthropogenic pressures.

Economic Botany 52(3):316-319.

Garkoti SC (1992) High altitude forests of central Himalaya: Productivity and nutrient

cycling. Ph.D. Theisis (Botany), Kumaun University, Nainital.

Garkoti SC, Singh SP (1992) Biomass productivity and nutrient cycling in alpine

Rhododendron community of central Himalaya. Oecologica Montana 2:21-32.

Garkoti SC, Singh SP (1994) Nutrient cycling in three central Himalayan forests

ranging from close canopied to open canopied tree line forests, India. Arctic and

Alpine Research 20:339-348.

149

Garkoti SC (1995) Shrub layer productivity in central Himalayan high elevation

forests. Proceedings of Indian National Science Academy B-6:45-50.

Garkoti SC, Singh SP (1995) Variation in forest biomass and net primary productivity

in the high mountains of central Himalaya. Journal of Vegetation Science 6:23-

28.

Garkoti SC (1996) Nutrient dynamics I high altitude shrubs of central Himalaya.

Proceedings of Indian National Science Academy (Biological Sciences) 62:281-

286.

Garkoti SC, Singh SP (1997) Structure and function of herbaceous vegetation in high

mountains of central Himalaya. Tropical Ecology 38:153-156.

Garkoti SC, Singh SP (1999) Litter decomposition and nutrient release in central

Himalayan high altitude forests. Tropical ecology 40:19-26.

Garkoti SC (1999) Changes in weight loss and nutrient composition of woody litter in

three forests on high altitudinal zone of central Himalaya. Tropical Ecology 40:

129-136.

Garkoti SC (2008) Estimates of biomass and primary productivity in a high-altitude

maple forest of the west central Himalaya. Ecological Research 23:41-49.

Garten CT, Post WM, Hanson PJ, Cooper LW (1999) Forest soil carbon inventories

and dynamics along an elevation gradient in the southern Appalachian

Mountains. Biogeochemistry 45:115-145.

Gautam AP, Edward LW, Ganehs PS, Zoebisch MA (2003) Land use dynamics and

landscape change pattern in mountain watershed in Nepal. Agriculture,

Ecosystems and Environment 99: 83-96.

Gaur RD (1999) Flora of District Garhwal, North West Himalaya with ethnobotanical

notes. Transmedia Publication, Srinagar, Garhwal.

Ghildyal BN (1957) A Botanical trip to the Valley of Flowers. Journal of Bombay

Natural History Society 54:365-386.

Giri L (2012) Studies on in vitro propagation and genetic variability in Habenaria

edgeworthii and H. intermedia in Uttarakhand. Ph.D. Thesis, Kumaun

University, Nainital.

Goel AK, Bhattacharya UC (1983) Rare flowering plants of Garhwal Himalaya. In:

Jain SK, Rao RR (eds.). An assessment of threatened plants of India. Botanical

Survey of India, Calcutta, pp. 13-17.

150

Gopalan AKS (1998) IRS-1C/1D Mission, Key Note Paper, 8th

user interaction

workshop to commemorate one decade of IRS operations, 17-18 march,

Hyderabad.

Goparaju L, Tripathi A, Jha CS (2005) Forest fragmentation impacts on

phytodiversity – An analysis using remote sensing and GIS. Current Science

88:1264-1274.

Gorrie RM (1933) The Sutlej Deodar, its ecology and timber production: Indian

Forest Production. Indian Forest Record 17: 1-240.

Greig-Smith P (1957) Quantitative Plant Ecology. Academic Press, New York.

Grime JP (2001) Plant Strategies, Vegetation Processes, and Ecosystem Properties.

2nd edition. John Wiley & Sons, Chichester.

Grytnes JA, Vetaas OR (2002) Species richness and altitude: a comparison between

Null Models and interpolated plant species richness along the Himalayan

altitudinal gradient, Nepal. The American Naturalist 159:294-304.

Grytnes JA, Beaman JH (2006) Elevational Species richness patterns for vascular

plants on Mount Kinabalu, Borneo. Journal of Biogeography 33: 1838-1849.

Guisan A, Edwards TC, Hastie T (2002) Generalized linear and Generalized Additive

Models in studies of species distributions: setting the scene. Ecological

Modelling 157: 89-100.

Guo Q (2004) Slow recovery in desert perennial vegetation following prolonged

human disturbance. Journal of Vegetation Science 15(6):757–762.

Hajra PK (1983) A contribution to the botany of Nanda Devi National Park. Botanical

Survey of India, Howrah.

Hajra PK, Balodi B (1995) Plant wealth of Nanda Devi Biosphere Reserve. Botanical

Survey of India, Culcutta.

Hajra PK, Rao RR, Singh DK, Uniyal BP (1995) Flora of India, Vol 12, Asteraceae

(Anthemideae-Heliantheae). BSI, Calcutta.

Hajra PK, Nair VJ, Daniel P (1997) Flora of India, Vol 4, Malpighiaceae-

ichapetalaceae. BSI, Calcutta.

Hansen M, DeFries R, Townshend JRG, Sohlberg R (2000) Global land cover

classification at 1 km resolution using a decision tree classifier. International

Journal of Remote Sensing 21: 1331-1365.

151

Hansen MC, DeFries RS (2004) Detecting long-term global forest change using

continuous field of tree-cover maps from 8 km advanced very high resolution

radiometer (AVHRR) data for the years 1982-99. Ecosystems 7: 695-716.

He MZ, Zheng JG, Li XR, Qian YL 2007) Environmental factors affecting vegetation

composition in the Alxa Plateau, China. Journal of Arid Environments 69(3):

473-489.

Hemond HF, Niering WA, Goodwin RH (1983) Two decades of vegetation changes

in the Connecticut Arboretum Natural Area. Bulletin of Torrey Botanical Club

110: 184-194.

Hobbs RJ, Atkins L (1988) Effect of disturbance and nutrient addition on native and

introduced annual in plant communities in the western Australian wheatbelt.

Australian Journal of Ecology 13: 171-179.

Hobbs RJ, Huenneke LF (1992) Disturbance, diversity and invasion: implications for

conservation. Conservation Biology 6: 324-337.

Hochrein H (2008) Corridors and plant invasions: A comparative study of the role of

roadsides and hiking trails on plant invasions in Moorea, French Polynesia.

UCB Moorea Class: Biology and Geomorphology of Tropical Islands, Berkeley

Natural History Museum, UC Berkeley.

http://escholarship.org/uc/item/5d39b0w7

Hooker JD (1872-1897) Flora of British India. Vol. 1-7, Reeve & Co., London.

Howard JA (1991) Remote sensing of forest resources-Theory and application.

University Press, Cambridge pp. 1-405

IPCC (2001) Climate change 2001. Impacts, adaptation, and vulnerability,

Cambridge. Cambridge University Press, UK.

IPCC (2007) Climate Change 2007. The Physical Science Basis. Summary for

Policymakers. Contribution of Working Group I to the Fourth Assessment

Report of the Intergovernmental Panel on Climate Change. Cambridge

University Press, UK.

Ise T, Moorcroft PR (2006) The global-scale temperature and moisture dependencies

of soil organic carbon decomposition: an analysis using a mechanistic

decomposition model. Biogeochemistry 12:2370-2390.

Ives JD, Messerli B (1990) The Himalayan dilemma. London and New York: United

Nations University and Routledge.

152

Jain SK, Rao RR (1977) A handbook of field and herbarium methods. Today and

tomorrow’s Printers and Publishers, New Delhi.

Jain SK, Rao RR (1983) An assessment of threatened plants of India. Botanical

Survey of India, Howrah.

Jayakumar S, Arockiasamy DI, Britto SJ (2002) Conserving forests in the Eastern

Ghats through remote sensing and GIS – A case study in Kolli hills. Current

Science 82:1259-1267.

Jha CS, Dutt CBS, Bawa KS (2000) Deforestation and land use changes in Western

Ghats, India. Current Science 79:231-238.

Jobbagy EG, Jackson RB (2000) The vertical distribution of soil organic carbon and

its relation to climate and vegetation. Ecological Applications 10:423-436.

Johnson RC (1990) The interception, throughfall and stemflow in a forest in Highland

Scotland and the comparison with other upland forests in the U.K. Journal of

Hydrology 118:281-287.

Joshi HC, Arya SC, Samant SS (2000) Diversity, distribution and indigenous uses of

plant species in Pindari area of Nanda Devi Biosphere Reserve- II. Indian

Journal of Forestry 24:514-536.

Joshi HC (2002) Assessment of habitat diversity, forest vegetation and human

dependence in the buffer zone of Nanda Devi Biosphere Reserve of west

Himalaya. Ph.D. theis (Botany), Kumaun University, Nainital.

Joshi HC, Samant SS (2004) Assessment of forest vegetation and conservation

priorities of communities in part of Nanda Devi Biosphere Reserve, West

Himalaya. Part I. International Journal of Sustainable Development and World

Ecology 11:326-336

Joshi NK, Tiwari SC (1990) Phytosociological analysis of woody vegetation along an

altitudinal gradient in Garhwal. Indian Journal of Forestry 13: 322-328.

Kaab A (2005) Remote sensing of mountain environments. UNESCO Publication:

Proceedings second and third GLOCHAMORE Workshop, pp. 92-99.

Kala CP (1998) Ecology and conservation of alpine meadows in the valley of Flowers

National Park, Garhwal Himalaya. Ph.D. Thesis, Forest Research Institute,

Dehradun.

Kala CP, Rawat GS, Uniyal VK (1998) Ecology and conservation of the Valley of

Flowers National Park, Garhwal Himalaya. Report, Wildlife Institute of India,

Dehradun, India.

153

Kala CP (2004) Pastoralism, plant conservation, and conflicts on proliferation of

Himalayan knotweed in high altitude protected areas of the Western Himalaya.

Biodiversity and Conservation 13(5): 985-995.

Kala CP, Shrivastava RJ (2004) Successional changes in Himalayan alpine

vegetation: Two decades after removal of livestock grazing. Weed Technology

18: 1210-1212.

Kala CP, Farooquee NA, Dhar U (2004) Prioritization of medicinal plants on the basis

of available knowledge, existing practices and use value status in Uttaranchal,

India. Biodiversity and Coservation 13: 453-469.

Kala CP (2005) Indigenous uses, population density and conservation of threatened

medicinal plants in Protected Areas of the Indian Himalayas. Conservation

Biology 19(2): 368-378.

Kala CP, Dhayani PP, Sajwan BS (2006) Developing the medicinal plants sector in

northern India: challenges an opportunities. Journal of Ethnobiology and

Ethnomedicine 2(32): 1-15.

Kalakoti BS, Pangtey YPS, Saxena AK (1986) Quantitative analysis of high altitude

vegetation of Kumaun Himalaya. Journal of the Indian Botanical Society

65:384-396.

Kanoyer LA (1921) Forest formation and succession in Satlej Valley, Kumaun

Himalaya. Journal of Indian Botanical Society 2: 8-9.

Kari K (2005) Climate change effects on species interactions in an alpine plants

community. Journal of Ecology 93: 127-137.

Kashyap SR (1932) Vegetation of western Himalaya and western Tibet in relation to

their climates. Journal of Indian Botanical Society 4: 327-334.

Keeling RF, Piper SC, Heimann M (1996) Global and hemispheric CO2 sinks deduced

from changes in atmospheric O2 concentration. Nature 381: 218-221.

Kawabata A, Yamaguchi Y (2001) Global monitoring of inter-annual changes in

vegetation activities using NDVI and its relationships to temperature and

precipitation. International Journal of Remote Sensing 22: 1377-1382.

Keenan RJ, Kimmins JP (1993) The ecological effect of clearcutting. Envrionmental

Reviews 1: 121-144.

Keller F, Kienast F, Beniston M (2000) Evidence of response of vegetation to

environmental change on high-elevation sites in the Swiss Alps. Regional

Environmental Changes 1:70-77.

154

Kelly AE, Goulden ML (2008) Rapid shifts in plant distribution with recent climate

change. Proceedings of National Academy of Sciences, USA 105:11823-11826.

Kershaw, KA (1973) Quantitative and dynamic plant ecology. Second edition.

Edward Arnold Limited, London.

Khan ML, Rai JPN, Tripathi RS (1987) Population structure of some tree species in

disturbed and protected sub-tropical forests of north-east India. Acta Oecologia-

Oecologica Applicata 8:247-255.

Khera N, Kumar A, Ram J, Tiwari A (2001) Plant biodiversity assessment in relation

to disturbances in mid elevational forest of Central Himalaya. Tropical Ecology

42: 83-95.

Khumbongmayum AD, Khan ML, Tripathi RS (2006) Biodiversity conservation in

sacred groves of Manipur, northeast India: population structure and regeneration

status of woody species. Biodiversity and Conservation 15:2439-2456.

Khuroo AA, Weber E, Malik AH, Dar GH, Reshi ZA (2010) Taxonomy and

biogeographic patterns in the native and alien woody flora of Kashir Himalaya,

India. Nord Journal of Botany 28: 685-696.

Khuroo AA, Weber E, Malik AH, Reshi ZA, Dar GH (2011) Altitudinal distribution

pattern of the native and alien woody flora in Kashmir Himalaya, India.

Environmental Research 111:967-977.

Kim Min-Kook, Daigle JJ (2010) Detecting vegetation cover change on the summit of

Cadillac Mountain using multi-temporal remote sensing datasets: 1979, 2001,

and 2007. Environmental Monitoring and Assessment 180(1-4): 63-75.

Kimothi MM, Murthy TVR, Singh TS et al. (2002a) IAPRS & SIS, “Resource and

Environmental Monitoring”, Hyderabad, India, Vol. 37, pp. 1043-1048.

Kimothi MM, Joshi V, Naithani AK, Garg JK (2002b) Study of Chamoli Earthquake

and its Impact Assessment Using IRS 1C/1D Data. Himalayan Geology 23:87-

94.

Kleijn D, Verbeek M (2000) Factors affecting the species composition of arable field

boundary vegetation. Journal of Applied Ecology 37(2): 256–266.

Kneeshaw DD, Bergeron Y (1996) Ecological factors affecting the abundance of

advance regeneration in Quebec's southwestern boreal forest. Canadian Journal

of Forest Research 26(5): 888-898.

Korner C (1998) A re-assessment of high elevation tree line positions and their

explanation. Oecologia 115: 445-459.

155

Korner C (1999) Alpine plant life. Springer Verlag, Berlin.

Korner C (2000a) Biosphere responses to CO2 enrichment. Ecological Applications

10:1590–1619.

Korner C (2000b) Why are there global gradients in species richness? Mountains may

hold the answer. Trends in Ecology and Evolution 15:513–514.

Korner C (2004) Mountain biodiversity, its causes and functions. Ambio 13: 11-17.

Krauchi N, Brang P, Schönenberger W (2000) Forests of mountainous regions: Gaps

in knowledge and research needs. Forest Ecology and Management 132:73–82.

Kuchler AW (1988) Mapping dynamic vegetation. In: Kuchler AW, Zonneveld IS

(eds.). Vegetation mapping. Handbook of vegetation science. Kluwer Academic

Publishers, Dordrecht, Vol 10, pp. 321-329.

Kumar R, Singh AK, Abbas SG (1994) Change in population structure of some

dominant tree species of dry Peninsular Sal Forest. Indian Forester 120: 343-

348.

Kumar A, Panigrahi G (1995) The family Rosaceae in India - Revisionary studies on

Cotoneatster Medik. Bishen Singh Mahendra Pal Singh, Dehradun, Vol 3, pp. 1-

292.

Lakshmi VU, Murthy, MSR Dutt CBS (1998) Efficient forest resources management

through GIS and remote sensing. Current Science 75:272-282.

Lele S, Rajashekhar G, Hegde VR, Kumar GP, Saravanakumar P (1998) Meso-scale

analysis of forest condition and its determinants: A case study from the Western

Ghats region, India. Current Science 75:256-263.

Lenihan JM, Drapek R, Bachelet D, Neilson RP (2003) Climate change effects on

vegetation distribution, carbon, and fire in California. Ecological Applications

13(6): 1667-1681.

Li A, Liang S, Wang A, Qin J (2007) Estimating crop yield from multi-temporal

satellite data using multivariate regression and neural network techniques.

Photogrammetric Engineering and Remote Sensing 73: 1149-1159.

Li A, Deng W, Liang S, Huang C (2010) Investigation on the pattern of global

vegetation change using a satellite-sensed vegetation index. Remote Sensing

2:1530-1548.

Lillesand TM, Kiefer RW (1994) Remote Sensing and Image Interpretation, 3rd

edition, John Wiley & Sons New York

,Chichester,Brisbane,,Toronto,,Singapore, pp. 750.

156

Lotsch A, Friedl MA, Anderson BT, Tucker CJ (2003) Coupled vegetation

precipitation variability observed from satellite and climate records.

Geophysical Research Letters 30: 1774.

Macdonald IAW, Loope LL, Usher MB, Hamann O (1989) Wildlife conservation and

the invasion of nature reserves by introduced species: a global perspective. In:

Drake JA, Mooney HA, Castri F.di. et al. (eds.). Biological Invasions: a global

perspective. John Willy & Sons, Scope 37, pp. 281-300.

Mahar G, Dhar U, Rawal RS, Bhatt ID (2009) Implications of location specific data

and their usefulness in conservation planning: an example from Indian

Himalayan Region (IHR). Biodiversity and Conservation 18: 1273-1286.

Maheshwari JK (1962) Studies on naturalized flora of India. In: Maheshwari P, Johri

BM, Vasil IK (eds.). Proceedings of Summer School of Botany. Sree Saraswati

Press, Calcutta, pp. 156-170.

Maikhuri RK, Rao KS, Nautiyal S et al. (2002) Management options for Nanda Devi

Biosphere Reserve. In: Traditional ecological knowledge for managing

Biosphere Reserves in south and central Asia. Oxford and IBH Publishing Co.

Pvt. Ltd., New Delhi, pp. 49-70.

Maikhuri RK, Rao KS, Nautiyal S (2003) Land use land cover change impacts and

strategies for rehabilitation of degraded land: A case study from the central

(lesser) Himalaya (Uttaranchal). In: Rawat MSS (eds.). Central Himalaya

Environment and Development - Potentials, Actions and Challenges. Printmedia

Publication, Srinagar, Garhwal, pp. 223-229

Mallen-Cooper J, Pickering CM (2008) Linear declines in exotic and native plant

species richness along an increasing altitudinal gradient in snowy mountains,

Australia. Australian Journal of Ecology 33: 684-690.

Mani MS (1974) Ecology and Biogeography in India. The Hague, Netherlands.

Miller-Rushing AJ, Primack RB (2008) Global warming and flowering time in

Thoreau’s Concord: a community perspective. Ecology 80:332-341.

Millette TL, Tuladhar AR, Kasperson RE, Turner BL (1995) The use and limits of

remote sensing for analyzing environmental and social change in the Himalayan

Middle Mountains of Nepal. Global Environmental Change-Human and Policy

Dimensions 5:367-380.

Misra R (1968) Ecological Work Book. Oxford & IBH Publishing Company,

Calcutta.

157

Mishra BP, Tripathi RS, Tripathi OP, Pandey HN (2003) Effect of disturbance on the

regeneration of four dominant and economically important woody species in a

broadleaved subtropical humid forest of Meghalaya, northeast India. Current

Science 84:1449-1453.

Mishra BP, Tripathi OP, Tripathi RS, Pandey HN (2004) Effect of anthropogenic

disturbance on plant diversity and community structure of a sacred grove in

Meghalaya, northeast India. Biodiversity and Conservation 13:421-436.

Mohan NP (1933) Ecology of Pinus longifolia with particular reference of Kangra

and Hoshiyarpur forest division. In: Proceedings of Punjab Forestry Conference,

Lahore.

Mohan NP, Prui GS (1955) The Himalayan conifers III: The succession of forest

communities in oak-conifer forests of Bashar Himalayas. Indian Forester 81:

461-487, 549-562, 646-652, 705-711.

Moinde-Fockler NN, Oguge NO, Karere GM, Otina D, Suleman MA (2007) Human

and natural impacts on forests along lower Tana river, Kenya: implications

towards conservation and management of endemic primate species and their

habitat. Biodiversity and Conservation 16:1161-1173.

Mukherjee PK, Constance L (1993) Umbelliferae (Apiaceae) of India. Oxford & IBH,

Calcutta.

Muller MJ (1982) Selected climate data for a global set of standard stations for

vegetation science. W. Junk, The hague, Netherlands.

Muller-Dombois D, Ellenberg H (1974) Aims and methods of vegetation ecology.

John Wiley and Sons, New York.

Munsi M, Areendran G, Joshi PK (2012) Modeling spatio-temporal change patterns

of forest cover: a case study from the Himalayan foothills (India). Regional

Environmental Change 12(3): 619-632.

Mura KS, Setty RS, Ganeshaiah KN, Shaanker RU (1998) Does forest

typeclassification reflect spatial dynamics of vegetation? An analysis using GIS

techniques. Current Science 75:220-227.

Nainwal HC, Chaudhary M, Rana N et al. (2007) Chronology of the Late Quaternary

glaciation around Badrinath (upper Alaknanda Basin): Preliminary observations.

Current Science 93:90-96.

158

Nainwal HC, Negi BDS, Chaudhary M, Sajwan KS, Gaurav A (2008) Temporal

changes in rate of recession: evidence from Satopant and Bhagirath Kharak

glaciers, Uttrakhand, using Total Station Survey. Current Science 94:653-660.

Naithani BD (1984) Flora of Chamoli district. Vol. I & II. Botanical Survey of India,

Howrah.

Nautiyal S, Maikhuri RK, Rao KS, Saxena KG (2001) Medicinal Plant Resources in

Nanda Devi Biosphere Reserve in Central Himalayas. Journal of Herbs, Spices

& Medicinal Plants 8:47-64.

Nautiyal BP, Prakash V, Chauhan RS et al. (2005) Cultivation of Aconitum Species.

Journal of Tropical Medicinal Plants 6:193–200.

Nautiyal S, Shibasaki R, Rajan KS, Maikhuri RK, Rao KS (2005) Impact of land use

changes on subsidiary occupation: a case study from Himalayas of India.

Environmental Information Archives 3:14-23

Nautiyal S, Kaechele H (2007) Conserving the Himalayan forests: approaches and

implications of different conservation regimes. Biodiversity and Conservation

16:3737-3754.

Negi GCS, Rikhari HC, Singh SP (1992) Phenological features in relation to growth

forms and biomass accumulation in an alpine meadow of the central Himalaya.

Vegetatio 101:161-170

Negi GCS, Rikhari HC, Garkoti SC (1998) The hydrology of three high altitude

forests in Central Himalaya, India: A reconnaissance study. Hydrological

Processes 12: 343-350.

Negi GCS, Samal PK, Kuniyal JC et al. (2012) Impact of climate change on the

western Himalayan mountain ecosystems: An overview. Tropical Ecology

53(3): 345-356.

Negi PS, Hajra PK (2007) Alien flora of Doon Valley, Northwest Himalaya. Current

Science 92: 968-978.

Neilson RP, Pitelka LF, Solomon AM et al. (2005) Forecasting Regional to Global

Plant Migration in Response to Climate Change. BioScience 55(9):749-759

Nemani R, White M, Thornton P et al. (2002) Recent trends in hydrologic balance

have enhanced the terrestrial carbon sink in the United States. Geophysical

Research Letters 29: 1468.

159

Nezlin NP, Kostianoy AG, Li BL (2005) Inter-annual variability and interaction of

remote-sensed vegetation index and atmospheric precipitation in the Aral Sea

region. Journal of Arid Environment 62: 677-700.

Nogues-Bravo D, Araujo MB, Errea MP, Marlienz-Rica JP (2007) Exposure of global

mountain systems to climate warming during the 21st Century. Global

Environmental Change 17:420-428.

Nogues-Bravo D, Araujo MB, Romdal T, Rahbek C (2008) Scale effects and human

impacts on the elevational species richness gradients. Nature 453:216-220.

Notaro M (2008) Response of the mean global vegetation distribution to inter-annual

climate variability. Climate Dynamics 30: 845-854.

Notaro M, Mauss A, Williams JW (2012) Projected vegetation changes for the

American Southwest: combined dynamics modelling and bioclimatic-envelope

approach. Ecological Applications 22:1365-1388.

Novosamsky I, Houba VJG, Eck Van R, Vark Van W (1983) A novel digestion

technique for multi element plant analysis. Soil Science Plant Analysis 14: 239-

249.

Ohsawa M (1991) Montane evergreen broad- leaved forests of the Bhutan. In:

Ohsawa M (eds.). Life zone ecology of Bhutan Himalaya II. Chiba University

Press, Japan, pp. 89-156.

Oliver CD, Larson BC (1990) Forest stand dynamics. MC. Graw Hill, Inc., New

York.

Osmaston AE (1922) Notes on forest communities of Garhwal Himalaya. Journal of

Ecology 10: 129-167.

Ottersen G, Planque B, Belgravio A (2001) Ecological effects of the Northern

Atlantic Oscillation. Oecologia 128: 1-14.

Palni LMS, Rawal RS (2012) Conservation of Himalayan bioresources: An

ecological, economical and evolutionary perspective. In: Sharma VP (eds.).

Nature at Work: Ongoing Saga of Evolution. Springer, India, pp. 369-402.

Palni LMS, Rawal RS, Rai RK, Reddy SV (2012) Introduciton and progression of the

Biosphere Reserve programm in India. In: Palni LMS, Rawal RS, Rai RK,

Reddy SV (eds.). Compendium on Indian Biosphere Reserve. Progression

during two decades of conservation, Ministry of Environment and Forests,

Government of India.

160

Pancer-Koteja E, Szwagrzyk J, Guzik M (2009) Quantitative estimation of vegetation

changes by comparing two vegetation maps. Plant Ecology 205:139-154.

Pangtey YPS, Kalakoti BS, Rawat GS, Pandey PC (1982) Observation on the flora of

Pindari area. Himalayan Research and Development 1: 56-60.

Pangtey YPS, Rawal RS, Bankoti NS, Samant SS (1990) Phenology of high altitude

plants of Kumaun in central Himalaya, India. International Journal of

Biometeorology 34:122-127.

Pangtey YPS, Samant SS (1988) Observation on the threatened, rare and endangered

flowering plants and ferns in the flora of Kumaun Himalaya. Advances in

Forestry Research in India 3: 65-74.

Panigrahy RK, Kale MP, Dutta U et al. (2010) Forest cover change detection of

Western Ghats of Maharashtra using satellite remote sensing based visual

interpretation technique. Current Science 98(5): 657-664.

Parendes LA, Jones JA (2000) Role of light availability and dispersal in exotic plant

invasion along road and streams in the H.J. Andrews Experimental Forest,

Oregon. Conservation Biology 14:64-75.

Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change

impacts across natural systems. Nature 421:37–42.

Parmesan C (2006) Ecological and evolutionary responses to recent climate change.

Annual Review of Ecology, Evolution, and Systematics 37: 637-669.

Paster J, Post WM (1988) Response of northern forest to Co2 induced climate change.

Nature 334: 55-58.

Pauli H, Gottfried M, Grabherr G (2001) High summits of the Alps in a changing

climate. The oldest observation series on high mountain plant diversity in

Europe. In: Walther G R, Burga CA and Edwards PJ (eds.). Fingerprints of

climate change. Adapted behavior and shifting species ranges. Kluwer

Academic/Plenum Publishers, New York, US.

Payette S, Filion L, Delwaide A, Begin C (1989) Reconstruction of tree-line

vegetation response to long-term climate change. Nature 341: 429-432.

Perira JS, Kozlwski TT (1977) Water relation and draught resistance of young Pinus

banksiana and Pinus resinosa plantation trees. Canadian Journal of Forest

Research 7: 132-137.

161

Piao SL, Fang JY, Chen AP (2003) Seasonal dynamics of terrestrial primary

production in response to climate change in China. Acta Botanica Sinica 45:

269-275.

Pielou EC (1975) Ecological diversity. Willey-Interscience, New York, pp. 165.

Poldini L, Martini F, Ganis P, Vidali M (1991) Floristic database and phytogeograhic

analysis of a territory: an example concerning Italy. In: Nimis PL, Crovello TJ

(eds.). Quantitative approaches to phytogeography. Kluwer Academic

Publishiers, Netherlands. pp. 159-181.

Pounds JA, Fogden MPL, Campbell JH (1999) Biological response to climate change

on a tropical mountain. Nature 398:611-615.

Poveda G, Salazar LF (2004) Annual and inter-annual (ENSO) variability of spatial

scaling properties of a vegetation index (NDVI) in Amazonia. Remote Sensing

and Environment 93: 391-401.

Potter C, Tan PN, Kumar V et al. (2005) Recent history of large scale ecosystem

disturbances in North America derived from the AVHRR satellite record.

Ecosystems 8: 808-824.

Prakash C, Uniyal VK (1999) Structure of forest vegetation along an altitudinal

gradient in the Valley of Flowers National Park and its vicinity, Western

Himalaya. Annals of Forestry 7:60-69.

Prasad SN (1998) Conservation planning for the Western Ghats of Kerala: II.

Assessment of habitat loss and degradation. Current Science 75:228-235.

Prasad SN, Vijayan L, Balachandran S, Ramachandran VS, Verghese CPA (1998)

Conservation planning for the Western Ghats of Kerala: I. A GIS approach for

location of biodiversity hot spots. Current Science 75:211-219.

Pregitzer KS, Barnes BV (1982) The use of ground flora to indicate edaphic factors in

upland ecosystems of the McCormik experimental forest, upper Michigan.

Canadian Journal of Forest Research 12:661-672.

Pregitzer KS, Barnes BV, Lemme GD (1983) Relationship of topography to soils and

vegetation in an upper Michigan ecosystem. Soil Science Society of America

Journal 47: 117-123.

Price MF, Gurung AB, Dourojeanni P, Muselli D (2006) Social monitoring in

Mountain Biosphere Reserves – conclusion from EU – GLOCHAMORE

project. Mountain Research and Development 26: 174-180.

162

Proll G, Dullinger S, Dirnbock T, Kaiser C, Richter A (2011) Effect of nitrogen on

tree recruitment in a temperate montane forest as analysed by measured

variables and Ellenberg indicator values. Preslia 83: 111-127.

Puri GS and Gupta AC (1951) The Himalayan conifers II: The ecology of humus in

conifer forest of Kullu Himalaya. Indian Forester 77: 56-63, 124-129.

Pysek P (1993) Factors affecting the diversity of flora and vegetation in central

European settlements. Vegetatio 106(1): 89-100.

Rabinowitz DA (1981) Seven forms of rarity. In: Synge H (eds.). The biological

aspects of rare plant conservation. John Willy & Sons Ltd., USA, pp. 205-217.

Rahbek C (1995) The elevational gradient of species richness: A uniform pattern?

Ecography 18: 200-205.

Rai ID, Adhikari BS, Rawat GS, Basgali K (2012) Community Structure along

timberline ecotone in relation to micro-topography and disturbances in

western Himalaya. Notulae Scientia Biologicael 4: 41-52.

Ralhan PK, Saxena AK, Singh JS (1982) Analysis of forest vegetation at and around

Nainital in Kumaun Himalaya. Proceedings of National Science Academy B 48:

122-138.

Ram J, Singh JS, Singh SP (1989) Plant biomass, species diversity and net primary

production in central Himalayan High altitude grassland. Journal of Ecology 77:

456-468.

Ram J (1992) Effect of clipping on aboveground biomass and total herbage yield in

grassland above treeline in central Himalaya, India. Arctic and Alpine Research

24: 78-81.

Ramachandra TV, Kumar U, Joshi NV (2012) Landscape dynamics in western

Himalaya – Mandhala Watershed, Himachal Pradesh, India. Asian Journal of

Geoinformatics 12(1). Open access

(wgbis.ces.iisc.ernet.in/energy/paper/ajg_mandhala/methods.htm).

Ramakrishnan PS, Vitousek PM (1989) Ecosystem level processes and the

consequences of biological invasion. In: Drake JA, Mooney HA, Castri F.di et

al. (eds.). Biological invasion: a global perspective. John Willy & Sons, Scope

37, pp. 281-300.

Rao MA (1961) Flora of the Nanda Devi Sanctuary. Bulletin of Botanical Survey of

India 3: 215-251.

163

Rao TA (1960) A botanical tour to Pindari glacier and Kumaun hill stations. Bulletin

of Botanical Survey of India 2:61-94.

Rastogi A (1993) Conservation status of forests in the buffer zone. In: Scientific and

ecological expedition to Nanda Devi. Report, Army Head Quarters, New Delhi,

pp. 96-98

Rawal RS, Bankoti NS, Samant SS, Pangtey YPS (1991) Phenology of tree layer

species from the timberline around Kunaum in central Himalaya, India.

Vegetatio 93:109-118.

Rawal RS, Pangtey YPS (1993) Vegetation diversity at timberline in Kumaun, central

Himalaya. In: Dhar U (eds.). Himalayan Biodiversity Conservation Strategies.

Gyanodaya Prakashan, Nainital, pp. 219-229.

Rawal RS, Pangtey YPS (1994a) Distribution and structural-functional attributes of

trees in the high altitude zone of central Himalaya, India. Vegetatio 112:29-34.

Rawal RS, Pangtey YPS (1994b) High altitude forests with special reference to timber

line in Kumaun, central Himalaya. In: Pangtey YPS, Rawal RS (eds.). High

Altitudes of the Himalaya. Gyanodaya Prakashan, Nainital, pp. 353-399.

Rawal RS, Bankoti NS, Pangtey YPS (1994) Broad community identification of high

altitude forest vegetation in Pindari catchment of Kumaun. Proceedings Indian

National Science Academy B 60: 553-556.

Rawal RS, Dhar U (1997) Sensitivity of timberline flora in Kumaun Himalaya, India:

Conservation implications. Arctic and Alpine Research 29:112-121.

Rawal RS, Dhar U (2001) Protected area network in Indian Himalayan region: Need

for recognizing values of low profile protected areas. Current Science 81:175-

184

Rawal RS, Pandey B, Dhar U (2003) Himalayan Forest Database – thinking beyond

dominants. Current Science 84: 990-994.

Rawal RS and Rawat B (2012) Nanda Devi Biosphere Reserve-west Himalaya, India.

In: Palni LMS, Rawal RS, Rai RK, Reddy SV (eds.). Compendium on Indian

Biosphere Reserve. Progression during two decades of conservation. Ministry

of Environment and Forests, Government of India.

Rawal RS, Gairola S, Dhar U (2012) Effect of disturbance intensities on vegetation

patterns in oak forests of Kumaun, west Himalaya. Journal of Mountain Science

9: 157-165.

164

Rawat GS, Sathyakumar S, Prasad SN (1999) Plant species diversity and community

structure in the outer fringes of Kedarnath Wildlife Sanctuary, western

Himalaya: conservation implications. Indian Forester 125:873-882.

Rawat GS, Kala CP, Uniyal VK (2001) Plant species diversity and community

composition in the Valley of Flowers National Park, Western Himalaya. In:

Pandey PC, Samant SS (eds.). Plant Diversity of the Himalaya. Gyanodaya

Prakashan, Nainital, pp. 277-290.

Reasoner M, Bugmann H, Schaaf T (2003) Background and Concepts for

Collaborative Work: Global Change Research in Mountain Biosphere Reserves.

In: Proceedings of the International Launching Workshop.Entlebuch Biosphere

Reserve, Switzerland 10–13 November. C. M. Carr, Curran Publishing Services.

The United Nations Educational, Scientific and Cultural Organization.

Rejmanek M, Randall JM (1994) Invasive alien plants in California: 1993 summary

and comparison with other areas of North America. Madrono 41: 161-177.

Rikhari HC, Adhikari BS, Rawat YS (1997) Woody species composition of temperate

forests along an elevational gradient in Indian Central Himalaya. Journal of

Tropical Forest Science 10:197-211.

Rikhari HC, Palni LMS, Sharma S, Nandi SK (1998) Himalayan yew: Stand structure,

canopy damage, regeneration and conservation strategy. Environmental

Conservation 25(4): 334-341.

Rinella MJ, Mangold JM, Espeland EK, Shelly RL, Jacobs JS (2012) Long-term

population dynamics of seeded plants in invaded grasslands. Ecological

Applications 22:1320-1329.

Root TL, Price JT, Hall KR, Schneider SH, Rosenzweig C, Pounds JA (2003)

Fingerprints of global warming on wild animals and plants. Nature 421:57-60.

Rosenzweig C, Casassa DS, Karoly A et al. (2007) Assessment of observed changes

and responses in natural and managed system. In: Parry ML, Canziani OF,

Palutidof JP et al. (eds.). Climate Change: Impacts, Adaptation and

Vulnerability Cambridge University Press, UK, pp. 79–131

Royle JF (1839-1840) Illustration of the botany and other branches of the natural

history of the Himalayan mountains and the flora of Cashmere. Todays &

Tomorrow’s Printers & Publishers, New Delhi. (Reprinted 1970).

165

Sahai B, Kimothi MM (1994) Remote sensing of Nanda Devi Biosphere Reserve for

biodiversity conservation. In: Proceedings of Seminar on Biodiversity

Conservaiton, National Conservation Congress WWF, India, pp. 131-137.

Samant SS (1993) Diversity and status of plants in Nanda Devi Biosphere Reserve.

Scientific and Ecological Expedition to Nanda Devi. Report, Army

Headquarters, New Delhi, pp. 54-85.

Samant SS, Dhar U, Rawal RS (1993) Botanical hot spots of Kumaun Himalaya:

Conservation perspectives. In: Dhar U (eds.). Himalayan Biodiversity

Conservation Strategies. Gyanodaya Prakashan, Nainital, pp. 377-400.

Samant SS, Pangtey YPS (1993) Rediscovery of some rare and endangered shrubs

and climbers of Kumaun Himalaya. Journal of Economic and Taxonomic

Botany 17(3): 509-512.

Samant SS (1994) An assessment on the diversity and status of alpine plants of the

Himalaya. In: Pangtey YPS, Rawal RS (eds.). High altitudes of Himalaya.

Gyanodaya Prakashan, Nainital, pp. 115-127.

Samant SS, Dhar U, Rawal RS (1996a) Conservation of rare-endangered plants: the

context of Nanda Devi Biosphere Reserve. In: Ramakrishnan et al. (eds.).

Conservation and Management of Biological Resources in Himalaya, Oxford

and IBH Publishing Co. Pvt. Ltd., New Delhi, pp. 521-545.

Samant SS, Dhar U, Rawal RS (1996b) Natural resources used by some natives of

Nanda Devi Biosphere Reserve in West Himalaya. Ethnobotany 8:40-50.

Samant SS, Dhar U (1997) Diversity, endemism and economic potential of wild

edible plants of Indian Himalaya. International Journal of Sustainable

Development and World Ecology 4: 179-191.

Samant SS (1998) Forest types and diversity of fodder resource in Kumaun Himalaya.

In: Proceeding of the Seminar Fodder problems Faced by the Himalayan Region

in India. SHERPA, Lucknow, pp. 111-123.

Samant SS, Dhar U and Palni LMS (1998a) Medicinal Plants of Indian Himalaya:

Diversity Distribution Potential Values. Gyanodaya Prakashan, Nainital.

Samant SS, Dhar U, Rawal RS (1998b) Biodiversity status of a protected area of west

Himalaya. 1-Askot Wildlife Sanctuary. International Journal of Sustainable

Development and World Ecology 5:194-203.

166

Samant SS (1999) Diversity, nativity and endemism of vascular plants in a part of

Nanda Devi Biosphere Reserve in west Himalaya I. Himalayan Biosphere

Reserves 1(1&2): 1-28.

Samant SS, Joshi HC, Arya SC (2000) Diversity, nativity and endemism of vascular

plants in Pindari area of Nanda Devi Biosphere Reserve-II. Himalayan

Biosphere Reserves 2:1-29.

Samant SS, Joshi HC, Pant S, Arya SC (2001) Diversity, nativity and endemism of

vascular plants of Valley of Flowers National Park. Himalayan Biosphere

Reserve 3:1-17.

Samant SS, Joshi SC (2003) Floristic diversity, community pattern and changes of

vegetation in Nanda Devi National Park. Biodiversity Monitoring Expedition.

Uttaranchal Forest Department, pp. 39-44.

Samant SS, Joshi HC, Arya SC, Pant S (2005) Diversity, distribution and

conservation of Pteridophytes in Nanda Devi Biosphere Reserve, West

Himalaya. Indian Fern Journal 22:100-111

Sampson FB, Knopf FL (1982) In search of a diversity ethic for wildlife management.

Transactions of the North American Wildlife and Natural Resources

Conference 47: 421-431.

Sarkar S, Kafatos M (2004) Inter-annual variability of vegetation over the Indian sub-

continent and its relation to the different meteorological parameters. Remote

Sensing and Environment 90: 268-280.

Saxena AK, Singh JS (1982) A phytosociological analysis of woody species in forest

communities of a part of Kumaun Himalaya. Vegetatio 50:3-22.

Saxena KG (1991) Biological invasion in the Indian subcontinent: review of invasion

by plants. In Ramakrishnan PS (eds.). Ecology of biological invasion in the

tropics. International Scientific Publications, New Delhi, pp. 53-73.

Schaaf T (2009) Mountain Biosphere Reserves – a people centred approach that also

links global knowledge. Sustaining Mountain Development (ICIMOD) 55:13-

15.

Schmid E (1938) Contribution to the knowledge of flora and vegetation in the central

Himalaya. Journal of Indian Botanical Society 17: 269-278.

Schlaepfer MA, Sax DF, Olden JD (2011) The potential conservation value of non-

native species. Conservation Biology 25:428-437.

167

Schulte PJ, Marshall PE (1983) Growth and water relation of black locust and Pine

seedlings exposed to controlled water-stress. Canadian Journal of Forest

Research 13:334-338.

Semwal DP, Saradhi PP (2004) Monitoring Land Use and Land Cover Changes in a

Part of Central Himalaya-Contribution towards Regional and Global

Environmental Study.

http://www.gisdevelopment.net/proceedings/gisdeco/2004/poster/semwal.htm (site

accessed on 03/07/08).

Shah NC (1974) A botanical survey in Nanda Devi Sanctuary. Himalayan Journal

35:210-214.

Shankar U (2001) A case study of high tree diversity in a sa1 (Shorea robusta)-

Dominated lowland forest of Eastern Himalaya:Floristic composition,

regeneration and conservation. Current Science 81:776-786.

Shannon CZ, Weiner W (1963) The mathematical theory of communications. Univ.

Illinois Press, Urbane.

Sharma BD, Balakrishnan NP (1993) Flora of India, Vol 2, Papaveracea-

Caryophyllaceae. BSI, Calcutta.

Sharma BD, Sanjappa M (1993) Flora of India, Vol 3, Portulacaceae-Ixonanthaceae.

BSI, Calcutta.

Sharma BD, Balakrishnan NP, Rao RR, Hajra PK (1993) Flora of India, Vol 1,

Rannunculaceae-Balrclayaceae. BSI, Calcutta.

Sharma S, Roy PS (2007) Forest fragmentation in the Himalaya: A Central

Himalayan case study. International Journal of Sustainable Development and

World Ecology 14:201-210.

Shigehara K (1991) Phonological observation data in Japan to be utilized as an

indicator of climatic variation. In: Proceedings of the international conference

on climatic impacts on the environment and society. University of Tsukuba,

Ibaraki, Japan, pp. C1-C6.

Shipley B, Keddy PA (1987) The individualistic and community-unit concepts as

falsifiable

hypotheses. Vegetatio 69: 47-55.

Shrestha BB (2003) Quercus semecarpifolia Sm. in the Himalayan region: Ecology,

exploitation and threats. Himalayan Journal of Sciences 1:126-128.

168

Shrestha BB, Ghimire B, Lekhak HD, Jha PK 2007. Regeneration of treeline Birch

(Betual utilis D. Don) forest in a trans-Himalayan dry valley in central Nepal.

Mountain Research and Development. 27: 259-267.

Silori CS (2001) Status and distribution of anthropogenic pressure in the buffer zone

of Nanda Devi Biosphere in western Himalaya, India. Biodiversity and

Conservation 10:1113-1130.

Sims ZR, Nielsen GA (1986) Organic carbon in Montana soils as related to clay

content and climate. Soil Science Society of America Journal 50:1269-1271.

Singh G (2008) Diversity pattern of vascular plants in some part of Kedarnath

Wildlife Sanctuary (western Himalaya). Ph.D. Thesis, Kumaun University,

Nainital.

Singh JS, Pandey U, Tiwari AK (1984) Man and forest: A Central Himalayan case

study. Ambio 13:80-87.

Singh JS, Singh SP (1987) Forest vegetation of the Himalaya. Botanical Review 52-

53.

Singh JS, Singh SP (1992) Forest of Himalaya: Structure, Functioning and Impact of

Man. Gyanodaya Prakashan, Nainital.

Singh JS, Roy PS, Murthy MSR, Jha CS (2010) Application of Landscape Ecology

and Remote Sensing for Assessment, Monitoring and Conservation of

Biodiversity. Journal of the Indian Society of Remote Sensing 38: 365–85.

Singh KN, Gopichand AK, Lal B (2008) Species diversity and population status of

threatened plants in different landscape elements of the Rohtang Pass, western

Himalaya. Journal of Mountain Science 5: 73-83.

Singh SP (1991) Structure and function of the low and high altitude grazing

ecosystems and impact of the livestock component in the central Himalaya.

Final technical report. Ministry of Environment and Forests, Government of

India, New Delhi.

Singh SP, Singh RP, Rawat YS (1992) Patterns of soil and vegetation and factors

determining their forms and hydrologic cycle in NDBR. Final technical report,

Ministry of Environment and Forest, Government of India, New Delhi.

Singh SP, Adhikari BS, Zobel DB (1994) Biomass productivity, leaf longevity and

forest structure along an altitudinal gradient in central Himalaya. Ecological

Monograph 64:401-421.

169

Singh SP, Rikhari HC, Negi GCS (1995) Community patterns in an alpine meadow of

Indian central Himalaya. Journal Indian Botanical Society 74:529-538.

Singh SP, Adhikari BS, Garkoti SC, Rawat YS (1996) Structural and functional

characteristics of the forest ecosystems around Nanda Devi Biosphere Reserve.

In Ramakrishnan PS, Purohit AN, Saxena KG, Rao KS, Maikhuri RK (eds.).

Conservation and Management of Biological Resources in Himalaya, New

Delhi. Oxford & IBH Publishing Co. Limited. 413-432.

Singh SP, Rawat YS, Garkoti SC (1997) Failure of brown oak (Quercus

semecarpifolia) to regenerate in central Himalaya: a case of environmental

semisurprise. Current Science 73: 371-374.

Singh SP (1998) Chronic disturbance, a principal cause of environmental degradation

in developing countries. Environmental Conservation 25(1): 1-2.

Singh SP, Singh V, Skutsch M (2010) Rapid warming in the Himalayas: ecosystem

response and development option. Climate and Development 2:221-232.

Smith WW (1913) The alpine and sub-alpine vegetation of south-east Sikkim.

Records of Botanical Survey of India 4: 323-431.

Songer M, Aung M, Senior B, DeFries R, Leimgruber P (2009) Spatial and temporal

deforestation dynamics in protected and unprotected dry forests: a case study

from Myanmar (Burma). Biodiversity and Conservation 18: 1001-1018.

Souza CM, Robbert DA, Monteiro AL (2005) Multitemporal analysis of degraded

forests in the southern Brazilian Amazon. Earth Interactions 9: 1-25.

Spurr SH, Barnes BV (1980) Forest Ecology. John Wiley and Sons, New York.

Srivastava N, Sharma V, Kamal B, Jadon VS (2010) Aconitum: need for sustainable

exploration (with special reference to Uttarakhand. International Journal of

Green Pharmacy. DOI 10.4103/0973-8258.74129.

Strachey R and Winterbottom JE (1882) Scientific botanty. In: Atkinson ET (eds.).

The Himalayan districts of the NW provinces of India. Cosmo Publication, New

Delhi.

Sykora KV, Kalwij JM, Keizer Peter-Jan (2002) Phytosociological and floristic

evaluation of a 15-year ecological management of roadside verges in the

Netherlands. Preslia-Praha 74: 421-436.

Tambe S, Arrawatia ML, Sharma N (2011) Assessing the priorities for sustainable

forest management in Sikkim Himalaya, India: A Remote Sensing based

170

approach. Journal of Indian Society of Remote Sensing. DOI 10.1007/s12524-

011-0110-6.

Task Force Report (2010) Planning commission of India. Compendium of statistics

for the states of Indian Himalayan Region. G.B.Pant Institute of Himalayan

Environment & Development, Kosi-Katarmal, Almora, Uttarakhand, India.

Consul Printers, Nainital, India.

Ter Braak CJF (1986) Canonical correspondence analysis: a new eigenvector

technique for multivariate direct gradient analysis. Ecology 67: 1167-1179.

Ter Braak CJF (1987) CANOCO - a FORTRAN program for canonical community

ordination by [partial] [detrended] [canonical] correspondence analysis,

principal components analysis and redundancy analysis (version 2.1), Report 87

ITI A 11, Institute of Applied Computer Science, Wageningen.

Ter Braak CJF (1991) Permutation versus bootstrap significance tests in multiple

regression and ANOVA. In: Jöckel KH (eds.). Bootstrapping and related

resampling techniques. Springer Verlag, Berlin.

Tewari JC (1982) Vegetational analysis along altitudinal gradients around Nainital.

Ph.D. Thesis, Kumaun University, Nainital.

Tiwari K, Khanduri K (2011) Land use / Land cover change detection in Doon Valley

(Dehradun Tehsil), Uttarakhand: using GIS& Remote Sensing Technique.

International Journal of Geomatics and Geosciences 2(1): 34-41.

Thadani R, Ashton PMS (1995) Regeneration of banj oak (Quercus leucotrichophora

A. Camus) in the central Himalaya. Forest Ecology and Management 78:217-

224.

Theurillat JP, Guisan A (2001) Potential impact of climate change on vegetation in

the European alps: a review. Climate Change 50:77-109.

Thomas CD, Cameron Green RE, Bakkenes M et al. (2004) Extinction risk from

climate change. Nature 427:145-147.

Thomson T (1851) Sketch of the climate and vegetation of the Himalaya. Journal of

Horticultural Science 6: 245-258.

Thomson T (1852) Western Himalaya and Tibet. A narrative of the journey through

the mountains of northern India during the years 1847-48, London.

Tierney GL, Faber-Langendoen D, Mitchell BR, Shriver WG, Gibbs JP (2009)

Monitoring and evaluating the ecological integrity of forest ecosystems.

Frontiers in Ecology and the Environment 7: 308-316.

171

Tripathi RS, Khan ML (1990) Effect of seed weight and micro-site characteristics on

seed germination and seedling fitness in two species of Quercus in a subtropical

wet hill forests. Oikos 57:289-296.

Tyser RW, Worley CA (1992) Alien flora in grasslands adjacent to road and trail

corridors in Glacier National Park, Montana (USA). Conservation Biology

6:253-262.

Udaya LV, Dutt CBS (1998) Paper presented in the Workshop on Experiences

Sharing on GIS for Sustainable Forest Management, 16-17 March, Hyderabad.

UNESCO-MAB (2004) Proceedings “Global change research in Mountain Biosphere

Reserves, Switzerland, pp. 10-13

Uniyal SK (2001) A study on the structure and composition of forests along an

altitudinal gradient in upper Bhagirathi catchment, Garhwal Himalaya. Ph. D.

Thesis, FRI, Deemed University, Dehradun.

Uniyal SK, Awasthi A, Rawat GS (2002) Current status and distribution of

commercially exploited medicinal and aromatic plants in upper Gori valley,

Kumaon Himalaya, Uttaranchal. Current Science 82:1246-1252.

Uniyal BP, Sharma JR, Chowdhery U, Singh DK (2007) Flowering plants of

Uttarakhand (A checklist). Bishen Singh Mahendra Pal Singh, Dehradun.

Valdiya KS (1979) An outline of the structural setup of the Kumaun Himalaya.

Journal of Geographical Society of India 20:143-157.

Vasseur L, Potvin C (1998) Natural pasture community response to enriched carbon

dioxide atmosphere. Plant Ecology 135:31-41.

Vetaas OR, Grytnes JA (2002) Distribution of vascular plant species richness and

endemic richness along the Himalayan elevation gradient in Nepal. Global

Ecology and Biogeography 11: 291-301.

Vitousek PM (1986) Biological invasions and ecological properties: can species make

a difference? In: Mooney HA, Drake JA (eds.). Ecology of biological invasions

of North America and Hawaii. Springer-Verlag, New York, pp. 163-178.

Wadia DN (1931) The syntaxis of the north west Himalaya, its rocks, tectonics and

orogeny. Quarterly Journal of Geology and Mineral Society of India 65:189-

200.

Wadia DN (1966) Geology of India. McMillan, London.

172

Waite TA, Corey SJ, Campbell LG et al. (2009) Satellite sleuthing: does remotely

sensed land-cove change signal ecological degradation in a protected area?

Diversity and Distribution 15:299-309.

Wales BA (1967) Climate, microclimate and vegetation relationships on north and

south forest boundaries in New Jersey. William L. Hutcheson Memorial Forest

Bulletin 2:1-67.

Walkley A, Black IA (1934) An examination of Degtjiareff method for determining

soil organic matter and a proposed modification of chromic acid titration

method. Soil Science 37: 29-38.

Walther G-R, Beissner S, Burga CA (2005) Trends in the upward shift of alpine

plants. Journal of Vegetation Science 16:541-548.

Weiher E, Keddy P (1999) Assembly rules as general constraints on community

composition.

In: Weiher E, Keddy P (eds.). Ecological Assembly Rules. Perspectives,

advances, retreats Cambridge University Press, Cambridge, pp. 251-271.

Wester L, Jurvik JO (1983) Roadside plant communities on Mauna Loa, Hawaii.

Journal of Biogeography 10:307-316.

Westhoff V, van der Maarel E (1978) The Braun-Blanquet approach. In: Whittaker

RH (eds.). Classification of Plant Communities, 2nd edition, Junk, The Hague,

pp. 287-297.

Whittaker RH (1975) Communities and ecosystems, II edition. MacMillan Publishing

Co. Inc., New York, pp. 385.

Whittaker RH (1978) Approaches to Classifying Vegetation. In: Whittaker RH (eds.).

Classification of Plant Communities, 2nd edition, Junk, The Hague, pp. 1-31.

Wilcove DS, Rothstein D, Dubow J, Phillips A, Losos E (1998) Quantifying threats to

Inmperical spceis in the United States 48(8): 607-615.

Williams JW, Jackson ST (2007) Novel climates, no-analog communities, and

ecological surprises. Frontiers in Ecology and the Environment 5:475–482.

Wilson JB, Agnew ADQ (1992) Positive-feedback switches in plant communities.

Advances in Ecological Research 23: 263-336.

Wilson JB, Gitay H, Steel JB, King W McG (1998) Relative abundance distributions

in plant communities: effects of species richness and of spatial scale. Journal of

Vegetation Science 9: 213-220.

173

Wilson JB (1999) Assembly rules in plant communities. In: Weiher E, Keddy P

(eds.). Ecological Assembly Rules. Perspectives, advances, retreats. Cambridge

University Press, Cambridge, pp. 130-164

Wing SL, Harrington GJ, Smith FA et al. (2005) Transient floral change and rapid

global warming at the Paleocene-Eocene boundary. Science 310:993–996.

Wittig R, Alberternst B (1999) The importance of geobotanical methods for

biomonitoring in protected areas. Phytocoenosis Suppl. Cartografiae

Geobotanicae 11:155-160.

Xu X, Zhou Y, Ruan H, Luo Y, Wang J (2010) Temperature sensitivity increased

with soil organic carbon recalcitrance along an elevational gradient in the Wuyi

Mountains, China. Soil Biology and Biochemistry 42: 1811-1815.

Yugi M (1979) Geology and metamorphism of the Nanda Devi Region, Kumaun

higher Himalaya, India. Himalayan Geology 9:3-17.

Zhou HA, Van Rompaey A, Wang JA (2009) Detecting the impact of the “Grain for

Green” program on the mean annual vegetation cover in the Shaanxi province,

China using SPOT-VGT NDVI data. Land Use Policy 26: 954-960.

Zhou L, Tucker CJ, Kaufmann RK (2001) Vriation in northern vegetation activity

inferred from satellite data of vegetation index during 1981 to 1999. Journal of

Geophysical Research 106: 20069-20083.

Zonneveld IS (1988) Monitoring vegetation and surveying dynamics. In: Kuchler

AW, Zonneveld IS (eds.). Vegetation mapping. Handbook of vegetation science,

Kluwer Academic Publishers, Dordrecht, Vol 10, pp. 331-334.

Zobel DB, Singh SP (1997) Himalayan forests and ecological generalizations.

Bioscience 47: 735-745.

174

Appendix 1

The changes in compositional features during two time periods in PSK (1990-2010) and LTP (2002-2010) sites

Compositional Features Communities

RIS RISP RIT ABS ABSP ABT DIS DISP DIT DO

PSK site

AN 0 4 0 825 50 25 0.05 0.54 0.12 9.17 MOD 2 2 0 5300 103 40 0.07 0.44 0.11 19 HS 1 1 1 833 124 280 0 0.08 0.43 1.57 QF 8 5 1 2400 27 3 0.11 0.08 0.26 13.64 QS 1 6 1 913 2 17 0.26 0.52 0.24 5.52 MD 8 4 0 717 50 10 0.08 0.08 0.02 2.28 MSO 0 0 0 675 40 20 0.04 1.6 0.02 0.29 MSRM 3 4 0 150 40 33 0 0.16 0.21 25.57 AP 0 3 2 150 85 15 0.28 0.38 0.22 0.99 MBS 3 0 0 800 35 10 0.17 0.23 0.23 2.93 BU 0 1 1 350 25 35 0.01 0.3 0.14 5.82 LTP site

CD 1 1 0 40 36 6 0.21 0.09 0.04 0.15 JPM 1 1 0 160 30 10 0.06 0.07 0.01 0.42 APM 7 2 0 50 30 20 0.35 0.1 0.02 0.62 PW 2 1 0 273 46 2 0.05 0.01 0.07 5.84 AS 1 0 0 490 39 21 0.2 0.08 0.05 4.21 TAM 1 0 0 126 21 9 0.15 0.15 0.12 3.18 AP 1 1 0 470 143 19 0.18 0.07 0.15 3.49 BU 0 0 0 228 19 8 0.04 0 0.05 7.29 [AN- A. nepalensis, MOD- Mixed Oak deciduous, HS- H. salicifolia, QF- Q. floribunda, QS- Q. semecarpifolia, MD- Mixed deciduou, MSO- Mixed Silver fir-Oak, MSRM-

Mixed Silver fir-Rhododendron-Maple, AP- A. pindrow, MBS- Mixed Birch-Silver fir, BU- B. utilis, CD- C. deodara, JPM- J. regia-P. cornuta mixed, APM- A. caesium-P.

cornuta Mixed, PW- P. wallichiana, AS- A. spectabilis, TAM- T. wallichiana-A. pindrow mixed; RIS, ABS, DIS= richness, abundance and diversity of seedlings, RISP,

ABSP, DISP= richness, abundance and diversity of saplings; RIT, ABT, DIT, DO= richness, abundance, diversity and dominance of tree species]

175

Appendix 2

Weightage values w.r.t compositional features in PSK and LTP sites- Change Sensitivity

Vegetation parameters Communities

RIS RISP RIT ABS ABSP ABT DIS DISP DIT DO CSS

Rank

PSK site

AN 0 67 0 16 40 9 18 34 28 36 25 8

MOD 25 33 0 100 83 14 25 28 26 74 41 3

HS 13 17 50 16 100 100 0 5 100 6 41 4

QF 100 83 50 45 22 1 39 5 60 53 46 1

QS 13 100 50 17 2 6 93 33 56 22 39 5

MD 100 67 0 14 40 4 29 5 5 9 27 7

MSO 0 0 0 13 32 7 14 100 5 1 17 11

MSRM 38 67 0 3 32 12 0 10 49 100 31 6

AP 0 50 100 3 69 5 100 24 51 4 41 2

MBS 38 0 0 15 28 4 61 14 53 11 22 9

BU 0 17 50 7 20 13 4 19 33 23 18 10

LTP site

CD 14 50 0 8 25 29 60 60 27 2 27 6

JPM 14 50 0 33 21 48 17 47 7 6 24 7

APM 100 100 0 10 21 95 100 67 13 9 51 2

PW 29 50 0 56 32 10 14 7 47 80 32 5

AS 14 0 0 100 27 100 57 53 33 58 44 3

TAM 14 0 0 26 15 43 43 100 80 44 36 4

AP 14 50 0 96 100 90 51 47 100 48 60 1

BU 0 0 0 47 13 38 11 0 33 100 24 8

[AN- A. nepalensis, MOD- Mixed Oak deciduous, HS- H. salicifolia, QF- Q. floribunda, QS- Q. semecarpifolia, MD- Mixed deciduou, MSO- Mixed Silver fir-Oak, MSRM-

Mixed Silver fir-Rhododendron-Maple, AP- A. pindrow, MBS- Mixed Birch-Silver fir, BU- B. utilis, CD- C. deodara, JPM- J. regia-P. cornuta mixed, APM- A. caesium-P.

cornuta Mixed, PW- P. wallichiana, AS- A. spectabilis, TAM- T. wallichiana-A. pindrow mixed; RIS, ABS, DIS= richness, abundance and diversity of seedlings, RISP,

ABSP, DISP= richness, abundance and diversity of saplings; RIT, ABT, DIT, DO= richness, abundance, diversity and dominance of tree species, CCS= Community Change

Sensitivity]

176

Appendix 3

Weightage values for community richness, representativeness, uniqueness and threat parameters PSK and LTP sites

Phytosociologial Uniqueness Threat Site RS RSP RT DS DSP DT DIS DISP DIT TBA CNP CRI RN DN CRPI RE DE CUI

CII RNN DNN DST

CTI

PSK site

AN 47 50 61 65 55 86 88 89 71 43 78 67 76 77 77 63 73 68 71 100 75 66 80 MOD 68 67 91 100 37 90 91 100 100 85 100 85 81 66 74 63 100 82 80 94 84 100 93

HS 26 17 17 39 100 79 28 24 22 12 63 39 77 64 71 72 42 57 56 100 85 59 81 QF 100 100 87 69 27 71 98 93 84 84 86 82 99 47 73 93 94 94 83 69 100 82 84 QS 84 78 100 57 32 88 90 79 81 91 61 77 90 56 73 79 48 64 71 80 93 79 84 MD 84 83 91 38 31 76 93 93 98 58 79 75 89 82 86 97 63 80 80 83 71 45 66

MSO 47 72 91 41 23 49 90 90 95 59 69 66 85 71 78 70 80 75 73 88 80 62 77 MSRM 74 83 100 44 32 69 100 100 96 100 79 80 91 100 96 97 89 93 90 80 55 37 57 AP 47 56 74 26 30 60 83 96 80 30 75 60 92 92 92 80 89 85 79 79 62 27 56 MBS 47 61 43 32 33 66 99 99 60 23 75 58 88 96 92 69 69 69 73 83 59 41 61

BU 26 33 35 18 37 100 60 68 46 18 81 48 100 61 81 100 61 81 70 67 89 41 66

LTP site

CD 25 55 22 28 42 51 54 63 39 100 85 51 85 60 73 81 67 74 66 96 99 91 95 JPM 38 55 39 51 7 62 74 56 100 19 78 53 100 83 92 100 63 82 75 71 81 100 84 APM 44 36 28 29 83 79 20 56 60 89 96 56 83 87 85 53 33 43 61 100 75 82 86 PW 100 100 100 39 36 52 100 85 76 43 76 73 100 76 88 84 79 82 81 70 86 91 82

AS 69 55 50 53 43 49 32 100 94 37 49 57 99 60 80 76 91 84 73 73 99 36 69 TAM 50 45 22 58 83 100 61 60 65 48 64 60 86 100 93 75 100 88 80 95 67 45 69 AP 50 64 50 100 100 68 64 91 88 76 70 75 96 66 81 79 70 75 77 77 95 78 83 BU 56 64 56 40 46 71 74 63 75 71 100 65 96 59 78 90 52 71 71 78 100 58 79

[AN- A. nepalensis, MOD- Mixed Oak deciduous, HS- H. salicifolia, QF- Q. floribunda, QS- Q. semecarpifolia, MD- Mixed deciduou, MSO- Mixed Silver fir-Oak, MSRM-

Mixed Silver fir-Rhododendron-Maple, AP- A. pindrow, MBS- Mixed Birch-Silver fir, BU- B. utilis, CD- C. deodara, JPM- J. regia-P. cornuta mixed, APM- A. caesium-P.

cornuta Mixed, PW- P. wallichiana, AS- A. spectabilis, TAM- T. wallichiana-A. pindrow mixed; RS, DS, DIS= richness, desnity and diversity of seedlings, RSP, DSP,

DISP= richness, desnity and diversity of saplings; RT, DT, DIT, TBA= richness, desnity, diversity and total basal area of tree species; RN, RNN, RE, DN, DNN, DE=

richness and density of native, non-native and endemic species; CNP= canopy cover; DST= canopy disturbance, CRI= Community Richness Index; CRPI= Community

Representativeness Indix; CUI= Community Uniqueness Index; CII= Community Importance Index; CTI= Community Threat Index]

177

PUBLICATIONS

Research papers

1. Rawat B, Rawat JM, Mishra S and Mishra SN (2013) Picrorhiza kurrooa: current

status and tissue culture mediated biotechnological interventions. Acta

Physiologiae Plantarum 35: 1-12.

2. Rawat JM, Mehrotra S and Rawat B (2013) ISSR and RAPD based evaluation of

genetic fidelity and active ingredient analysis in tissue culture raised plants of

Picrorhiza kurrooa. Acta Physiologiae Plantarum (DOI: 10.1007/s11738-013-1217-

x).

3. Rawat JM, Rawat B and Mehrotra S (2013) Enhanced production of picrotin and

picrotoxinin from in vitro products of Picrorhiza kurrooa through elicitation.

Biotechnology Letters (DOI: 10.1007/s10529-013-1152-3).

4. Rawat B, Sekar KC and Gairola S (2013) Ethnomedicinal plants of Sunderdhunga

valley, western Himalaya, India - traditional use, current status and future scenario.

Indian Forester 139 (1): 61-68.

5. Rawat B, Negi VS and Rawat JM (2013) Potential contribution of wildlife

sanctuary to forest conservation-a case study from western Himalaya. Journal of

Mountain Science (Under revision).

6. Sekar KC and Rawat B (2011) Diversity, utilization and conservation of ethno-

medicinal plants in Devikund - A high altitude, sacred wetland of Indian Himalaya.

Medicinal Plants 3(2): 105-112.

7. Rawat B, Gairola S and Bhatt A (2010) Habitat characteristics and ecological

status of Paeonia emodi Wallich ex Royle: A high value medicinal plant of West

Himalaya. Medicinal Plants 2(2): 121-125.

8. Sekar KC, Rawat B and Rawal RS (2010) Taraxacum lanigerum Van Soest

(Asteraceae) – A new record from Uttarakhad. Annals of Forestry 18 (2): 331-332.

9. Sekar KC, Rawal RS, Gairola S and Rawat B (2009) Arnebia nandadeviensis

(Boraginaceae) a new species from India. American Journal of Science 5 (2): 105-

106.

178

10. Sekar KC, Gairola S, Rawat B and Rawal RS (2008) Avena fatua subsp.

meridionalis Malz. (Poaceae) – A new record from Uttarakhad. Annals of Forestry

16 (2): 361-362.

11. Rawat B, Majghain S and Joshi H (2007) An Ecological Study of the status,

regeneration, conservation and management of three commercially important trees

of Almora. Vegetos 20(1): 71-77.

Abstract /Presentations/Workshops/Symposia

International

1. Rawat B, Patel Lavkush and Rawal RS (6 Dec-8 Dec, 2010) Trend of changes in

vegetation composition and its future implications: a case study from Nanda Devi

Biosphere Reserve (NDBR), west Himalaya, India. Organized by G.B. Pant Institute

Himalayan Environment and Development, Kosi-Katarmal, Almora, Uttarakhand,

India. (Poster presentation).

2. Rawat B and Rawal RS (31 July- 4 August, 2010) Assessment and future

implications of changes in vegetation pattern in and around Nanda Devi Biosphrere

Reserve (NDBR), west Hiamalay, India. Organized by Botanical Society of

America at Providence, Rhode Island, USA (Oral presentation).

National

1. Rawat B and Rawal RS (2011) Population structure and regeneration behavior of

forest communities in last two decades in Nanda Devi Biosphere Reserve, western

Himalaya. 6th Uttarakhand state science and technology congress, 14-16 November,

2011 (Oral presentation).

2. Rawat B, Gairola S and Rawal RS (2-7 January, 2010) Forest Vegetation Changes

in and Around Nanda Devi Biosphere Reserve, West Himalaya. 97th Indian Science

Congress, Thiruvananthapuram. (Abstract).

3. Participation in Pre Conference ‘Geomatics-2009 Tutorials’ on “Geomatics in

Disaster Management” (2-3 February, 2009) sponsored by Indian Society of

Geomatics, Ahmedabad, India and organized at IIRS Campus, Dehradun, India.

4. Participation in Consultation Workshop on Himalayan Biosphere Reserves-

Defining role under global change scenarios of climate and human economics (6-7

July, 2007) organized by GBPIHED, Kosi Katarmal Almora, Uttarakhand, India.

179

5. Participation in National Seminar and Workshop on “Application of RS & GIS in

the Natural Resources Management, Sustainability and Uses (1-3 February, 2006)

organized at Department of Geology, HNB Garhwal University, Srinagar

(Garhwal), Uttarakhand, India.

6. Participation in symposia on Himalayan Biodiversity- Issues and Options for

Priority Research (27-28 March, 2006) organized by GBPIHED, Kosi Katarmal

Almora, Uttarakhand, India.

7. Participation as a resource person in different training workshops on “People

Participation in Biodiversity Conservation” (2006-2009). Organized by

Conservation of Biological Diversity Core of G. B. Pant Institute of Himalayan

Environment and Development, Kosi-Katarmal, Almora, Uttarakhand, India.

Book chapters/proceedings/reports/compendium

1. Rawal RS and Rawat B (2012) Nanda Devi Biosphere Reserve-west Himalaya,

India. In: Compendium on Indian Biosphere Reserve. Progression during two

decades of conservation (eds. Palni LMS, Rawal RS, Rai RK and Reddy SV).

Ministry of Environment and Forests, Government of India.

2. As an organizing team and resource person in “Voices of School Children-

Children’s Discussion and Expression Session(s)” a report (2011) A Joint Venture

of ICIMOD, Kathmandu, Nepal and GBPIHED, Almora, India towards celebration

of Global Environment Day-2011 and International Year of Forests.

3. As a Resource person in “Kailash Sacred Landscape Conservation Initiative”

feasibility assessment report (June 2010) Submitted by GB Pant Institute of

Himalayan Environment and Development, Kosi-Katarmal, Almora (India) to

Ministry of Environment and Forest, Government of India