Effects of Rotational Shepherding on Plant

Dispersal and Gene Flow in Fragmented

Calcareous Grasslands

by

Yessica Rico Mancebo del Castillo

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy

Department of Ecology and Evolutionary Biology

University of Toronto

© Copyright by Yessica Rico Mancebo del Castillo, 2012

Effects of Rotational Shepherding on Plant Dispersal and Gene Flow in

Fragmented Calcareous Grasslands

Yessica Rico Mancebo del Castillo

Doctor of Philosophy

Department of Ecology and Evolutionary Biology University of Toronto

2012

Abstract

Understanding dispersal and gene flow in human-modified landscapes is crucial for effective

conservation. Seed dispersal governs colonization, recruitment, and distribution of plant

species, whereas both pollen and seed dispersal determine gene flow among populations. This

PhD thesis tests the effect of rotational shepherding on seed dispersal and gene flow in

fragmented calcareous grasslands. Calcareous grasslands (Gentiano-Koelerietum pyramidatae

vegetation) in Central Europe are semi-natural communities traditionally used for rotational

grazing that experienced a decline of plant species during the 20th century due to abandonment

of shepherding. This PhD profits from a management project started in 1989 in Bavaria,

Germany to reconnect previously abandoned calcareous grasslands in three non-overlapping

shepherding systems. Two vegetation surveys in 1989 and 2009 revealed colonizations in

previously abandoned grasslands reconnected by shepherding. First, I propose a comprehensive

approach to identify determinants of community-level patch colonization rates based on 48

habitat specialist plants by testing competing models of pre-dispersal and dispersal effects and

accounting for post-dispersal effects. Mean source patch species occupancy in 1989, and

structural elements in focal patches related to establishment explained community-level patch

ii

colonization rates. Secondly, by adapting the community analysis to all 31 individual species of

the same community with sufficient data, I corroborate the role of shepherding to support

dispersal for a range of species, even if they lack seed morphological traits related to zoochory.

Thirdly, for the habitat specialist Dianthus carthusianorum, I genotyped 1,613 individuals from

64 populations at eleven microsatellites to test the effect of dispersal by sheep on spatial genetic

structure at the landscape scale. Genetic distances between grazed patches of the same herding

system were related to distance along herding routes, whereas ungrazed patches showed

isolation by geographic distance. Lastly, within individual grassland patches, shepherding

significantly decreases the degree of relatedness among neighboring individuals (kinship

structure) and increases genetic diversity. My thesis contributes towards understanding the

effects of zoochory on spatial dynamics in plant populations across scales.

iii

Acknowledgments

My PhD thesis would not be possible without the support from people involved in all aspects of

my life such as my professional career, my family, and friends. First of all, I am deeply thankful

to my advisor and mentor Dr. Helene Wagner because working with her represents a turning

point in my professional development. Helene has guided me through my entire PhD program,

supporting me in difficult moments of my PhD when the field and lab work, the results from

data analyses, or my writing didn’t flow well and in the planned direction. More importantly, I

have learned from her passion how to do good science and that there is always an exciting story

to tell about the way we understand the complexity of nature. I am also thankful to my

committee members Dr. Marie-Josée Fortin, Dr. Sasa Stefanovic, and Dr. Rolf Holderegger for

their thoughtful comments, criticisms, and advice on my PhD research. In particular, thanks for

the suggestions and encouragement from Marie-Josée to finalize my PhD thesis, and to Rolf for

his helpful comments about the analyses and interpretation of my genetic data.

My PhD research benefited greatly from the collaboration with Rolf Holderegger during

two stays at the WSL Swiss Federal Research Institute. At the WSL, I had valuable learning

experiences about lab techniques and analysis of genetic data, besides it being a great place

where I profited from the interaction with enthusiastic scientists related to my research interests.

Special thanks to Dr. Juergen Boehmer, who is a fundamental collaborator of my PhD research

since his early work as an ecologist in my study area provided the research basis of this PhD

project; as well as for hosting me twice at the Interdisciplinary Latin America Center ILZ at the

University of Bonn in Germany. I greatly appreciate that Juergen shared with me his passion

and knowledge of the flora and social-natural history of the beautiful landscape of Franconia in

Bavaria, by which I was captivated since the first day of my field work.

Also thanks to the people involved in the conservation project on calcareous grasslands

in Franconia, Karlheinz Dadrich and Doris Baumgartner (Untere Naturschutzbehörde,

Landkreis Weissenburg-Gunzenhausen), Bernd Raab (Landesbund für Vogelschutz in Bayern),

Stefanie Haacke (Landschaftspflegeverband Mittelfranken), Jens Sachteleben (PAN, Munich),

and the shepherds Erich Beil, Erich Neulinger, and Alfred Grimm for providing valuable

information of the management practices in the area. Special thanks to Henry Lehnert for field

support and René Graf for support during my lab work experience at the WSL. Thanks to the

iv

Biology Department and to the Micro-Electronics group at the University of Toronto,

Mississauga for their assistance.

Financial support to do my PhD studies at the University of Toronto was provided by

the National Council on Science and Technology of Mexico, CONACYT and by the Secretary

of Public Education of Mexico, SEP. I also received funding for my research stay in Germany

by the German Academic Exchange Service, DAAD. Additional funding was provided by the

Natural Sciences and Engineering Research Council of Canada, NSERC, through a Discovery

grant to Helene H. Wagner, and by the Department of Biology and Ecology and Evolutionary

Biology at the University of Toronto.

I feel thankful and lucky to have as colleagues and friends Ilona and Shekhar for sharing

with me their minds and hearts and for walking together the ups and downs along these four

significant years of my life. Thanks to many good friends that I have made at different places,

especially to Carmen, Liz, Chang, To, Gil, Maria Luisa, Carolina, Adrián, Pera, and friends

from San Cristóbal, Chiapas, for listening my experiences and thoughts, and cheering me up

when I most needed it.

Lastly but not less importantly I want to thank to all the members of my family who

have always supported my decisions for pursuing my dreams. Especially to my parents, Martha

and Luis, my brothers Luis and Abraham, my grandparents Purecita and Manuel, because no

matter that I am not there with them at home, they always encouraged and reminded me that I

can accomplish whatever goal I propose to do.

v

Table of Contents

Contents

Acknowledgments .......................................................................................................................... iv

Table of Contents ........................................................................................................................... iv

List of Tables ................................................................................................................................. ix

List of Figures ................................................................................................................................. x

List of Appendices ......................................................................................................................... xi

Chapter 1 ....................................................................................................................................... 1

1.1 Effect of Habitat Fragmentation in Plants .......................................................................... 1

1.2 Investigating Functional Connectivity in Plants .................................................................. 3

1.3 Importance of Directed Dispersal for Plant Genetic Connectivity ...................................... 5

1.4 Study System: Calcareous Grasslands ................................................................................. 9

1.5 Study organism: Dianthus carthusianorum L. ................................................................... 11

1.6 Objectives and Outline of the Thesis ................................................................................. 12

Chapter 2 ..................................................................................................................................... 17

2.1 Abstract .............................................................................................................................. 17

2.2 Introduction ........................................................................................................................ 18

2.3 Methods .............................................................................................................................. 22

2.3.1 Study site .................................................................................................................. 22

2.3.2 Actual functional connectivity data ......................................................................... 23

2.3.3 Management records ............................................................................................... 24

2.3.4 Models of potential functional connectivity ............................................................. 25

2.3.5 Pre-dispersal effects ................................................................................................. 26

2.3.6 Post-dispersal effects ............................................................................................... 27

2.3.7 Data analysis ........................................................................................................... 27

2.4 Results ................................................................................................................................ 27

2.5 Discussion .......................................................................................................................... 29

2.6 Conclusions ........................................................................................................................ 32

vi

Chapter 3 ..................................................................................................................................... 41

3.1 Abstract .............................................................................................................................. 41

3.2 Introduction ........................................................................................................................ 42

3.3 Methods .............................................................................................................................. 45

3.3.1 Study area and species occupancy data ................................................................... 45

3.3.2 Functional connectivity model ................................................................................. 47

3.3.3 Species traits ............................................................................................................ 49

3.3.4. Species occupancy analysis .................................................................................... 49

3.3.5 Genetic sample collection and microsatellite analysis ............................................ 50

3.3.6 Genetic data analysis ............................................................................................... 52

3.4 Results ................................................................................................................................ 53

3.4.1 Functional connectivity models at the species level ................................................ 53

3.4.2 Connectivity effects on patch occupancy and gene flow in Dianthus

carthusianorum ...................................................................................................... 54

3.5. Discussion ......................................................................................................................... 55

3.5.1 Connectivity by shepherding supports dispersal of calcareous grassland plants ... 55

3.5.2 Contribution of source and focal patch properties to landscape species

occupancy ............................................................................................................. 58

3.5.3 Consistency of ecological and genetic data in functional connectivity

assessments of Dianthus carthusianorum .............................................................. 59

3.6 Conclusions ........................................................................................................................ 60

Chapter 4 ..................................................................................................................................... 69

4.1 Abstract .............................................................................................................................. 69

4.2 Introduction ........................................................................................................................ 70

4.3 Methods .............................................................................................................................. 73

4.3.1 Study site and species ............................................................................................... 73

4.3.2 Sampling and microsatellite analysis ...................................................................... 74

4.3.3 Analysis of landscape-scale patterns of genetic structure ....................................... 75

4.3.4 Isolation by geographic distance and connectivity by shepherding ........................ 76

4.4 Results ................................................................................................................................ 77

4.4.1 Landscape-scale patterns of genetic structure ........................................................ 77

vii

4.4.2 Isolation by geographic distance (IBD) and directed dispersal by sheep ............... 78

4.5 Discussion .......................................................................................................................... 79

4.5.1 Effect of directed dispersal by shepherding on genetic structure at the

landscape .............................................................................................................. 79

4.5.2 Effect of IBD vs. directed dispersal by shepherding on genetic connectivity .......... 81

Chapter 5 ..................................................................................................................................... 88

5.1 Abstract .............................................................................................................................. 88

5.2 Introduction ........................................................................................................................ 89

5.3 Methods .............................................................................................................................. 93

5.3.1 Study site .................................................................................................................. 93

5.3.2 Study species and sampling ..................................................................................... 94

5.3.3 Microsatellite analysis ............................................................................................. 95

5.3.4 Quantification of SGS, genetic diversity, and inbreeding ........................................ 96

5.3.5 Statistical test of SGS, genetic diversity, and inbreeding coefficients among

groups ................................................................................................................... 98

5.4 Results ................................................................................................................................ 99

5.4.1 Strength of fine-scale spatial genetic structure (SGS) ............................................. 99

5.4.2 Estimates of neighborhood size and gene dispersal .............................................. 100

5.4.3 Estimates of genetic diversity and inbreeding ....................................................... 100

5.5 Discussion ........................................................................................................................ 102

5.6 Conclusions ...................................................................................................................... 107

Chapter 6 ................................................................................................................................... 115

6.1 Directed Dispersal by Shepherding Promotes Functional Connectivity in Calcareous

Grasslands ....................................................................................................................... 115

6.2 Shepherding Effects on Seed-Mediated Gene Flow across Spatial Scales in Dianthus

carthusianorum ............................................................................................................... 118

6.3 Conservation Implications and Future Directions............................................................ 121

References .................................................................................................................................. 125

viii

List of Tables

Table 1. Distance models description……………………………………………………….….34

Table 2. Variation partitioning and sensitivity analysis of the final model……………………36

Table 3. Description of dispersal traits………………………………………………………....61

Table 4. Best performing Si connectivity indices of each species……………………………...62

Table 5. Analysis of molecular variance among shepherding systems and ungrazed patches in

D. carthusianorum……………………………………………………………………………...87

Table 6. Estimates of SGS across 49 populations in D. carthusianorum…………………….108

Table 7. Estimates of gene dispersal and neighborhood size for six treatment groups in D.

carthusianorum …………………………………………………………………………….....111

Table 8. Estimates of genetic diversity for six treatment groups in D. carthusianorum……...112

ix

List of Figures

Fig. 1 Model species, Dianthus carthusianorum……………………………………………....16

Fig. 2 Conceptual model of plant functional connectivity…………………………………......38

Fig. 3 Relative importance of each parameters in the Si connectivity models…………………39

Fig. 4 Side-by-side boxplots of the community-level patch colonization rates……………......40

Fig. 5 Conceptual diagram of genetic and demographic connectivity in plants…………….....66

Fig. 6 Percentage of best ranked dispersal models by dispersal mode………………………...67

Fig. 7 Relative importance of Si connectivity parameters in D. carthusianorum……………...68

Fig. 8 Ordination plot of PCA of population allele frequencies in D.carthusianorum...............84

Fig. 9 Pie charts of population genetic membership scores for two genetic clusters in D.

carthusianorum……………………………………………………………………..........85

Fig. 10 Isolation by geographic distance and distance along shepherding routes on population

genetic distances in D. carthusianorum…………..…………...........................................86

Fig. 11. Sketch of spatial locations of populations analyzed for SGS in D.

carthusianorum.........................................................................................................113

Fig. 12 Interaction plot of the Sp statistics as a function of sheep grazing and population size

factors in D. carthusianorum ………………………………………………………....114

x

List of Appendices

A1. Distribution of calcareous grassland patches in the study area…………………………...152

A2. Optimized α-values for each dispersal model and for each analyzed species…………….153

A3. Genetic diversity indices per loci in D. carthusianorum…………………………….........155

A4. Multiple comparisons with Tukey’s HSD of interclass PCA axes…………………….....156

A5. Plot of DIC average values and Posterior estimates of cluster memberships………….....157

A6. Genetic diversity estimates, inbreeding coefficients, and location coordinates of D.

carthusianorum populations ………….….................................................................................158

xi

1

Chapter 1

General Introduction

1.1. Effect of Habitat Fragmentation in Plants

Habitat loss and fragmentation, due to human activities, change a formerly continuous

distribution of populations into disconnected habitat patches varying in size and spatial

isolation, causing plant biodiversity decline at the scales of individual patches and the entire

landscape (Saunders et al. 1991). The degree to which these processes will affect plant

population persistence will depend on the composition and spatial arrangement of the

landscape elements, the quality of the remaining habitat, the effective dispersal of seeds

through the landscape matrix to reach suitable habitat patches and on the exchange of genes

among remaining populations (Taylor 1993; Tischendorf and Fahring 2000; Moilanen and

Hanski 2001).

In plants, increased habitat isolation is likely to decrease effective seed dispersal and

gene flow among populations (where a population is defined ecologically as all individuals of

a species in a habitat patch) (Sork et al. 1999). On one hand, lack of gene flow through pollen

and seeds will “erode” genetic diversity within populations by drift and inbreeding, which will

be stronger in small and isolated populations (Templeton et al. 1990; Young et al. 1996;

Young and Clark 2000; Frakham 2005; Aguilar et al. 2008). On the other hand, limited seed

2

dispersal may prevent small isolated populations from being “rescued” from extinction due to

lack of seed immigration (Nathan and Muller-Landau 2000). Importantly, patch re-

colonization after local extinction can only occur through seed dispersal, which is fundamental

for the long-term species persistence in fragmented landscapes (Cain et al. 2000; Levin et al.

2003; Nathan 2006; Levey et al. 2008).

Restoring habitat connectivity is thus expected to enhance dispersal and gene flow

among fragmented plant populations having a positive effect on the maintenance of landscape

biodiversity (Opdam et al. 2006). As pointed out by Fischer and Lindenmayer (2007) in a

review of habitat fragmentation effects on biodiversity, species conservation research will

benefit from addressing the following six research priorities: (1) focusing on species dispersal

by a combination of ecological and genetic approaches, (2) assessing changes in biodiversity

patterns not only at the patch level but through entire modified landscapes, (3) implementing

and evaluating natural experiments to study larger spatial and temporal scales than typically

investigated, (4) focusing on plants and invertebrates to overcome the bias in connectivity

studies in birds, mammals, and amphibians, (5) investigating cascading effects of landscape

change; and (6) evaluating trade-offs between biodiversity conservation goals and land-use

benefits in collaboration with conservation practitioners and policy makers. My PhD, which

investigates determinants of seed dispersal and gene flow in calcareous grasslands, addresses

the first fourth points based on “natural experiment” of a landscape management project that

has been ongoing for more than 20 years.

3

1.2. Investigating Functional Connectivity in Plants

Conservation planning in fragmented landscapes often focuses on restoring landscape

connectivity, assuming that dispersal and gene flow will be enhanced by linking habitat

patches (Baguette and Van Dyck 2007). To effectively restore connectivity, it is essential to

distinguish two components: (1) structural connectivity that refers to the spatial pattern, such

as habitat area, the distance between habitat patches and the composition of the intervening

matrix, and (2) functional connectivity, which refers to the species interaction with pollen and

seed vectors and the matrix resulting in effective dispersal and gene flow (Taylor et al. 1993;

Uezu et al. 2005). Two aspects of functional connectivity can be further distinguished as: (1)

potential functional connectivity, which refers to models of species’ (or their pollen or seed

vectors) behavioral response to the matrix, and (2) actual functional connectivity, which

corresponds to the quantification of effective dispersal, such as observed dispersal rates,

colonization events, or estimates of gene flow (Calabrese and Fagan 2004; Fagan and

Calabrese 2006).

To effectively inform conservation efforts, it is necessary to apply more comprehensive

approaches not only focusing on the process of species dispersal per se (potential functional

connectivity), but also on the importance of habitat site characteristics influencing pre- and

post-dispersal processes and thus actual functional connectivity in terms of realized seed

dispersal and gene flow (Nathan and Muller-Landau 2000; Mortelliti et al. 2010). Source

patches varying in population size may unequally contribute with emigrant seeds and amount

of pollen flow, while environmental characteristics of focal patches, i.e. relating to habitat

quality, may affect probability of establishment and growth (e.g., Honnay et al. 1999;

Fleishman et al. 2002; Johansson and Ehrlen 2003).

4

While functional connectivity is assumed to be a species-specific property (Taylor et

al. 2006) it is logistically and economically unfeasible to investigate and manage connectivity

for each species across the landscape. Alternatively, large-scale empirical studies at the

community-level of analysis may reveal general patterns of species responses to habitat

fragmentation (Lindenmayer 2009; Minor et al. 2009; Schleicher et al. 2011), which may

provide useful information to conservation managers aiming to protect multiple species

simultaneously.

Another aspect of consideration when modeling connectivity is the trade-off between

the nature of the data and information gain (Fagan and Calabrese 2006). Most empirical

studies at the plant-community level have modeled connectivity simply as a function of the

physical distances between habitat patches without considering the influence of the matrix and

the role of vectors (Nathan et al. 2000; Erik and Priya 2003; Jordano et al. 2007). Such a

simplistic approach may lead to the erroneous conclusion that connectivity is not a key factor

if dispersal and gene flow rates do not depend on distance alone but on matrix resistance, or on

the interaction of the vectors with the matrix.

Neutral genetic markers, such as nuclear microsatellites, are a common approach to

analyze the effect of landscape structure on rates of gene flow (Ouborg et al. 1999). For

instance, to infer genetic connectivity across populations scattered in the landscape, either pair-

wise estimates of genetic differentiation (FST) are regressed against inter-patch distances, or

patch-level estimates of population genetic diversity are regressed against patch connectivity

indices, assuming that populations within structurally well connected patches are likely to

experience high rates of gene flow (e.g., Keyghobadi 2005; Honnay et al. 2007; Bizoux et al.

2008; Neel 2008). Such, inferences of gene flow assessed with nuclear markers are affected by

5

both seed dispersal and pollen flow, which may differ considerably in their rates and spatial

scales (Ellstrand 1992; Ennos 1994; McCauley 1997; Petit et al. 2005). However, genetic

connectivity at the landscape scale may largely depend either on pollen or seeds, depending

upon their interaction with dispersal vectors and the matrix. The ratio of pollen- vs. seed-

mediated gene flow can be quantified from combining markers with different mode of

inheritance such as nuclear and chloroplast DNA microsatellites. For instance, in most

angiosperm species chloroplast DNA (cpDNA) is maternally inherited only via seeds. The

differences in genetic structure between markers thus allow disentangling the contributions of

seeds vs. pollen to rates of gene flow (McCauley 1997). However, the above approach is often

limited since cpDNA polymorphism is often insufficient to reveal enough genetic structure

within and among populations at the landscape scale (Holderegger and Wagner 2008).

Alternatively, indirect information may be gained by contrasting demographic data of species

dispersal (i.e. colonization rates) and estimates of gene flow based on nuclear markers against

alternative and competing models of dispersal that are based on alternative assumptions of

vector interactions and matrix effects. A combination of approaches is needed to fully

understand plant functional connectivity in human modified landscapes.

1.3. Importance of Directed Seed Dispersal for Plant Genetic

Connectivity

Gene flow in plants is a scale-dependent process (McCauley 1997) that is largely determined

by the dispersal ability of seeds and pollen (Loveless and Hamrick 1984; Ennos 1994).

According with the prevalent view, pollen-mediated gene flow at the landscape scale is

expected to be more substantial than seed-mediated gene flow (Cruse-Sanders and Hamrick

6

2004; Petit et al. 2005), whereas at local scales, i.e. within habitat patches, the restricted

dispersal of seeds is expected to create fine-scale spatial genetic structure (e.g., Hamilton

1999; Heuertz et al. 2003; Trapnell and Hamrick 2004; 2005; Cruse-Sanders and Hamrick

2004; Escudero et al. 2006; Bittencourt and Sebbenn 2007; Wang et al. 2011). However, a

growing number of empirical studies revealed that the contribution of seed- relative to pollen-

mediated gene flow across scales depend to a large extent on seed dispersal mechanisms and

landscape heterogeneity (e.g., Cruse-Sanders and Hamrick 2004; Jordano et al. 2007; Zhou et

al. 2007; Freeland et al. 2012).

Plants exhibit a large variety of seed dispersal vectors, influencing the distance,

direction, and destination at which seeds are deposited away from the source to reach suitable

sites for establishment and growth (Shupp 1993; Nathan and Muller Landau 2000; Shupp

2011). For instance, plants without morphological seed dispersal adaptations are likely to be

dispersed over very short distances (often < 1 m), resulting in clustering of related genotypes

within the vicinity of the source (e.g., Hamrick and Nason 1996; Epperson and Alvarez-Buylla

1997; Cruse-Sanders and Hamrick 2004). On the other hand, seeds adapted to dispersal by

wind will travel longer distances (Tackenberg 2003; Soons et al. 2004), but the random

direction of dispersal and thus the chance to arrive in suitable sites for establishment is highly

stochastic (Nathan 2000; 2006). Directed seed dispersal by animal vectors can also occur over

long distances, but in contrast to wind dispersal, seed deposition in suitable sites with high

probability of establishment can be substantial (Howe and Smallwood 1982; Wenny 2001).

Directed seed dispersal with animal vectors (zoochory) has been shown to influence the

structure of populations and plant communities (Wenny 2001; Aukema and del Rio 2002;

Purves et al. 2005; Briggs et al. 2009) and to determine, spatial patterns of genetic structure

7

within and among populations (e.g., García et al. 2007; Jordano et al. 2007; Karubian et al.

2010; Kloss et al 2011). Available evidence from tree and shrub species dispersed by

frugivores found that directed dispersal may create strong spatial clustering of related

genotypes even with extensive pollen flow (Grivet et al. 2005; García et al. 2009; Torimaru et

al. 2007). The resulting pattern of fine-scale spatial genetic structure was explained by the

clumped seed deposition in preferable microsites used by the vector, regardless of the long-

distance dispersal away for the source (e.g., Torimaru et al. 2007). In contrast, directed

dispersal may also result in a homogenization of the spatial distribution of genetic variation by

the recurrent mixing of seeds from several population sources (e.g., Karubian et al. 2010).

These conflicting results likely are related to the behavior of the animal vector and its

interaction with the landscape structure.

In the case of frugivory, plants invest in the production of fruits that are attractive to

their animal vectors, and animals deliberately consume on these fruits (Traveset and Perez

2008; Lorts et al. 2008). In contrast, animals may inadvertently transport seeds, e.g. attached to

their fur or hooves (epizoochory) or if seeds are ingested during foraging (endozoochory)

(Janzen 1984; Fisher et al. 1996; Iravini et al. 2011). Some plants have developed

morphological seed adaptations to promote epizoochory, e.g. with awns or bristles (Hughes et

al. 1994; Lorts et al. 2008). In agricultural landscapes, grazing by herds of domestic ungulates

such as sheep, horses, or cattle may thus support dispersal of a range of herbaceous species

(Fischer et al. 1996; Couvreur et al. 2004; Cosyns et al. 2005; Bruun and Poschlod 2006;

Auffret et al. 2012). In grazed calcareous grasslands communities, rotational shepherding is

presumed to be one of the key factors sustaining local and regional species richness (Fischer et

al. 1996; Schrautzer et al. 2009; Kuiters and Huiskes 2010; Piqueray et al. 2011; Wagner et al.

8

2012). In contrast to seed dispersal by wind (anemochory), directed seed dispersal by

rotational shepherding increases the chances of successful long-distance dispersal, as seeds

may be retained for extended periods of time and travel several kilometers to suitable sites as

herds move between pastures (Manzano and Malo 2006). However, whether rates of seed

dispersal among spatially isolated plant populations connected along shepherding routes can

be substantial enough to influence landscape-scale patterns of genetic structure has not yet

been shown.

At a local scale, seed dispersal determines the spatial distribution of individuals within

a habitat patch and thus is the major determinant of fine-scale spatial genetic structure

(hereafter SGS) (Heywood 1991; Epperson 1993). SGS is characterized by the degree and

spatial extent of relatedness between adjacent plants (kinship structure), which arises from

restricted seed dispersal near to the source. If in addition, pollen flow is restricted, biparental

inbreeding would increase. Thus SGS influences the outcomes of subsequent ecological

processes such as mating patterns, effective population size, selection, and progeny fitness

(Loveless and Hamrick 1984; Heywood 1991; Ouborg and Van Treuren 1994). Life history

traits such as reproduction type (selfing vs. outcrossing), and the type of seed dispersal vector,

and demographic factors such as population size and history, are likely to affect the strength of

SGS (Loveless and Hamrick 1984; Hamrick et al. 1993; Hamrick and Nason 1996; Epperson

2003; Vekemans and Hardy 2004; Jones et al. 2006; Troupin et al. 2006; Hamrick and

Trapnell 2011).

If grazing by domestic ungulates promotes seed dispersal into populations by long-

distance dispersal (Fisher et al. 1996; Manzano and Malo 2006; Rico et al. 2012) while also

moving seeds within grassland patches, these effects are likely to modify spatial patterns of

9

relatedness within plant populations (Kleij and Steingner et al. 2002; Kloss et al. 2011). In

general, zoochory tends to increase the variance of seed dispersal and deposition patterns

(Jordano et al. 2007). Increased variance in dispersal distances tend to increase the overlap of

offspring (seed shadows) from different mother plants, causing a gradual decay of relatedness

over distance (Degen et al. 2001; Chung et al. 2002; Karubian et al. 2010).This may increase

effective population size, as nearby individuals become more genetically different on average

(Vakemans and Hardy 2004; Hamrick and Trapnell 2011). While long-distance dispersal by

shepherding is recognized as a major determinant of spatial dynamics for grazed calcareous

grasslands (Adler et al. 2001; Kuiters and Huiskers et al. 2010; Reitalu et al. 2010; Wagner et

al. 2012), its potential to contribute to seed-mediated gene flow and to influence spatial genetic

structure across scales has been little investigated.

1.4. Study System: Calcareous Grasslands

Calcareous grasslands (Gentiano-Koelerietum pyramidatae vegetation; Oberdorfer 1978) in

Central Europe are semi-natural communities of medieval origin traditionally used for sheep

grazing and hay production (Ellenberg 1996). They have high floristic and faunistic species

richness of important conservation value (WallisDeVries et al. 2002). Abandonment of

transhumance shepherding during the early 20th century, followed by shrub encroachment and

natural reforestation (Hakes 1987), led to a dramatic decline in the number and extent of

calcareous grasslands with subsequent loss of biodiversity (Poschlod and WallisDeVries 2002;

Butaye et al. 2005). Secondary succession after abandonment of traditional land use practices

typically leads to relatively species-poor beech forests with abundant species that are of

10

limited interest for conservation, whereas calcareous grasslands have high numbers of habitat

specialist plants and arthropod. Within this anthropogenic landscape, which was partly

deforested and subject to traditional land use since Roman times, calcareous grasslands have

become important refugia for numerous endangered species and hotspots for biodiversity at the

landscape scale. As a consequence, this anthropogenic habitat type has received protection

status at regional, national, and European levels.

Habitat specialist plants of calcareous grasslands are highly vulnerable to current trends

of habitat fragmentation (Poschlod and WallisDeVries 2002) as most species do not form a

persistent seed bank and do not disperse far (Coulson et al. 2001; Fenner and Thompson 2005;

Joshi et al. 2006; Zeiter et al. 2006). Thus, it is doubtful whether impoverished patches where

many species have gone locally extinct after abandonment may restore their species richness

without artificial sowing (Poschlod et al. 1996; Kahmen et al. 2002).

Available evidence from connectivity studies in calcareous grasslands showed no

consistent trends. For instance, some studies have found that species richness is mostly

explained by the physical distance among patches (e.g., Brunn et al. 2000; Geertsema 2005;

Adriaens et al. 2006; Joshi et al. 2006; Bruckman et al. 2010), whereas other studies have

found that patch area is the main predictor (e.g., Krauss et al. 2004; Bisteau and Mahy 2005),

while only a few have incorporated the effect of connectivity by shepherding on species

richness (e.g., Reitalu et al. 2010; Wagner et al. 2012).

In the study area in the Southern Franconian Alb (Bavaria, Germany), calcareous

grasslands typically are found along the steep slopes and on un-tillable wasteland of the karstic

plateau. The natural vegetation of the area would be characterized by orchid-rich beech forest

(Cephalanthero-Fagion community) or sedge-rich beech (Carici-Fagetum community;

11

Adalbert 1978). Calcareous grasslands (Gentiano-Koelerietum pyramidatae vegetation;

Oberdorfer 1978) within the study area were grazed since medieval times in approximately ten

communal shepherding systems following defined routes around municipal land (Hornberger

1959; Jacobeit 1961). By the 19th century, regional transhumance shepherding systems

involved the grasslands along the steep slopes of the Franconian Alb, while calcareous

grasslands located on the plateau were still grazed as communal land. During the first half of

the 20th century, due to socio-economic land use changes, calcareous grassland were

progressively abandoned (“previously abandoned patches”), with an estimated decline in the

study area from 970 ha to 302 ha by the early 1990 (Dolek and Geyer 2002). Some of the

larger and more easily accessible grassland patches remained grazed in local herding systems

(“core areas”), and these cores areas typically have high species richness of habitat specialist

plants.

1.5. Study species: Dianthus carthusianorum L.

Dianthus carthusianorum (Caryophyllaceae) is a mainly outcrossing, diploid, and perennial

herb, and a habitat specialist of Central European calcareous grasslands (A1 see Appendix). D.

carthusianorum forms a rosette of unramified shoots of 30 to 35 cm in height, and has tubular-

shaped flowers. Flowering occurs from June to October. Pollination is carried out by

specialized Lepidoptera species such as Satyrus ferula, Papilio machaon, Macroglossum

stellarathum, Thymelicus sylvestri (Bloch et al. 2005). Dianthus carthusianorum do not form a

persistent seed bank, and it reproduces by seed while also vegetatively (Klotz et al. 2002).

Seeds are likely to be dispersed by gravity (authochory) or by wind guts (boleochory) since

12

they lack morphological adaptations to anemochory or zoochory (Klotz et al. 2002). I selected

D. carthusianorum as a model species because it is a characteristic forb species of calcareous

grasslands in the study region (Boehmer et al. 1990), it occurs in many patches in the study

area but never in high densities, and showed numerous colonizations of previously abandoned

patches, increasing its occurrence in such patches from 40% in 1989 to 90% in 2009. In

addition, nuclear microsatellite markers were available for the species or for closely related

species.

1.6. Objectives and Outline of the Thesis

My PhD research profits from an ongoing long-term landscape management project as a

natural experiment. In 1989, the County of Weißenburg-Gunzenhausen initiated a landscape

management project aimed to restore calcareous grasslands by reconnecting core areas with

previously abandoned calcareous grasslands in three non-overlapping herding systems (A1,

Appendix). Large-flocks of approximately 400 to 800 ewes are herded along predefined routes

up to 55 km and covering around 140 ha of calcareous grasslands (Wagner et al. 2012).

Previous empirical evidence for the study area based on a baseline survey from 1989/1990 and

an evaluation survey in 2008/2009 revealed numerous species colonizations in previously

abandoned patches that were reconnected by shepherding as compared to ungrazed patches

(Lehnert 2008). These documented colonizations provided an excellent research opportunity to

study the effects of seed dispersal by rotational shepherding.

The main objective of this research was to investigate the role of directed dispersal in

plant functional connectivity in fragmented calcareous grasslands. By assessing functional

13

connectivity in herbaceous plant communities and individual species, this research helps

overcome the bias towards mammals, amphibians, and birds in empirical studies of functional

connectivity (Fischer and Lindenmayer 2007), and as well as the bias towards tree and shrub

species in landscape genetics studies of plants. The remainder of the thesis comprises four

chapters that each address a main research question as outlined below, followed by a final

synthesis chapter.

Chapter 2 investigates which are the main determinants of functional connectivity

for the community of calcareous grassland plants. Based on mean patch colonization rates

of 48 habitat specialist plants I quantify the relative contribution of (i) pre-dispersal effects

(source patch properties) by including patch area and mean species occupancy; (ii) dispersal

effects measured by five competing models of potential functional connectivity: geographic

proximity between patches, matrix resistance, and three alternative models of dispersal by

shepherding; and (iii) post-dispersal effects (focal patch properties) measured by the number of

dynamic structural elements present in focal patches. This chapter highlights that actual

functional connectivity in plants, as quantified by patch-level colonization rates, cannot be

approximated by structural connectivity based on physical distance alone, but depends on the

number of patches traversed by sheep along shepherding routes, and that predictions of actual

functional connectivity need to consider pre- and post-dispersal processes.

Chapter 3 builds on chapter 2 to contrast the predictions from the community

level assessment of functional connectivity with analysis for individual species of the

same plant community and with molecular data. The specific goals are (i) to assess whether

shepherding effectively promotes dispersal at the landscape scale for a majority of habitat

14

specialist plants irrespective of seed morphological adaptation to zoochory, and (ii) to evaluate

whether pre- and post-dispersal processes importantly contributed to explain patch occupancy

at the species level. For one characteristic species, Dianthus carthusianorum, I obtained

population genetic data from nuclear microsatellite markers (iii) to assess whether assessments

with patch occupancy and genetic data consistently reveal the potential of seed dispersal by

sheep using ecological patch occupancy and population genetic diversity data. By assessing

the consistency between common approaches to quantify connectivity in plants, this chapter

illustrates that when a dispersal vector is shared between species, community-level assessment

may be sufficient to reveal general patterns for a range of species. In addition, this chapter

highlights that contrasting ecological and genetic data in connectivity models can provide a

better understanding of the mechanisms of dispersal and gene flow in plant populations across

the landscape.

Chapter 4 determines the effect of seed-mediated gene flow by large-flock

shepherding on landscape-scale patterns of population genetic structure in Dianthus

carthusianorum. Specifically, I (i) test if there is significant genetic differentiation between

the three non-overlapping shepherding systems, and (ii) contrast the effects of isolation by

geographic distance (IBD) and (iii) determine distance along shepherding routes on population

genetic structure. This chapter provides evidence for a substantial contribution of seed-

mediated gene flow by directed dispersal from shepherding on landscape-scale patterns of

population genetic structure in a calcareous grassland plant.

Chapter 5 focuses on assessing the effect of directed seed dispersal by rotational

shepherding on fine-scale spatial genetic structure (SGS) in Dianthus carthusianorum.

Specifically, I assess if (i) populations of grazed grassland patches show higher genetic

15

diversity and weaker SGS compared to populations of ungrazed grasslands. Moreover, I test if

(ii ) large populations will show higher genetic diversity and weaker SGS than small

populations Furthermore, for populations of grazed grasslands I compare genetic diversity and

SGS between populations of grassland patches colonized since 1989 and pre-existing

populations to infer (iii) if recently colonized populations were founded by colonists from a

variety of populations sources. By contrasting multiple populations (replicates) within a

landscape, this chapter provides evidence on the role of directed dispersal by shepherding on

spatial patterns of genetic structure within populations of D. carthusianorum.

16

Fig. 1 Dianthus carthusianorum L.

17

Chapter 2

Determinants of Actual Functional Connectivity

for Calcareous Grassland Communities Linked

by Rotational Shepherding

Contents of this chapter have been published in the Journal of Landscape Ecology. Permission to use this published material in this dissertation has been obtained from

the publisher (license copyright number 2951391258260): Rico, Y., H. J. Boehmer, H. H. Wagner. 2012. Determinants of actual functional

connectivity for calcareous grassland communities linked by rotational sheep grazing. Landscape Ecology 27: 199-209

A link to the published paper can be found at:

http://www.springerlink.com/content/h4l30247813222t5/

2.1. Abstract

In fragmented landscapes, plant species persistence depends on functional connectivity in

terms of pollen flow to maintain genetic diversity within populations, and seed dispersal to re-

colonize habitat patches following local extinction. Connectivity in plants is commonly

modeled as a function of the physical distance between patches, without testing alternative

dispersal vectors. In addition, pre- and post-dispersal processes such as seed production and

establishment are likely to affect patch colonization rates. Here, we test alternative models of

18

potential functional connectivity with different assumptions on source patch effects (patch area

and species occupancy) and dispersal (relating to distance among patches, matrix composition,

and sheep grazing routes) against empirical patch colonization rates at the community level

(actual functional connectivity), accounting for post-dispersal effects in terms of structural

elements providing regeneration niches for establishment. Our analyses are based on two

surveys in 1989 and in 2009 of 48 habitat specialists in 62 previously abandoned calcareous

grassland patches in the Southern Franconian Alb in Bavaria, Germany. The best connectivity

model Si, as identified by multi-model inference, combined distance along sheep grazing routes

including consistently and intermittently grazed patches with mean species occupancy in 1989

as a proxy for pre-dispersal effects. Community-level patch colonization rates depended to

equal degrees on connectivity and post-dispersal processes. Our study highlights that actual

functional connectivity of calcareous grassland communities cannot be approximated by

structural connectivity based on physical distance alone, and modeling of functional

connectivity needs to consider pre- and post-dispersal processes.

2.2. Introduction

Habitat fragmentation threatens the persistence of plant populations by reducing habitat

connectivity and thus affecting dispersal of pollen and seeds between habitat fragments (Sork

and Smouse 2006). While pollen flow may be sufficient to maintain genetic diversity and

avoid inbreeding effects (Young and Clark 2000; Keller and Weller 2002), habitat re-

colonization after local extinction can only occur by propagule dispersal (Josht et al. 2002).

Given that in most grassland plants propagules are dispersed within 1m in the vicinity of the

19

source (Coulson et al. 2001; Fenner and Thompson 2005), lack of connectivity by seed

dispersal will limit species’ ability to reach empty patches and establish new populations

(Soons et al. 2004). Moreover, the colonization process may increase population genetic

differentiation if propagules come from a few sources only (Withlock and McCauley 1990;

Panell and Dorken 2006), which is likely to be the case with increased fragmentation.

Restoring connectivity is thus expected to avoid deleterious demographic and genetic effects

mostly in small isolated populations (Frakham 2005; Aguilar et al. 2008).

Connectivity comprises two components: structural connectivity, which is defined by

the spatial habitat configuration without reference to organism movement behavior, and

functional connectivity, which refers to the individual behavioral response to the landscape

pattern, including the scaling of inter-patch distances by maximum dispersal distance or the

transversability of different land-covers in the intervening matrix, and the resulting dispersal

and gene flow (Taylor et al. 1993; 2006). Assessing functional connectivity in plants remains a

methodological challenge, because even for a single species it is not feasible to observe

dispersal events over long distances, for more than a single source population, or over multiple

seasons and even less so for entire species assemblages (Fischer and Lindenmayer 2007).

Commonly, empirical studies of plant communities model connectivity as a function of

patch area and geographic distance between patches alone and test model predictions against

species occupancy data, as empirical data on colonization rates are often lacking (but see

Soons et al. 2004; Herrera et al. 2011). These approaches are unlikely to capture the

mechanism behind functional connectivity. As recommended by Murphy and Lovett-Dousst

(2004), it is necessary to incorporate a functional approach for modeling landscape

connectivity including variables associated with effective dispersal trough the matrix (i.e.,

20

dispersal vectors, predation) and with local establishment (i.e., resource availability,

competition), which together determine long-term species persistence. Empirical studies thus

need to combine an assessment of landscape structure with biological assumptions on

organism dispersal into realistic models of potential functional connectivity, and test these

models against empirical estimates of effective dispersal (e.g., colonization rates or migration

events inferred by assignment test with genetic data) that correspond to actual functional

connectivity (Fig. 2; Calabrese and Fagan 2004; Fagan and Calabrese 2006).

Beyond dispersal per se, pre- and post-dispersal processes are likely to influence patch

colonization success (Nathan and Muller-Landau 2000). For instance, population size in

source patches may determine the quality and the potential number of emigrant propagules.

The patch connectivity index, Si, of the incidence function model (IFM, Hanski 1994) includes

the area of source patches as a proxy for population size assuming that the carrying capacity of

the focal patch is proportional to its area (Ovaskainen and Hanski 2004). While most studies

include source patch variables at least in terms of patch area, focal patch properties affecting

establishment have rarely been considered in landscape connectivity models (Clobert et al.

2004). For instance, availability of resources in focal patches would influence seedling

establishment and thus colonization success. Hence, most measures of actual functional

connectivity are likely to confound dispersal effects with post-dispersal processes when testing

predictions of potential functional connectivity. To effectively inform management efforts, it is

crucial to disentangle the contribution of dispersal per se from pre- and post-dispersal

processes. We propose a comprehensive approach to assess and disentangle determinants of

actual functional connectivity for plant species at the community level (Fig. 2). Our approach

considers that actual functional connectivity (colonization rates) depends on the emigrant pool

21

in source patches, a dispersal function reflecting the main dispersal vector, and the

establishment probability of propagules in focal patches.

We apply this framework to study plant community connectivity of calcareous

grasslands in Germany. Calcareous grasslands are semi-natural communities traditionally used

for sheep grazing or hay production (Ellenberg 1996). They typically are nutrient poor,

unfertilized and free of herbicide or pesticide application, resulting in a high floristic and

faunistic species richness of important conservation value (WallisDeVries et al. 2002). In

Central Europe, since the late 19th century abandonment of transhumance sheep grazing

followed by encroachment of natural forest succession led to a dramatic decline in calcareous

grasslands with a consequent loss of biodiversity (Poschlod and WallisDeVries 2002; Bender

et al. 2005). Paradoxically, preservation of natural succession is not a goal of nature

conservation here because it typically leads to relatively species poor beech forests with

predominantly ubiquitous species that are of no particular interest for conservation, whereas

calcareous grasslands are very diverse with high numbers of habitat specialist arthropod and

plant species. Within this old cultural landscape, which was deforested and subject to

traditional land use since Roman times, calcareous grasslands have become important refugia

for numerous endangered species and hotspots for biodiversity at the landscape scale.

In 1989 in the Franconian Alb in Bavaria, Germany, a conservation project was

established aimed to restore abandoned calcareous grasslands and re-connect them with

existing core areas by rotational sheep grazing. Due to extensive baseline data from 1989 for

all previously abandoned patches (Boehmer et al. 1990), this project represents a unique

research opportunity to study connectivity of grassland communities. Based on an evaluation

survey in 2009, empirical colonization rates were observed for all 48 habitat specialist plants.

22

Wagner et al. (2012) showed that rotational sheep grazing significantly increased species

richness of previously abandoned patches, although structural connectivity based on the

physical distance between patches had no effect on species richness. Here, we use patch

colonization rates at the community level as a measure of actual functional connectivity to test

multiple competing models of potential functional connectivity, including pre- and post-

dispersal effects (Fig. 3). By pooling colonization events across the 48 habitat specialist

species, we are able to overcome data limitations that would prevent statistical analysis at the

species level.

2.3. Methods

2.3.1. Study site

The study area of approximately 10 km x 15 km in the Southern Franconian Alb near

Weissenburg, Bavaria, Germany, comprises valleys and limestone plateaus with agricultural

fields, forests, grasslands, orchards, and settlements. Between 1900 and 1960, pasture

abandonment led to a dramatic regional decrease of calcareous grassland cover from 15% to

1% today (Bender et al. 2005). In the study area, calcareous grasslands declined from 970 ha

in 1830 to 302 ha by the early 1990s (Dolek and Geyer 2002). In 1989, the County of

Weissenburg-Gunzenhausen initiated a pilot project aimed to preserve and reconnect

calcareous grasslands by implementing three independent rotational grazing systems, which

connected larger, consistently grazed patches (“core areas”) with previously abandoned

patches experiencing secondary succession (“abandoned patches”; A1 Appendix).

23

2.3.2. Actual functional connectivity data

All previously abandoned calcareous grasslands of at least 25 m2 that had remnants of

Gentiano-Koelerietum pyramidatae vegetation were surveyed as a basis for implementing the

conservation project (Boehmer et al. 1990). During summer and fall of 1989 and spring of

1990, complete species lists of vascular plants were recorded with Braun-Blanquet abundance

information on all 62 previously abandoned calcareous grasslands in the study area (baseline

survey). All 48 habitat specialist species were surveyed again during summer 2008 and spring

and fall 2009 (evaluation survey) on all previously surveyed patches and in all core areas. Core

areas were fully surveyed in the evaluation survey but only aggregate data are available for the

baseline survey, consisting of the frequency of occurrence of each species among 11 sampled

core areas Only previously abandoned patches were included in the statistical analysis, but

core areas were included in the calculation of Si connectivity models.

Consistency between baseline and evaluation surveys was high as both were led by the

same scientist. Comparisons between independent surveys by two different observers

confirmed that species were reliably detected even if reproductive structures were absent,

possibly with the exception of Allium oleraceum. As summer visits for the baseline survey

were primarily done during late summer 1989, this may have affected the detectability of four

plant species that flower during early summer: Leontodon hispidus, Ranunculus bulbosus,

Ajuga genevensis, and Linum catharticum. To assess sensitivity to detectability, we repeated

all analyses without the above-mentioned five species.

We calculated mean patch colonization rate CRi = Ci / (48 – Richness.1989i) at the

community level including all 48 habitat specialist plants. i.e., for each previously abandoned

24

patch i, the number Ci of species present in the evaluation survey, but absent in the baseline

survey was divided by the number of species absent in patch i in the baseline survey. We thus

scaled the observed number of colonization events in each patch by the maximum possible net

number of colonizations among the 48 specialist species.

The interpretation of CRi as community-level patch colonization rate relies on the

assumptions that species were reliably detected in both surveys and that colonization did not

occur from the seed bank. The majority of habitat specialist species in this system is known to

have a transient or short-term persistent seed bank. However, long-term persistent seed bank

has been reported for eight species: Ranunculus bulbosus, Medicago lupulina, Lotus

corniculatus, Sanguisorba minor, Thymus pulegioides, Gentiana cruciata, Euphorbia

cyparissias, and Linum catharticum (Poschlod et al. 2003). To assess sensitivity to seed bank

persistence, we repeated all analyses without these eight species.

2.3.3. Management records

The grazing regime 1989 - 2009 of each of the 62 previously abandoned patches was classified

based on archived management records and current maps of grazing routes combined with

shepherd interviews. Thus, 26 of the previously abandoned patches were consistently grazed

since the beginning of the conservation project, which means that 400 – 800 ewes were herded

through each patch 3-5 times per year; 13 patches were intermittently grazed, i.e., not grazed

all years from 1990 to 2009, or only later in the season, or they were only grazed initially for a

few years after the start of the project. The remaining 23 patches were not included in the three

grazing systems and thus remained ungrazed from 1989 to 2009 (A1, Appendix).

25

2.3.4 Models of potential functional connectivity

We parameterized a patch connectivity index, Si, to test competing dispersal models and

source patch effects. The Si index quantifies distances (dij) between focal patch i and each

source patch j using a negative exponential dispersal kernel with a constant scaling parameter

α (Hanski 1994; Ovaskainen and Hanski 2004). Parameter Aj refers to the area (ha) of source

patch j, and in a single species model, parameter pj indicates source patch occupancy (present

= 1, absent = 0). For our community-level model, we averaged baseline species occupancy pjk

over all 48 species k, resulting in pj = Σk pjk/48, so that 0 ≤ pj ≤ 1. All core areas received the

value of pj = 0.75 derived from the aggregate baseline data. Substituting patch-level occupancy

data from the evaluation survey did not improve model fit.

We modeled the effect of potential dispersal vectors with five alternative dispersal

models (Table 1) by modifying the distance parameter dij in the Si index. The simplest model,

geographic distance (Table 1a), is a null model that assumes seeds are dispersed by wind as a

simple diffusion process without an effect of the landscape matrix. The second model, matrix

resistance (Table 1b), assumes that seeds are dispersed by simple diffusion, but seeds are

intercepted by forest in the intervening matrix. The remaining three models assume sheep to be

the main dispersal vector: the consistently grazed model (Table 1c) assumes that grazing needs

to be consistent, i.e., every year and throughout the season, to effectively transport seeds

between patches along the grazing route; the model implies that distance in terms of the

number of patches traversed between two patches i and j matters (i.e., distance effect). The

consistently or intermittently grazed model (Table 1d) is similar to the previous model, but

26

some degree of grazing is assumed sufficient to effectively transport seeds. The grazed within

the same system model (Table 1e) is a null model for dispersal by sheep as it includes no

distance effect, i.e., it assumes that seeds transported by sheep are equally likely to reach all

grazed patches within the same grazing system.

Dispersal capacity accounted for by parameter α is unknown for our set of 48 specialist

species. In a sensitivity analysis varying α from 0.1 to 50 with increments of 0.1, we found that

α = 0.2 resulted in the best or second best fit between patch colonization rates, CRi, and each

of the five dispersal models (i.e., highest positive Pearson correlation), hence we used this

value for the final models.

2.3.5. Pre-dispersal effects

For each distance measure, dij, we calculated four alternative Si connectivity indices with

different assumptions on source patch effects. Model 1 does not include source patch effects Aj

or pj, and thus assumes that all patches are equal sources of propagules, so that patch

connectivity Si depends only on distance dij between patches. Model 2 includes patch area (Aj),

assuming that seed production is proportional to habitat area. Model 3 includes only mean

patch species occupancy pj. Model 4 includes both source patch parameters, assuming that the

emigrant propagule pool depends on habitat area (Aj) and species occupancy (pj).

27

2.3.6. Post-dispersal effects

We recorded in each previously abandoned grassland how many types of dynamic structural

elements were present that are likely to create regeneration niches for the establishment of

habitat specialist species (post-dispersal effect): rock debris, erosion, ant hills, and small

mammal burrows.

2.3.7. Data analysis

We used multi-model inference with the function dredge in the R library MuMIn to rank each

of the 20 Si connectivity models for explaining patch colonization rates, CRi. For each

parameter in the Si connectivity index, we summed Aikake model weights wm over all Si

models that contained the parameter of interest to assess its relative importance. For the best

performing Si connectivity index, we performed significance tests and residual analysis and

assessed model fit with adjusted R2. Subsequently, for the full regression model including the

best Si connectivity index and the number of structural elements in 2009, we applied variation

partitioning (Legendre and Legendre 1998) to assess the unique and shared contributions of

each factor.

2.4. Results

Based on multi-model inference, consistently or intermittently grazed was by far the best

dispersal model explaining patch colonization rates (CRi: relative importance = 0.96, Fig. 3A).

For the pre-dispersal effects, the best model was Model 3 with mean species occupancy in

28

1989 (pj: relative importance = 0.63, Fig. 3B), followed by Model 1 (null: relative importance

= 0.35, Fig. 3B) that only incorporated distance effects. Interestingly, when source patch area

(Aj) was included (Model 2 and 4) the model performed worse than without pre-dispersal

effects (Fig. 3B). Thus, the best Si connectivity index included dij as consistently or

intermittently grazed and mean species occupancy pj as pre-dispersal effect. This model (AIC

= -62.6, w = 0.46) had well behaved residuals and explained 24% (R2adj, df = 1 and 59, F =

19.5, p = 0.001) of the variation in community-level patch colonization rates CRi. Neither the

geographic distance model (p = 0.28) nor the matrix resistance model (p = 0.15), both

including mean species occupancy pj, were statistically significant for explaining patch

colonization rates CRi.

Post-dispersal effects in terms of the number of structural elements present alone

explained 26% of the variation in CRi (R2adj, df = 1 and 59, F = 22.3, p = 0.0001; Fig. 4).

Combining Si and the number of structural elements in the full regression model significantly

increased the variance explained to 37% (R2adj, df = 2 and 58, F = 18.3, p = 0.0001). This

model had well-behaved residuals without influential outliers. Variation partitioning showed

that unique contribution of the number of structural elements was 13% of the total variation

and the unique contribution of the Si connectivity index was 11%, with a shared variance of

13%. Repeating the analysis without five species with potential issues of detectability between

surveys and eight species likely presenting a long-term persistent seed bank did not change the

nature or statistical significance of the results (Table 2).

The consistently and intermittently grazed model potentially confounds the effects of

connectivity by grazing per se and a distance effect. Omitting the ungrazed patches from the

final model estimation reduced the variance explained overall and both by the connectivity

29

model Si and the structural elements, but all effects remained statistically significant (Table 2).

In contrast, further omitting the distance effect by substituting the grazed within the same

system distance model resulted in an overall non-significant regression model. Similarly,

omitting the pre-dispersal effect pj resulted in non-significant contributions of both the

connectivity model Si and the structural elements (Table 2).

2.5. Discussion

Our results clearly demonstrate the importance of connectivity in terms of sheep as dispersal

vector for patch colonization at the community level for grassland plant species. Wagner et al.

(2012) found no significant effect of connectivity as calculated from the physical distance

between patches, which corresponds to our geographic distance model without pre-dispersal

effects. We tested alternative dispersal models and found strong evidence that dispersal does

depend on distance but in terms of the number of patches that sheep need to traverse between

two sites along grazing routes. This effect remained significant when omitting the variation

due to grazed vs. ungrazed patches. Although we cannot rule out that a species may have been

overlooked in a survey or colonized a patch from the seed bank, our results appear to be robust

as omitting species with potential issues of detectability or ability to form a long-term

persistent seed bank did not change the nature or statistical significance of the results.

Available evidence from empirical studies in calcareous grasslands based on patch

occupancy data showed no consistent trends regarding the effects of habitat loss and isolation.

For instance, some studies found that species richness is mostly explained by geographic

proximity between patches (e.g., Geertsema 2005; Adriaens et al. 2006; Joshi et al. 2006;

30

Bruckman et al. 2010), whereas other studies found that patch area is the main predictor of

patch species richness (e.g., Krauss et al. 2004; Bisteau and Mahy 2005). The lack of robust

and consistent trends may be due to an oversimplified assessment of habitat connectivity and

the use of indirect measures of actual functional connectivity, such as patch occupancy

patterns instead of colonization rates. For these studies, functional connectivity was assumed

to depend exclusively on the source patch area and the physical distances between patches,

which ignored the biological processes behind actual functional connectivity (Taylor et al.

1993; 2006).

Probability of patch re-colonization after extinction will decrease as habitat isolation

increases (Geertsema 2005; Joshi et al. 2006). However, for plants, dispersal vectors that are

likely to transport propagules over longer distances enable plants to partly overcome such

limitation (Bruun and Fritzbøger 2002; Nathan et al. 2008). For habitat specialists of

calcareous grassland pastures (Gentiano-Koelerietum pyramidatae vegetation), sheep are

assumed to act as the main dispersal vector (Fischer et al. 1996). Empirical evidence, however,

is limited to small field experiments measuring seed adhesive potential and seed distance

traveled on few tamed sheep or with experimental coats (Moussie et al. 2005; Manzano and

Malo 2006), by germination experiments of dung samples (Kuiters and Huiskes 2010), or

indirectly inferred by contrasting species occupancy data between grazed patches with varying

grazing history (Reitalu et al. 2010). Our results thus confirm with empirical data at the

landscape scale the role of sheep as dispersal vector for the maintenance of calcareous

grassland biodiversity.

From the strong support for connectivity in terms of consistent or intermittent grazing,

we conclude that some amount of rotational sheep grazing may be sufficient for many species.

31

However, further research is needed to assess whether early flowering species were equally

likely to be dispersed to patches that are grazed only later in the season after crop harvesting in

surrounding fields. The clear support for a distance effect in terms of number of patches

traversed by sheep between two patches suggests that most seeds dispersed by sheep do not

stay on the sheep for a long time. This is consistent with previous experimental results, where

most seeds fell off the wool within the first days, although both morphologically adapted and

non-adapted species were found to persist in the wool for over a month (Fischer et al. 1996).

Repeating our analysis for ungrazed patches only showed no significant effect of

connectivity, neither for simple diffusion models (geographic distance model, p = 0.8) nor for

the models that assume seeds to be intercepted by intervening forest (matrix resistance, p =

0.9). Since some ungrazed patches experienced colonization events, these events may depend

on turbulent wind conditions, where the distance and direction of dispersal may be

unpredictable (Soons et al. 2004; Bolli 2009), or other dispersal vectors such as machinery,

wild or domestic animals, or humans. Further research using molecular methods may be able

to identify likely sources of known colonizations and thus provide further insight into

connectivity.

Source patch area is widely used as a proxy of population size and thus of seed

production (Moilanen and Hanski 2006). However, including patch area decreased rather than

increased explanatory power of the Si connectivity index, suggesting that habitat area may not

be a good proxy for population size for most calcareous grassland plants. Here we did not

include population size data as this would require modeling at the species level with binary

response data, for which a considerably larger data set would be needed.

32

In contrast to habitat area, mean species occurrence in 1989 (pj) significantly improved

the model fit of the Si connectivity index. It is reasonable to expect that if source patches

provide a more diverse propagule pool, community-level patch colonization rates of nearby

connected patches will be higher. This positive association supports existing empirical

evidence showing that after local species extinction, restoration success of calcareous

grasslands depends on diversity of the species pool in nearby patches (Kahmen et al. 2002).

Once a viable seed arrives in a patch of suitable habitat, seedling establishment will be

influenced by species interactions (e.g., predation, competition, population density; Orrok et

al. 2006) and local environmental conditions (regeneration niches, disturbance; Boehmer 1994;

Rusch and Fernandez-Palacios 1995; Willems and Bik 1998). Accounting for post-dispersal

processes in terms of the number of dynamic structural elements providing regeneration niches

improved model fit R2adj markedly from 0.24 to 0.37. While connectivity and post-dispersal

effects were correlated, their unique contributions were of the same magnitude (0.11 and 0.13,

respectively). Given the highly stochastic nature of the dispersal process and the additional

variation that is likely introduced by further post-dispersal factors affecting establishment,

growth and mortality (Clobert et al. 2004), the R2adj of 0.37 can be considered rather high for a

community-level analysis. Further analysis is needed to assess to what degree these results

depend on species traits.

2.6. Conclusions

Our results show that patch colonization rates at the community level for habitat specialist

plant species of calcareous grasslands depend on: (1) the availability of propagules in source

33

patches, (2) the presence of sheep as dispersal vector, at least intermittently, and distance

related to the time consumed by sheep herds between source and focal patches, and (3) the

number of structural elements providing a variety of regeneration niches for propagule

establishment. Modeling connectivity for plant communities based on the physical distance

between patches alone (structural connectivity) without considering dispersal vectors and how

they respond to landscape structure (potential functional connectivity) may lead to erroneous

conclusions about the determinants and importance of functional connectivity in plants. It is

equally important to recognize that measures of actual functional connectivity like

colonization rates are the result of dispersal and post-dispersal processes. Thus post-dispersal

effects may introduce noise when the focus of interest is on dispersal per se. Based on our

comprehensive approach (Fig. 2), it was possible to assess the unique contribution of each

process to actual functional connectivity, which is an important concern for species

conservation.

This study fills an important research gap regarding the determinants of actual

functional connectivity in plant communities (Erik and Priya 2003; Fischer and Lindenmayer

2007) by testing competing connectivity models of seed dispersal with empirical colonization

data at the community level. This direct approach is a considerable improvement over indirect

methods based on patch occupancy data (Fagan and Calabrese 2006). Comprehensive

landscape connectivity assessments that use direct estimates of actual functional connectivity,

such as colonization rates and consider pre- and post-dispersal effects are much needed to

effectively inform conservation efforts aimed to mitigate, revert or prevent biodiversity loss in

fragmented landscapes.

34

Table 1. Description of the distance models included for the estimation of each incidence

function model (IFM) of the patch connectivity Si.

Table 1. Continue on the following page

Distance measure

(dij)

Description Units

a) Geographic distance

Straight line distance from the center of the

focal patch i to the center of each other source

patch j

Km

b) Matrix resistance

Straight line distance cutting through forest

from the center of the focal patch i to the

center of each other source patch j

Km

c) Consistently grazed

Number of patch-to-patch steps from focal

“consistently” grazed patch i to each other

“consistently” grazed patch j within each

grazing system.

A value of 100 was assigned to ungrazed

patches, to “intermittently” grazed patches,

and to grazed patches from different grazing

systems.

Integer

35

Distance measure (dij) Description Units

d) Consistently or

intermittently grazed

Number of patch-to-patch steps from

“consistently” or “intermittently” grazed

focal patch i to each other “consistently” or

“intermittently” grazed patch j within the

same grazing system.

A value of 100 was assigned to ungrazed

patches and to grazed patches from

different grazing systems.

Integer

e) Grazed within the

same system

Same value of 1assigned to all grazed

patches within the same grazing system,

whereas a value of 100 was assigned to

ungrazed patches or grazed patches from

different grazing systems.

1 or 100

36

Table 2. Variation partitioning and sensitivity analysis of the final model. Each line shows the

total variance explained (R2adj), the unique variance explained by post-dispersal effects

(Structural elements) and by the connectivity model (Si only) as well as their shared variance

explained (Shared). Models differ by the species or patches included (Data) and by the

inclusion of pre-dispersal effect pj in the connectivity model Si. Asterisks indicate statistical

significance of the regression model (R2adj) and of partial regression coefficients (based on

Type II sums of squares) for post-dispersal effects (Structural elements) and connectivity (Si

only) (‘***’: p < 0.001; ‘**’: p < 0.01; ‘*’: p < 0.05; ‘.’: p< 0.1, ‘n.s.’: p ≥ 0.1).

Data Distance dij pj R2adj Structural

elements Shared

Si only

All consistently or intermittently grazed

Yes 0.37*** 0.13*** 0.13 0.11***

Without 5 species with detectability issues

consistently or intermittently grazed

Yes

0.31***

0.12**

0.11

0.07**

Without 8 species with long-term persistent seed bank

consistently or intermittently grazed

Yes

0.36***

0.16***

0.13

0.07**

Table 2. Continue on the following page

37

Data Distance dij pj R2adj Structural

elements Shared Si only

Only grazed patches

Grazed within the same system

Yes 0.08 (n.s.) 0.08 0 0

Only grazed patches

consistently or intermittently grazed

No

0.12*

0.07

0.02

0.03

38

Fig. 2 Conceptual model of functional connectivity including effects relating to pre-dispersal (e.g., seed production) and post-dispersal

processes (e.g., availability of regeneration niches) that interact with potential functional connectivity to determine actual functional

connectivity.

Emigrant pool

Dispersal Pre-dispersal

Potential functional connectivity model

Establishment

probability

Post-dispersal

Actual functional connectivity

Landscape structure

Species dispersal behavioral model

39

null pj Aj Aj pj

A B

Fig. 3 Relative importance of parameters in the Si connectivity model. Each bar shows for one

version of the distance parameter dij. (A) or pre-dispersal effects (B): null = no pre-dispersal

effect, pj = mean patch occupancy in baseline survey, Aj = patch area, and Apj = Aj * pj) the sum

of Akaike model weights wm of all candidate models m of connectivity Si containing that

parameter. Abbreviations of the distance models dij: geographic distance (Eu), matrix resistance

(Matrix), consistently grazed (Shecte), consistently and intermittently grazed (Sheint), and

grazed within the same grazed system (Shenu).

Eu Matrix Shecte Sheint Shenu

Aka

ike

mo

del

wei

ght (w

m)

40

(n= 7) (n= 13) (n= 21) (n= 17) (n= 3)

Number of structural elements

0 2 3 1

4

Fig. 4 Side-by-side boxplots of community-level patch colonization rates CRi for different

numbers of structural elements providing regeneration niches (rock debris, small mammal

burrows, erosion, ant hills) present in focal habitat patches in 2009.

Pat

ch c

olo

niza

tion

s ra

tes

41

Chapter 3

Plant Connectivity Assessment at the Community,

Species, and Genetic Level Consistently Reveals

Dispersal by Shepherding

3.1. Abstract

Functional connectivity is species-specific and reflects species response to habitat fragmentation.

Therefore any community-level assessment of connectivity may be insufficient to protect

multiple species simultaneously. Yet when a dispersal vector is shared within a community, the

community-level assessment can provide valuable information about community response to

fragmentation. This chapter evaluates the extent to which the results of species-level assessments

are consistent with the predictions of functional connectivity at the community level based on

aggregate species data for 48 calcareous grassland plants. For 31 species of the same plant

community, I tested competing models of dispersal, as well as source and focal patch effects to

explain patch occupancy using presence-absence data of two surveys from 1989 and 2009. I

evaluated if effects of connectivity by shepherding were limited to species with dispersal

adaptations. Species-level assessments showed patterns consistent with community-level results,

identifying a distance-dependent effect of shepherding connectivity for almost all species.

Population size and patch area of source patches were, in most cases, not important predictors of

42

patch occupancy. While all zoochorous species strongly responded to connectivity by

shepherding, dispersal by sheep was also supported for most species without zoochory

adaptations. Molecular genetic analysis for a selected species, Dianthus carthusianorum, using

eleven microsatellite markers showed consistency between connectivity models based on patch

occupancy and genetic data, identifying sheep as main seed dispersal vector. This study

illustrates that if an effective dispersal vector is shared between species, the community-level

assessment may be sufficient to reveal general patterns and determinants of functional

connectivity for a range of species, even if they vary in dispersal-related traits.

3.2. Introduction

For plants in fragmented landscapes, increased habitat isolation of remnant patches commonly

decreases effective seed dispersal and pollen flow between populations, with negative effects on

population dynamics (Young et al. 1996). Seed dispersal limitation may prevent small, isolated

plant populations from being “rescued” from extinction by seed immigration, and may

jeopardize species persistence by the lack of habitat re-colonization after local extinction (Fig. 5;

Cain et al. 2000; Piessens et al. 2005). Hence, seed dispersal is a crucial process determining

population demographic connectivity and species persistence at the landscape scale (Fig. 5). On

the other hand, population genetic connectivity is provided by the exchange of genes from the

dispersal of both seeds and pollen. Theoretical predictions and empirical evidence suggests that

genetic connectivity via seeds is expected to be lower than the contribution from pollen flow

(Ellstrand 1992; Petit et al. 2005), and pollen is therefore assumed to support genetic

connectivity at the landscape scale (McCauley 1997; Sork et al. 1999; Fig. 5). However, this may

43

not be the case for all plants, for instance, in the case of species whose seeds are effectively

dispersed through zoochory (e.g., Herrera and Jordano 1981; Jordano et al. 2007; Zhou et al.

2007). Lack of gene flow and thus genetic connectivity may lead to increased population genetic

differentiation and to impoverishment of genetic diversity within populations due to increased

rates of inbreeding and drift (Young et al. 1996; Lowe et al. 2005; Aguilar et al. 2008; Vranckx

et al. 2011).

Plant species’ ability to effectively exchange pollen and seeds between populations, i.e.,

plant functional connectivity, largely depends on the interaction of dispersal vectors with the

landscape matrix (Taylor et al. 1993). Functional connectivity is expected to be a species-

specific property, because dispersal vectors and response to the matrix may vary between species

(Taylor et al. 2006). This implies that predictions from functional connectivity assessments may

not be generalizable from one species to another. However, assessing functional connectivity for

each species in an ecological community is impractical, especially for very rare or abundant

species for which statistical analysis is often not feasible. Assessment of connectivity at the

community level, which would aggregate responses of all species in a community, may

overcome the challenge of collecting large amounts of data for individual species. Instead of

fitting a large number of single species models, connectivity may be analyzed by contrasting

species richness among habitat patches varying in spatial isolation (e.g., Lindborg and Eriksson

2004; Geertsema et al. 2005; Brudvig et al. 2009), or by classifying species according to life

history traits associated with dispersal (e.g., Dupre and Ehrlen 2002; Kolb and Diekmann 2005;

Schleicher et al. 2011). Community-level assessment may reveal general patterns of plant species

response to habitat modification and fragmentation (Minor et al. 2009; Schleicher et al. 2011)

and thus provide valuable information for conservation managers aiming to protect multiple

44

species simultaneously. However, whether the predictions at the community level effectively

identify determinants of functional connectivity for a range of species requires empirical testing.

Understanding the mechanisms and patterns of dispersal and gene flow in human

modified landscapes is crucial for managing and conserving plant populations. Commonly,

inference of genetic connectivity is evaluated by the amplification of nuclear markers to compare

the degree of genetic differentiation or genetic diversity between populations varying in spatial

isolation (e.g., Keyghobadi 2005; Honnay et al. 2007; Bizoux et al. 2008; Neel 2008). In plants

estimates of gene flow using nuclear markers reflect gene flow events of both pollen and seeds.

Consequently, the assessment of population demographic connectivity may be an insufficient

predictor of genetic connectivity and vice versa, in the situation where pollen-mediated gene

flow is more extensive than that of seeds. Moreover, assessment of habitat connectivity using

estimates of species dispersal to infer and protect genetic variation at the landscape-scale may

misinform conservation decisions, for instance, of reserve design (Grenwald 2010).

Calcareous grasslands are semi-natural ecosystems traditionally used for sheep grazing,

and are of special conservation concern since they present the highest floristic and faunistic

species richness of Central Europe, including Germany (Poschlod and WallisDeVries 2002). For

most calcareous grassland plants of the Gentiano-Koelerietum pyramidatae vegetation type

(Oberdorfer 1978), sheep have been suggested to promote seed dispersal at the landscape scale

(Fischer et al. 1996; Poschlod et al. 1998; Kuiters et al. 2010). In particular, Rico et al. (2012)

(chapter 2) tested alternative seed dispersal models at the community level and found that

functional connectivity in terms of mean patch colonization rates was explained by a distance-

dependent effect of dispersal along shepherding routes. Furthermore, habitat area of source

patches had no effect, whereas the presence of structural elements in focal patches, which is

45

expected to affect probability of establishment, also contributed to explain patch colonization

rates.

Here, I applied the community-level assessment of functional connectivity of calcareous

grassland specialist plants from chapter 2 (Rico et al. 2012) to all individual species of the same

plant community with sufficient data for statistical analysis. Specifically, I aimed to assess the

extent to which the results from the species-level assessment are consistent with the results of

Rico et al (2012) at the community level. Since calcareous grassland habitat specialist plants

differ in a range of morphological traits related to seed dispersal, I assessed whether all species,

including those without adaptations to zoochory were dispersed by sheep. Subsequently, I

evaluated if patch area and population size in source patches, and the presence of structural

elements in focal patches, importantly contributed to explain patch occupancy for each species.

Finally, for one characteristic calcareous grassland species, Dianthus carthusianorum, I obtained

population genetic data from nuclear microsatellite markers to assess whether connectivity

assessments from patch occupancy and genetic data reflect different patterns of connectivity.

3.3. Methods

3.3.1. Study area and species occupancy data

The study area located in the Southern Franconia Alb near Weissenburg, Bavaria, Germany has

an extent of approximately 10 x 15 km and includes 96 patches of calcareous grassland

embedded in a heterogeneous matrix of settlements, agricultural fields, meadows, orchards, and

forest. In the area, abandonment of traditional shepherding caused a rapid decline of calcareous

46

grasslands from 970 ha in 1830 to 302 ha by 1989 (Dolek and Geyer 2002). Local conservation

agencies initiated a landscape-scale management project in 1989 aiming at restoring and

reconnecting previously abandoned calcareous grasslands (abandoned at least since 1960) with

remaining “core areas” (i.e., calcareous grasslands that were not abandoned) in three non-

overlapping rotational shepherding systems (A1, Appendix). Each herd has approximately 400 to

800 ewes, which are herded along predefined routes of up to 55 km and covering up to 140 ha of

calcareous grasslands (Wagner et al. 2012). Of 62 previously abandoned calcareous grasslands,

26 were grazed three to five times per year since 1989 (“consistently grazed”), 13 were grazed

only later during the season or just for a few years (“intermittently grazed”), and 23 remained

ungrazed; whereas core areas (n = 34) were never abandoned.

All 62 previously abandoned grasslands with remnants of the Gentiano-Koelerietum

pyramidatae vegetation were surveyed in summer and fall 1989 and spring 1990, establishing

complete plant species lists (1989 survey; Boehmer et al. 1990; for further details refer to

Wagner et al. 2012). Vegetation surveys were repeated (by the same scientist) for 48 habitat

specialist plants in all 62 previously abandoned grasslands and all 34 core areas in summer 2008

and spring and fall 2009 (2009 survey). Species presence-absence data of the two surveys were

used in logistic regressions as follows: for previously abandoned patches, each species was

assigned a value of 0 (absent 1989 and 2009), a value of 1 (present 1989 or 2009), or a value of 2

(present in both 1989 and 2009). Core areas were individually surveyed only in 2009, and thus

each species in core areas was assigned either a value of 0 (absent 2009) or 2 (present 2009). To

check the effect of replacing the missing data of 1989 with the data of 2009 for core areas,

statistical analyses were repeated for all 96 patches using the 2009 data only. I did not observe

differences in overall trends for each species (i.e., change in connectivity model selected) and

47

thus I present the results of the 1989-2009 survey data. From the 48 habitat specialist plants,

analyses were performed for only those 31 species for which sufficient data for statistical

analysis was available. These 31 species had an occurrence between 15% and 85% in the 2009

survey.

Population size and patch area in source patches were measured to assess the effect of

source patch properties on patch occupancy. Population size in the 2009 survey was quantified

using four ordinal size classes: 1 = 1-3 individuals, 2 = 4- 39 individuals, 3 = 40- 99 individuals

and 4 = ≥100 individuals. Area of patches was digitized from orthophotos in ArcGis 9.3 to

estimate patch area (ha). Focal patch properties were quantified by counting the number of four

dynamic structural elements present in each patch: rock debris, anthills, small mammal burrows,

and erosion. The presence of structural elements has been suggested to provide microsites for

establishment and thus increasing patch colonization rates of habitat specialist species (Rico et

al. 2012; Wagner et al. 2012).

3.3.2. Functional connectivity model

To determine functional connectivity of populations, I fitted the same Si connectivity models for

each species as proposed in chapter 2 (Rico et al. 2012): �� = ∑ ����−∝ ��� �� · ����� , where

Si is the connectivity of the focal patch i, Aj refers to area of patch j, pj indicates source patch

occupancy (absent = 0, present = 1), dij is the distance between the focal patch i and patch j, and

α is a constant scaling parameter (Hanski 1994). Alternatively, I substituted Aj for the parameter

Nj that refers to population size of patch j.

48

Species dispersal (αdij) was modeled by five alternative dispersal models. (1) The

geographic distance model assumes that seeds are dispersed by a simple diffusion process

without matrix effects (Euclidean distance). (2) The matrix resistance model assumes that seeds

are dispersed by simple diffusion, but seeds are intercepted by intervening forest. (3) The

consistently grazed model assumes that seeds are dispersed along shepherding routes and

includes a distance effect in terms of the number of patches that sheep traverse from patch i to

patch j. The model assumes that grazing needs to occur yearly and three to five times throughout

the season to effectively transport seeds. (4) The intermittently or consistently grazed model

includes all grazed patches not distinguishing between grazing treatments. The model also

includes a distance effect, but intermittent grazing during a few years only or for a few times at

the end of the season is sufficient to effectively transport seeds. The last model (5) grazed within

the same grazing system, differs from the previous two models as it does not include a distance

effect, thus seeds are equally likely to be transported anywhere within the same herding system.

For each dispersal model αdij, I analyzed three models of source patch effects. Model 1

included parameter pj, assuming that all occupied patches are equal sources of seeds. Model 2

included patch area Aj · pj, (in ha) assuming a positive relationship between area and population

size. In model 3, instead of including Aj, I used population size Nj (ordinal categories: 0, 1, 2, 3,

4) from the 2009 survey. I tested additional transformations with interval midpoints for

population size (values of 2, 20, 70, and 150) to assess whether the classification (1 to 4) had an

influence on the results of logistic regressions. I did not observe differences in overall trends,

therefore I present only the results of population size with the four ordinal categories.

The constant α of the Si index is a species-specific dispersal constant that was optimized

for each species by varying α from 0.1 to 10 with increments of 0.1. Because α is a constant

49

scaling the distance parameter dij, the value of α was optimized separately for each dispersal

model using the Si index with the occupancy parameter pj. The α-value with the lowest deviance

of logistic regressions with the two-time survey occupancy data (1989 and 2009) was selected

for parameterizing each dispersal model dij (A2, Appendix). The combination of the five distance

models with the three source patch properties resulted in 15 Si connectivity models for each

species.

3.3.3. Species traits

Species were group by traits related to dispersal: release height, seed shape, seed length,

vegetative form, zoochory, and anemochory dispersal syndrome (Table 3). Seed shape was

estimated as the ratio between seed width and seed length. There was no significant correlation

between traits (pairwise Pearson correlation between quantitative variables, point bi-serial

correlation between quantitative and binary variables), thus I included all selected traits in

statistical analysis. Trait data were obtained from two plant databases: BIOLFLOR (Klotz et al.

2002) and LEDA (Kleyer et al. 2008; Table 3).

3.3.4. Species occupancy analysis

For each species, I fitted logistic regressions using patch occupancy data from the two surveys in

1989 and 2009 as defined above. I used multi-model inference with the function dredge in the R-

library MuMIn (Burnham and Anderson 2002) to rank and summarize each of the 15 Si

connectivity models for predicting occupancy for each species. Akaike model weights wm were

50

summed for each parameter over all candidate Si connectivity models (m) containing the

repetitive parameter to determine its relative importance. I used one-sided significance tests of

the regression coefficient to evaluate only positive associations.

I applied multiple logistic regressions of the best performing Si connectivity index and the

number of dynamic structural elements present on focal patches to test the effect of post-

dispersal processes on patch occupancy. Subsequently, I applied variation partitioning (Legendre

and Legendre 1998) to determine the unique contributions of both predictors to patch occupancy.

I performed a chi-square test to compare the proportion of the best ranked dispersal

models for zoochorous, anemochorous, and species with no specific dispersal adaptations to test

whether the effect of shepherding connectivity was associated with dispersal adaptions. In

addition, to assess if there were dispersal traits that largely contributed to connectivity by

shepherding, I performed multiple linear regressions using the pseudo-R2 of the Si connectivity

index of each species as a measure of the strength of the response to connectivity (response

variable). I applied stepwise model selection with AIC to rank and select the best full linear

regression model. For the best performing model, I performed significance tests and residual

analysis and assessed model fit with adjusted R2. All statistical tests were conducted using R (R

Development Core Team 2010).

3.3.5. Genetic sample collection and microsatellite analysis

I selected Dianthus carthusianorum L. for genetic analysis as this species showed 18 patch

colonizations between 1989 and 2009, and nuclear microsatellite markers were available. D.

51

carthusianorum has no persistent seed bank and no specialized adaptations to wind or animal

seed dispersal (Klotz et al. 2002). In summer 2009, I collected leaf samples from 1,613

individuals from 64 patches (considered here as populations) including core areas, previously

abandoned patches, and six grass verges along roads or forest edges where D. carthusianorum

was also present. In the southern half of the study area, all patches were sampled, whereas in the

northern half, all but 11 patches were sampled. Leaf tissue samples were immediately dried in

silica.

Genomic DNA was extracted following the DNeasy 96 Plant kit protocol (QIAGEN). I

amplified 15 microsatellite loci developed for related Dianthus species (MS-DINMADSBOX,

MS-DCDIA30, MS-DCAMCRBSY, MS-DINCARACC, DCA 221, DCD 224, DCB140,

Smulders et al. 2000; DCB109, Smulders et al. 2003; CB018a, CB057a; CB004a, CB027a,

CF003a, CB011a, CB020a, Kimura et al. 2009). Microsatellite amplifications were done using

the QIAGEN Multiplex kit as follows: 0.2 to 0.4 µl of each primer (5µM), 4.7 µl of Master-mix,

and 5-10 ng of genomic DNA in a total reaction volume of 10 µl. PCR conditions followed those

described in Smulders et al. (2003). Fluorescent labeled PCR products were run on an ABI

3730X Automated Sequencer (Applied Biosystems) with 500 LIZ size standard.

Electrophenograms were analyzed using GENMARKER 1.91(Softgenetics). I detected only a

high proportion of non-amplifications likely due to null alleles at three microsatellite loci

(DCA221, DCD224, DCD140), which were discarded from analysis.

52

3.3.6. Genetic data analysis

Departure from Hardy-Weinberg (HW) equilibrium and linkage disequilibrium (LD) for each

population were tested using probability tests with 10000 Markov chain iterations in GENEPOP

3.4 (Raymond and Rousset 1995). Locus DC020a significantly deviated from HW equilibrium in

50 populations and was discarded from further statistical analyses. Some locus combinations

showed statistically significant LD in some populations, but there were no recurrent patterns

across locus pairs. The final genetic dataset included 1,613 individuals genotyped for eleven

polymorphic microsatellite loci (A3 Appendix). Genetic diversity estimates such as allelic

richness (Ar), observed (Ho) and expected heterozygosity (He) were estimated using FSTAT

(Goudet 2002) and GENALEX (Peakall and Smouse 2006). I corrected for the effect of sample

size in allelic richness measures using rarefaction in HP-RARE (Kalinowoski 2005).

I used the rarified corrected measure of allelic richness (Ar) as response in linear

regression analyses since it was the genetic diversity estimate producing the best model fit (i.e.,

homoscedasticity and residual distribution), and the highest R2 coefficient. Although statistical

inferences were similar (i.e. positive linear relationship) among the three genetic diversity

estimates, logarithmic or square root transformations on Ho and He did not improve model fit in

comparison with models using Ar estimates. Populations with less than four individuals were

excluded from statistical analysis. I applied multi-model inference of multiple linear regressions

to rank the relative importance of each Si parameter on genetic diversity. I performed

significance tests and residual analysis for the best Si connectivity index. Lastly, I added the

number of dynamic structural elements in focal patches with the best performing Si connectivity

index in a full linear regression model. Small populations (n ≤ 4 individuals) were discarded

from statistical analysis.

53

3.4. Results

3.4.1. Functional connectivity models at the species level

Almost all species had significant positive regression coefficients for the best ranked Si

connectivity models with pseudo-R2 values ranging from 0.06 (Ajuga genevensis) to 0.3

(Scabiosa columbaria, Table 4), except for Hippocrepis comosa which had a negative coefficient

for the matrix resistance model. Distance models (dij) of dispersal by sheep were the best ranked

models for 28 of the 31 species tested (Table 4). Specifically, consistently grazed was the most

supported model for 15 species, while consistently or intermittently grazed was the best ranked

model for 13 species. Only for two species, Ononis repens and Euphorbia verrucosa, the

geographic model was the best-ranked dispersal model (Table 4). For 15 out of the 31 species,

patch occupancy pj was the best supported source patch parameter, population size (Nj) was the

best parameter for six species, and patch area (Aj) only for one species (on the basis of relative

importance values higher than 0.6; Table 4). Thus, the best two performing Si connectivity

indices included dij as consistently grazed, followed by consistently or intermittently grazed, both

with pj.

Post-dispersal effects quantified by the number of structural elements in focal patches

significantly increased the model variance explained in most species (except Phleum phleoides),

resulting in pseudo-R2 values ranging from 0.15 (Pulsatilla vulgaris and Koeleria pyramidata) to

0.38 (Cirsium acaule, Table 4). Variation partitioning showed that Si connectivity alone had a

larger contribution than the number of structural elements for 22 of the 31 species. The opposite

was shown for three species (Prunella grandiflora, Carlina acaulis, and Phleum pheloides;

Table 4).

54

There were no significant differences in the proportion of the best ranked models selected

among species with adaptations to zoochory, anemochory, or without dispersal adaptations (X 2 =

7.4, df = 6, p = 0.28; Fig. 6). I further aimed to predict the strength of association with

connectivity Si from dispersal-related traits. The best lineal regression model based on stepwise

model selection of the presudo-R2 of the Si connectivity index of each species (response variable)

included zoochory and seed length as predictors. This model was significant and showed the

lowest AIC (-160.3) with R2adj = 0.17 (df = 2 and 27, F = 4.07, p = 0.03), and it had well-behaved

residuals without influential outliers. However, only zoochoory was statistically significant.

3.4.2. Connectivity effects on patch occupancy and gene flow in Dianthus

carthusianorum

Multi-model inference showed that dispersal models of shepherding were the best ranked models

for both patch-occupancy and genetic diversity data of D. carthusianorum (Fig. 7). For both data

types, the consistently or intermittently grazed model was the best ranked dispersal model

(relative importance value wm of 0.92 and 0.75; Fig. 7a and 7c respectively). For the patch-

occupancy data, model selection was not conclusive for neither of the source patch properties

(Fig. 7b), whereas for the genetic diversity data, presence of the species pj had higher support (wm

= 0.6) than population size (wm = 0.37) and with no support for patch area (wm = 0.02; Fig. 7d).

Results from linear regression analysis of the best performing Si connectivity index with

genetic diversity data (consistently and intermittently grazed with population size) showed a

significant positive relationship of patch connectivity Si and allelic richness (Ar) (Fig. 7). This

model had well-behaved residuals and explained 17% of the total variation (R2adj = 0.17, F =

55

12.47, 1 and 55 df, p = 0.0008). Including the presence of the number of dynamic structural

elements in focal patches in a full regression model had no effect (R2adj = 0.05 df = 2 and 45, F =

2.2, p = 0.12).

3.5. Discussion

3.5.1. Connectivity by shepherding supports dispersal of calcareous grassland

plants

For most of the 31 species for which species-level models could be fitted, I found substantial

consistency with the functional connectivity assessment at the community level (Rico et al.

2012) identifying: (i) shepherding as the main dispersal vector, with a distance-dependent effect,

(ii) a lack of association between focal patch occupancy with source patch area and population

size, which suggests that species merely have to be present in the landscape to act as source for

habitat colonization along sheep grazing routes, and (iii) the occurrence of structural elements as

a significant post-dispersal effect for establishment in focal patches.

While consistently or intermittently grazed was the best ranked model at the community

level (Rico et al. 2012), at the species level the consistently grazed model was selected for 48%

of the species, whereas consistently or intermittently grazed was the best ranked model in 42% of

the species. In contrast, for most of the 31 species, neither geographic isolation nor the context of

the intervening matrix (forest) explained patch occupancy. The two selected shepherding models

contained a distance-dependent effect in terms of the number of patches that sheep traverse along

the route. The clear support of a distance-dependent effect suggests that most seeds do not stay

56

on the sheep for a long time. This interpretation is consistent with previous experimental results

where a high proportion (> 50%) of seeds fell off the wool within the first days (Fischer et al.

1996), although some proportion of seeds were found to persist adhered in the wool for more

than a month (Fischer et al. 1996; Manzano and Malo 2006) and to travel long distances over

400 km (Manzano and Malo 2006). For species in which the consistently grazed model was the

best predictor of patch occupancy, intermittent grazing may not be effective for propagule influx.

However, this interpretation needs to be taken with caution since I cannot rule out other

confounding effects, for instance, related to site characteristics.

Species with adaptation to zoochory and with longer seeds tended to show a stronger

response to dispersal models of shepherding than other species, though only zoochory had a

statistically significant effect. Observational and experimental studies have shown a higher

probability of seed attachment to the fur by specialized seed appendages related to anemochory

or zoochory (e.g., hooks, hairs, sticky coats) and by longer seeds that are morphologically more

likely to be caught in wool and thus remain attached to the fur (Fisher et al. 1996; Couvrer et al.

2005). Although there is a strong association for zoochorous species, this result does not imply

that species without adaptations to zoochory are not transported by sheep. The above is evident

from the lack of significant differences between the proportions of dispersal models selected for

zoochorous, anemochorous, and species without specific adaptations to dispersal. In fact, more

than half of the latter species responded strongly to dispersal by shepherding (Fig. 6). These

findings of connectivity provided by shepherding regardless of dispersal mode is in agreement

with experimental studies that have found seeds without dispersal adaptations attached to the fur

in substantial numbers (Fisher et al. 1996; Couvrer et al. 2004; Rommerman et al. 2005).

Remarkably, I did not find an effect of release height. Adhesion to the wool is less likely for

57

plants that release seeds at height lower than 40 cm (Mouissie et al. 2005). However given that

there was no such effect, seeds may also be effectively dispersed by hooves or dung of sheep

(Fisher et al. 1996).

For 35% of the species, no species-level models could be fitted due to insufficient

numbers of presences or absences in habitat patches, which highlights data limitations for

individual species for statistical analysis. For instance, habitat specialist species such as

Brachyopodium pinnatum or Festuca ovina were very abundant and occurred in more than 90%

of the patches, whereas e.g., Campanula glomerata and Gentiana germanica were rare species

that occurred in less than 5% of the patches. Among the 17 species for which dispersal models

could not be fitted, nine (all zoochorous) were frequent species (occurring in ≥ 85% of the

patches), and four (two anemochorous and two without dispersal adaptations) were rare (< 15%

of the patches).

It is possible that grazing per se may affect ecological conditions of individual patches,

such as gap creation by trampling, removal of biomass, or others, thus indirectly influencing

establishment, a previous analysis in this same study system teased apart both confounding

effects by showing that sheep grazing connectivity (as quantified by the Si index from Rico et al.

2012) rather than grazing treatments had a higher power to explain the increase in species

richness in previously abandoned patches based on the surveys of 1989 and 2009 (Wagner et al.,

2012). The results from the individual species assessments thus confirm the important effect of

sheep grazing to support species dispersal of calcareous grassland specialist plants.

58

3.5.2. Contribution of source and focal patch properties to landscape species

occupancy

Similar to the community-level assessment (Rico et al. 2012), including patch area as a proxy of

seed production reduced model fit compared to patch occupancy alone. Interestingly, the results

showed that population size was also not a good source patch predictor. In the field data, I did

not find a strong relationship between species-specific population size and patch area (results not

shown). Taken together, these results indicate that independently of patch area or population

size, presence of a species in source patches was sufficient to sustain colonization of focal

patches (Hanski et al. 2004; Tremlova and Munzbergova 2007). This suggests that landscape-

scale occupancy patterns for most calcareous grassland plants are largely related to the

distribution of patches acting as potential sources, rather than local abundances in source

patches; a fact that simplifies parameterization of landscape connectivity models.

The presence of the number of dynamic structural elements in focal patches, related to

post-dispersal effects, increased the total variance in patch occupancy explained for most species.

This result was in agreement with the community-level analysis (Rico et al. 2012). However,

variation partitioning showed that Si connectivity (including pre-dispersal and dispersal

processes) had a larger contribution to explain patch occupancy for 88% of the species, except

for Ajuga genevensis, Carlina acaulis, and Prunella grandiflora in which structural elements in

focal patches had a higher contribution. This result underscores that patch connectivity supported

by shepherding was the main predictor of patch occupancy (Piessens et al. 2005; Bruckmann et

al. 2010).

59

3.5.3. Consistency of ecological and genetic data in functional connectivity

assessments of Dianthus carthusianorum

Both connectivity models for D. carthusianorum, with patch-occupancy and with genetic

diversity based on the corrected rarified measure of mean allelic richness (Ar), identified the

‘consistently or intermittently grazed’ model as the best-ranked dispersal model. Although

estimates of population genetic diversity are an indirect measure of gene flow between

populations, the clear support of a seed dispersal model by sheep suggests the potential influence

of seed dispersal on genetic connectivity for D. carthusianorum. This finding was remarkable

given that genetic diversity estimates from nuclear markers incorporate gene flow from both

seeds and pollen, so that we might expect a lack of association with seed dispersal models in the

case of a large contribution of pollen flow to genetic connectivity. In conjunction with recent

evidence, the results emphasize that gene flow mediated by seed dispersal at the landscape-scale

may be more important than commonly expected (e.g., Bacles et al. 2006; Iwaizumi et al. 2010;

Freeland et al. 2012). However, these interpretations are limited since I cannot directly estimate

the relative contribution of pollen vs. seeds to gene flow.

The higher regression coefficient of the connectivity model that included as a response Ar

instead of Ho and He is likely related to the higher sensitivity of the number of alleles to

contemporary changes of habitat fragmentation, while measures that take into account the

relative frequency of alleles such as Ho and He, are expected be affected at slower rates than Ar.

This difference has previously been reported in similar studies of habitat fragmentation effects

on population genetic diversity in plants (Honnay and Jacquemyn 2006; Aguilar et al 2008). On

the other hand, presence of structural elements in focal patches had a significant effect on patch

occupancy, but no influence on population genetic diversity. While the presence of dynamic

60

structural elements may be related to establishment of seeds (Wagner et al. 2012), this not

necessarily implies that larger numbers of established seeds in local patches would have a direct

effect on allelic richness within populations.

3.6. Conclusions

To effectively protect species and maintain species diversity at the landscape scale, we need to

understand what determines functional connectivity. Performance and predictive ability of

connectivity models are constrained by a trade-off between data amount (i.e., number of

populations sampled), scale of analysis (i.e., local, landscape scale), and choice and number of

target species. This study illustrates that if a dispersal vector is shared among plant species,

community-level assessment may be sufficient to identify determinants of functional

connectivity for a broad range of species. However, I recognize that connectivity is a species-

specific property (Taylor et al. 2006) and variation in species-specific responses should not be

neglected, especially for species of conservation concern.

Contrasting ecological and genetic data can provide insights in the mechanisms of plant

dispersal and gene flow at the landscape scale. The results on the basis of genetic diversity and

patch-occupancy data for D. carthusianorum revealed that dispersal by sheep not only explained

patch occupancy, but may also have contributed substantially to population genetic diversity.

Further research is needed to assess the relative contributions of gene flow by seeds vs. pollen to

population genetic structure.

61

Table 3. Description of dispersal traits included in Pearson correlation analyses

Dispersal trait Values Sample size Source

Seed length Continuous (mm) 30 Klotz et al. 2002

Seed shape Continuous (mm) 30 Klotz et al. 2002

Anemochory dispersal 1: present

0: absent

8

22

Klotz et al. 2002

Zoochory dispersal 1: present

0: absent

13

18

Klotz et al. 2002

Vegetative propagation 1: below ground

0: above ground

14

4

Klotz et al. 2002

Release height class 1: max < 30cm

2: min < 30 and max ≥ 30cm

3: max & min ≥ 30cm

8

6

17

Kleyer et al. 2008

62

Table 4. Best performing Si connectivity index parameters as ranked by the sum of Akaike model weights, and relative

variance contribution of each factors to the total variance of the final model.

Best Si index c Final model

d

Species Dispersal mode

a

N b Distance dij Source

Si

z-coefficiente

Si Elem Pseudo-R

2 e

Sanguisorba minor Animal 79 Sheint (0.97) pj (0.77) 1.07*** 0.04 0.07 0.24*

Arabis hirsute Animal 40 Shecte (0.9) pj (0.93) 1.047*** 0.22 0.02 0.30**

Centaurea jacea Animal 76 Sheint (0.88) Nj (0.47) 0.62** 0.03 0.0 0.07*

Koeleria pyramidata Animal 66 Sheint (0.99) pj (0.85) 0.716*** 0.06 0.03 0.15**

Linum catharticum Animal 48 Shecte (0.92) pj (0.68) 1.175*** 0.18 0.05 0.33**

Medicago lupulina Animal 62 Shecte (0.93) pj (0.84) 0.772*** 0.09 0.03 0.19**

Plantago media Animal 77 Shecte (1.0) Nj (0.7) 1.45*** 0.11 0.05 0.24**

Polygala comosa Animal 48 Shecte (0.76) pj (0.88) 1.152*** 0.15 0.08 0.35*

Table 4. Continue on the following page

63

Best Si index c Final model d

Species Dispersal mode a N

b Distance dij Source Si

z-coefficiente

Si Elem Pseudo-R

2 e

Prunella grandiflora Animal 73 Sheint (0.9) pj (0.62) 0.886*** 0.07 0.13 0.29**

Ranunculus bulbosus Animal 71 Shecte (0.92) Nj (0.59) 0.976*** 0.16 0.04 0.30*

Salvia pratensis Animal 75 Sheint (0.63) Aj (0.61) 0.932*** 0.07 0.05 0.16**

Scabiosa columbaria Animal 67 Shecte (1.0) Nj (0.99) 1.614*** 0.24 0.02 0.30**

Hieracium pilosella Wind 57 Shecte (0.98) pj (0.86) 1.332*** 0.21 0.04 0.37**

Leontodon hispidus Wind 54 Sheint (0.97) Nj (0.8) 1.352*** 0.20 0.05 0.35**

Anthyllis vulneraria Wind 28 Shecte (0.98) pj (0.91) 0.894*** 0.18 0.01 0.21 **

Campanula rotundifolia Wind 77 Shecte (0.75) pj (0.66) 0.78*** 0.07 0.08 0.22 **

Carlina acaulis Wind 62 Shecte (0.76) Nj (0.4) 0.66*** 0.04 0.07 0.17 *

Table 4. Continue on the following page

64

Best Si index c Final model d

Species Dispersal a mode N

b Distance dij Source Si

z-coefficiente

Si Elem Pseudo-R

2

Cirsium acaule Wind 52 Sheint (0.99) Nj (0.99) 1.401*** 0.26 0.04 0.38**

Pulsatilla vulgaris Wind 44 Shecte (0.63) Nj (0.59) 0.796*** 0.12 0.01 0.15**

Ajuga genevensis None 52 Shecte (0.84) pj (0.43) 0.586*** 0.02 0.07 0.20*

Asperula cynanchica None 49 Sheint (0.98) pj (0.98) 1.038*** 0.20 0.06 0.20**

Dianthus carthusianorum None 74 Sheint (0.97) pj (0.79) 0.919*** 0.10 0.06 0.20**

Euphorbia cyparissias None 77 Sheint (0.8) Nj (0.78) 1.01*** 0.08 0.02 0.14**

Euphorbia verrucosa None 26 Eu (0.9) pj (0.61) 1.014*** 0.20 0.0 0.22**

Hippocrepis comosa None 66 Matrix (0.99) pj (0.77) n.s NA NA N.A

Ononis repens None 59 Eu (0.44) Nj (0.52) 0.698*** 0.10 0.06 0.19**

Ononis spinosa None 17 Shecte (0.77) Nj (0.92) 1.2*** 0.30 0.02 0.33***

Table 4. Continue on the following page

65

Best Si index c Final model d

Species Dispersal mode a N

b Distance dij Source Si

z-coefficiente

Si Elem Pseudo-R

2 e

Onobrychis viciifolia None 39 Sheint (0.99) pj (0.62) 0.123*** 0.12 0.020 0.17**

Phleum phleoides None 34 Shenu (0.49) pj (0.52) 0.447*** 0.01 0.08 0.12n.s.

Stachys recta None 19 Sheint (0.89) pj (0.45) 0.92*** 0.17 0.03 0.20**

Trifolium montanum None 41 Shecte (0.95) pj (0.45) 1.143*** 0.19 0.03 0.31**

a Number of occupied patches in the 2009 vegetation survey for each of 31 species. b Dispersal mode of each species (Klotz et al. 2002). c Relative importance values shown in parentheses and z-coefficient of logistic regressions of best performing Si index and patch

occupancy. Definitions of distance dij models: geographic distance (Eu), consistently grazed (Shecte), consistently or intermittently

grazed (Sheint), grazed within the same grazing system (Shenu), and matrix resistance (matrix). Source patch parameters: patch

occupancy pj, population size Nj, and patch area Aj. d Variation partitioning analysis of best connectivity index (Si), number of structural elements present (Elem); Pseudo-R2 value of

the final model. e Statistical significance of one-sided tests ***p < 0.0001; **p < 0.001; *p < 0.01; n.s. p ≥ 0.1.

66

Fig. 5 Conceptual diagram showing the main roles of pollen flow (genetic connectivity) and seed dispersal (demographic

connectivity) to determine landscape connectivity. Grey boxes indicate determinants and resulting effects, while white boxes

represent the biological processes of dispersal and gene flow with resulting effects on species persistence. Direct arrows indicate

effects and the line width main contribution (i.e., role at the landscape-scale).

Pollen flow/ // dispersal

Range expansion

Extinction rescue

Gene flow

Gene flow

Colonization

Recruitment

Genetic connectivity

Demographic

connectivity

Vectors and matrix interactions

Seed dispersal

Genetic diversity maintenance/ reduced genetic differentiation

Genetic diversity maintenance/ reduced genetic differentiation

67

Fig. 6 Percentage of the best ranked dispersal models for zoochorous (n = 12) and anemochorous

(n = 7) species, and for species with no specific adaptations to dispersal (n = 11). Dispersal

model abbreviations: geographic distance (Eu), consistently grazed (Shecte), consistently or

intermittently grazed (Sheint), and grazed within the same grazing system (Shenu). For none of

the 31 species was matrix resistance selected as the best ranked dispersal model with significant

positive regression coefficients.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Zoochory Anemochory None

Shenu Eu Sheint ShecteP

erce

nta

ge

of s

pec

ies

68

Fig. 7 Relative importance of each parameter in the Si connectivity index for Dianthus

carthusianorum based on the sum of Akaike model weights (wm) over all Si candidate models

(m) containing the same parameter. Each bar shows one version of the distance (dij) or source

patch parameters (pj, Aj, and Nj) for models of patch-occupancy data (A and B, n = 96) and for

models on genetic diversity (C and D, n = 58). Distance model abbreviations: geographic

distance (Eu), consistently grazed (Shecte), consistently or intermittently grazed (Sheint), grazed

within the same grazing system (Shenu), and matrix resistance (Matrix).

Eu Matrix Shecte Sheint Shenu

Eu Matrix Shecte Sheint Shenu

Aka

ike

mo

del

wei

ght (w

m)

Aka

ike

mo

del

wei

ght (w

m)

pj Aj pj Nj

pj Aj pj Nj

A) B)

C) D)

69

Chapter 4

Directed dispersal by grazing affects seed-

mediated gene flow

4.1. Abstract

Directed seed dispersal by animal vectors can have a large effect on the structure and dynamics

of plant populations and has been found to influence genetic structure in plants dispersed by

frugivores. Yet, empirical data are lacking on the potential of directed seed dispersal by grazing

of domestic animals to mediate gene flow across the landscape. Here, I investigated the potential

effect of large-flock shepherding on landscape-scale genetic structure in the calcareous grassland

plant Dianthus carthusianorum. Based on eleven nuclear microsatellite loci, I found a significant

pattern of genetic structure differentiating calcareous grassland patches of three non-overlapping

shepherding systems and populations of ungrazed patches. Among ungrazed patches, I found a

significant and strong effect of isolation by distance (IBD; Mantel correlation = 0.55, p = 0.001).

In contrast, genetic distance between grazed patches within the same herding system was

unrelated to geographic distance but significantly related to distance along shepherding routes,

i.e., the number of intervening patches traversed by sheep (Mantel correlation =0.45, p = 0.001).

The distance-dependent effect of shepherding connectivity suggests that gene flow occurs mostly

between adjacent populations similar to the stepping stone model of gene flow. While this study

70

used nuclear markers that integrate gene flow from pollen and seeds, the significant differences

in genetic structure between ungrazed patches and patches connected by large-flock shepherding

indicate a substantial potential of directed dispersal by grazing to mediate patterns of gene flow

across the landscape-scale.

4.2. Introduction

Seed dispersal and pollen flow are central processes influencing ecological and evolutionary

dynamics of plant populations across the landscape. Seed dispersal supports colonization and

recruitment, and influences the spatial structure of populations (Nathan and Muller-Landau 2000;

Levin et al. 2003; Clobert et al. 2004), whereas both seed dispersal and pollen flow contribute to

gene flow between populations (Levin 1981; Hamrick and Trapnell 2011). Because pollen is

likely to travel across the landscape over larger distances and in larger numbers, gene flow

mediated by pollen is expected to be much more substantial than seed-mediated gene flow

(Ellstrand 1992; Ennos 1994; McCauley 1997; Petit et al. 2005). However, empirical evidence

from a growing number of species suggests that rates of seed-mediated gene flow depend to a

large extent on seed dispersal mechanisms (e.g., Cruse-Sanders and Hamrick 2004; Jordano et al.

2007; Zhou et al. 2007; Freeland et al. 2012).

Understanding the link between spatial patterns of genetic structure and dispersal

mechanisms is fundamental for the long-term conservation of plant populations in human-

modified landscapes. Plants exhibit a large variety of seed dispersal vectors, which affect the

distance, direction, and destination at which seeds are deposited away from the source. For

instance, seeds of wind-dispersed species can travel longer distances (Tackenberg 2003; Soons et

71

al. 2004), but the chance to arrive at suitable sites is often stochastic (Nathan 2000; 2006). In

contrast, seed-dispersal by animals often is non-random (Kollman 2000; Spiegel and Nathan

2007), and some vectors may disperse seeds in substantial numbers into sites with high

probability of establishment (Howe and Smallwood 1982; Wenny 2001). This process known as

directed dispersal has been shown to influence spatial dynamics of plant populations (Aukema

and del Rio 2002; Purves et al. 2005; Briggs et al. 2009), but less evidence has been gathered on

its effects to influence spatial patterns of gene flow across the landscape. Most empirical studies

have focused on species dispersed by endozoochory, especially trees and shrubs adapted to

frugivory (Godoy and Jordano 2001; Jordano et al. 2007). Several studies showed that directed

dispersal tends to generate strong spatial genetic structure resulting from the aggregated

deposition of related individuals in particular microsites for establishment, even in cases where

there is extensive pollen flow (Grivet et al. 2005; García et al. 2007; 2009; Torimaru et al. 2007).

Yet, the opposite effect has also been found, where recurrent directed long-distance dispersal by

frugivores over time resulted in spatial homogenization of genetic variation (Karubian et al.

2010). Such contrasting patterns highlight the complexity of interactions between plants and their

dispersal vector.

Empirical data on the effect of directed dispersal on gene flow are biased towards

frugivore-dispersed tree species of temperate and tropical ecosystems. However, zoochory by

large flocks of domestic ungulates may play an important role for maintaining grassland

connectivity, as in the highly fragmented semi-natural calcareous grasslands in Central Europe

(Fisher et al. 1996; Couvrer et al. 2005). In these low-intensity agricultural systems, large flocks

of sheep, horses, or goats have the potential of transporting a large number of seeds over long

distances for a range of habitat specialists, which include mostly forbs and grasses (Janzen 1984;

72

Moussie et al. 2005; Manzano and Malo 2006). Since herds move along predefined grazing

routes, seeds dispersed either by seed attachment to the fur or hooves or by seed consumption

have a higher chance to be deposited in distant grasslands across the landscape (Fischer et al.

1996; Cosyns et al. 2005; Bruun and Poschlod 2006; Auffret et al. 2012). Despite the importance

of directed dispersal to largely influence the structure and composition of grassland plant

communities (Bruun and Fritzbøger 2002; Schrautzer et al. 2009; Kuiters and Huiskes 2010;

Piqueray et al. 2011; Wagner et al. 2012), to date there is no empirical information on the effects

of directed dispersal by grazing on patterns of gene flow across a landscape.

In this study chapter, I assessed whether directed dispersal by large-flock shepherding has

affected landscape-scale patterns of genetic structure. As study species, I selected the calcareous

grassland habitat specialist Dianthus carthusianorum. In the study area, most calcareous

grasslands are grazed by sheep in three non-overlapping herding systems (between 400-800

ewes) following defined routes up to 55 km and covering approximate 140 ha of calcareous

grasslands (Wagner et al. 2012). If directed dispersal by shepherding is substantial, I

hypothesized that (i) spatial patterns of genetic structure are in association with shepherding

systems. Subsequently, by comparing genetic distances of populations of grazed vs. ungrazed

patches, I hypothesized that (ii) genetic structure in ungrazed patches can be explained by

isolation by geographic distance (IBD) resulting from a lack of connectivity from seed dispersal

by shepherding, and that (iii) populations connected within the same herding system show

population genetic structure associated with directed dispersal along shepherding routes.

73

4.3. Methods

4.3.1. Study site and species

The study area was located in the Southern Franconian Alb near Weissenburg, Bavaria Germany,

and covered approximately 10 x 15 km. In this region, calcareous grasslands of the Gentiano-

Koelerietum pyramidatae vegetation association (Oberdorfer 1978) declined from 970 ha in 1830

to 302 ha by 1989 due to the abandonment of traditional shepherding practices (Dolek and Geyer

2002). In 1989, a conservation project was started to reconnect previously abandoned calcareous

grassland patches (abandoned at least since 1960) with still grazed high-quality grasslands (“core

areas”) in three non-overlapping shepherding systems. Today, sheep flocks of approximately 400

to 800 ewes are herded in both directions following predefined routes (A1 Appendix1). Grazing

season lasts from March until early November. Of the 62 previously abandoned calcareous

grasslands, 26 were grazed three to five times per year since 1989, 13 were only grazed later in

the season or only for a few years, and the remaining 23 grasslands were not grazed. Core areas

(n = 34) had never been abandoned.

Dianthus carthusianorum L. (Caryophyllaceae) is a perennial herbaceous habitat specialist of

calcareous grasslands in the region (Oberdorfer 1978). The species is diploid and has an

outcrossing mating system (Bloch et al. 2005). There is no persistent seed bank (Klotz et al.

2002). Flowering time is from June to October. Pollination is by few specialized Lepidoptera

species (Bloch et al. 2005), and the species has no specialized adaptations for seed dispersal by

wind or animals (Klotz et al. 2002). However, since release height is 30-35 cm and seed

production falls within the main period of grazing in the area, seeds dispersal by attachment to

74

the fur is possible, as well as attachment to hooves or transportation via ingestion and dung

deposition (endozoochory).

4.3.2. Sampling and microsatellite analysis

A total of 58 calcareous grassland patches (here considered as populations) including core areas

and previously abandoned patches were sampled, representing about 86% of the identified

populations of D. carthusianorum in the study area. In the southern half of the study area, all

existing populations were sampled (n = 38). In addition to the above 58 populations in calcareous

grassland patches, D. carthusianorum also occurred and was sampled along six grass verges

along the roads or along forest edges. I collected leaf material from 30 to 40 individuals sampled

across the patch for grasslands with more than 40 individuals, whereas all individuals were

sampled from patches with less than 40 individuals. Leaf tissue samples were immediately dried

in silica. Genomic DNA was extracted following the DNeasy 96 Plant kit protocol (QIAGEN). I

amplified 15 microsatellite loci developed for related Dianthus species (MS-DINMADSBOX,

MS-DCDIA30, MS-DCAMCRBSY, MS-DINCARACC, DCA 221, DCD 224, DCB140;

Smulders et al. 2000; DCB109, Smulders et al. 2003; CB018a, CB057a; CB004a, CB027a,

CF003a, CB011a, CB020a; Kimura et al. 2009). Microsatellite amplifications were done with the

QIAGEN Multiplex kit as follows: 0.2 to 0.4 µl (5µM) of each primer, 4.7 µl of Master-mix, and

5-10 ng of genomic DNA in a total reaction volume of 10 µl. PCR conditions followed those

described in Smulders et al. (2003). Fluorescent labeled PCR products were run on an ABI

3730X Automated Sequencer (Applied Biosystems) with 500 LIZ size standard.

Electrophenograms were analyzed using GENMARKER 1.91(Softgenetics). I detected a high

75

proportion of non-amplifications at three microsatellite loci (DCA221, DCD224, DCD140),

which were discarded from analysis.

Departure from Hardy-Weinberg (HW) equilibrium and linkage disequilibrium (LD) for

each population were tested using probability tests with 10000 Markov chain iterations in

GENEPOP 3.4 (Raymond and Rousset 1995). Locus DC020a significantly deviated from HW

equilibrium in 50 populations and was discarded from further statistical analyses. Some locus

combinations showed statistically significant LD in some populations, but there were no

recurrent patterns across locus pairs. The final genetic dataset included 1,613 individuals

genotyped at eleven polymorphic microsatellite loci (A3 Appendix).

4.3.3. Analysis of landscape-scale patterns of genetic structure

I performed principal component analysis (PCA) of population allele frequencies to assess

patterns of population genetic structure at the landscape scale as constrained by shepherding

systems using the function dudi.pca of the adegenet package (Jombart 2008) in the R software

(R development core team 2010). The first three PCA axes containing the largest variance were

retained for an interclass PCA among four groups: populations within each of the three

shepherding systems and populations in ungrazed patches. The interclass PCA was applied using

the function bca of the ade4 package (Dray and Dufour 2007) in the R software. Because the

first interclass PCA axes maximize the variance explained constrained by the defined groups, I

used the PCA scores to test whether populations from the three shepherding systems and

ungrazed patches significantly differ. Pairwise comparisons with Tukey’s HSD were performed

using as the response the three interclass PCA scores. I applied Bonferroni corrections to adjust

76

for the multiple tests performed as alpha = 1 - 0.05/k, where k denotes number of tests.

Additionally, I performed analysis of molecular variance (AMOVA) as implemented in

ARLEQUIN (Excoffier et al. 2005) by partition the genetic variance among the three

shepherding systems and ungrazed patches to test for significance genetic differences among

groups using 1000 permutations.

In addition to PCA analysis I performed Bayesian clustering methods using TESS 2.3

(Chen et al. 2007), which incorporates the spatial locations of individuals by constructing a

neighbourhood network to assess spatial patterns of genetic structure. I performed 40 runs with

the admixture model, using the default spatial parameter ψ at = 0.6, burn-in lengths of 100,000

and with 5000 sweeps for of each kmax ranging from two to ten. The lowest values of the

deviance information criterion (DIC) were used to select the optimum number of clusters k as

suggested by Chen et al. (2007).

4.3.4. Isolation by geographic distance and connectivity by shepherding

To assess if spatial patterns of genetic structure were explained by directional dispersal along

shepherding routes or by isolation of geographic distance (IBD), I performed Mantel and partial

Mantel correlation tests using two predictor matrices: inter-patch geographic distances and

distance along shepherding routes. The distance along shepherding routes was calculated as the

number of patches that sheep traverse along the route from patch i to path j, including all grazed

patches without distinguishing between consistently and intermittently grazed patches (see

chapter 2). As the response variable, a matrix of genetic distances among populations was

calculated using the Cavalli-Sforza and Edwards (1967) chord distance (Dc), which has been

77

shown to be more appropriate for microsatellite data than other types of genetic distances

(Takezaki and Nei 1996). I performed a Mantel test between population genetic distances, Dc,

distances and Euclidean distances to test for isolation by geographic distance (IBD) for pairs of

populations in ungrazed patches (n = 12). For populations of patches connected by shepherding

(n = 47), I tested for significant differences in the regression slopes and intercepts among the

three shepherding grazing systems by including grazing system as a random (blocking) factor in

the multiple linear regression. To partial out the effect of geographic distance from the distance

along shepherding routes, I performed a partial Mantel test controlling for Euclidean distance.

Significance of Mantel correlation coefficients was tested by permuting observations within each

shepherding system 1000 times. To account for non-linearity Spearman rank correlation

coefficients (rho) were calculated. Although the use of Mantel test has been criticized in

landscape genetics due to lower statistical power compared to traditional linear models

(Legendre and Fortin 2010), so far it is the most appropriate method when hypotheses testing are

clearly defined in terms of distances, such as inferences of gene flow through neutral markers

and landscape structure (i.e. inter-patch distances or distance as tested here in terms of

shepherding connectivity; Cushman and Landguth 2010).

4.4. Results

4.4.1. Landscape-scale patterns of genetic structure

I found clear patterns of population genetic structure among the three non-overlapping

shepherding systems, as revealed by an interclass principal component analysis (PCA) that

factored in the three non-overlapping herding systems and a fourth group containing all ungrazed

78

patches (Fig. 8). Although the genetic variation explained by the three retained interclass PCA

axes was not high (axis 1: 6.25%; axis 2: 3.2%; axis 3: 2.2%), the first interclass PCA axis

significantly distinguished herd 2 populations from those grazed by herds 1 and 3 (Tukey-HSD:

p = 0.0001; A4 Appendix), the second interclass PCA axis significantly distinguished herd 3

populations from herd 1 (Tukey-HSD: p = 0.0001; A4 Appendix), and the third axis significantly

distinguished ungrazed populations from those in the three herding systems (Fig. 8; Tukey-HSD:

p = 0.0001; A4 Appendix). According to the results of AMOVA the proportion of the genetic

variation explained between the three herding systems and ungrazed patches is relative low

(0.6%), while most of the genetic variation was found within populations (97%). However, there

was significant genetic differentiation among herding systems and ungrazed patches (Table 5).

Bayesian clustering analysis, which did not take into account herding systems, identified two

genetic groups separating populations in the east (herd 2) and the west (herd 1 and 3) of the study

area (Fig. 9; A5 Appendix). This east-west genetic differentiation was consistent with the results

shown from the first interclass PCA axis.

4.4.2. Isolation by geographic distance (IBD) and directed dispersal by sheep

There was clear evidence of IBD effects for populations in ungrazed patches, as there was a

strong and significant positive association between inter-patch geographic distances with

population genetic distances (Mantel correlation = 0.55, p= 0.001; Fig. 10A). There were no

significant differences in the regression slopes among herding systems for Euclidean distances

(ANOVA test of overall effect of interaction between Euclidean distance and shepherding

system: t= 1.17, p = 0.18; herd 2: t = -0.52, p = 0.8; herd 3: t = -0.06, p = 0.9) and shepherding

distances (overall: t= 0.86, p = 0.28; herd 2: t = 1.15, p = 0.57; herd 3: t = 0.51, p = 0.78).

79

Similarly there were no significant differences among the regression intercepts among

shepherding systems for the relationship of genetic distances with Euclidean distances (overall:

t= 2.12, p = 0.19; herd 2: t = 0.75, p = 0.19; herd 3: t = -2.17, p = 0.8), but I found significant

differences for the regression intercept of herd 3 from herd 1 for the shepherding distances

(overall: t= 8.2, p = 0.001, herd 2: t = -0.53, p = 0.19; herd 3: t = -2.8, p = 0.001). Hence, the

final model contained a common slope and different intercepts for shepherding systems.

I found no significant effect of IBD on population genetic distances (Mantel correlation = 0.18, p

= 0.14; Fig. 10B). Instead, the distance along shepherding routes (measured as the number of

intervening patches traversed by the sheep) had a strong and significant effect on population

genetic distances (Mantel correlation = 0.43, p = 0.001; Fig. 10C). In fact, this relatively strong

association even increased slightly after partialling out the effect of IBD to account for potential

confounding of geographic distance and distance along shepherding routes (partial Mantel

correlation = 0.45, p = 0.001).

4.5. Discussion

4.5.1. Effect of directed dispersal by shepherding on genetic structure at the

landscape

I found evidence to support the main hypothesis that large-flock shepherding influences

landscape-scale patterns of genetic structure through directed seed dispersal in the calcareous

grassland forb Dianthus carthusianorum. In calcareous grasslands, traditional land use by

rotational sheep grazing is recognized to influence the structure and composition of this plant

80

community, as many habitat specialist plants are effectively dispersed among patchy grasslands

by sheep (Fisher et al. 1996; Kahmen et al. 2002; Kuiters and Huiskers et al. 2010; Reitalu et al.

2010). This is the first study to show that directed dispersal by grazing can result in sufficient

rates of seed-mediated gene flow to affect spatial patterns of genetic structure. This result is

especially important as D. carthusianorum lacks any seed morphological adaptations for

zoochory (Klotz et al. 2002).

Only two empirical studies have investigated patterns of population genetic structure in

association with shepherding practices in calcareous grassland plants. Willerdirg and Poschlod

(2002) studied the grass Bromus erectus, but did not find a clear association of patterns of

population genetic structure with shepherding routes, although seeds of B. erectus were found to

be transported by sheep in large numbers (Fischer et al. 1996). This lack of association might be

explained by the small number of populations studied (n = 12), and the generally large

population size of this species in calcareous grasslands. Honnay et al. (2006) investigated the

association of population genetic diversity and structure with temporal patterns of landscape

connectivity (as assessed from maps from 1850 to 1984) in the calcareous grassland plant

Anthyllis vulneraria, but did not find an effect. These authors speculated that directed dispersal

by shepherding may have homogenized spatial genetic variation in this species. In contrast to

these studies, I found significant population genetic structure related shepherding systems within

a relatively small study area (10 x 15 km).

81

4.5.2. Effect of IBD vs. directed dispersal by shepherding on genetic connectivity

When comparing populations within shepherding systems and controlling for the effect of

geographic distance between grazed populations, I found a significant and strong correlation of

population genetic distances with distance along shepherding routes. Specifically, there is no

data from observational or experimental studies reporting the presence of D. carthusianorum

seeds to be attached in the fur of animals, but other similar calcareous grasslands specialist

species lacking dispersal adaptations to zoochory (e.g. Asperula cynanchica; Fischer et al 1996)

have been found to become attached to the fur of sheep and travelling long distances up to 400

km (Fischer et al. 1996; Couvreur et al. 2004; Manzano and Malo 2006). Frequent long-distance

dispersal would be expected to homogenize spatial genetic structure across the landscape.

However, the finding of a distance-dependent effect similar to IBD but instead associated with

the distance along shepherding routes, suggests that gene flow is spatially restricted and mostly

occurring between neighboring populations along shepherding routes, thus supporting the

stepping-stone model of gene flow (Kimura and Weiss 1964). As required by local conservation

agencies, sheep are kept in designated paddocks for rumination, where consumed seeds are most

likely to be deposited. Sheep flocks move back and forth along grazing routes and animals do not

stay within a grassland patch for more than three days on smaller patches. Therefore, directed

dispersal into calcareous grassland patches may depend largely on epizoochory In a field

experiment, a large proportion of seeds of a variety of calcareous grassland specialists fell off

from the sheep wool within a few hours after grazing (Fischer et al. 1996; Couvrer et al. 2005),

which may explain the pattern of genetic structure found in populations connected along

shepherding routes.

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Remarkably, the substantial effect of IBD of populations in ungrazed patches suggests

that in the absence of directed dispersal by sheep, seeds of D. carthusianorum may not travel far.

Experimental studies have documented that seeds of most calcareous grassland plants are

dispersed over short distances, i.e., often within 1m of the source plant (Coulson et al. 2001).In

the context of species conservation, a lack of connectivity by rotational sheep grazing may

endanger the long-term persistence of calcareous grassland specialist plants such as D.

carthusianorum by reducing the species’ ability to colonize grasslands after local extinction

(Soons et al. 2004; Rico et al. 2012) and by inhibiting gene flow and thus increasing the risk of

deleterious effects of inbreeding and genetic drift in local populations (Young et al. 1999).

Although the data do not allow quantification of the relative contributions of pollen- and

seed-mediated gene flow, the contrast between spatial genetic structure among ungrazed patches

and among patches within the same herding system, all within the same study area, suggests that

shepherding increased rates of seed-mediated gene flow markedly. Yet, the relative contribution

of seed-mediated gene flow to genetic diversity and spatial genetic structure depends also on the

rate of pollen flow. On one hand, pollen-mediated gene flow may be high in D. carthusianorum

as the species is mainly outcrossing and pollinated by specialized butterflies (Bloch et al. 2005),

which may travel long distances. On the other hand, habitat fragmentation has been shown to

negatively affect the effectiveness of pollinators for transferring pollen among plant populations

(Allen-Wardell 1998). The study system that is characterized by a heterogeneous matrix of

forest, intensive agricultural fields, orchards, and settlements is likely to have an effect on the

behavior of pollinators. In such a situation, a shortage of pollinators of D. carthusianorum due to

habitat fragmentation might lower rates of pollen flow, both for grazed and ungrazed patches,

thus increasing the importance of seed-mediated gene flow.

83

4.6. Conclusions

Overall, this study involving directed dispersal by grazing of domestic ungulates confirms

previous evidence found from plants dispersed by frugivores (e.g., Bacles et al. 2006; Freeland et

al. 2012). The significant differences found in genetic structure between ungrazed patches and

patches connected by large-flock shepherding indicate a substantial potential of directed seed

dispersal by grazing to mediate spatial patterns of gene flow across the landscape. Further

research is needed to assess whether the landscape-scale genetic structure found in D.

carthusianorum is typical for other characteristic species of calcareous grasslands, and to

elucidate the effect of traditional land-use such as sheep herding on spatial genetic structure in

plants.

84

Axi

s 2

Axis 1

Axi

s 3

Axis 1

Fig. 8 Ordination of the first two interclass PCA axes based on population allele frequencies

constrained by three non-overlapping shepherding systems and ungrazed patches (n = 59). Inset

shows ordination of the first and third interclass PCA axes differentiating ungrazed patches. Grey

squares populations in ungrazed patches, black diamond’s populations of herd 1, white triangles

populations of herd 2, and white circles populations of herd 3.

Herd 1

Herd 2 Herd 3 Ungrazed

85

Fig. 9 Pie charts of population membership scores for two genetic clusters based on TESS.

Background map shows in light grey the forested areas of the Upper Fraconia Jura plateau and

the colored lines correspond to the three non-overlapping herding routes connecting calcareous

grasslands (circles). The inset map shows the distribution of calcareous grasslands (grey areas) in

Germany and the location of the study area (orange box, map modified from Beinlich and

Plachter 1995).

86

Fig. 10 Effects of isolation by geographic distance (IBD) and distance along shepherding routes

on population genetic distances. A) IBD for pairs of population in ungrazed patches (n = 12); B)

IBD for pairs of population in grazed patches within the same herding system (n = 47), and C)

distances along shepherding routes for pairs of population in grazed patches within the same

herding system (n = 47). Symbols on plots B and C differentiate shepherding systems: herd 1=

open circles, herd 2= blue crosses, and herd 3= red triangles.

C)

Geographic distance (km)

0 2 4 6 8 10 12 14

0.1

0.2

0.3

0.4

0.5 A)

Gen

etic

dis

tanc

e

Dc

Gen

etic

dis

tanc

e

Dc

Sheep grazing distance

0 5 10 15

0.15

0.25

0.35

0.45

Gen

etic

dis

tanc

e

Dc

Sheep grazing distance

B)

0 5 10 15

0.15

0.25

0.35

0.45

r = 0.55, p= 0.001 r = 0.18, p= 0.14

r = 0.45, p= 0.001

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Table 5. Analysis of molecular variance (AMOVA) and F-statistics by factor. Groups are

defined by three shepherding systems and ungrazed patches with Dianthus carthusianorum.

Source of variation

Sum of Squares

Variance component

Percentage of variation

F-statistc

Among herding systems and ungrazed

65.8 0.019 0.56 0.006*

Among populations within groups

393.4 0.07 2.12 0.021*

Within populations 10263.5 3.26 97.32 0.027*

Total 10722.7 3.35

*p < 0.05

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Chapter 5

Genetic Consequences of Directed Seed

Dispersal by Shepherding in Fragmented

Calcareous Grasslands

5.1. Abstract

In fragmented landscapes, increased habitat isolation is likely to decrease rates of seed dispersal

and pollen flow among plant populations. Lack of gene flow will erode genetic diversity due to

genetic drift and inbreeding, which are stronger in small and isolated populations. Within

fragments, restricted seed dispersal at shorter distances will create spatial clustering of

genetically related individuals (fine-scale spatial genetic structure, SGS). However, directed seed

dispersal by zoochory promoting effective dispersal across the landscape will increase the

mixing of seeds from a variety of mother plants, thus affecting levels of genetic diversity and

SGS at the local scale, within a population. Here, I investigated the effects of directed seed

dispersal by rotational shepherding on the strength of SGS and genetic diversity, using eleven

nuclear microsatellites for 1,613 individuals from 49 populations of the calcareous grassland

specialist Dianthus carthusianorum. Populations connected by shepherding showed significantly

weaker SGS and significantly higher genetic diversity than populations in ungrazed grasslands,

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suggesting that seed dispersal among spatially isolated grasslands is a major determinant

reducing plant relatedness within grassland patches. Independent of grazing treatment, small

populations (< 40 individuals) showed significantly stronger SGS and lower genetic diversity

than larger populations, likely due to genetic drift and inbreeding, which may further reduce

effective population size in small populations. A lack of significant differences in the strength of

SGS and genetic diversity between populations that were recently colonized and pre-existing

populations suggested that populations colonized after the reintroduction of shepherding were

likely founded by colonists from diverse source populations, reducing relatedness during the

initial colonization process. I conclude that directed long-distance dispersal by rotational

shepherding has the potential to increase genetic diversity and reduce SGS within D.

carthusianorum populations. This study thus highlights the importance of considering the

mechanisms of seed dispersal across spatial scales to better understand the nature of spatial

genetic structure in plant populations.

5.2. Introduction

Human modification of the landscape often changes a formerly continuous distribution of a

population into a set of smaller and spatially disconnected populations (Taylor et al. 2006). In

plants, increased habitat fragmentation is likely to decrease rates of seed dispersal and pollen

flow among remaining populations such that gene flow may be insufficient to counteract the loss

of genetic diversity caused by increased rates of drift and inbreeding in small and isolated plant

populations (Young et al. 1996; Lowe et al. 2005; Aguilar et al. 2008; Vranckx et al. 2011). In

isolated fragments, plant spatial dynamics are expected to be stronger, as restricted pollen flow

90

will increase rates of inbreeding, while restricted seed dispersal near to the mother plant will lead

to higher spatial overlap of offspring, increasing kinship structure and the probability of mating

among relatives (Heywood 1991; Wells and Young 2002). These effects will be more

pronounced in small populations, which may experience rapid loss of genetic diversity (Leimu et

al. 2006), thus potentially decreasing effective population size that will in turn affect offspring

fitness and population viability (Heywood 1991; Ellstrand and Ellam 1993; Sork et al. 1999;

Young and Clarke 2000; Keller and Weller 2002). Investigating spatial patterns of genetic

structure is fundamental for understanding ecological and evolutionary dynamics of plant

populations, such as demography, mating patterns, selection, and long-term persistence (Ouborg

and Van Treuren 1994; Hamrick and Nason 1996).

Restricted dispersal of seeds creating fine-scale spatial genetic structure, i.e., non-random

distribution of genetically related individuals (hereafter SGS; Heywood 1991; Epperson 1995;

2003), has been reported from many plant populations (e.g., Loiselle et al. 1995; Epperson 2000;

Vekemans and Hardy 2004; Trapnell and Hamrick 2004; 2005; Escudero et al. 2006; Sato et a.

2006; De-Lucas et al. 2009; Barluenga et al. 2010; Volis et al. 2010; Hamrick and Trapnell 2011;

Ndiade-Bourobou et al. 2011; Sebbenn et al. 2011; Wang et al. 2011). In general, evidence from

analyses across species suggests that the strength of the association of relatedness over physical

distance is largely determined by seed dispersal mode. Species with seed dispersal by gravity are

prone to show higher SGS than species with extensive seed dispersal provided by wind or animal

vectors (Hamrick and Nason 1996; Hamrick and Trapnell 2011). In the case of directed dispersal

by animals, the strength of SGS can decrease or increase as a function of the vector’s behavior.

Frugivores (endozoochory) that are specialized to forage on few plant sources and cache high

proportions of genetically related seeds in microsites suitable for establishment, will increase

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SGS despite long-distance dispersal (Grivet et al. 2005; García et al. 2009; Torimaru et al. 2007).

In contrast, animals that forage on varied plant sources (endozoochory) or that inadvertently

transport seeds by attachment to the fur or hooves (epizoochory) will lead to higher mixing of

seeds from different mother sources, thus decreasing SGS within a population (Sezen et al. 2005;

Karubian et al. 2010; Kloss et al. 2011).

Directed long-distance dispersal by zoochory may also influence occurrence of SGS

resulting from colonization events. If populations are founded by numerous and unrelated

colonists from varied population sources, SGS will be rather low at first than if only a few seed

sources provided the colonists of the founded population (Withlock and McCauley 1990; Panell

and Charlesworth 2000). Colonization sets up the initial template of SGS on which seed and

pollen dispersal will further act, influencing subsequent mating patterns (Kalisz et al. 1999;

2001; Chung et al. 2003). Thus, to better understand the processes and consequences of SGS in

plant populations, it is important to consider the mechanisms determining seed dispersal at both

the landscape and local scales.

In agricultural landscapes, directed seed dispersal by domestic ungulates, such as sheep,

is recognized to provide directed long-distance dispersal for a range of calcareous grassland

plants (Fischer et al. 1996; Couvrer et al. 2005; Manzano and Malo 2006). As shepherding

supports effective seed dispersal among calcareous grasslands (Fischer et al. 1996; Auffret et al.

2012; Rico et al. 2012; Wagner et al. 2012), the likely mixing of seeds from different sources

will tend to decrease genetic relatedness among individuals, thus decreasing SGS within plant

populations. There are few studies on the effects of grazing on spatial patterns of genetic

structure, which showed no consistent trends (e.g., Kleijn and Steinger 2002; Rudman et al.

2007; Reisch and Poschlod 2009; Smith et al. 2009; Kloss et al. 2011). For instance, increased

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aggregation of identical genotypes has been found to be enhanced by grazing (Kleijn and

Steingner 2002), while the opposite effect of lower SGS has also been reported (Kloss et al.

2011). However, since detailed data on management practices are often lacking (i.e., grazing

history, grazing routes), so far there is no empirical evidence showing the link between the

spatial patterns of genetic structure observed within grassland patches and directed long-distance

seed dispersal by shepherding.

Here I investigate the effects of directed seed dispersal by shepherding on SGS in the

perennial herb and specialist of calcareous grasslands Dianthus carthusianorum (Oberdorfer

1978). Although, D. carthusianorum lacks seed-dispersal traits related to zoochory (Klotz et al.

2002), previous empirical evidence has shown that the species responds to dispersal by

shepherding by showing increased habitat colonization after the reintroduction of sheepherding

in the study system in 1989 (see chapter 3) and by showing spatial patterns of population genetic

structure at the landscape scale associated with shepherding routes (see chapter 4). Specifically, I

hypothesize (i) that populations in grazed grassland patches will show weaker SGS and higher

genetic diversity than populations of ungrazed grasslands due to effective long-distance seed

dispersal increasing seed mixing within populations. Also, I expect that (ii ) large populations will

show higher genetic diversity and weaker SGS than small populations, where genetic drift and

inbreeding are expected to be stronger to reduce effective population size. For grazed grasslands,

I further compare SGS and genetic diversity between populations colonized since 1989 and pre-

existing populations. I expect to find (iii) weak SGS and moderately high genetic diversity due to

effective long-distance seed dispersal from varied seed population sources.

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5.3. Methods

5.3.1. Study site

The study was conducted in the Southern Franconian Alb near Weissenburg, Bavaria Germany

covering an area of approximately 10 x 15 km. Calcareous grasslands of the Gentiano-

Koelerietum pyramidatae vegetation association (Oberdorfer 1978) in the study area are mainly

located on the steep slopes between the Upper Franconia Jura plateau and the valleys. In the 20th

century, progressive abandonment of rotational sheep grazing of calcareous grasslands led to a

substantial decrease in species richness of habitat specialist plants caused by local extinction due

to shrub encroachment and reforestation (Hakes 1987; Butaye et al. 2005). A conservation

management project was initiated in 1989 to reconnect remaining consistently grazed grassland

patches (“core areas”) with patches abandoned at least since 1960 by sheep grazing in three non-

overlapping herding systems. Flocks of about 400 to 800 ewes are herded along defined routes

up to 55 km and covering up to 140 ha of calcareous grasslands (Wagner et al. 2012). From 62

previously abandoned grassland patches, 26 are now grazed three to five times annually

throughout the season, 13 are grazed only towards the end of the season or were grazed only

during a few years at the beginning of the project, and 23 grassland patches remained ungrazed.

In the study area, sheep are kept in designated paddocks for rumination as prescribed by

calcareous grassland conservation management. In this situation, consumed seeds are unlikely to

be deposited in grassland patches and thus dispersal may depend mostly on epizoochory.

94

5.3.2. Study species and sampling

Dianthus carthusianorum L. (Caryophyllaceae) is a perennial herb of 30-45 cm in height. It has a

predominantly outcrossing mating system (Bloch et al. 2005), does not form a persistent seed

bank, and reproduces both sexually and vegetatively, although clonal shoots do not detach from

the rosette (Klotz et al. 2002). Flowering and seed shed occurs from June to October. Hence seed

dispersal by sheep is possible as the grazing season in the study area lasts from March until early

November. Pollination is carried out by specialized Lepidoptera species (Bloch et al. 2005) and

seeds lack morphological adaptations to dispersal by wind or animals (Klotz et al. 2002). In a

1989 baseline survey, D. carthusianorum occurred in 40 % of the 62 previously abandoned

patches and in 90 % of the core areas (Boehmer et al. 1990). An evaluation survey in 2009 (Rico

et al. 2012; Wagner et al. 2012) showed that successful colonization increased the species’

occurrence to 90 % in previously abandoned patches, whereas occurrence in unconnected

(ungrazed) patches remained at 40 %.

Leaf material was sampled in spring of 2009. I sampled all 38 grassland patches with

occurrence of D. carthusianorum (here denoted as populations) in the southern half of the study

area, where most occurrences of the species are concentrated, and additional 20 grassland

patches from the northern part of the study area. I also sampled D. carthusianorum from six

grass verges along forest edges, resulting in a total of 64 sampled patches, i.e. populations,

including 50 from grazed calcareous grasslands and eight from ungrazed grasslands. I sampled

leaf material from all individuals in patches with less than 40 individuals (here defined as small

populations), whereas I sampled across a patch approximately 30 to 40 leaf samples from

individuals in patches with more than 40 individuals (large populations). The latter implied that

sampling was spread out within patches of large populations such that the nearest sampled

95

individuals are not likely to be the actual nearest neighbors and thus the estimation of pairwise

kinship coefficients at the shortest distances are likely to be underestimated. Leaf tissue was

preserved in silica gel. The geographic coordinates of each sampled individual were recorded

using a Trimble® GeoXT™ 2008 GPS receiver with sub-meter resolution based on differential

GPS post-processing.

5.3.3. Microsatellite analysis

Genomic DNA was extracted following the DNeasy 96 plant kit protocol (QIAGEN). I amplified

15 microsatellite loci developed for related Dianthus species (MS-DINMADSBOX, MS-

DCDIA30, MS-DCAMCRBSY, MS-DINCARACC, DCA 221, DCD 224, DCB140, Smulders et

al. 2000) and specifically for Dianthus caryophyllus (DCB109; Smulders et al. 2003; CB018a,

CB057a, CB004a, CB027a, CF003a, CB011a, CB020a; Kimura et al. 2009). Microsatellite

amplifications were carried out with the QIAGEN Multiplex kit as follows: 0.2 to 0.4 µl (5µM)

of each primer 4.7 µl of Master-mix, and 1 µl of genomic DNA (approximately 5-10 ng/µl) in a

total reaction volume of 10 µl. PCR conditions were as described in Smulders et al. (2003).

Fluorescently labeled PCR products were run on an ABI 3730X Automated Sequencer (Applied

Biosystems) with 500 LIZ as size standard. Electrophenograms were analyzed using

GENMARKER v1.3. I only detected a high proportion of null amplifications, likely due to null

alleles in three microsatellite loci (DCA221, DCD224, DCD140), which were consequently

discarded from analysis.

Departure from Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD) for

each population was assessed using probability tests with 10000 Markov chain iterations in

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GENEPOP v. 3.4 (Raymond and Rousset 1995). Locus DC020a significantly deviated from

HWE in 50 populations and was discarded from further analyses. Few locus combinations were

statistically significant for LD in some populations, but there were no congruent patterns.

Identical genotypes (< 2%), which likely corresponded to clones were excluded from analysis.

The final genetic data set included 1,613 individuals from 64 populations genotyped at eleven

polymorphic microsatellite loci (A3 Appendix). The same data set was previously used for

analysis of chapter 3 and 4.

5.3.4. Quantification of SGS, genetic diversity, and inbreeding

To evaluate the patterns of SGS, I used a multilocus measure of spatial genetic structure at the

individual level, the pairwise kinship coefficient of Ritland (1996) Fij as implemented in

SPAGEDI (Hardy and Vakemans 2002). The Fij coefficient measures the probability that two

alleles are identical by descent. Negative values of Fij may occur if allele frequencies between

two compared individuals differ more than expected at random from the entire data set. To assess

the strength of SGS, I estimated the Sp statistic of Vekemans and Hardy (2004) for each patch.

The Sp statistic has been widely applied in empirical studies of SGS since it provides an estimate

of SGS under isolation by distance (higher values of Sp indicate higher SGS; Vekemans and

Hardy 2004). If Fij tends to decrease linearly with the logarithm of distance, the strength of SGS

is quantified by the Sp statistic as bF / (1- F1), where bF represents the regression slope of the

pairwise Fij relatedness coefficients between individuals against the logarithmic of the distance,

while F1 is the mean Fij between pairs of individuals in the first distance class (Hardy and

Vekemans 2004). Since there were differences in sampling of individuals between small and

large populations, such that there is high variation among patches in the range of distances

97

between pairs of individuals within a patch, equal distance classes to compare all patches as

traditionally implemented in most studies could not be used. Instead F1 was defined as the

regression intercept, i.e., the value where the logarithm of distance is 0, which corresponds to the

value at a distance of 1 m. Higher values of the Sp statistic indicate a strong relationship of

pairwise Fij relatedness over distance. Significance of the regression slope in each population

was tested by permuting the pairwise Fij coefficients within populations 1000 times. Analyses

were implemented using R (R Development core team, 2010).

When SGS is the result of isolation by distance and assuming drift-dispersal equilibrium

in two-dimensional space, neighborhood size Nb and gene dispersal σ2 can be estimated from the

parameters of the Sp statistics (Hardy and Vakemans 1999). Under the above assumption I

estimated Nb, which represents the unit of genetic structure (Wright 1943), for each treatment

group as follows: Nb = (1- F1)/bF. Similarly, gene dispersal σ2 representing half the average of

the square axial parent-offspring distance was derived from the Sp statistic as: σ = 1/(4πSpδ),

where δ represents the estimated effective mating density of a population (although typically it

is only estimated by counting the number of individuals per square meter, which likely

overestimate effective mating densities). Since I did not have estimates of population density for

all 64 sampled patches, I estimated an average population density using the abundance data from

patches with complete census of individuals (i.e. patches having small populations). Plant density

thus was estimated as 0.028 ± 0.04 individuals per m2. For calculations of σ2 and Nb I only

included populations with significant values of the regression slope bF, as non-significant

populations produced extreme values for both parameters.

Genetic diversity indices including allelic richness (Ar) as well as observed (Ho) and

unbiased expected heterozygosity (He) were estimated for each population using GENALEX

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(Peakall and Smouse 2006). I corrected the measures of allelic richness for the effect of sample

size using the rarefaction procedure in HP-RARE (Kalinowoski 2005). Inbreeding coefficients

FIS for each population were calculated in FSTAT v2.9.3.2 (Goudet 1995). Indices of genetic

differentiation among populations FST (Weir and Cockerham 1984) and their standard deviation

were estimated by jackknifing loci over populations using SPAGEDI (Hardy and Vakemans

2002).

5.3.5. Statistical test of SGS, genetic diversity, and inbreeding coefficients among

groups

To test for main effects and interaction of the two factors grazing and population size on genetic

diversity indices (He, Ho, and Ar), FIS inbreeding coefficients, and the Sp statistics, I performed a

two-way ANOVA for each response variable. To account for an unbalanced design, I used Type

II sums of squares. To normalize the distribution of residuals, we applied square-root

transformation to the Sp statistic, while FIS and genetic diversity indices were not transformed.

Negative values of the Sp statistic were set to zero. Model assumptions of normal distribution of

residuals and homogeneity of variances were checked using Anderson-Darling and Bartlett tests.

In addition, I tested for statistical differences in the degree of spatial isolation among grazed and

ungrazed patches using as a predictor the patch Si connectivity index based on inter-patch

geographic distances previously estimated in chapter 2 (Table 7). Since population history (i.e.,

presence of D. carthusianorum in 1989) is not confirmed for all core areas, statistical analysis of

population history was only performed for populations with known history. Due to this reduced

sample size, history was not included as an additional factor in the two-way ANOVA with the

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factors grazing and population size. Additionally, I excluded populations with less than ten

individuals from analysis, one population with a minimum distance between neighboring

individuals > 10m, and four populations from patches that were grazed only during a few years

but remained ungrazed for the last 15 years. Thus, statistical analyses were based on 49

populations (Table 6, A6 Appendix; Fig. 11). All statistical analyses were performed in R (R

Development Core Team 2010).

5.4. Results

5.4.1. Strength of fine-scale spatial genetic structure (SGS)

Estimates of SGS for each population are shown in Table 6. The strength of SGS as quantified

by the Sp statistic was significantly related to both grazing and population size and without a

significant interaction (two-way ANOVA: sheep grazing: F1, 45= 4.74, p= 0.035; population size:

F1, 45 = 4.28, p = 0.044; interaction F1, 45 = 0.003, p = 0.95). The model showed normally

distributed and homoscedastic residuals (Anderson-Darling’s A = 0.73, p = 0.05; Bartlett's K2 =

3.75, p = 0.28). Specifically, grazed populations showed significantly weaker SGS than ungrazed

populations (i.e., lower Sp values), and independent of grazing, small populations showed

significantly higher Sp values than large populations (Fig. 12). Moreover, the regression slopes

bF of all ungrazed patches, independent of population size, were significant, while, for grazed

grasslands small populations showed a higher number of significant cases of SGS than large

populations (small: 6 out of 11, large: 13 out of 32 populations; Table 6). On the other hand,

there were no significant differences in the degree of spatial isolation between populations of

ungrazed and grazed grassland patches (ANOVA, F1, 47=1.05, p = 0.3).

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Within grazed grasslands, I found no significant differences between the strength of SGS

in colonized and pre-existing populations (ANOVA, F1, 14 = 0.7, p = 0.41). This result did not

change when accounting for population size (two-way ANOVA population size: F1, 12 = 2.7, p =

0.12; interaction: F1, 12 = 1.26, p = 0.28). The model showed normally distributed and

homoscedastic residuals (A = 0.35, p = 0.4; K2 = 0.57, p = 0.49).

5.4.2. Estimates of neighborhood size and gene dispersal

Assuming that plant density is constant within patches, grazed grasslands showed higher

estimates of gene dispersal (small σ = 14.35, large σ = 19.67) than ungrazed grasslands (small σ

= 12.8, large σ = 15.6; Table 7). A similar pattern was observed for estimates of neighborhood

size, with the highest value for large populations in grazed grasslands (Nb = 150.94). Comparing

the values for small populations in which I had complete sampling, as expected small

populations of grazed patches showed a larger estimate of neighborhood size (Nb = 78.5; Table

7) than small populations in ungrazed patches (Nb = 61.9; Table 7). For colonized and pre-

existing populations, both gene dispersal and neighborhood size were very similar (Table 7).

5.4.3. Estimates of genetic diversity and inbreeding

Average estimates of genetic diversity, Ho, He, and Ar tended to be higher in populations of

grazed patches than in populations of ungrazed patches (except for Ho in ungrazed-small vs.

grazed-small; Table 8), but only differences in mean allelic richness were statistically significant,

where both sheep grazing and population size had a significant effect for allelic richness (two-

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way ANOVA: sheep grazing: F1,45 = 7.09, p = 0.01; population size: F1,45 = 6.3, p = 0.01). The

model showed homoscedastic residuals (K2 = 1.26, p = 0.74), whereas the normality test was

significant (A = 0.91, p = 0.02). However, visual inspection showed no specific patterns or

influential outliers; hence residual distribution could not be improved by transformation.

No significant differences were observed for Ho (two-way ANOVA: sheep grazing: F1, 45

= 4.0, p = 0.05; population size: F1, 45 = 0.85, p = 0.36) and He (two-way ANOVA: sheep grazing:

F1, 45 = 0.48, p = 0.49; population size: F1, 45 = 3.17, p = 0.08). Colonized populations had higher

mean values of He (0.6) and mean allelic richness (Ar = 4.32) than pre-existing populations (He=

0.58, Ar = 4.14; Table 8), but no significant differences were found (ANOVA: Ar: df = 2, F =

0.39, p = 0.54; Ho: df = 2, F = 1.12, p = 0.31; He: df = 2, F = 0.26, p = 0.62).

The values of FST were higher for populations of ungrazed patches than for populations in

grazed patches independent of population size (small-ungrazed FST = 0.057 vs. small-grazed FST

= 0.041; Table 7). Average inbreeding FIS in D. carthusianorum was rather low (FIS = 0.095).

However and contrary to expectation, small ungrazed populations showed lowest inbreeding (FIS

= 0.06; Table 8). The two-way ANOVA showed a significant interaction between population

size and grazing (F1, 45 = 8.2, p = 0.006). Pairwise comparisons with Tukey’s HSD revealed that

small populations of ungrazed grasslands had significantly lower FIS values than all other groups.

Residuals showed normal distribution and homogeneity of variance (A = 0.23, p = 0.79; K2 =

3.37, p = 0.33). There were no significant differences in FIS between colonized and pre-existing

populations (ANOVA: df = 2, F = 0.36, p = 0.7).

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5.5. Discussion

Analysis of SGS in D. carthusianorum, a typical calcareous grassland plant species, showed

significant patterns of SGS in 26 out of 49 populations. This result indicates that restricted gene

flow within populations’ affects genetic structure at local scales following isolation by distance

(Wright 1943), resulting from restricted seed dispersal close to the mother plant (Crawford 1984;

Epperson 1993; Epperson and Alvarez-Buylla 1997). However, as expected I found that the

strength of SGS across populations within a landscape was affected by the seed vector, as

populations of grazed grassland patches showed significantly weaker SGS than ungrazed

patches. This result was based on comparing only small populations, which unlikely large

populations (with 40≥ individuals) did not suffer from potential underestimation of the shortest

distances between neighboring plants since all individuals were sampled. These results are

consistent with previous empirical evidence showing that species dispersed by animals (but

mostly frugivores) have weak SGS, which is explained by effective seed dispersal over larger

distances (Degen et al. 2001; Fuchs and Hamrick 2010; Barluenga et al. 2011; Hamrick and

Trapnell 2011; Wang et al. 2011). Here, I showed that this also holds true for seed dispersed in

the fur of animals, such as directed seed dispersal by shepherding.

The above result was in agreement with related empirical studies in grassland plants that

have found that grazed plots present weaker patterns of SGS (Smith et al. 2009; Kloss et al.

2011). In contrast to these previous studies, by comparing levels of genetic diversity and SGS

within populations known to be connected within rotational shepherding systems, it is possible to

provide inferences on the ways in which grazing can decrease SGS in D. carthusianorum. First,

seed dispersal by sheep within patches will increase the variance of seed dispersal distances,

leading to higher overlap of seed shadows from different mothers and hence higher seed mixing

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within sites. Secondly, directed dispersal by shepherding promotes effective long-distance

dispersal among grasslands connected along herding routes (see chapter 4). This process will

likely increase and maintain within-patch genetic diversity by seed-mediated gene flow, while

also reducing genetic relatedness of established seeds by interspersing seeds from external

sources within the patch . According to my results showing that populations of grazed grasslands

had higher genetic diversity than populations of ungrazed grasslands, long-distance seed

dispersal is likely an important determinant.

Populations of grazed patches clearly showed higher estimates of gene dispersal (σ)

within populations than ungrazed patches. Larger dispersal distances, also are expected to lead to

larger neighborhood sizes Nb in populations of grazed patches as sheep will increase the variance

of seed dispersal distances within sites. However, inferences made to estimate gene dispersal and

neighborhood size assume drift-dispersal equilibrium among populations (Wright 1946).

Contemporary land-use changes in the study system, such as increased calcareous grassland

isolation due abandonment of shepherding and recent colonization events, may prevent

populations from reaching drift-dispersal equilibrium (Vakemans and Hardy 2004). However,

since information on plant density was lacking for most large populations and I thus used the

assumption that plant density was equal in all patches, my estimates of gene dispersal and

neighborhood size need to be interpreted with caution and are only indicative of the likely

variation among populations determined by factors of seed dispersers, plant demography, and

population history.

In addition I found that population size is another factor modifying SGS and genetic

diversity. Independent of grazing treatment, small populations had significantly stronger SGS

and significantly lower genetic diversity than large populations. Small populations are more

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prone to suffer from drift, which reduce effective population size, and thus erode genetic

diversity. Evidence based on meta-analyses across studies in plants showed that population size

is an important factor associated with levels of population genetic diversity (Honnay and

Vackemyn 2007; Aguilar et al. 2008; Vranckx et al. 2011). Furthermore, evidence showed that

small populations with reduced genetic diversity experience reduced fitness and population

viability (Leimu et al. 2006). Although I found that small populations have stronger SGS than

large populations, which may be due to reduced effective population size, this finding is partially

limited since individuals in large populations were not completely sampled but spread out across

the patch, and thus the nearest sampled individuals may not be the actual nearest neighbors. This

may have an effect on the estimation of the Sp statistics in large populations of both grazed and

ungrazed patches, thus limiting my interpretations on the effect of population size on SGS.

Further research will be needed to include estimates of plant density, instead of population size,

to analyze likely changes on SGS by population density within a patch.

In accordance with my expectations, I did not find differences in the strength of SGS and

genetic diversity between pre-existing and recently colonized grazed populations. This result

provides some information on the colonization process after the reintroduction of shepherding by

conservation management in 1989. Theoretical genetic models of colonization predict that when

populations are founded by colonists from a single seed source, population genetic diversity will

be low and genetic differentiation among recently colonized populations will be higher as

compared to older populations, whereas higher genetic diversity and lower genetic differentiation

will be found if colonists are from diverse seed sources (Withlock and McCauley 1990; Panell

and Charlesworth 2000; Pannell and Dorken 2006). The lack of significant differences in genetic

diversity, SGS, and FST values between recently colonized and pre-existing populations in grazed

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patches, suggests that historical gene flow, at least partly mediated through directed seed

dispersal by shepherding was high enough to maintain similar levels of genetic diversity within

populations and low levels of genetic differentiation among populations connected by

shepherding.

It is important to contrast estimates of SGS, genetic diversity, and inbreeding to infer

patterns of gene flow within populations, and to understand gene flow effects in small and

isolated populations. Contrary to expectation, small ungrazed populations showed significantly

lower levels of inbreeding than all other groups. Inbreeding in plants, here measured by FIS and

theoretically defined by non-random mating reducing He as expected under HW, could be the

result of bi-parental inbreeding or selfing (Mimura and Aitken 2004; Gonzales et al. 2006). In the

absence of further information (e.g., parentage analysis) to distinguish whether inbreeding is

mainly due to selfing or biparental inbreeding, one can approximate the question by comparing

values of F1 and FIS (Vakemans and Hardy 2004). If F1 is higher than FIS, biparental inbreeding

may explain the pattern of inbreeding, whereas if F1 is lower than FIS, selfing is suggested

(Vakemans and Hardy 2004). Although the level of inbreeding was not particularly high in D.

carthusianorum (average FIS = 0.095), FIS was higher than F1 in all treatments groups (Table 6),

suggesting that selfing might be important.

However, evidence from studies based on meta-analysis in outcrossing plants (Leimu et

al. 2006), have found that inbreeding as measured by the FIS coefficient is often not associated

with population size as expected as large populations showed higher levels of FIS than small

populations. The above is explained by a primary effect of genetic drift rather than inbreeding

resulting in a more rapid loss of alleles than the decrease of He (Leimu et al. 2006). Specifically

the FIS values observed within all six compared groups in D. carthusianorum (Table 8) were

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below 0.15 (overall FIS= 0.1). Values above 0.15 correspond to a kinship structure of half-sibs,

while values above 0.25 indicate relatedness of full-sibs (Wright 1921). It is important to

consider that the FIS coefficient as calculated based on estimates of mean He within

subpopulations represents a measure of inbreeding over several generations, and may not

necessarily reflect the current situation of the population.

D. carthusianorum is partially outcrossing and insect-pollinated by specialized

butterflies (Bloch et al. 2005). In the study system, calcareous grasslands are embedded in a

heterogeneous matrix composed of forest, intensive agriculture, meadows, orchards, and

settlements. Habitat fragmentation may negatively affect the effectiveness of pollinators to

transfer pollen among patchy plant populations (Allen-Wardell 1998; Kearns et al. 1998).

Butterfly pollinators that are specialists of calcareous grasslands have been shown to be affected

by increased habitat isolation (Bruckmann et al. 2010). Reduced rates of pollen transfer between

grasslands by pollinators may increase biparental inbreeding, thus reducing the contribution of

pollen to gene flow, while seed-mediated gene flow by shepherding may have a major

importance in D. carthusianorum. The lack of significant differences in the geographic isolation

among grasslands does not necessarily imply that movement of pollinators across the landscape

may not be affected by the composition of the matrix. However, with nuclear markers, I cannot

estimate the relative contribution of pollen- vs. seed-mediated gene flow to determine whether

gene flow by pollen was less important in D. carthusianorum than gene flow by seed (Ellstrand

and Ellam 1993; Ennos 1994). Estimating the ratio of pollen to seed flow will add important

information relevant for species conservation (Leimu et al. 2006; Aguilar et al. 2008). For plants,

increased rates of selfing and biparental inbreeding will reduce genetic diversity within

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populations with negative consequences for offspring fitness and the long-term persistence of

plant populations (Young et al. 1999; Keller and Weller 2002; Frakham 2005).

5.6. Conclusions

The majority of empirical studies of fine-scale spatial genetic structure have investigated one or

few populations, assuming that patterns of SGS are homogeneous across populations within a

landscape. By contrasting multiple populations of D. carthusianorum in calcareous grassland

patches, I showed substantial variation in SGS among populations. More importantly, my results

revealed that directed seed dispersal by sheep is an important factor reducing SGS within

populations. Another factor affecting genetic diversity within populations and SGS is population

size (Leimu et al. 2006). The lack of differences in the strength of SGS between grazed

populations varying in population age suggests that colonized populations are likely to be the

result of effective seed dispersal among physically disconnected calcareous grasslands after the

reintroduction of sheep grazing in 1989. This result adds evidence of the successful restoration of

abandoned grassland patches by reestablishing rotational shepherding in the study area. Further

research is needed to investigate if differential rates of inbreeding in populations of grazed

grasslands as compared to ungrazed grasslands may be the result of different rates of selfing and

biparental inbreeding.

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Table 6. Estimates of spatial genetic structure SGS across 49 populations of D. carthusianorum: N, number of individuals genotyped;

F1, kinship coefficient at ln(distance = 1m); slope (bF) of the regression kinship coefficient on ln(distance); Sp statistic reflecting the

intensity of SGS; larger values of Si indicates higher connectivity. Significant p-values (α = 0.05) of the regression slopes in bold.

Population Minimum distance

N Connectivity index Si

Grazing Population size

Population history

F1

Slope

bF Sp

P-value

A03 0.3 29 0.943 grazed small colonized 0.064 -0.0126 0.0135 0.001

A05 0.3 26 1.497 grazed small colonized 0.054 -0.0127 0.0134 0.001

A08 0.5 23 1.599 grazed small colonized 0.011 0.0003 -0.0003 0.564

A26 0.3 26 2.346 grazed small colonized 0.032 -0.0062 0.0064 0.026

A31 0.3 23 2.773 grazed small colonized 0.050 -0.0099 0.0104 0.001

A33 0.4 35 1.805 grazed small colonized 0.097 -0.0180 0.0200 0.002

A45 0.7 15 1.262 grazed small colonized 0.021 -0.0020 0.0021 0.272

C08 0.3 30 0.819 grazed small unknown 0.025 -0.0015 0.0015 0.203

A12 0.3 12 1.325 grazed small pre-existing 0.055 -0.0150 0.0159 0.148

A25 0.5 15 1.572 grazed small pre-existing 0.103 -0.0444 0.0495 0.001

E07 2.2 15 0.092 grazed Small unknown -0.068 0.0328 -0.0307 0.91

A06 0.5 30 1.673 grazed Large colonized 0.038 -0.0100 0.0104 0.017

Table 6. Continue on the following page

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Table 6. Continue on the following page

Population Minimum distance

N Connectivity index Si

Grazing Population size

Population history

F1

Slope

bF Sp

P-value

A07a 2.0 30 1.207 grazed large colonized 0.008 0.0019 -0.0019 0.678

N05 0.5 28 0.119 grazed large colonized 0.021 -0.0016 0.0016 0.334

A14 0.4 16 1.714 grazed large pre-existing 0.128 -0.0346 0.0397 0.001

A18 1.2 29 1.560 grazed large pre-existing 0.081 -0.0125 0.0136 0.005

A28 0.4 33 2.574 grazed large pre-existing 0.014 0.0004 -0.0004 0.558

A29 0.8 28 2.644 grazed large pre-existing 0.012 -0.0034 0.0034 0.012

A38 2.4 30 1.041 grazed large pre-existing 0.010 -0.0006 0.0006 0.456

E01 0.3 36 0.456 grazed large unknown 0.040 -0.0047 0.0049 0.004

G01 0.6 29 0.322 grazed large unknown 0.051 -0.0051 0.0053 0.11

G13 0.3 41 0.092 grazed large unknown 0.014 -0.0010 0.0011 0.363

G16 1.4 29 0.904 grazed large unknown 0.018 -0.0038 0.0038 0.01

G20 1.8 30 0.556 grazed large unknown 0.006 -0.0001 0.0001 0.477

G21 0.3 30 0.895 grazed large unknown 0.034 -0.0064 0.0066 0.001

G23 2.8 28 0.905 grazed large unknown 0.065 -0.0104 0.0112 0.001

G26 0.6 38 1.586 grazed large unknown 0.026 -0.0039 0.0040 0.015

G28 0.3 30 0.790 grazed large unknown 0.002 0.0000 0.0000 0.535

G29 3.3 30 1.447 grazed large unknown -0.007 0.0039 -0.0039 0.808

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Population Minimum

distance

N Connectivity

index Si

Grazing Population

size

Population

history

F1

Slope

bF

Sp

P-value

G30 0.3 30 1.098 grazed large Unknown 0.036 -0.0079 0.0082 0.005

G31 0.7 30 1.411 grazed large Unknown -0.002 0.0015 -0.0015 0.786

G37 0.3 27 1.322 grazed large Unknown -0.011 0.0028 -0.0028 0.831

G40 0.3 31 0.716 grazed large Unknown 0.003 0.0004 -0.0004 0.593

G45 5.2 31 0.646 grazed large Unknown 0.009 -0.0001 -0.0001 0.523

G46 0.3 30 1.193 grazed large Unknown 0.002 -0.0004 0.0004 0.432

G47 2.0 28 1.426 grazed large Unknown -0.001 0.0007 -0.0007 0.589

G48 0.3 29 1.340 grazed large Unknown 0.028 -0.0048 0.0050 0.056

G49 0.5 30 1.269 grazed large Unknown 0.019 -0.0014 0.0014 0.343

G50 0.3 28 1.030 grazed large Unknown 0.085 -0.0235 0.0257 0.001

G05a 0.8 27 1.036 grazed large Unknown 0.047 -0.0060 0.0063 0.163

Gzim 0.3 30 0.211 grazed large Unknown 0.017 -0.0026 0.0027 0.041

N03 0.3 29 0.302 grazed large Unknown 0.044 -0.0104 0.0109 0.001

E03 0.3 15 0.941 ungrazed small Unknown 0.091 -0.0166 0.0182 0.001

E04 0.3 30 0.421 ungrazed small Unknown 0.052 -0.0179 0.0189 0.005

E09 0.3 32 1.338 ungrazed small Unknown 0.047 -0.0083 0.0087 0.001

Nroth 0.4 14 0.573 ungrazed small Unknown 0.106 -0.0362 0.0404 0.001

A37 0.3 31 1.205 ungrazed Large Colonized 0.067 -0.0121 0.0130 0.001

N10 0.3 25 0.733 ungrazed Large Unknown 0.030 -0.0103 0.0106 0.001

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Table 7. Estimates of gene dispersal σ, and neighborhood size Nb, F1 kinship coefficient at

ln(distance = 1m), for the six treatment groups in Dianthus carthusianorum. SD indicates

standard deviation of the mean.

Group σ (SD) Nb (SD) F1 (SD)

Ungrazed- small 12.8 ± 3.9 61.9 ± 38.0 0.074 ± 0.03

Ungrazed- large 15.6 ± 1.1 85.79 ± 12.1 0.048 ± 0.03

Grazed- small 14.35 ± 4.5 78.48 ± 45.7 0.040 ± 0.04

Grazed-large 19.67 ± 6.7 150.94 ± 92.7 0.028 ± 0.03

Colonized-grazed

Pre-established-grazed

15.84 ± 3.0

14.79 ± 9.8

91.07 ± 35.8

102.2 ± 127.6

0.042 ± 0.03

0.039 ± 0.03

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Table 8. Summary of genetic diversity measures and fixation indices of the six treatment groups

in Dianthus carthusianorum. N, sample size; Ho observed heterozygosity; He expected

heterozygosity; rarefied Ar allelic richness; FIS, inbreeding coefficient, FST fixation index of

genetic differentiation. SD standard deviation of the mean.

Group N Ho (SD) He (SD) Ar (SD) FIS (SD) FST (SD)

Ungrazed- small 4 0.53 ± 0.05 0.56 ± 0.04 4.11 ± 0.28 0.04 ± 0.05 0.057 ± 0.02

Ungrazed- large 2 0.50 ± 0.003 0.56 ± 0.02 4.02 ± 0.08 0.12 ± 0.02 0.032 ± 0.01

Grazed- small 11 0.52 ± 0.04 0.58 ± 0.02 4.21 ± 0.22 0.12 ± 0.05 0.041 ± 0.00

Grazed-large 32 0.54 ± 0.02 0.59 ± 0.03 4.44 ± 0.23 0.09 ± 0.03 0.018 ± 0.00

Colonized-grazed

Pre-established-

grazed

9

7

0.53 ± 0.03

0.54 ± 0.04

0.60 ± 0.02

0.58 0.02

4.32 ± 0.20

4.14 ± 0.31

0.097 ± 0.07

0.098 ± 0.04

0.03 ± 0.004

0.04 ± 0.009

Total 49 0.54 ± 0.03 0.59 ± 0.03 4.34 ± 0.27 0.095 ± 0.02 0.03 ± 0.002

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Fig 11. Sketch of spatial locations of patches (populations) analyzed for fine-scale spatial genetic

structure (SGS) in Dianthus carthusianorum. Black circles denote larger populations and white

circles correspond to small populations (n < 40 individuals). Lines indicate shepherding routes

connecting calcareous grassland patches in three non-overlapping herding systems. Ungrazed

populations thus are showed as not connected by lines.

Map produced by Adrián Sarabia Rangel

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Fig. 12 Interaction plot of the mean square-root transformed Sp statistic as a function of the

factors grazing and population size in populations of Dianthus carthusianorum. The dashed line

indicates small population size (n < 40 individuals) and the full line denotes large population size

(n ≥ 40 individuals).

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Chapter 6

Synthesis and Future Directions

6.1. Directed Dispersal by Shepherding Promotes Functional

Connectivity in Calcareous Grasslands

To effectively protect and maintain plant biodiversity in human-modified landscapes, we need to

understand what regulates functional connectivity. For plants, investigating determinants of

effective seed dispersal (Shupp 1993) is critical for species conservation, as seed dispersal

supports colonization, recruitment, range expansion, and ultimately species persistence (Nathan

and Muller-Landau 2000; Levin et al. 2003; Clobert et al. 2004). Animal vectors are important

determinants of connectivity because they can effectively disperse seeds through the matrix to

deposit them in suitable sites for establishment (directed dispersal) (Shupp 1993; 2011).

Despite the importance of animal vectors for effective long-distance dispersal of seeds,

most empirical studies of fragmented plant communities have modeled connectivity only as a

function of the physical distances between habitat patches, ignoring the role of seed dispersal

vectors, and thus failing to approximate functional connectivity. This has been the typical case of

most empirical studies of connectivity in calcareous grassland communities (e.g., Krauss et al.

2004; Geertsema 2005; Adriaens et al. 2006; Joshi et al. 2006; Bruckman et al. 2010). Contrary

to previous studies, my PhD research (chapter 2) shows that functional connectivity of

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calcareous grassland communities cannot be comprehensively understood by quantifying only

structural connectivity as a function of inter-patch distances alone, e.g., with simple dispersal

kernels or distance thresholds. In chapter 2, by testing competing models of potential functional

connectivity with different assumptions of dispersal relating the physical distances between

patches, vegetation compositions of the matrix, and dispersal by shepherding, I showed that

functional connectivity in terms of mean patch colonization rates is associated with directed seed

dispersal by large-flock shepherding. Furthermore, the analysis implemented in chapter 2 using

estimates of actual functional connectivity such as patch-level colonization rates represents an

important improvement over studies using indirect estimates of functional connectivity such as

data of patch-level species richness (Fagan and Calabrese 2006).

Modeling functional connectivity at the community level offers general insights in

species’ responses to habitat fragmentation (Minor et al. 2011; Schleicher et al. 2011). However,

by pooling colonization events across species, species-specific responses may be neglected

(Taylor et al. 2006), such as in the case of species varying in dispersal-related traits. In

calcareous grasslands, traditional land-use by rotational shepherding has been suggested to

support dispersal of habitat specialist plants (Fischer et al. 1996; Poschlod et al. 1998; Schrautzer

et al. 2009; Kuiters and Huiskes 2010; Reitalu et al. 2010; Piqueray et al. 2011). However,

habitat specialists of calcareous grasslands vary in seed dispersal syndromes, such that not all

species may respond to connectivity by shepherding. In chapter 3, by contrasting the results of

the community-level assessment (chapter 2) with analyses of 31 individual species of the same

plant community, I showed that rotational shepherding effectively supports dispersal for most of

these species regardless of the presence of seed morphological adaptations to zoochory (e.g.,

bristles, hooks, awns). Based on the results from chapters 2 and 3, I found evidence

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corroborating the pivotal role of large-flock shepherding to maintain species richness in

calcareous grassland communities by supporting effective seed dispersal among physically

isolated calcareous grasslands.

The development of comprehensive approaches for assessing functional connectivity in

plants requires investigating not only the processes governing seed dispersal per se, but in

addition focusing on the factors that determine the migrant pool (pre-dispersal) and that facilitate

establishment and growth (post-dispersal) (Murphy and Lovett-Doust 2004). By testing

alternative source patch effects, in terms of patch area, population size (both proxies of seed

production), and species presence in source patches, I found that the diversity of seeds of the

source patch at the community level (chapter 2) or species occurrence at the individual level of

analysis (chapter 3) were sufficient predictors of connectivity. On the other hand, ecological

factors of focal patches affecting establishment and thus colonization rates have not commonly

been considered (Clobert et al. 2004). By including the number of dynamic structural elements

present in focal patches (i.e., small mammal burrows, rock debris, erosion, and ant hills) I found

a significant increase of model performance for both, community- and species-level assessments.

Importantly, since colonization rates as a measure of actual functional connectivity reflect the

outcome of dispersal and post-dispersal processes, assessing the relative importance of these

processes is essential for effective landscape management (Nathan and Muller Landau 2000).

Based on the comprehensive approach proposed in chapter 2, dispersal and post-dispersal

processes were found to be equally important predictors of patch colonization rates at the

community level. Therefore, maintenance of calcareous grasslands not only depends on the

presence of shepherding to support dispersal, but also on the diversity of microsites facilitating

establishment within patches (Wagner et al. 2012).

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Understanding maintenance of genetic diversity in fragmented populations is another

important element of species conservation (Vellend and Geber 2005; Leimu et al. 2006; Hughes

et al. 2008). Genetic diversity within populations is the result of several processes including gene

flow, mutation, drift, and demographic processes (Slatkin 1987). Gene flow tends to homogenize

the spatial genetic variation among populations and maintain similar levels of genetic diversity

within populations (Levin et al. 2003). Connectivity models with ecological (presence-absence)

and genetic data (nuclear microsatellites) in D. carthusianorum showed that dispersal by

shepherding not only influenced patch occupancy across the landscape, but also is likely to

influence genetic diversity (as observed by estimates of mean allelic richness) within populations

through seed-mediated gene flow.

6.2. Shepherding Effects on Seed-Mediated Gene Flow across

Spatial Scales in Dianthus carthusianorum

As suggested by the results in chapter 3, rotational shepherding is likely an important

determinant of seed-mediated gene flow in D. carthusianorum. In chapter 4, I specifically tested

specific hypotheses relating to the effect of directed seed dispersal by shepherding on spatial

patterns of genetic structure at the landscape scale. In the study area, most calcareous grasslands

have been grazed in three non-overlapping herding systems since 1989 (after the progressive

abandonment of shepherding during the first half of the 20th century). If directed dispersal by

sheep substantially contributes to seed immigration into patches connected by shepherding, I

expected to find genetic differentiation among non-overlapping herding systems and ungrazed

patches. Remarkably and as supported consistently with different analyses (e.g., PCA and

Bayesian clustering methods), there was clear evidence of landscape-scale genetic differentiation

119

among herding systems and ungrazed patches given the short time elapsed since introduction of

the herding systems in 1989. Although sheep grazing has been recognized to determine species

composition and community structure of calcareous grassland communities (Kahmen et al. 2002;

Reitalu et al. 2010; Piqueray et al. 2011; Wagner et al. 2012), the analysis presented in chapter 4

is the first to show that directed long-distance dispersal by sheep can result in sufficient rates of

seed-mediated gene flow to determine spatial patterns of genetic structure at the landscape scale

in a calcareous grassland plant.

Furthermore, by specifically disentangling the confounding effects of IBD and

shepherding connectivity on population genetic distances within each herding system, the

analysis carried out in chapter 4 goes beyond to other related studies (e.g., Willendirg and

Poschold 2002). Interestingly, the association of genetic distances and shepherding connectivity,

in terms of the number of patches that sheep need to traverse between two sites along grazing

routes, suggests that gene flow occurs mostly between nearby populations, whereas geographic

distance per se showed no significant association with population genetic distances.

Most empirical studies of seed-mediated gene flow related to directed dispersal have

focused on endozoochorous species, mostly trees and shrubs dispersed by frugivores (e.g., Grivet

et al. 2005; Jordano et al. 2007; Garcia et al. 2009; Karubian et al. 2010). Dispersal by domestic

ungulates like sheep and goats likely occurs mostly by epizoochory. In particular, in this study

system sheep do not stay long within a patch since animals are herded along grasslands, while

they are kept to ruminate in specific paddocks, where most dung is deposited. Hence,

endozoochory likely plays a considerably smaller role than epizoochory is this system. Spatial

patterns of genetic structure resulting from directed dispersal by epizoochory may depend to

some extent on the adhesive interaction between seeds and furs (Courvrer et al. 2004). Species

120

with adhesive seed appendages or specialized structures to promote epizoochory are prone to be

transported in higher numbers to travel over larger distances (Fisher et al. 1996). In contrast, D.

carthusianorum lacks seed morphological adaptations to zoochory (Klotz et al. 2002), and most

seeds may not stay long in the sheep wool, leading to a distance-dependent effect of shepherding

connectivity along grazing routes at the landscape scale.

At the local scale, i.e., within a patch, seed dispersal is a major determinant of genetic

structure as it influences the spatial distribution of the offspring, while also dispersing both

maternal and paternal alleles (Heywood 1991; Epperson 1993; Ouborg 1999). Given that

directed seed dispersal by large flocks of sheep has the potential to support effective seed

immigration from connected grasslands into a population (chapter 4) and to move seeds within a

patch, I expected to find an effect on fine-scale genetic structure (SGS) in D. carthusianorum

populations. Chapter 5 specifically addressed this hypothesis. Analysis of SGS across multiple

populations differing in grazing treatment suggested that sheep grazing is a major determinant of

SGS, as it significantly decreased the degree of spatial relatedness among conspecifics.

Interestingly, populations of grazed patches had significantly higher levels of genetic diversity

(allelic richness) than ungrazed patches, which suggests that effective seed dispersal among

grasslands is likely to play a major role in seed mixing and reducing relatedness of neighboring

individuals (kinship structure).

Other intrinsic factors of a population can also modify patterns of SGS. Plant

demography, determining effective population size, is known to influence SGS (Hardy and

Vekemans 2004). I found that independent of grazing treatment, large populations had

significantly weaker SGS and higher genetic diversity than small populations. This result is

likely to be related to the combined effects of genetic drift and inbreeding (biparental inbreeding

121

or selfing) acting more strongly in small populations, which are likely to reduce effective

population size and increase the strength of SGS (Ellstrand and Ellam 1993; Young and Clarke

2000; Keller and Weller 2002; Charlesworth 2003; Hardy and Vakemans 2004).

6.3. Conservation Implications and Future Directions

Calcareous grasslands in the Franconian Alb are listed in Bavaria and in Germany as an

endangered plant community (Walentowski et al. 1991). The conservation management project

for calcareous grassland protection initiated in the study area in 1989 opted to reintroduce

shepherding as the main restoration practice. Results of connectivity assessments at the

community- and species-levels emphasize the important role of shepherding to support effective

dispersal and thus restore species composition of previously abandoned patches by enabling

habitat recolonization after local extinction. These results were consistent with a scientific

evaluation of this 20 year-old management project, which showed the improvement in species

richness of previously abandoned calcareous grasslands that were reconnected by shepherding

since 1989 compared to grasslands that remained ungrazed (Wagner et al. 2012).

Habitat fragmentation not only will affect plant demography and probability of habitat

colonization by limiting seed dispersal, but in addition will have genetic consequences for plant

populations (Aguilar et al. 2008; Vranckx et al. 2011). Thus, ecological data alone (presence-

absence data or colonization rates) cannot tell whether gene flow is sufficient to maintain

genetically diverse populations. In this study system, the effectiveness of landscape management

by shepherding to restore dispersal and gene flow among previously abandoned grasslands can

be inferred from the genetic data in D. carthusianorum by contrasting patterns of SGS between

122

grasslands colonized since 1989 with pre-existing populations. Importantly, the lack of

significant differences in SGS and genetic diversity between colonized and pre-existing

populations in grazed grasslands indicates that recently colonized populations were founded by

seeds from a variety of source populations, whereas ungrazed patches generally lacked

colonization.

Another important finding for species conservation management refers to the result that

population genetic structure of D. carthusianorum in ungrazed patches was significantly

associated with isolation by distance. In addition, ungrazed patches showed significantly stronger

patterns of kinship structure, lower genetic diversity, and the highest FST index of population

genetic differentiation. These results suggest that in the absence of sheep promoting effective

seed dispersal among physically isolated grasslands, seeds of D. carthusianorum do not disperse

far. These data are particularly important since D. carthusianorum lacks dispersal traits related to

anemochory or zoochory. Thus, dispersal by shepherding has important genetic implications for

the conservation of this species by preventing loss of genetic diversity and development of

genetic differentiation among populations by supporting effective seed dispersal among spatially

isolated grasslands.

Using nuclear microsatellites, it is not possible to quantify the relative contribution of

pollen- and seed-mediated gene flow to spatial patterns of genetic structure (Ennos 1994). D.

carthusianorum has a mixed mating system and is pollinated by specialized butterflies (Bloch et

al. 2005). The agricultural landscape of the Southern Franconian Alb is characterized by a

heterogeneous matrix composed of intensive agricultural fields, beech and pine forest, meadows,

orchards, and settlements. Habitat fragmentation may disrupt plant-insect interactions such as

pollination (Allen-Wardell 1998; Kearns et al. 1998). Pollinator species of calcareous grasslands

123

such as bees and butterfly species have been shown to be affected by habitat fragmentation

(Goverde et al. 2002; Dewenter and Tscharntke 2002). Specifically, butterfly specialists of the

ecological conditions of calcareous grasslands are vulnerable to habitat fragmentation, which

may lead to a population decline or a change in their foraging behavior (Bruckmann et al. 2010).

A shortage of pollinators of D. carthusianorum will likely reduce rates of pollen transfer between

grasslands, thus leading to relatively lower rates of pollen-mediated gene flow than seed-

mediated gene flow, in the case, where grasslands are connected by shepherding. Although

inbreeding coefficients were not high across populations in D. carthusianorum, surprisingly

average inbreeding was lowest within small ungrazed patches. Thus, investigating the

contribution of pollen flow within and among populations to overall gene flow would be an

important research step to complement our knowledge regarding determinants of functional

connectivity in calcareous grasslands.

For instance, using paternity analysis make feasible to test if selfing or biparental

inbreeding is occurring within populations, and more importantly, identifying which landscape

factors affect rates of pollen flow across the landscape. Equally important, would be to test the

potential of shepherding dispersal for seed-mediated gene flow in other habitat specialist plants.

Corroborating the findings of spatial patterns of genetic structure at the landscape scale (i.e. west

and east genetic differentiation) in other calcareous grassland species would show whether such

patterns are exclusive of the biology of D. carthusianorum or to the management history of this

landscape. Furthermore, it will be interesting to contrast if the result of a distance-dependent

effect similar to IBD but in terms of the number of patches traversed by sheep along shepherding

routes depends on dispersal syndrome. For instance, paternity analysis would allow testing

whether selfing or biparental inbreeding occur within populations, and more importantly,

124

identifying which landscape factors affect rates of pollen flow across the landscape. Equally

important would be to test the potential of seed dispersal by sheep for seed-mediated gene flow

in other habitat specialist plants. Corroborating the findings of spatial patterns of genetic

structure at the landscape scale (i.e. west and east genetic differentiation) in other calcareous

grassland species would show whether such patterns are exclusive of the biology or population

history of D. carthusianorum or related to the management history of this landscape.

Furthermore, it will be interesting to contrast if the finding of a distance-dependent effect along

shepherding routes on spatial genetic structure depends on dispersal syndrome. For instance,

species with seeds adapted to zoochory may likely not show such a pattern, as higher rates of

seed dispersal may tend to homogenize the spatial genetic variation across the landscape. On the

other hand, studying flagship species of this calcareous grassland community such as the

endangered Pulsatilla vulgaris would provide important information on the likely effects of

habitat fragmentation in species on conservation concern.

In conclusion, this PhD study contributes towards understanding what directs functional

connectivity in plants using calcareous grasslands as a model system. This study addresses most

of the research needs identified by Fischer and Lindenmayer (2007; see chapter 1) as by a

combination of genetic and ecological data, this research provides comprehensive empirical

evidence on patterns and mechanisms of dispersal and gene flow in plants at the landscape scale.

Specifically, I found evidence both at the community and species levels that highlights the

important role of directed seed dispersal by large-flock shepherding for determining spatial

patterns of species occupancy in a range of habitat specialist species, as well as patterns of seed-

mediated gene flow at the spatial scales of the landscape and within individual patches as shown

for the habitat specialist forb Dianthus carthusianorum.

125

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Appendices A1. Distribution of calcareous grasslands in the study area located in the Southern Fraconian Alb

(map from Wagner et al. 2012). Square symbols correspond to previously abandoned grassland

patches that were reconnected with core areas (red polygons) in three non-overlapping

shepherding systems (red, blue, and green bold lines). Sheep grazing treatments (consistently and

intermittently) since 1989 and patches that had remained ungrazed are indicated by the shading

pattern of the square symbols. Inset map shows the distribution of calcareous grasslands (grey

areas) in Germany, while the orange box indicates the location of the study area. This map has

previously been published by the Journal of Ecography: Wagner et al. 2012. Permission to

use this published material in this dissertation has been obtained from the publisher

153

A2. Optimized α-values for each dispersal model and for each analyzed calcareous grassland plants. Bold numbers indicate the α-value for

the best performing dispersal model.

Table A2. Continue on the following page

Species Geographic distance (Eu)

Matrix resistance (Matrix)

Consistently grazed (Shecte)

Consistently and intermittently grazed (Sheint)

Grazed within the same grazed system (Shenu)

Hieracium pilosella 0.85 1.56 0.72 0.81 2.42

Leontodon hispidus 2.50 0.97 1.16 1.52 0.67

Sanguisorba minor 2.50 2.50 2.50 2.50 2.26

Arabis hirsute 1.19 2.50 1.57 0.85 1.05

Centaurea jacea 2.50 1.68 1.36 2.50 1.53

Koeleria pyramidata 0.66 0.47 2.50 1.96 1.78

Linum catharticum 0.72 1.34 0.67 0.56 1.78

Medicago lupulina 0.91 0.10 0.55 1.00 2.10

Plantago media 2.50 0.53 0.83 0.32 1.80

Polygala comosa 0.78 2.50 0.92 1.06 1.99

Prunella grandiflora 0.10 1.35 0.86 0.87 1.36

Ranunculus bulbosus 2.50 2.06 0.65 0.54 1.02

Salvia pratensis 2.30 2.50 0.11 0.10 2.15

Scabiosa columbaria 2.50 2.50 2.50 1.25 1.09

154

Species Geographic distance (Eu)

Matrix resistance (Matrix)

Consistently grazed (Shecte)

Consistently and intermittently grazed (Sheint)

Grazed within the same grazed system (Shenu)

Anthyllis vulneraria 1.07 0.66 2.50 2.50 2.22

Campanula rotundifolia 0.89 1.50 0.67 0.80 2.26

Carlina acaulis 1.84 1.53 0.63 0.72 1.51

Cirsium acaule 0.83 0.10 1.81 1.56 1.04

Pulsatilla vulgaris 0.87 2.50 2.50 2.50 1.92

Ajuga genevensis 2.50 0.40 0.37 2.50 1.04

Asperula cynanchica 0.60 2.29 2.50 1.13 1.02

Dianthus carthusianorum 1.86 2.20 0.65 1.45 2.43

Euphorbia cyparissias 2.50 2.50 2.50 2.40 1.58

Euphorbia verrucosa 0.68 0.45 2.50 0.37 2.30

Hippocrepis comosa 0.51 2.50 0.52 0.72 1.14

Ononis repens 0.28 0.53 0.91 0.46 2.09

Ononis spinosa 0.41 0.67 2.50 2.50 1.63

Onobrychis viciifolia 0.47 0.43 2.50 0.87 1.93

Phleum phleoides 0.41 0.43 0.13 2.50 1.58

Stachys recta 1.06 0.53 2.46 1.48 0.74

Trifolium montanum 2.50 1.40 0.71 0.64 1.57

155

A3. Genetic diversity indices of eleven loci amplified for 1,613 individuals from 64 populations

in Dianthus carthusianorum. Number of alleles per loci, Ho, observed heterozygosity; He,

expected heterozygosity, Ar mean allelic richness, FST, fixation index. SD, standard deviation of

the mean.

Locus No. alleles Ho (SD) He (SD) Ar (SD) FST

DC109 12 0.68 ± 0.02 0.69 ± 0.01 4.40 ± 1.0 0.035

MSACC 13 0.20 ± 0.02 0.2 ± 0.01 1.83 ± 0.39 0.039

MSBOX 6 0.48 ± 0.02 0.53 ± 0.01 2.40 ± 0.44 0.042

MSA30 7 0.35 ± 0.02 0.37 ± 0.02 2.56 ± 0.63 0.035

MSBSY 21 0.65 ± 0.02 0.80 ± 0.01 5.18 ± 1.1 0.037

CB004 11 0.67 ± 0.02 0.68 ± 0.02 4.08 ± 0.93 0.046

CB057 18 0.81 ± 0.01 0.87 ± 0.01 6.27 ± 1.4 0.044

CF003 19 0.77 ± 0.01 0.88 ± 001 6.60 ± 1.4 0.044

CB011 2 0.04 ± 0.01 0.04 ± 0.01 1.20 ± 0.25 0.045

CB027 11 0.65 ± 0.02 0.71 ± 0.07 4.10 ± 0.77 0.038

CB018 13 0.69 ± 0.02 0.76 ± 0.01 4.50 ± 1.0 0.056

Total 133 0.55 ± 0.25 0.59 ± 0.28 4.04 ± 1.7 0.042 ± 0.002

156

A4. Multiple comparisons with Tukey’s HSD (family-wise significance level of alpha = 0.05) of

mean scores for the three interclass PCA axes accounting for three shepherding systems and

populations in ungrazed patches. Significant values are in bold.

Pairwise groups Difference Lower limit Upper limit p-value

adjusted

PCA axis 1:

herd 2 vs. herd 3 -5.485 -7.750 -3.219 0.0001

herd 1 vs. herd 3 -0.460 -2.889 1.969 0.936

ungrazed vs.herd 3 -2.599 -5.140 -0.059 0.014

herd 1 vs. herd 2 5.024 3.112 6.937 0.0001

ungrazed vs. herd 2 2.885 0.833 4.937 0.0003

ungrazed vs. herd 1 -2.139 -4.371 0.092 0.023

PCA axis 2:

herd 2 vs. herd 1 -3.570 -4.922 -2.218 0.0001

herd 3 vs. herd 1 -5.703 -7.153 -4.254 0.0001

ungrazed vs. herd 3 -2.902 -4.418 -1.386 0.0001

herd 3 vs. herd 2 -2.133 -3.274 -0.992 0.0001

ungrazed vs. herd 2 0.668 -0.556 1.892 0.344

ungrazed vs. herd 3 2.801 1.469 4.132 0.0001

PCA axis 3:

Herd 2 vs. herd 3 0.426 -0.984 1.837 0.788

Herd 1vs. herd 3 0.729 -0.783 2.242 0.454

Ungrazed vs. herd 1 4.121 2.539 5.704 0.0001

Herd 1 vs. herd 2 0.302 -0.888 1.493 0.862

Ungrazed vs. herd 2 3.695 2.416 4.973 0.0001

Ungrazed vs. herd 3 3.392 2.002 4.782 0.0001

157

A5. A) Plot of the lowest values of the deviance information criterion (DIC) averaged over 10

run using TESS with admixture models varying K from 2 to 10, ψ = 0.6, burn-in lengths of

100,000 and with 5000 sweeps. Although the plateau starts at kmax = 4, we observed that

additional clusters of runs kmax = 3 to 10 were ambiguously identified as they had very low

membership probabilities. B) Posterior estimates of cluster memberships for kmax 2 to 5. The

two main clusters at the east and the west of the study area remained relatively consistent

among runs, with little variation of membership scores of the added clusters for each k.

A)

B)

A)

B)

kmax = 2

kmax = 3

kmax = 4

kmax = 5

158

A6. Estimates of genetic diversity calculated for eleven polymorphic loci amplified in 49 Dianthus carthusianorum populations

defined by population size, population history, and shepherding system. Populations with less than ten individuals are not included. N,

sampled size; Ho, observed heterozygosity; He, expected heterozygosity, Ar allelic richness corrected by rarefaction; FIS, inbreeding

coefficient.

Population Latitude Longitude N

Population

size

Population

history

Shepherding

system Ho He Ar FIS

E01 48º99’85.6”N 10º 96’01.9”E 34 big unknow herd 1 0.553 0.613 4.54 0.099

G01 49º 01’37.0”N 10º96’85.0”E 29 big unknow herd 1 0.566 0.607 4.27 0.03

G100 48º 99’44.5”N 10º96’56.9”E 58 big unknow herd 1 0.547 0.603 4.45 0.123

G16 48º 97’39.7”N 10º97’01.3”E 20 big unknow herd 1 0.524 0.598 4.49 0.093

G20 48º 97’09.0”N 10º95’56.3”E 29 big unknow herd 1 0.566 0.602 4.5 0.107

N03 48º 98’75.5”N 10º95’67.5”E 27 big unknow herd 1 0.545 0.637 4.7 0.108

N05 49º 02’68.4”N 11º00’51.6”E 28 big unknow herd 1 0.513 0.573 4.33 0.066

A45 48º 97’44.4”N 10º94’83.7”E 16 small colonized herd 1 0.592 0.603 4.32 0.02

A06 48º 99’08.2”N 11º05’60.3”E 29 big colonized herd 2 0.542 0.638 4.58 0.097

A07a 48º 98’37.7”N 11º06’08.5”E 27 big colonized herd 2 0.548 0.592 4.35 0.085

A14 48º 95’93.0”N 11º02’21.7”E 16 big pre-existing herd 2 0.528 0.542 3.56 0.11

A18 48º97’18.8”N 11º00’32.4”E 27 big pre-existing herd 2 0.567 0.591 4.35 0.045

Table A6. Continue on the following page

159

Population Latitude Longitude N

Population

size

Population

history

Shepherding

system Ho He Ar FIS

E07 49º00’36.0”N 11º06’41.2”E 15 big unknow herd 2 0.596 0.477 4.45 0.102

G05a 49º04’65.6”N 11º03’19.9”E 27 big unknow herd 2 0.538 0.598 4.45 0.145

G13 49º00’79.1”N 11º06’30.4”E 41 big unknow herd 2 0.536 0.599 4.67 0.069

G37 48º98’29.6”N 11º01’59.2”E 26 big unknow herd 2 0.515 0.606 4.6 0.10

G40 48º97’79.6”N 11º03’93.8”E 30 big unknow herd 2 0.563 0.570 4.26 0.153

G45 48º97’42.5”N 11º05’57.4”E 29 big unknow herd 2 0.487 0.566 4.17 0.111

G46 48º97’67.4”N 11º02’53.1”E 28 big unknow herd 2 0.521 0.583 4.44 0.05

G47 48º97’74.3”N 11º01’50.2”E 26 big unknow herd 2 0.549 0.591 4.42 0.121

G48 48º98’93.5”N 11º06’06.7”E 26 big unknow herd 2 0.558 0.613 4.56 0.108

G49 48º98’61.2”N 11º05’50.1”E 30 big unknow herd 2 0.511 0.576 4.26 0.065

G50 48º98’06.8”N 11º05’52.2”E 29 big unknow herd 2 0.544 0.582 4.45 0.126

A03 48º99’78.1”N 11º05’99.4”E 26 small colonized herd 2 0.517 0.571 3.89 0.096

A05 48º99’27.3”N 11º05’62.1”E 25 small colonized herd 2 0.509 0.577 4.21 0.09

A08 48º97’01.8”N 11º02’60.1”E 20 small colonized herd 2 0.486 0.582 4.27 0.146

A12 48º96’61.4”N 11º02’60.8”E 12 small pre-existing herd 2 0.462 0.587 4.28 0.22

C08 48º95’56.7”N 11º02’89.5”E 30 small unknow herd 2 0.494 0.525 3.84 0.14

A28 48º95’56.8”N 10º94’13.8”E 33 big pre-existing herd 3 0.565 0.598 4.21 0.056

A29 48º95’47.4”N 10º94’26.2”E 27 big pre-existing herd 3 0.571 0.600 4.38 0.048

Table A6. Continue on the following page

160

Population Latitude Longitude N

Population

size

Population

history

Shepherding

system Ho He Ar FIS

A38 48º93’66.0”N 10º98’19.3”E 29 big pre-existing herd 3 0.552 0.598 4.45 0.078

G21 48º96’60.8”N 10º97’35.2”E 29 big unknow herd 3 0.554 0.612 4.68 0.127

G23 48º96’17.8”N 10º96’53.9”E 27 big unknow herd 3 0.558 0.632 4.49 0.096

G26 48º95’57.3”N 10º95’09.4”E 35 big unknow herd 3 0.555 0.597 4.63 0.037

G28 48º94’86.8”N 10º95’66.9”E 26 big unknow herd 3 0.539 0.589 4.55 0.044

G29 48º95’04.2”N 10º94’93.4”E 29 big unknow herd 3 0.509 0.575 4.41 0.072

G30 48º94’22.0”N 10º97’77.0”E 28 big unknow herd 3 0.512 0.609 4.44 0.13

G31 48º94’23.4”N 10º98’35.6”E 30 big unknow herd 3 0.544 0.604 4.55 0.117

Gzim 48º92’48.2”N 10º98’96.8”E 28 big unknow herd 3 0.564 0.637 4.92 0.099

A26 48º95’91.9”N 10º94’27.4”E 25 small colonized herd 3 0.538 0.603 4.52 0.11

A31 48º95’37.8”N 10º94’51.0”E 23 small colonized herd 3 0.514 0.594 4.38 0.138

A33 48º94’85.9”N 10º93’68.9”E 35 small colonized herd 3 0.542 0.602 4.37 0.101

A25 48º96’04.1”N 10º94’21.8”E 11 small pre-existing herd 3 0.521 0.582 4.05 0.108

A37 48º94’75.9”N 10º98’46.7”E 30 big colonized ungrazed 0.499 0.576 4.1 0.136

N10 48º95’99.9”N 10º97’91.1”E 23 big unknow ungrazed 0.505 0.540 3.93 0.102

E03 48º94’52.7”N 10º93’75.8”E 15 small unknow ungrazed 0.577 0.577 4.1 0.0

E04 48º95’01.3”N 11º01’52.8”E 30 small unknow ungrazed 0.578 0.579 4.01 0.034 E09 48º97’02.8”N 10º95’03.9”E 32 small unknow ungrazed 0.531 0.605 4.37 0.034

Nroth 49º04’22.4”N 11º00’16.6”E 14 small unknow ungrazed 0.429 0.475 3.64 0.107