Srinivas Vaidyanathan Devcharan Jathannapdf.usaid.gov/pdf_docs/PA00K5BG.pdf · A Preliminary Assessment of Wildlife in the Eastern Forest Complex of Nuristan, Afghanistan Report Submitted

A Preliminary Assessment of Wildlife

in the Eastern Forest Complex of

Nuristan, Afghanistan

Report Submitted to WCS‐Afghanistan

By

Srinivas Vaidyanathan Devcharan Jathanna

2007

A Preliminary Assessment of Wildlife

in the Eastern Forest Complex of

Nuristan, Afghanistan

Report Submitted to WCS‐Afghanistan

By

Srinivas Vaidyanathan Devcharan Jathanna

2007

Contents

Summary ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 7

Acknowledgements ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 9

I. Introduction ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 11

II. Local Capacity Building ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 14

III. Field Surveys ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 21

IV. Analytical Methods ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 29

V. Results ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 30

VI. Discussions ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 35

VII. References ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 41

Appendix I: List of species of interest ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 43

Appendix II: Results in detail ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 44

Appendix III: Field identification key ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 50

Appendix IV: Data forms ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ 62

Page 5

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Summary

The Eastern Forest Complex of Afghanistan is one the most biologically diverse

areas and the largest contiguous patch of forests in the country. Earlier studies

have highlighted the biological significance and the threats to wildlife in this area,

but no surveys have been carried out in the last 3 decades. With years of political

instability and war, the extent of impacts on wildlife in this region are unknown. A

preliminary assessment of the Eastern Forests in the Nuristan and Kunar Province,

covering an area of approximately 1000 km2 was undertaken by the WCS –

Afghanistan Program. Prior to actual surveys, field personnel were identified and

adequate training was provided in various field methods and data collection

procedures. An occupancy‐based survey was carried out in an area similar to a 1977

assessment carried out by Petocz and Larsson. WCS preliminary surveys resulted in

the detection of 12 species over a sampling period of four months. Wolf, jackal, and

fox were most frequently encountered, with occupancy rates of over 0.8. But the

detection probability for all species of interest was extremely low, less than 0.2.

One of the key factors determining observed occupancy patterns across species

was hunting, and this highlights the need for immediate measures to reduce

hunting pressures to ensure long term sustenance and viability of these species.

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Acknowledgements

We are grateful to USAID for providing the funding support and facilitating this

study.

We thank the Ministry of Agriculture for giving us the required permissions for

carrying out this work. we gratefully acknowledge the support extended by the

regional security forces to the field teams and for facilitating the team's work.

The team members including Abdul Ghafoor, Ghulam Haider, Himmat, Jamal Khan,

and Abdul Qaher from the Government of Afghanistan and Ahmed Farid, Abdul

Haq, Mohammed Ismail, Mohammed Joma, Bahadur Khan, Abdullah Nuristani,

Abdul Zaher from the Wildlife Conservation Society‐Afghanistan program were

invaluable without whose enthusiasm and hard work the field data collections

would not have been possible. We thank them.

The American Museum of Natural History, USA and the Wildlife Conservation

Society, New York, USA identified the scats. We thank them for their help.

We thank Drs. Ullas Karanth and Peter Zahler for providing us this opportunity to

work in Afghanistan, Dr. Alex Degan and Ms. Kara Stevens for their inputs and

suggestions while implementing this project, and all staff of WCS‐Afghanistan for

the hospitality extend to us in Kabul.

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I. Introduction

The eastern forests region of Afghanistan, situated in the provinces of Nuristan,

Kunar and Laghman, is one of the most biologically diverse areas in the country.

The eastern forests receive considerable precipitation because of the Indian

monsoon, over 1000 mm annually, compared to the average annual rainfall of

about 200 mm in other parts of Afghanistan (Habibi, unpublished). Due to this, the

region supports the only extensive forest tracts in the country, harboring wildlife

species of global conservation concern, such as the markhor, snow leopard, urial,

and the black bear, among others.

The last ecological survey carried out in this region was in the late seventies by

Petocz and Larsson (1977). Based on a 6‐week field survey that covered “plant

ecology, range conditions, large mammal population status, and socio‐economic

status of local peoples” the report makes specific conservation recommendations

for the region. Petocz and Larsson (1977) described extensive hunting and

increasing logging in the area. Habibi (unpublished report) describes rapid

deforestation due to uncontrolled logging in this region, to meet the ever‐

increasing demand for fuelwood and timber. He also draws attention to increased

hunting as a result of proliferation of weapons, a direct consequence of war and

political instability. Sayer and van der Zon (1981) also list overgrazing as an

important reason for the decline of biodiversity and ecosystem services of

rangelands and forests, in addition to intense logging and hunting. Given this

scenario, frequent and continued monitoring of the status of wildlife is not only

desirable, but critical. However, due to the political situation in Afghanistan since

the early eighties, there have been no surveys of wildlife, systematic or otherwise.

Not only is there a paucity of data on the current population sizes of various species

of global conservation concern, even basic information on species occurrences and

distribution is absent. This underlines the importance and urgency of assessing the

status of wildlife in the region, using systematic surveys.

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Wildlife in the Eastern Forest Complex of Nuristan

Satellite ana

lyses in Nuristan, Kun

ar and

Nan

garhar Source: U

NEP

Page 12


52% loss of forest cover

by 200

2

The Wildlife Conservation Society‐Afghanistan Program (WCS‐Afghanistan) initiated

field surveys in the region in 2006. Because the region is relatively insecure for

foreigners, the surveys were to be carried out by Afghan staff of WCS‐Afghanistan,

along with local guides, after being suitably trained in the latest developments in

survey techniques. This report highlights the strengthening of local capacities , field

methods and results of surveys carried out in Eastern Forest Complex in late 2006

and early 2007.

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Forests in Nuristan

II. Local capacity building

The authors were invited by WCS‐Afghanistan to conduct a training workshop on

concepts and techniques for conducting wildlife surveys, from November 14th ‐26th,

2006. The aim of the workshop was to build local capacity, as recommended by

Sayer and van der Zon (1981), to a level where they were able to carry out surveys

independently, taking advantage of the latest advances in field and analytical

methods. The workshop was conducted at the WCS‐Afghanistan office in Kabul, at

the Kabul Zoo and at a field site about 20 km outside Kabul. The instructors were

invited by the WCS‐Afghanistan Program to train 12 participants in all aspects of

wildlife surveys to prepare them to independently conduct surveys in the forests of

the eastern provinces of Nuristan and Kunar. The surveys, which commenced in late

December 2006, aimed to estimate habitat occupancy patterns of a range of

species (see list of species of interest, Appendix I). Participants included recent

veterinary graduates, officials from the Department of Agriculture, and local

shepherds from the survey area (to act as local guides). The workshop was

conducted with the help of a translator, since most participants spoke little or no

English.

The workshop covered the following broad topics:

• Species and sign identification of species of interest, standardization of names

to be used

• Establishing line transects, line transect and occupancy survey field protocols

• Concepts of sampling, survey designs, distance sampling, capture‐recapture

sampling and occupancy surveys.

• Design of line transect and occupancy surveys

• Use of GPS, maps and other field equipment

• Recording data using forms

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Species and sign identification

Species and sign identification were covered in great detail, as the quality of the

data ultimately depends on these skills. Many participants from the study area

were reasonably familiar with the local wildlife, and were able to provide accurate

descriptions of the species or their natural history. However, they were unfamiliar

with identification of signs. To help with identification of species and signs, a trip to

the Kabul Zoo was organized, where diagnostic features of several species and their

sign (scats/ pellets, tracks) were pointed out. Some scats and pellets had already

been collected from the zoo by WCS‐Afghanistan staff, and these were used to train

the participants. Participants were taught to take photographs of signs correctly

(with a scale) and systematically measure diameter, collect, label and preserve

scats and pellets. Following the zoo visit, participants were taught basic taxonomy

(i.e. assigning animals to one of 5 orders—primates, rodents, lagomorphs,

carnivores and ungulates; families within these orders, such as dog and cat families;


Page 15

Participants at the Kabul Zoo learning diagnostic features of several species and their signs

species within each family). The purpose of teaching basic taxonomy was to help

the participants systematically narrow down identification at least to the level of

order or family, when ever species‐level identification was not possible. Emphasis

was laid on not forcing species identification, and recording ‘unknown’ or (for

example, ‘unknown cat’) when identification was uncertain. The approach also

allowed teaching of some basic biology along with identification and natural

history. The instructors prepared a detailed identification key (Appendix III), with

descriptions, photographs and signs, which was then given to the WCS‐Afghanistan

staff for further modification and translation into Dari. However, due to the non‐

availability of scats/ pellets and tracks of several species of interest, participants

may not have been able to always correctly identify signs in the field. Further

training is required in this regard. Biologists familiar with some of the study species

also indicated that species‐level identification of some species may not always be

possible.

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Participants were familiarized with basic taxonomy and species identification

Since participants spoke Dari, Pashto as well as Nuristani, species names needed to

be standardized. Based on the participants’ familiarity with names, Dari, and in a

few cases, Nuristani names were chosen. For mountain ungulates, participants

were asked to learn the English names, since all species were known by one generic

name.

Use of field equipment

Participants were thoroughly trained in the use of GPS and compasses, as well as in

reading maps, plotting points, extracting coordinates from maps and using these to

navigate. The GPS training included checking and, if necessary, changing settings,

marking waypoints, tracking and navigation. At the end of the training, all

participants were comfortable with using GPS, and are now able to use them in the

field with ease. A manual on the use of GPS units (Garmin 12XL and 60) was

prepared and given to the Afghanistan Program staff for translation into Dari.


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Devcharan training the participants in the use of compass

Participants were also trained in the use of other field equipment such as digital

cameras, Vernier callipers, compasses and laser rangefinders, and are now able to

use these correctly and properly.

Establishing field survey protocols

Participants were trained in marking and measuring line transects. In a day long

exercise, they were trained to mark a transect at the field training site (Lake

Qargah). Following this, they were trained in walking transects where issues of

speed and noise were discussed. Emphasis was also laid on collecting data correctly

and training on sighting, identifying and counting animals, marking group centers,

measuring distance and bearings, and recording data on the forms was covered in

great detail. The participants are now well versed with marking transects, and

collecting line transect data.

Participants were also trained in collection of occupancy data, with a focus on

recording signs (scats/ pellets, tracks). They were shown how to photograph signs

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A map reading exercise undertaken in the WCS office

correctly with the use of a reference scale to enable species‐ or family‐level

identification. They were also trained to systematically collect measure signs (e.g.

pugmark length, scat diameter).

Recording data using forms

The participants had previously never carried out any surveys that required

meticulous data collection and had never used data forms. Thus it was important

that they were introduced to the concept of data forms and to ensure that they use

the same during surveys. With the help of pre‐formatted data forms (Appendix IV),

participants were shown how to record data correctly, for occupancy and line

transect surveys. The forms were translated into Dari, with the help of WCS‐

Afghanistan staff and the translator.


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A field training session, explaining the line transect and occupancy survey field protocols to

the participants

Concepts of sampling and survey designs

Because it was important that participants understood the rationale underlying the

field techniques to be used, the instructors introduced them to the basic concepts

underlying sampling, survey designs, distance sampling, capture‐recapture

sampling and occupancy surveys. Since the participants will not be required to

analyse the field data, the training aimed only to provide an intuitive understanding

of these concepts.

Midway into the course, the participants were given a short test, to help the

instructors assess participants understanding of the subjects covered. The objective

type test covered species and sign identification, use of field equipment, map

reading and basics of line transect surveys. While performance was variable, most

participants performed reasonably well. Three participants (the Nuristani locals)

were unable to take the test, as they could not read or write.

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Participants learning to use a compass

III. Field surveys

Survey region

The eastern forests are influenced by the Indian monsoon, and receive substantial

rainfall, thus supporting deciduous, oak and coniferous forests, as well as alpine

meadows (Petocz and Larsson 1997). The vegetation type depends mainly on

altitude, precipitation and exposure. Detailed descriptions of these habitat types

can be found in Petocz and Larsson (1977) As the forestry law is still being drafted,

currently the forests and wildlife have no legal status nor legal protection.

However, there is a presidential decree banning the cutting of forests. Government

presence in Nuristan is minimal and the forest areas are managed by village shura.

For the preliminary field survey a study area of approximately 1000 km2 was

identified (See Map), based on descriptions in Petocz and Larsson (1977) and

consultation with local shepherds from the region. The survey region covered the


Page 21

Coniferous forests in Nuristan

areas of Waygal, Wama, Kosht, Kantiway and Nishigram within Nuristan province

and Mano Gai and Chapadara in Kunar.

The altitude varied from 950m to 4750m and the terrain was highly rugged, with

75% of the areas characterized by slopes greater than 25%. Given the limited time

and manpower, carrying out occupancy based surveys was deemed to be better

suited than carrying out line transects or camera trapping surveys.

Design of occupancy surveys

During the preliminary assessments, the field teams carried out occupancy surveys,

focusing on large and medium carnivores, and large herbivores. Occupancy surveys

(described in MacKenzie et al. 2002, 2005; MacKenzie and Royle, 2005) focus on

estimating the proportion of area occupied (PAO) by a species, taking into account

the fact that species are not always detected even when present. Traditionally,

investigators would visit a number of sites (e.g. forest patches, ponds, grid cells),

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Participants during a field survey

and simply record the proportion of cells in which a species of interest, or its sign,

was recorded. However, for sites where the species’ presence was not recorded,

there could be two possibilities: the species may be genuinely absent from that site,

or the species may have been present but not detected. Hence, equating the

proportion of sites where the species was recorded to the proportion of sites

occupied by the species will inevitably result in underestimation of the PAO.

Occupancy‐based approaches now address this problem by estimating the

probability of detecting the species, if it is present in a site. The modeling approach

used to estimate this detection probability is similar to modeling of capture

recapture data. Based on replicated (either temporal or spatial) visits within a site,

a detection history is constructed for each site, which is used to estimate detection

probability (p, the probability of detecting species in a site, given that it is present).

Often different sites have varying detection probabilities, and this heterogeneity

can be accounted for by modeling detection probability as a function of covariates.

Covariates can either be site‐specific and constant across replicates (site covariates;

e.g. tree density), or they may vary over the replicates (e.g. temperature). In

addition, probability of a site being occupied (referred to as psi) can also be

modeled as a function of site covariates. This allows one to assess the support for

various alternate models of ecological interest, and thus ask questions such as “…

what ecological or other factors determine occupancy patterns of a species? What is

the relative importance of various factors in driving these patterns?” As all models

are based on maximum likelihood estimation, models can be evaluated using

objective criteria such as Akaike’s Information Criteria (AIC; Burnham and Anderson

2002). AIC is a measure of how well different models fit the data, while penalizing

models for complexity; thus AIC helps select the simplest model that fits the data

adequately. As AIC is a relative measure, interest is usually focused on differences

in AIC between the best (i.e. lowest AIC) model, and other models. These AIC

differences are used to compute AIC weights for each model, which indicate the

amount of support the model receives from the data. Finally, AIC weights summed

over all models containing a certain factor (e.g. altitude) reflect the importance of


Page 23

that factor as a predictor. Because we had small to moderate sample sizes, we

based all our model selection on the small sample correction to AIC, referred to as

AICc (Burnham and Anderson 2002).

The occupancy‐based approach also has the potential to move from studying

patterns in occupancy, to estimating and studying patterns of abundance across

space and time, at least in the future, based on recent developments by Royle and

Nichols (2003), or other refinements thereof. These recent advances are based on

the fact that the largest amount of heterogeneity of species’ detection probability

between sites may be due to differences in abundance. This is especially important

because at these very large spatial scales, direct abundance estimation methods

such as camera trapping‐based capture recapture or line transect surveys become

impractical.

The grid cell size of 50 km2 was fixed based on prior knowledge of biological

attributes of the study species, such as home ranges and seasonal movement rates.

The biological basis for this is that the largest expected range for any of the species

of interest is about 30 km2.

The approach outlined above follows design recommendations for surveying low

density species (Mackenzie et al. 2005). We were interested in measuring true

occupancy rather than intensity of habitat use (that can be measured using cell

sizes much smaller than the animals’ home ranges), and also wanted to avoid a

“100% occupied” result, which could occur because of grid cells that are too large.

We expected that this survey design would yield occupancy parameter estimates

within the 30%‐80% range (Mackenzie et al. 2005).

Only grid cells containing more than 20% of forest cover were included in the

survey sampling frame. The logic is that species of interest cannot “live” in patches

smaller than these, although they may occasionally pass through them.

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Map

sho

wing the stud

y area

and

the sampling grids


Page 25

All cells within the sampling frame were to be surveyed. Each 1 km walked along

the sampled trails constituted a “spatial replicate”. The length of the spatial

replicate was set constant at 1 km, but the number of replicates per grid cell varied

proportionally, depending upon the extent of forest cover in the grid cell. For

analytical ease, the minimum number of 1‐km long replicates per grid cell was 4,

and the maximum was about ~ 10 (in a fully forested cell).

The design for occupancy surveys was developed on a GIS platform using ArcView

GIS package (ESRI, 1999). The survey design for the preliminary assessment resulted

in 27 grid cells, each of size 50km2.

Field data collection

Field data collection for the occupancy surveys was carried out from December

2006 to January 2007, and again from February 2007 to May 2007. The field

personnel were split into three teams. Occupancy data were collected from a total

of 25 grid cells. As the surveys focused primarily on detection of species’ signs,

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Track of wolf

spatial rather than temporal replication was used to estimate detection probability

(p, probability of detecting species in a cell, given that it is present). Each kilometer

walked within a grid cell was treated as a replicate, so that the detection probability

estimated was the probability of detecting the species over each kilometer walked.

The survey teams walked a pre‐decided length (decided based on amount of

natural habitat present in the cell) within each grid cell, and recorded all

identifiable signs/ direct sightings they encountered, using a preformatted data

sheet. Photographs of tracks and scats were taken with digital cameras, to confirm

species identification, and scat samples were collected for subsequent verification

using laboratory DNA analysis. For direct sightings, the number of animals and any

additional information were also recorded. The search routes were marked with

Garmin 60Csx GPS units, and elevation was recorded at each detection using the

GPS units. In addition to the field surveys, the survey teams also carried out

questionnaire surveys (see Appendix IV) of local shepherds, farmers and hunters.


Page 27

A participant collecting scat for subsequent verification using DNA analysis

The questionnaires covered species occurrences, perceptions of species declines,

intensity of hunting, in addition to questions to assess the reliability of the

respondents.

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A survey team talking to locals to fill in questionnaires

IV. Analytical methods

Occupancy modeling

Detection histories were constructed for each grid cell, by species, from the field

data. In addition to the detection histories, site and sampling covariates were also

computed. Forest type within a grid cell was treated as a sampling covariate, since

forest type varied over replicates. This sampling covariate was used as a potential

predicator of detection probability. In addition, a number of site covariates were

also developed. Modal altitude and modal slope were computed for each grid cell

from a digital elevation model (DEM) using the GIS software ArcView. The percent

forest cover for each cell was computed using a forest cover layer developed by

UNEP. The questionnaire survey data were used to compute a poaching intensity

score for each grid cell. The detection history matrices and covariates were then

imported into program PRESENCE (Hines, 2006) for further analysis.

Though the set of candidate models constructed varied between species, the

constant detection, constant occupancy model was included in the set for all

species. Other models included ones where detection was a function of habitat

type. Based on the biology of the species some or all of the following models of

occupancy were used for different species: occupancy as a function of altitude,

slope, percent forest cover and hunting intensity, and combinations thereof.

The various models were assessed based on AICc and AICc weights (Burnham and

Anderson 2002; see Design of occupancy surveys above). The importance of each

predictor in determining species’ occupancy was assessed through AICc weights

summed over all models containing that predictor (Burnham and Anderson 2002).


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V. Results

Sampling effort, numbers of detections, species detected

Of the 27 grid cells identified for sampling only 25 grid cells could be surveyed, 2

cells could not be surveyed due to snow and problems with security in Kunar

Province. A total of 368 km were walked across 25 grid cells.

The table above shows sampling effort by teams in each grid cell. Within each grid,

based on percent natural habitat available the minimum length to be walked and

sampled was planned prior to the commencement of the field work. In most cases,


Page 30

Grid no.

Team A (km)

Team B (km)

Team C (km)

Total distance (km)

231 8 0 5 13 253 8 0 7 15 254 0 0 7 7 275 5.8 0 6 11.8 276 9.8 0 10.6 20.4 277 5.8 0 7 12.8 297 11 0 10.2 21.2 298 9.8 0 10.2 20 299 7 0 8.8 15.8 229 0 4 0 4 251 0 4.4 0 4.4 252 0 9 0 9 273 4 0 0 4 274 7.6 10.4 0 18 295 7.4 11 0 18.4 296 14 11.2 0 25.2 316 0 4.6 0 4.6 317 0 10.4 11.8 22.2 318 10.6 9.6 0 20.2 319 0 10 10.2 20.2 320 0 10.8 11 21.8 339 0 0 10.6 10.6 340 0 0 11.2 11.2 341 0 9.6 8.8 18.4 342 0 10 9 19

the length covered exceeded the planned length for that grid. A total of 43 signs/

direct sightings were recorded during the December‐January surveys and 184

during the February –May surveys. The survey teams detected a total of 12 species

during the course of the surveys.

Patterns of occupancy

The detection probability or the likelihood of collecting evidence of a species when

it is present was calculated and varied across species. While wolfs were detected

70% of the time they were present in a grid, while species like the mountain

ungulates, black bear and leopard were detected less than 1% of the time. The

probability of detection of the red fox, jackal, and porcupine is around 10%.

The proportion of area occupied could be estimated for only four of the species of

interest due to low sightings recorded for the others. Results show that porcupine

occurs in an estimated 80% of the study area while the red fox and wolf occupy

87% and 88% respectively. The jackal was estimated to be found in 93% of the

study area. The best model that fitted the data was selected based on the AICc

statistics. This has been explained in detail in the appendix. The data indicates that

the most important factor affecting occupancy for several of the species is

poaching. Percentage forest cover and to a lesser extent altitude were the other

factors that affected occupancy. Occupancy of Wolf, in addition to poaching is

found to be influenced by altitude and to a much lesser extent by percent forest

cover.

The red fox occupancy is very strongly related to poaching intensity with other

factors having a negligible effect while, Porcupine occupancy was weakly

determined by poaching intensity followed by forest cover. Leopards seemed to be

weakly influenced by forest cover followed by the terrain and poaching intensity.

The mountain ungulates' occupancy was strongly limited by the forest cover and to

a lesser extent by the terrain and altitude. None of the covariates considered in this

study seemed to have a clear effect on the occupancy of Jackal and black bear.

However in the case of Jackals, altitude and poaching intensity and in the case of


Page 31

black bear the forest cover and altitude showed weak relationship with their

occupancy.


Page 32

1 Non

e of th

e measured covaria

tes seem

ed to

have a strong

effect o

n jackal occup

ancy.

2 Data consisted of only 5 recorded

occurrences due

to which no estim

ate of PAO was possible.

3 Occup

ancy of p

orcupine

sho

wed

a weak relatio

nship to th

e Intensity

of p

oaching which was re

flected

in th

e second

‐best m

odel

Species

Detection

Prob

ability

Naïve

PAO

Estimated

PAO

Most Impo

rtan

t factor

affecting Occup

ancy

Best M

odel

Wolf

0.71

0.56

0.88

Po

aching

psi(P

oaching),p(.)

Red Fox

0.12

0.68

0.87

Po

aching

psi(p

oaching),p(.)

Jackal

1 0.10

0.68

0.931

‐ psi(.),p

(.)

Leop

ard2

0.01

0.20

‐

Percen

t Forest C

over

psi(.),p

(.)

Snow

Leo

pard

‐ ‐

‐ ‐

‐

Black Be

ar

0.03

0.40

‐

‐ psi(.),p

(.)

Mou

ntain Ungulates

0.04

0.12

‐

Percen

t Forest C

over

psi(Forest cover),p

(.)

Porcup

ine3

0.097

0.60

0.80

Po

aching

psi(.),p

(.)

Questionnaire surveys

During the course of the survey a total of 136 interviews were carried out. Our

survey teams especially sought out ‘people who knew wildlife and hunters’ for

questioning. Out of the 136 respondents 66% had hunted at some time, and 62% of

these continue to hunt. The most commonly hunted species are mountain

ungulates (40%), bear (18%, probably black bear) and leopard (14%). The figure

below shows species hunted, by percentage. Hunting for meat and personal

consumption was the most common reason for hunting, followed by trade. It is also

important to note that hunting was completely carried out using guns and traps or

snares were no longer used. To a question on their perception of animal population

trends over the last 5 years, 65% of the respondents reported a stable or increasing

population for wolves and black bear, where as a declining trend for leopards, snow

leopard and brown bear.

In the case of carnivores, retaliatory killing seems to be prevalent, as the meat is

not consumed. 97% of the respondents reported livestock predation by carnivores,

and 22% reported human casualties due to carnivore attacks. The survey


Page 33

Chart shows the proportion of species hunted as indicated from the questionnaire surveys.

Markhor was the the most commonly hunted species

Jackal 1% Fox 5% Musk deer 5%

Wolf 8%

Birds 10%

Leopards 14%

Bears 18%

Markhor 39%

respondents also indicated that bulk of the depredation was caused by wolves

(42%) followed by leopards (34%) and bears (24%).

Page 34


Poaching was found to be an important factor in limiting the distribution of sev‐

eral species

A hunter in Nuristan Province uses a mask for camouflage

VI. Discussions

Limitations of the data

The field surveys, while being valuable as the first field assessment of the status of

wildlife in the Eastern Forests of Afghanistan in 20 years, have a number of

limitations that need to be considered while examining the results. The survey

focused primarily on animal signs rather than direct sightings, and the signs for

some similar species could not be distinguished between, in which case, data has

been pooled for analyses, as in the case of mountain ungulates. This may actually

obscure inter‐species differences in habitat occupancy patterns. In addition, the

identification of signs for some groups (e.g. leopards and snow leopards; black and

brown bears) were based partly on attributes of the sites themselves (e.g. altitude),

and modeling occupancy as a function of these covariates would be circular. Scats

collected in the field have been sent to laboratories of the American Museum of

Natural History, in partnership with WCS Great Cats program, for species

identification based on DNA analyses, and our results may change once those

results are available.

Status of wildlife in the eastern forests

Wolves, foxes and jackals seem to be common in the surveyed area, and occur in a

large proportion of sites. However, when interpreting these results, it is important

to consider the fact that for these preliminary surveys, we selected an area known

a priori to be good wildlife habitat. These results are unlikely to be representative

of the entire Eastern Forests region. Leopards and snow leopards appear to be

much rarer than the canids, though scarcity of data did not permit modeling of

occupancy taking into account detection probability for both these species. It is

likely that for these large, solitary and secretive felids, estimated detection

probability may be extremely low, so that estimated occupancy would be much

higher than the proportion of sites in which the species was detected at least once.

Petocz and Larsson (1977) reported a sizable population of Markhor in Nuristan,


Page 35

though our teams failed to detect any large herds, or collect evidence from new

areas. The earlier report indicates a large population of markhor from the Chaman

area, which was outside the present study area. However, considering limitations of

our dataset, such as low numbers of detections, and ambiguity in species

identification from sign, we would hesitate to conclude that the population has

drastically declined, or suffered reductions in its range. More data (collected so that

these problems are addressed) is required before a reliable assessment of markhor

populations can be made. It is also important to note that markhors are still

extensively sought and hunted for their meat.

Page 36


Markhor horns collected by local hunters

The occupancy modeling highlights the important role of poaching in limiting

species distributions—for wolf, red fox and porcupine, poaching intensity was

estimated to have the greatest effect in determining occupancy . The questionnaire

surveys carried out by the field teams also reflected the intensity of hunting in the

area. Although certain species are not hunted due to cultural and religious belief,

with the proliferation of guns, demand for wildlife products, political instability, the

intensity of hunting is expected to increase.

Conservation recommendations

As this is a preliminary survey of wildlife of the Eastern Forests region, it is difficult

to suggest strong conservation measures. Our results clearly indicate that hunting

has strong effects on species’ distributions, and must be targeted if populations of

large carnivores and ungulates are to be conserved. While hunting for personal

consumption may be reduced through control of firearms (Habibi, unpublished

report), hunting in response to livestock predation must be addressed through

measures that seek to minimize human‐wildlife conflict. Petocz and Larsson (1977)


Page 37

An old leopard trap being investigated by the survey team

recommend trophy hunting of Markhor as a conservation strategy to improve

wildlife in Nuristan. As acknowledged by the authors of the previous study their

results are likely to be biased as not all areas of Nuristan were accurately surveyed.

We do not hesitate to recommend that the decision on establishment of a trophy

hunting program of Markhor await a more thorough assessment of the current

distribution and status of Markhor populations in the region. As highlighted by

Shackleton (2001), initiation of a trophy hunting program first requires an

understanding of population sizes, proportion of trophy sized animals, population

dynamics (including birth, immigration, emigration and mortality rates, annual

turnover) and other vital rates, inorder to assess if the population can take the

additional off‐take without declining . While considering such initiatives we would

like to quote from Shackleton (2001) “It must be emphasised that the primary

purpose of Community‐based Trophy Hunting Programs is not the generation of

money or provision of community benefits ‐ it is the conservation of wildlife and

their habitats”. The success of such intervention can only be measured by collecting

biological data. Thus setting up wildlife monitoring programs and building local

capacities to carry out surveys should be given a higher priority. Although the

present study has several limitations, described above, it does provide a framework

addressing questions related to occupancy, site colonization and extinction rates

and population sizes.

Forest loss and degradation highlighted by Petocz and Larsson (1977), Sayer and

van der Zon (1981), UNEP (2002) and Habibi (unpublished report) must similarly be

addressed through interventions that consider both the proximate as well as

ultimate drivers of deforestation and degradation.

Next steps: monitoring and research

One of the key problems with the present surveys is the extremely low detection

probabilities. This needs to be addressed by increasing the search effort and spatial

coverage within the grid cell, and having a larger area identified for surveys. The

Chaman area from where a large number of Markhors were reported earlier

Page 38


(Petocz and Larsson, 1977) should be resurveyed to assess the status of the species

in this region. Surveys need to be carried out in the best possible season when

animals can be sighted, and when tracks and scats are easily found. The present

surveys were restricted to trails, which might be useful to survey species which

regularly use trails (such as leopards), whereas they might fail to adequately detect

species that avoid trails (such as ungulates). Another problem encountered with

these sign‐based surveys is the uncertainty in species identification. This can only

be overcome with adequate in situ field training provided in the best of the sites,

where the teams can watch and observe animals, and by developing a reference

key of species’ signs, based on first‐hand observations. In the case of species for

which this may not work, alternate techniques such as genetic analyses of scats and

pellets, and a combination of field surveys and camera trapping surveys need to be

explored.

The analyses presented in this report need to be revised once the species

identifications are confirmed using DNA analysis of scats. This preliminary survey of

wildlife in the Eastern Forests needs to be followed up with surveys to estimate

abundance and density of a few species of conservation concern, such as leopard,

snow leopard, wolf, black bear, brown bear, markhor, ibex, urial, among others. For

the large felids, camera trap sampling in conjunction with capture recapture

modeling (Karanth and Nichols 1998, 2002) may be used, while fecal DNA‐based

capture recapture sampling can be used for wolves and the two species of bears.

Line transect sampling (Buckland et al. 2001) may help estimate densities of the

mountain ungulate species, though true randomization of transect lines may be

difficult to implement, and low encounter rates may necessitate extremely large

survey effort. Also, more intensive occupancy‐based surveys can be used to

estimate animal abundance using the Royle and Nichols (2003) model, or as

described by Gopalaswamy (2006). The same will also provide a frame work to

estimate range contraction and expansion.

It is also suggested that landscape level changes be monitored as this will have a

direct bearing on the availability of suitable habitat for species of interest and


Page 39

studies on poaching, fuel wood, fodder and timber extraction need to be initiated

to address the issues of depleting resources.

Page 40


References

Burnham, K. P and Anderson. D. R. 2002. Model selection and multi‐model

inference: A practical Information‐Theoretic Approach. Springer‐Verlag, New York,

NY.

Environmental Systems Research Institute Inc. (1999). ArcView GIS 3.2,

Environmental Systems Resource Institute, Inc, www.esri.com

Gopalswamy, A. M. 2006. Estimating sloth bear abundance from repeated presence

‐absence data in Nagarahole‐Bandipur National Parks, India., University of Florida,

Gainesville, Florida, USA.

Habibi, K. Unpublished report. The war in Afghanistan and its effects on wildlife.

Hines, J. E. (2006). PRESENCE2‐Software to estimate patch occupancy and related

parameters. USGS‐PWRC. http://www.mbr‐pwrc.usgs.gov/software/presence.html

Karanth, K.U. and Nichols, J. D. 1998. Estimation of tiger densities in India using

photographic captures and recaptures. Ecology 79: 2852‐2862.

Karanth, K. U. and Nichols, J. D. (Eds). 2002. Monitoring tigers and their prey: A

manual for researchers, managers and conservationists in tropical Asia. Centre for

Wildlife Studies, Bangalore, India.

MacKenzie, D.I., Nichols, J.D., Lachman, G.B., Droege, S., Royle, J.A. & Langtimm,

C.A. 2002. Estimating site occupancy rates when detection probabilities are less

than one. Ecology, 83, 2248–2255.

MacKenzie, D.I., Nichols J.D., Royle J.A., Pollock K.H., Hines J. E. & Bailey, L. L. 2005.

Occupancy estimation and modeling: inferring patterns and dynamics of species

occurrence. Elsevier, San Diego, California, USA.

Mackenzie, D. I., and J. A. Royle. 2005. Designing occupancy studies: general advice

and allocating survey effort. Journal of Applied Ecology 42:1105‐1114.


Page 41

Petocz, R. G. and Larsson, J. Y. 1977. Ecological reconnaissance of Western Nuristan

with recommendations for management. Report to UNDP, FAO and Dept. of Forests

and Range, Ministry of Agriculture.

Royle, J. A. and Nichols, J. D. 2003. Estimating abundance from repeated presence‐

absence data or point counts. Ecology, 84, 777–790

Shackleton, D.M. 2001. A review of community‐based trophy hunting programs in

Pakistan. IUCN‐Pakistan, Islamabad, Pakistan. 59 pp.

Sayer, J. A. and van der Zon, A. P. M. 1981. National parks and wildlife

management, Afghanistan: A contribution to a conservation strategy. Volume 1.

FAO Technical report, Rome.

Page 42


Appendix I: List of species of interest

(* Scientific names follow Wilson and Reeder 2006)


Page 43

English Scientific* Dari Nuristani

Rhesus macaque Macaca mulatta Shadey

Musk deer Moschus chrysogaster Ahu khutan Ruswami

Markhor Capra falconeri Ahu Markhur Tsov

Ibex Capra ibex Ahu rung Tsov

Urial Ovis vignei Ahu nekhsheyr, mel

Wild pig Sus scrofa Khuge Sor

Black bear Selenarctos thibetanus Khers siyah Galo khe ots

Brown bear Ursus arctos Khers nasvary Ots

Jackal Canis aureus Shagal Denkar

Wolf Canis lupus Gurg Babar denkar

Red fox Vulpes vulpes Robae surkh Lew asha

Leopard Panthera pardus Palang

Snow leopard Uncia uncial Palangi barfi

Lynx Lynx lynx Siyah gosh

Pallas’ cat Felis manul Peshak kohi

Jungle cat Felis chaus Samuncha Denpsha

Leopard cat Prionailurus bengalensis Peshak jangali Palang pish

Wild cat Felis sylvestris Peshak dashti

Porcupine Hystrix indica Jarah, jirah

Appendix II: Results in details

Results are presented below by species. Though some species had too few

occurrences to meaningfully model occupancy patterns, we present all results, and

discuss these limitations in the section titled discussions. The results of the

occupancy modeling are presented in two tables. The first table shows the different

models fit to the data (e.g. constant probability of detection p with constant

probability of occupancy psi; or constant p‐ altitude dependent psi), AICc values of

each model, the difference in AICc between the best model and each of the other

models, and the AICc weight for each model (i.e. the amount of support each model

receives from the data (see Design of occupancy surveys above for descriptions of

these quantities). The second table presents the AICc weights, summed over all the

models containing a particular predictor of occupancy. This helps us assess the

relative importance of the different covariates (Burnham and Anderson 2002) in

determining probability of occupancy.

Wolf

The results of the wolf occupancy modeling show that the detection probability

(given presence) over each replicate was estimated at 0.0714 by the best model,

and naïve (no. of sites with species presence recorded/ total no. of sites surveyed)

and estimated proportions of occupied area (PAO) were 0.56, and 0.88,

respectively. Among the covariates used poaching seems to have the strongest

effect on the probability of a site being occupied by wolf. Given the level of conflict

with wolves in the region, and the results of the questionnaire surveys (see

Discussion), this is not surprising. Altitude seems to play a secondary role.

Red fox

Red fox occupancy is extremely strongly related to poaching intensity, with all the

other covariates having a negligible effect. Detection probability was estimated at

0.1229, while the naïve PAO was 0.68. The estimated PAO (± SE) was 0.8666

(0.0041).

Page 44


Model AICc ∆AICc AICc weights psi(Poaching),p(.) 180.06 0.00 0.437 psi(.),p(.) 182.05 1.98 0.162 psi(Altitude),p(.) 182.22 2.16 0.148 psi(Altitude + Poaching),p(.) 183.20 3.14 0.091 psi(Altitude + Forest cover),p(.) 184.06 4.00 0.059 psi(Forest cover),p(.) 184.56 4.50 0.046 psi(Slope),p(.) 185.65 5.59 0.027 psi(Altitude + Slope),p(.) 188.51 8.45 0.006 psi(Slope + Forest cover),p(.) 188.51 8.45 0.006 psi(Slope + Poaching),p(.) 188.51 8.45 0.006 psi(Forest cover + Poaching),p(.) 188.51 8.45 0.006 psi(Altitude + Slope + Forest cover),p(.) 191.67 11.61 0.001 psi(Altitude + Slope + Poaching),p(.) 191.67 11.61 0.001 psi(Slope + Forest cover + Poaching),p(.) 191.67 11.61 0.001

Wolf

Poaching intensity 0.543 Altitude 0.308 Percent forest cover 0.121 Slope 0.049

Model AICc ∆AICc AICc weights psi(poaching),p(.) 256.33 0.00 0.930 psi(.),p(.) 262.33 5.99 0.046 psi(forest cover),p(.) 266.21 9.88 0.007 psi(altitude),p(.) 266.21 9.88 0.007 psi(slope),p(.) 266.21 9.88 0.007 psi(poaching + altitude),p(.) 269.07 12.74 0.002 psi(forest cover + poaching),p(.) 269.07 12.74 0.002

Red Fox

Poaching intensity 0.934 Percent forest cover 0.008 Altitude 0.008 Slope 0.007


Page 45

Jackal

None of the measured covariates seemed to have a strong effect on jackal

occupancy. The naïve estimate of PAO was 0.68. The best model (constant p,

constant psi) estimated detection probability at 0.0998, and actual PAO (± SE) at

0.9311 (0.1246). However, this model did not have very clear support (AICc

difference of next best model was only 1.04). Estimated p from the second best

model (constant p, altitude‐dependent psi) was 0.0986, and PAO (± SE) was

estimated at 0.9112 (0.0422).

Leopard

The naïve estimate of PAO was 0.20. Because the data consisted of only 5 recorded

occurrences, the best model (constant p‐constant psi) failed to converge and give

an estimate of PAO, though detection probability was estimated at 0.0133.The

leopard modeling did not show strong relationships with any of the covariates.

Percent forest cover seemed to have a weak relationship with occupancy, of this

stalking predator.

Snow leopard

The snow leopard data were too scarce to carry out any meaningful analyses,

consisting of only 2 recorded occurrences. Therefore, we omitted snow leopard

from the occupancy modeling.

Black bear

No clear relationships were found between black bear occupancy and the

measured covariates. The selected best model also failed to estimate PAO, though

detection probability was estimated at 0.0346, and the naïve estimate was 0.40.

Page 46


Altitude 0.267 Poaching intensity 0.220 % Forest cover 0.163 Slope 0.088

Model AICc ∆AICc AICc weights psi(.),p(.) 237.07 0.00 0.378 psi(altitude),p(.) 238.10 1.04 0.225 psi(poaching),p(.) 239.66 2.60 0.103 psi(forest cover),p(.) 239.97 2.91 0.088 psi(slope),p(.) 239.97 2.91 0.088 psi(forest cover + poaching),p(.) 240.30 3.23 0.075 psi(altitude + poaching),p(.) 241.45 4.38 0.042

Jackal

Model AICc ∆AICc AICc weights psi(.),p(.) 57.68 0.00 0.297

psi(forest cover),p(.) 57.80 0.13 0.279

psi(slope),p(.) 58.99 1.32 0.154

psi(altitude),p(.) 60.27 2.60 0.081

psi(poaching),p(.) 60.27 2.60 0.081

psi(forest cover + poaching),p(.) 60.89 3.21 0.060 psi(altitude + forest cover),p(.) 61.29 3.61 0.049

Leopard

% Forest cover 0.338 Slope 0.154 Poaching intensity 0.141 Altitude 0.130

Model AICc ΔAICc AICc weights

psi(.),p(.) 117.58 0.00 0.350

psi(forest cover),p(.) 118.64 1.07 0.205

psi(altitude),p(.) 118.79 1.22 0.190

psi(slope),p(.) 119.88 2.31 0.110

psi(poaching),p(.) 120.17 2.60 0.095 psi(forest cover + poaching),p(.) 121.50 3.92 0.049

Black bear

Percent forest cover 0.254 Altitude 0.190 Poaching intensity 0.145

Slope 0.110


Page 47

Mountain ungulates (markhor, urial and ibex)

Occupancy of mountain ungulates is strongly limited by percent forest cover.

However, field data did not differentiate between the three species of mountain

ungulates found in the region (markhor, urial and ibex); this pooling may have

obscured some of the ecological patterns of habitat occupancy across the three

species, as each is know to occur in different habitat types. Naïve occupancy was

0.1200. The selected best model (constant p‐percent forest cover dependent psi)

failed to reach convergence, though it estimated detection probability at 0.04.

Porcupine

Porcupine occupancy seems weakly determined by poaching intensity. However the

best model is the constant p‐constant psi model. While the naïve estimate of PAO

was 0.60, estimated p and PAO (± SE) were 0.0970 and 0.8037 (0.1380),

respectively.

Model AICc ΔAICc AICc weights psi(Forest cover),p(.) 32.33 0.00 0.280 psi(altitude + slope),p(.) 33.06 0.73 0.195 psi(slope + Forest cover),p(.) 33.17 0.84 0.184 psi(altitude + Forest cover),p(.) 33.72 1.39 0.140 psi(Forest cover + Poaching),p(.) 35.19 2.86 0.067 psi(Altitude + slope + Forest cover),p(.) 36.33 4.00 0.038 psi(Altitude + Forest cover + Poaching),p(.) 36.88 4.55 0.029 psi(slope),p(.) 37.29 4.96 0.023 psi(Altitude + Slope + Poaching),p(.) 37.66 5.33 0.020 psi(.),p(.) 39.51 7.17 0.008 psi(Poaching),p(.) 39.99 7.66 0.006 psi(Slope + Poaching),p(.) 40.10 7.77 0.006 psi(Altitude),p(.) 42.10 9.77 0.002 psi(Altitude + Poaching),p(.) 42.74 10.41 0.002 psi(.),p(Forest type) 52.71 20.38 0.000

Mountain Ungulates

Percent forest cover 0.739 Slope 0.466 Altitude 0.425 Poaching intensity 0.123

Page 48


Model AICc ΔAICc AICc weights psi(.),p(.) 206.98 0.00 0.474 psi(Poaching),p(.) 207.83 0.86 0.309 psi(Forest cover),p(.) 209.42 2.45 0.139 psi(Altitude),p(.) 211.46 4.49 0.050 psi(Altitude + Poaching),p(.) 214.32 7.34 0.012 psi(Altitude + Forest cover),p(.) 214.32 7.34 0.012 psi(Altitude + Forest cover + Poaching),p(.) 217.48 10.50 0.002 psi(.),p(Forest type) 218.65 11.67 0.001

Porcupine

Poaching intensity 0.323 Percent forest cover 0.154 Altitude 0.077


Page 49

Appendix III: Field identification key

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Page 51

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Page 53

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Page 55

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Page 57

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Appendix IV: Data Forms

Field data form for occupancy surveys

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Page 62


Questionnaire data form

سروی اشغال حيوانات: پرسشنامه معلوماتی براساس تشخيص آگاھی دھنده

.نمبرپرسش نامه تاريخ خانه پری فورم.

ساحه يی که تمام معلومات فورمه مربوط به آن ميشود.

شماره گريد.

نام و آدرس خانه پری کننده فورمه.

جنس معلومات دھنده. زند يا مر (يکی آنرا دايره بکشيد)قريهء معلومات دھنده.

سن معلومات دھنده.

لطفا خانه خالی ھای مربوط به ھرسوال رادائيره بکشيد

.آيا درھمين ساحه سروی شده درسال گذشده کسی توسط کدام حيوانات گوشت خوار به قتل رسيد است 1-

بـــــلی نخير

(M) اگر بلی موقعيت ھا را در مرجع نقشه نشان کنيد.

اگر بـــــلی کدام نوع/جنس .2

گرگ پلنگ پلنگ برفی خرس نصواری سگ ھای وحشی خرس سياه

مالداری مورد شکارحيواناتوحشی قرار گرفته. ,يک سال گذشده اين ساحهء سروی شده در به طور مثال يادر جريان آ3-

شواھد درآن قسمت:بلی،اگر نخير بلی

3Aشواھد ازجزئيات درقسمت مورد شکارقرارگرفتن مالداری تان بدھيد وموقعيت ھا مذکوررا درمرجع نقشه نشانی کنيد. بلی، . اگر

سک ھا گاوھا بز/گوسفند مرغ خانگی ماکيان,مرغ وخرو

3 B .توسط کدام گونه حيوان بلیاگر ،

گرگ پلنگ پلنگ برفی خرس نصواری سک شکاری خرس سياه

چطورميدانيد که مال داری شما توسط کدام اين جنس ياگونه شکارشده؟ {ھرکدام که بکارميرود چک نمائيد4-

چشم ديد ردپاھا موادغايطهء گوشتخوار عالمهءکشتن ديگر( مشخصات)


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.- عکس ھر حيوان را نشانی کنيد و جدول مذ کور را تکميل نمائيد5

.روند تشخيص حيوان گوشت خواردرجريان ده سال گذشده درھمين ساحه سروی: 6 کم شدن، زياد شدن، ويا ثابت است؟) (مبنی برتجارب شما، آيا فکرميکنيد که نفوس درحال

درحال کاھش ثابت درحال زياد شدن بيدون معلومات

پلنگ پلنگ برفی گرگ

خرس سياه خرس نصوری

شواھد يا رخداد جنس يا قسم حيوان درجريان يکسال گذشته ( چطورميدانيد که اين جنس/قسم حيوان موجوداست) 7-

عالمات ديده شده ازطريق دوربين ديدن منابع ثانوی بيدون شواھد

(LPD) پلنگ

(SPD) پلنگ برفی

(WLF) گرگ

(BER) خــرس سياه

(BBR) خــرس نصواری

(LNX) سياه گوش

(PAL) پشک کوھی

(JUN) سمانچه

(LCA) پشک چنگلی

(WCA) پشک دشتی

(MRK) آھوی مارخور

(URL) يولایر

(IBX) آبکس

نشان عکس اسم حيوان چسيت؟ ايا در نورستان پيدا ميشود؟

پلنگ برفی روبا پلنگ خرس سالت پلنگ خالدار گرگ خرس قطبی خرس نصواری پلنگتازی خرس سياه

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موقعيت ھای که قسم/ جنس حيوانان درآنجا باشد درمرجع نقشه نشان کنيد

آيا شما ازکدام يکی اين نوع فعاليت ھای آتی درجريان سه سال گذشده باخبرھستيد: بيدون معلومات نخير/ بلی/ - شکارمنظم برای تجارت وجودارد:8

بيدون معلومات نخير / بلی / - بشکل تصادفی:9

بيدون معلومات نخير / بلی / شکار يادگاری توسط خارجی ھا:. 10

آيا شما ويا اعضای فاميل تان محصول ازحيوانات وحشی درحال حاضرداريد ويا درگذشته برای ھرمنظوری داشتيد؟.11

شکار ھرگزشکارنشده ديگرشکارنمشود اگرفعال شکارنمی شود، چه وقت متوقف شد ه است

اگرمصاحبه شونده ويا اعضای فاميلش درحال حاضرشکارميکند ويا شکارميکرد، اين جدول راخانه پری نمايد. 12

اسم جنس ياگونه

اين گونه حيوان راکدام وقت جای شکارشده سال شکارمی نمائيد

چطورشکارشد ,سک ھا ,دام ھا ,تفنگ)

(.وغيره

مقصدازشکار( مصرف شخصی، تجارت،

سرگرمی)

اگرشمابرای تجارت شکارميکنيد، به چه مقدارپول

ازپوست وگوشت حيوان بدست می آوريد؟

. اگرکدام معلومات يا حکايتی پيرامون موضوع باشد: 13


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Srinivas Vaidyanathan Devcharan Jathannapdf.usaid.gov/pdf_docs/PA00K5BG.pdf · A Preliminary Assessment of Wildlife in the Eastern Forest Complex of Nuristan, Afghanistan Report Submitted