And Regional-Level Population Biology, with Particular Reference to Gleditsia Triacanthos (Fabaceae): Landscape and Plant Ecology Across the Geographic Range

University of Windsor Scholarship at UWindsor

Electronic Theses and Dissertations Theses, Dissertations, and Major Papers

2005

Patch- and regional-level population biology, with particular reference to Gleditsia triacanthos (Fabaceae): Landscape and plant ecology across the geographic range.

Helen T. Murphy University of Windsor

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Recommended Citation Murphy, Helen T., "Patch- and regional-level population biology, with particular reference to Gleditsia triacanthos (Fabaceae): Landscape and plant ecology across the geographic range." (2005). Electronic Theses and Dissertations. 3016. https://scholar.uwindsor.ca/etd/3016

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. PATCH- AND REGIONAL-LEVEL POPULATION BIOLOGY, WITH PARTICULAR REFERENCE TO GLEDITSIA TRIACANTHOS (FABACEAE): LANDSCAPE AND PLANT ECOLOGY ACROSS THE GEOGRAPHIC RANGE.

Helen T. Murphy

A Dissertation Submitted to the Faculty of Graduate Studies and Research through the Department of Biological Sciences in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy at the University of Windsor

Windsor, Ontario, Canada 2005

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ABSTRACT Ecological analyses at large spatial scales have emerged over the past decade in response to increasing awareness of the importance of landscape- and regional-scale processes in structuring fragmented terrestrial communities. Even relatively large-scale ecological studies, however, are usually not large enough to observe the variation in conditions that a species experiences across its entire geographic range. The geographic ranges of tree species often encompass great latitudinal and altitudinal scope, and all the associated climatic and geological variability which subsequently affects a suite of ecological, physical and physiological factors.

In this thesis I examine the applicability of classic theories and current frameworks for understanding regional-scale population dynamics of plants across their geographic range. I have reviewed several literature bases from the general ‘tree’ perspective, including metapopulation- and landscape ecology, patch-matrix models, connectivity and species ranges. I analyze population performance parameters at 22 populations of the dioecious tree Gleditsia triacanthos in relation to position in the range, population size and density, and measures of the surrounding landscape structure at various spatial scales. I examine the distribution of abundance across the geographic range ofG. triacanthos with particular attention to the predictions of the central-peripheral model. I used GIS datasets of landcover to extract measures of the spatial structure of landscapes across the range, and determine whether variation in abundance can be explained by landscape spatial structure, and whether this is consistent across the range. Finally, I used GARP (Genetic Algorithm for Rule-Set Production) to develop habitat suitability models based on known occurrences in particular regional conditions, as well as conditions across the geographic range as a whole, and interpret the models in terms of niche breadth and overlap between central and peripheral populations.

Many factors influence the distribution of abundance and population performance across the range in Honey Locust, and effects of these factors appear to differ regionally and latitudinally. Abundance and performance of G. triacanthos populations were not simply related to position in the range as predicted by the central-peripheral model. Populations

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. at the western periphery of the species’ range have a broad niche, show active recruitment and high density and abundance, but high levels of developmental instability and low survivorship. Populations in the southern part of the range have a narrower niche breadth and show low recruitment, density, abundance and developmental instability, but high survivorship. Geographically central and northern parts of the range contain populations with a mix of demographic structures and a range of population performance values. Trees in the southern part of the range appear to have virtually no niche overlap with trees in the central part of the range. The niche space occupied by western trees overlaps to some degree with that of the central trees and to a lesser extent, with that of southern trees.

The results suggest abiotic conditions may be more limiting in the western region of the range, while competition likely plays a more significant role in limiting population performance, distribution and niche breadth in southern populations. The successional stage of the site probably influences population performance and abundance of trees in central and northern parts of the range and a lack of available suitable habitat may limit further extension of the range to the north. In general, I suggest a grid-based functional mosaic approach, utilizing relevant scale- and regional-specific gradients of abiotic, biotic, and historical and human impact-based parameters of importance to the particular species, is required to portray the fate of plant populations.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ACKNOWLEDGMENTS Many, many thanks to: An amazing supervisor - Jon Lovett-Doust And committee - Lesley Lovett-Doust, Peter Sale, Phil Graniero and Carl Freeman Funding sources - SHARCNET, Grad Studies - University of Windsor

Labmates - Nancy MacDonald, Jeremy VanDerWal, Lisa Tulen, Izabela Woznicksa Friends - Paul and Karen Chittaro, Chelsey Lumb, Mel Wickham, Rebecca Fisher

My family - Dad, Mum, Laurie, Mary, Victor and Cathie, Michael and Michaela, and Kate Kritzer family - John, Irene and Becky

And especially Jake and Caz.

I would like to thank the permitting agencies, managers/superintendents and staff at the following Parks, areas and agencies: Ohio Department of Natural Resources, and Ohio Division of Parks and Recreation, A.W. Marion State Park, Deer Creek State Park, East Harbor State Park, Delaware Wildlife Area; Illinois Department of Natural Resources, Giant City State Park, Lake Murphysboro State Park; Shawnee National Forest-Lake Glendale and Cedar Lake areas; Tennessee Department of Environment and Conservation, Big Cypress Tree State Natural Area; U.S. Department of Agriculture, Land Between the Lakes National Recreation Area; Ontario Ministry of Natural Resources, Fish Point Provincial Nature Reserve; Parks Canada, Point Pelee National Park; United States Department of the Interior Fish and Wildlife Service Louisiana, Tensas River National Wildlife Refuge, Bayou Cocodrie National Wildlife Refuge, Pomme de Terre Wildlife Management Area; Mississippi Department of Wildlife, Fisheries and Parks, Natchez State Park; United States Army Corps of Engineer, Kansas, Perry Lake, Tuttle Creek, Melvem Lake and Milford Lake.

permission of the copyright owner. Further reproduction prohibited without permission. STATEMENT OF ORIGNINALITY

I certify that this thesis, and the research to which it refers, are the product of my own work, and that any ideas or quotations from the work of other people, published or otherwise, are fully acknowledged in accordance with the standard referencing practices of the discipline. I acknowledge the helpful guidance and support of my supervisor, Dr Jon Lovett-Doust.

I hereby certify that the work embodied in this thesis is the result of original research and has not been submitted for a higher degree to any other University or Institution.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. TABLE OF CONTENTS

ABSTRACT...... iii ACKNOWLEDGEMENTS...... v STATEMENT OF ORIGINALITY...... vi LIST OF TABLES...... xii LIST OF FIGURES...... xiv LIST OF APPENDICES...... xvii

Chapter 1 - General Introduction...... 1 Background...... 1 Thesis objectives...... 2 Plant population ecology across the geographic...... range 2 Plant population ecology in a fragmented landscape...... 3 Thesis Structure...... 5 References...... 7 Chapter 2 - Living at the edge vs life at the centre: plant population ecology across the geographic range ...... 11 Introduction...... 11 The niche and geographic ranges...... 11 The central-peripheral model...... 12 The limits to species' ranges...... 13 History and temporal dynamics o f ranges...... 17 Species responses at edges...... 19 A note on terminology...... 19 Abundance ...... 20 Demography...... 22 Genetic diversity and adaptation ...... 24 Developmental instability...... 27 Different edges - different responses...... 29 Edge o f range effects: a matter o f scale?...... 30

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Living on the edge - not as 'stressful ’ as it sounds?...... 33 Alternatives to the central-peripheral model...... 35 Conclusion...... 36 References...... 37 Chapter 3 - Context and connectivity in plant metapopulations and landscape mosaics: does the matrix matter?...... 53 Introduction...... 53 The matrix from a plant perspective...... 56 Characterizing the matrix: toward a functional mosaic approach...... 58 Effects o f scale...... 61 Measuring connectivity...... 62 Incorporating the matrix in measures o f connectivity...... 63 Effect of landscape context on connectivity...... 65 Effect of corridors and “stepping-stones” on connectivity...... 67 Effect of edges on connectivity...... 69 Effects of matrix land u se...... 70 A functional mosaic approach...... 71 Conclusions...... 73 References...... 74 Chapter 4 - Effects of different edges versus central sites on patch- and landscape- level population biology inGleditsia triacanthos (Honey Locust)...... 87 Introduction...... 87 The central-peripheral model...... 87 Landscape fragmentation and plant population dynamics...... 90 Methods...... 91 Gleditsia triacanthos, Honey Locust...... 91 Study site locations...... 94 Vegetative and reproductive output...... 94 Population parameters...... 94 Landscape parameters ...... 95 Statistical analyses...... 96

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Results...... 96 Population size-frequency distributions...... 96 Population sex-ratios...... 97 Reproductive output...... 97 Vegetative output...... 99 Landscape spatial structure ...... 99 Discussion...... 100 Population demographic parameters...... 100 Landscape spatial structure ...... 103 References...... 106 Chapter 5 - Landscape-level effects on developmental instability: fluctuating asymmetry across the range of Honey Locust,Gleditsia triacanthos (Fabaceae).... 130 Introduction...... 130 Methods...... 132 Study organism...... 132 Fluctuating asymmetry...... 133 FA and trait size ...... 134 Measurement error...... 134 Statistical analysis...... 135 Results...... 135 Discussion...... 136 Correlations between FA characters...... 136 FA and landscape-level parameters ...... 137 Conclusions...... 139 References...... 140 Chapter 6 - Distribution of landscape spatial structure and abundance across the range of Gleditsia triacanthos (Honey Locust)...... 157 Introduction...... 157 The distribution of abundance and central-peripheral models ...... 157 Effects of landscape spatial structure on abundance ...... 159 Methods...... 161

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Study Species - Gleditsia triacanthos (Honey Locust)...... 161 Abundance estimates ...... 161 Landscape spatial structure...... 163 Results...... 164 Spatial autocorrelation in abundance...... 165 Landscape spatial structure and abundance ...... 166 Discussion...... 167 The central-peripheral model of distribution of abundance ...... 167 The distribution of abundance and landscape spatial structure ...... 168 Implications of scale...... 169 Conclusions...... 169 References...... 171 Chapter 7 - Niche differentiation and habitat suitability across the range in Gleditsia triacanthos (Honey Locust)...... 191 Introduction...... 191 Methods...... 193 Study species...... 193 Land cover...... 193 Soil variables...... 194 Digital elevation...... 194 GARP ecological niche model...... 194 Results...... 195 Discussion...... 197 The relationship between habitat and the niche ...... 198 References...... 201 Chapter 8 - Scaling problems in plant ecology and the importance of regionality and the landscape mosaic ...... 212 Introduction...... 212 General results...... 213 The central - peripheral model...... 213 Landscape spatial structure ...... 214

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Range limitation and niche differentiation...... 214 Regionally versus the central-peripheral model...... 216 Fragmentation from the perspective o f an early successional...... tree 218 Landscape connectivity and the functional mosaic approach...... 220 References...... 222 APPENDICES...... 226 COPYRIGHT RELEASES...... 235 VITA AUCTORIS...... 236

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LIST OF TABLES Table 3.1 - Components of a landscape mosaic approach to connectivity: parameters of structural (physical and spatial) and functional (community level and population level) contexts in a landscape...... 86 Table 4.1 - Population locations, arranged from most northerly to most southerly latitudes, with number of male, female and vegetative trees sampled, total population size (including unsampled individuals), approximate distance from centre of geographic range and the landcover type in which the sampled trees occur at the site (‘minor’ next to a landcover type indicates only a few individuals occur in that landcover type at the site)...... 114 Table 4.2 - Landcover classifications used in analysis. Landcover classes in bold were used in the correlation analysis...... 117 Table 4.3 - Sex ratio and results of Chi Squared tests of departure from a 1:1 sex ratio, for the overall population and for size classes 2, 3 and 4. (sex ratios > 1 indicate male bias) y2 values in grey shading are significant at p < 0.05...... 118 Table 4.4 - Results of correlation analysis between parameters of population performance. Correlation coefficients in grey shading are significant at (*) p<0.05 and (**) p < 0.01 ...... 119 Table 4.5 - Results of correlation analysis between population performance parameters and landscape factors. Correlation coefficients in grey shading are significant at (*) p<0.05 and (**) p < 0.01 ...... 120 Table 5.1 -Population locations arranged from most northerly to most southerly latitudes, with number of trees sampled, and site parameters, including nearest-neighbour (NN) distances between trees ...... 146 Table 5.2 - Pearson correlation coefficients between leaflet length FA (LFA), intemodal distance FA (DFA), size-adjusted LFA (LFAR) and DFA (DFAR) and leaf length and leaf width (n = 3062) ...... 148 Table 5.3 - Nested ANOVA results for the effects of landscape (region) and site on log leaflet length FA (logLFA) and intemodal distance FA (logDFA)...... 149 Table 5.4 - Result of principal component analysis of climatic factors - amount of variance explained by component 1 for each climatic parameter...... 150

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 5.5 - Results of Pearson correlations between leaflet length FA (LFA) and intemodal distance FA (DFA) measures and landscape-level and environmental parameters (N = 18). Note: no probabilities remained significant (p > 0.05) after Bonferroni adjustment for multiple comparisons...... 151 Table 5.6 - Results of Pearson correlation analysis between leaflet length FA (LFA) and intemodal distance FA (DFA) distance measures, and latitude and geographic distance measures (N = 306) ...... 152 Table 6.1 - Landcover classifications used in analysis. Landcover classes in bold were used in the regression analysis...... 176 Table 6.2 - Landscape parameters used in linear regression analysis ...... 177 Table 6.3 - Number and proportion of cells in each importance value (IV) category within the range ofG. triacanthos...... 178 Table 6.4 - Results of linear regression analysis of landscape parameters and importance value for all cells and for three of the range quadrants...... 179 Table 6.5 - Correlation matrix of landscape structure parameters and latitude and longitude (n = 212). In Landscape Parameters column, number denotes the landcover class; letters denote the landscape parameter abbreviation (see Table 6.2). 181 Table 7.1 - Parameters used in GARP habitat suitability modelling ...... 205 Table 7.2 - Suitability classifications based on the composite output of 20 GARP runs (e.g., grid cell value of 0 indicates 0 of 20 runs predicted presence in that cell).... 206 Table 7.3 - Proportion of trees in each region classified as occurring in any class of suitable habitat using the individual regional tree-models and the all-tree model.. 207 Table 7.4 - Proportion of each class of predicted habitat suitability summed over the study area, using the individual regional tree-models and the all-tree model 208

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LIST OF FIGURES Figure 4.1 - Range ofGleditsia triacanthos (in grey shading) (from Prasad and Iverson 2003) and site locations...... 121 Figure 4.2 - Mean proportion of individuals in each CBH size class (0 = <5 cm; 1 = <15 cm; 2 = 16-50 cm; 3 = 51-100 cm; 4 = > 100 cm) in (a) central, (b) northern, (c) southern, and (d) western populations...... 122 Figure 4.3 - Site rankings by proportion of individuals in the size class for classes 0 (< 5cm cbh), 1 (5-15 cm cbh), 2 (15-50cm cbh), 3 (15-50 cm cbh) and 4 (>100 cm cbh). The site with the highest proportion of individuals in a size class would be ranked 22, for that size class ...... 123 Figure 4.4 - Proportion of individuals of each sex (vegetative/unknown [u], male [m], female [f]) in each size class (0 = <5 cm; 1 = <15 cm; 2 = 16-50 cm; 3 = 51-100 cm; 4 = > 100cm) in (a) central, (b) northern, (c) southern, and (d) western populations...... 124 Figure 4.5 - Mean regional proportions of individual (a) males flowering, (b) females flowering and (c) females fruiting in year one (filled squares) and year two (unfilled squares)...... 125 Figure 4.6 - Mean number of male inflorescences at each site in year 1 and year 2...... 126 Figure 4.7 - Mean number of female (a) inflorescences and (b) fruits at each site in year 1 and year 2...... 127 Figure 4.8 - Mean number of branches per tree at each site ...... 128 Figure 4.9 - Mean proportion of the landscape composed of (o) agricultural land, (•) water, (□) upland deciduous forest, (■) pasture/grassland and (0) bottomland deciduous forest...... 129 Figure 5.1 - Range of Gleditsia triacanthos (in grey shading) (from Prasad and Iverson 2003) and site locations...... 153 Figure 5.2 - Leaflet length FA (LFA) measured along the length of the midvein of each leaflet, and intemodal distance FA (DFA) measured along the leaf rachis from the base of the leaf to each leaflet...... 154 Figure 5.3 - (a) Mean regional leaflet length FA (LFA) and (b) intemodal distance FA (DFA) and results of LSD post-hoc multiple comparison test. The same letters

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. above regions indicate no significant difference in mean values (NB Post-hoc comparisons were calculated on absolute log-transformed FA values)...... 155 Figure 5.4 - Mean (a) leaflet length FA (LFA) and (b) intemodal distance FA (DFA) values at each site arranged by latitude ...... 156 Figure 6.1 - Importance values across the geographic range ofG. triacanthos...... 182 Figure 6.2 - Proportion of unoccupied (solid bars) and occupied (hollow bars) cells at each distance category forG. triacanthos...... 183 Figure 6.3 - Mean importance value (IV) from the closest to the centre (1) to the edge of range (10) in 10% increments of the total range for G. triacanthos for (a) all cells and (b) occupied cells only. Note the different y-axis scales...... 184 Figure 6.4 - Mean importance value (IV) from the closest to the centre (1) to the edge of range (10) in 10% increments of the total range forG. triacanthos for (a) quadrant 1 (b) quadrant 2, (c) quadrant 3 and (d) quadrant 4 cells ...... 185 Figure 6.5 - Mean number of co-occurring species from the closest to the centre (1) to the edge of range (10) in 10% increments of the total range forG. triacanthos 186 Figure 6.6 - Mean importance value (IV) for four species of Fabaceae from the centre (1) to the edge of range (10) in 10% increments of the total range (a) all cells and (b) occupied cells only...... 187 Figure 6.7 - Mean importance value (IV) from the centre (1) to the edge of range (10) in 10% increments of the total range for all pioneer species (n = 27; see Appendix 6.1) for (a) all cells and (b) occupied cells only. Note the different y-axis scales 188 Figure 6.8 - Proportion of cells unoccupied (solid bars) and occupied (hollow bars) from (a) southern to northern latitudes and (b) western to eastern longitudes; and mean importance value from (a) southern to northern latitudes and (b) western to eastern longitudes for G. triacanthos...... 189 Figure 6.9 - Spatial autocorrelogram showing Moran’s / at increasing distances between cells. One unit on the horizontal axis indicates one interval of the maximum distance between cells, with 1 representing cells in the closest 10 % of the range of distances ...... 190 Figure 7.1 - Native geographic range ofGleditsia triacanthos (in stipple) and location of regions for which GARP habitat-suitability models were generated. Shown is the

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. GARP all-tree habitat suitability model for the western (Kansas), southern (Louisiana), and central (southern Illinois, Kentucky and Tennessee) regions. Darker shading indicates higher habitat suitability, as predicted by GARP ...... 209 Figure 7.2 - Proportion of unsuitable and very low- to high-suitability cells in (a) central, (b) western, and (c) southern regions, generated using the individual regional tree models...... 210 Figure 7.3 - Proportion of unsuitable and very low- to high-suitability cells in each region, generated using the all-tree model...... 211

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LIST OF APPENDICES Appendix 4.1 - Correlations between landscape fragmentation indices at four scales. LPD = Total landscape patch density...... 226 Appendix 5.1 -Location of nearest weather station to each site and historical climatic variables ...... 228 Appendix 5.2 - Mean, standard error, kurtosis and skew statistics for signed leaflet length FA (LFA) and intemodal distance FA (DFA) at each site ...... 230 Appendix 6.1 - Species considered to be pioneer and/or shade intolerant bottomland species...... 231 Appendix 7.1 - Landcover classifications used in analysis...... 232 Appendix 7.2 - Soil attribute data extracted from STATSGO soil survey maps 233

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter 1 - General Introduction

Background

Plants serve as the base of nearly all earth’s ecosystems, and trees (so much more than herbs and plants of other growth forms) bring prominence and some permanence to earth’s terrestrial ecosystems. Tree-dominated communities are among the richest in both ecological structure and biotic diversity, in both above- and below-ground aspects. Increasingly however, human land uses are impacting and altering the structure of terrestrial communities. For most tree species today, the physical arena — the geographic location —within which population dynamics, ecological processes, adaptation and evolution occur is becoming increasingly fragmented and otherwise degraded.

Most ecological processes and interactions depend on spatial scales much larger than that of a single patch of habitat and certainly larger than the locus of single plant individuals, and ecologists have become increasingly aware of the importance of linking spatial patterns with ecological processes at various scales (e.g., Turneret al. 2001; Bullock et al. 2002; Thies et al. 2003). Ecological analyses at large spatial scales have emerged over the past decade in response to increasing awareness of the importance of landscape- and regional-scale processes in structuring fragmented terrestrial communities (Freckleton & Watkinson 2002, Eriksson & Ehrlen 2001). Even relatively large-scale ecological studies, however, are usually not large enough to observe the variation in conditions that a species experiences across its entire geographic range. The geographic ranges of tree species often encompass great latitudinal and altitudinal scope, and all the associated climatic and geological variability which subsequently affects a suite of ecological, physical and physiological factors.

Although trees may seem particularly useful subjects for ecological research as a result of their prominence in terrestrial communities, and their sessile nature, their strong spatial structure and restricted dispersal, research in large-scale population dynamics unfortunately has lagged behind that of other organisms. Certain aspects of tree

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. population ecology (such as their longevity and their often sporadic reproduction) make them difficult to study in the average, rather short-term, research project.

Thesis objectives

Plant population ecology across the geographic range

To a first approximation, a species’ geographic range represents a spatial expression of its niche (Brown 1984). However, many other factors may influence the realization of that niche expression (e.g., Holtet al. 2005). Recent concepts of source-sink dynamics, metapopulation dynamics and dispersal limitation complicate any relationship between a species’ niche and its geographic distribution (Pulliam 2000), particularly in light of the increasing ‘patchiness’ of landscapes as a result of habitat fragmentation.

The central-peripheral range model posits that habitat suitability declines from the centre of a species range towards the edge (Hengeveld & Haeck 1982; Brown 1984; Lawton 1993; Guoet al. 2005). According to this widely held model (Sagarin & Gaines 2002), there will be a decreasing number of local sites where a species can occur at all and, even within these patches, population densities will tend to be lower because resources are scarce and/or conditions approach the limits that can be tolerated (Brown 1984). In addition, the performance of species at the range periphery is commonly assumed to decline due to the scarcity of resources and suboptimal conditions there (Lawton 1993). Peripheral populations are also expected to have less genetic variation than central

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. populations due to genetic drift, founder effects, bottlenecks and inbreeding, and diminished sexuality (Levin 1970; Lawton 1993; Lesica & Allendorf 1995).

Populations at the edge of a species range are noteworthy for a number of reasons, not the least of which is what they may reveal about the processes that circumscribe species ranges; moreover range borders provide an important subset of populations for studying biological rarity and its causes (Gaston & Kunin 1997). These populations may contain important genetic diversity since they likely face environmental conditions atypical of the species’ overall distribution (Kark et al. 1999). Peripheral populations are also of interest in understanding the rapid range expansions of non-native invasive species (Lonsdale 1999). Peripheral populations (and even isolated adventive populations beyond the range boundary) can serve as foci for range expansion in the face of changing climatic conditions (see e.g., Parshall 2002). Given the potential importance of edge-of-range populations, the relative paucity of both empirical and theoretical work is surprising.

Plant population ecology in a fragmented landscape

It is now widely recognized that most species are patchily distributed in nature and the way in which population dynamics of species are affected by this patchy landscape structure has become a major focus of ecological research. Most landscapes have been influenced by human land use and the resulting landscape mosaic is a mixture of natural and human-managed patches that vary in size, shape and arrangement.

Patches remaining in the fragmented landscape contain populations of smaller sizes, which are more isolated from each other than in continuous habitat. These populations are thought to have a greater risk of extinction due to demographic and genetic effects associated with the changes in landscape structure (Ouborg 1993; Widen 1993; Hanski & Gilpin 1997). Increased inbreeding and genetic erosion (Heschel & Paige 1995; Buza et al. 2000; Mavraganis & Eckert 2001), disruption of pollinator associations and connectivity for pollen and seed flow (Schnabel & Hamrick 1995; Nason & Hamrick 1997; Murphy & Lovett-Doust 2004a), and weed invasion and effects of increased edges (Aizen & Feinsinger 1994; Murcia 1995; Jules 1998) are all factors likely to impact on

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the persistence of small, fragmented populations. Resultant decreases in both long- and short-term population viability and average individual fitness are expected to result from the effects of habitat fragmentation (Lande 1988; Ouborg & Van Truen 1995). Patterns of developmental instability (usually measured as fluctuating asymmetry) have been proposed as a useful tool for quantifying the degree of environmental and genetic stress that individuals experience during their development (Kark et al. 2004). Associations between developmental stress and fluctuating asymmetry are particularly interesting from the general perspective of fragmented and marginal populations, which are thought to be exposed to greater levels of genetic and environmental stress (Murphy & Lovett-Doust 2004b).

Finally, metapopulation theory has gained increasing support amongst ecologists with the recognition that dynamics of natural populations should be addressed at a larger scale than that of the local population. According to the Levins classical metapopulation concept, all local populations have a substantial probability of extinction, and therefore long term persistence of a species is regulated at the regional, or metapopulation level (Hanski 1999). The key premises of the metapopulation approach to population dynamics are that local populations inhabit spatially disjunct habitat patches and that migration among the patches has some effect on local dynamics, including the possibility of recolonization of patches where populations have become extinct.

As Hanski (1999) noted, populations in nature exhibit continuous variation in their spatial structures and different approaches to understanding population dynamics are likely to be most effective in different kinds of systems. Recent reviews of evidence forplant metapopulation prevalence in nature have concluded that most species appear not to be arranged as metapopulations (Husband & Barrett 1996; Bullock et al. 2002; Freckleton & Watkinson 2002) —hence other frameworks may be necessary for understanding large- scale, regional dynamics in plants (Murphy & Lovett-Doust 2004a). Integration of realistic, spatially structured landscape elements and organism-based parameters of population biology into models of regional plant population persistence has not been easy, due in part to the complexities of the models required, but also to the lack of

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. empirical data describing how landscape structure affects aspects of the population biology of, particularly long-lived, plant species.

Thesis Structure

Chapter 1 (this chapter) gives a general introduction and an overview of the thesis.

Chapter 2 contains a review of the main factors considered to limit plant species ranges, and how populations of species respond to these limiting factors in terms of abundance, demography, genetic diversity and developmental instability. The potential for different factors limiting distributions and consequent species responses at different parts of the range edge, and potential confounding effects of scale are also discussed. I note the paucity of empirical results supporting the central-peripheral model in plants and suggest other models may be needed to explain variation in the responses of species at range edges.

Chapter 3 describes the dominant current frameworks for understanding large-scale, regional dynamics of plant populations. In particular, I compare related paradigms from the disciplines of landscape ecology and metapopulation ecology with emphasis on treatment of the matrix, that is, the landscape surrounding the habitat within which focal plant populations exist. I review important effects of the matrix - via composition and configuration of habitat patches, extent of edges, patterns of land use, etc., upon plant populations. Finally, I describe a functional landscape mosaic approach that treats structural and functional features of the landscape and show how these interact to determine the fate of plant populations.

In Chapter 4 I present the results of three years of field work measuring demographic parameters and reproductive effort in 22 populations ofG. triacanthos across its geographic range. Populations are located in northern, southern and western peripheral areas as well as in geographically central parts of the range. I analyse size distribution frequency patterns and variation in male and female reproductive effort in G. triacanthos

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. populations in relation to position in the range, population size and density, and measures of the surrounding landscape structure at various spatial scales.

A further component of my research involves measurement of the responseG. of triacanthos to stressors incurred during development, leading to developmental instability, and postulates that these stressors may be more pronounced in ecologically- marginal, or geographically-peripheral populations. InChapter 5 I report patterns of leaf fluctuating asymmetry, a common measure of developmental instability, in 18 populations across the geographic range ofG. triacanthos.

In Chapter 6 ,1 examine the distribution of abundance across the geographic range ofG. triacanthos with particular attention to the predictions of the central-peripheral model. I use GIS datasets of landcover to extract measures of the spatial structure of landscapes across the range, and determine whether variation in abundance can be explained by landscape spatial structure and whether this is consistent across the range.

I develop a gradient-based habitat suitability model for G. triacanthos based on known occurrences of the species and reported patterns of landcover, soil types, geology and elevation. In Chapter 7, suitability models based on occurrence in particular regional conditions, as well as conditions across the geographic range as a whole, are built and compared to determine whether abiotic axis of the species’ niche space vary across the geographic range.

In Chapter 8 1 first discuss the results in the context of traditional theories about species performance across the geographic range, and relationships between notions of the niche, spatial patterns in environmental variation, and habitat- and scale-specific species responses. I suggest how current frameworks for understanding plant population persistence should be updated or recast to account for regional differences in species responses to their environment and to the structure of the landscape around them.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Lawton, J.H. (1993) Range, population abundance and conservation.Trends in Ecology & Evolution, 8,409-413.

Lesica, P. & Allendorf, F.W. (1995) When are peripheral-populations valuable for conservation?Conservation Biology, 9, 753-760.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter 2 - Living at the edge vs life at the centre: plant population ecology across the geographic range

Introduction

Populations at the edge of a species range are noteworthy for a number of reasons, not the least of which is what they may reveal about processes circumscribing species ranges; moreover range borders provide an important subset of populations for studying biological rarity and its causes (Gaston & Kunin 1997). These populations may contain important genetic diversity since they likely face environmental conditions atypical of the species’ overall distribution (Kark et al. 1999). Peripheral populations are also of interest in understanding the rapid range expansions of non-native invasive species (Lonsdale 1999). Interest in determinants of range size and range limits has increased as a result of concerns about the effects of climate change on species distributions (e.g., Brzezieckiet al. 1995; Leathwick 1995; Flannigan & Bergeronl998; MacDonaldet al. 1998; Miller & Halpern 1998). Peripheral populations (and even isolated adventive populations beyond the range boundary) can serve as foci for range expansion in the face of changing climatic conditions (see e.g., Parshall 2002). In some cases, especially where species cross political or biogeographic boundaries, edge populations are of particular conservation interest as the sole representatives of their species in a given region or jurisdiction (Lennon et al. 2002). Given the potential importance of range-margin populations, the relative paucity of both sound empirical and theoretical work is surprising.

The niche and geographic ranges

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. be absent from portions of its fundamental niche. Now, to a first approximation, a species’ geographic range represents a spatial expression of its niche (Brown 1984). However, many other factors may influence the realization of that niche expression (Holt etal. 2005).

Recent concepts of source-sink dynamics, metapopulation dynamics and dispersal limitation complicate any relationship between a species’ niche and its geographic distribution (Pulliam 2000). Dispersal factors may relegate a species to habitats in which its niche requirements are not fully met (‘sink’ populations; Pulliam 2000). According to Pulliam (2000), this suggests that a species’ realized niche may sometimes be larger than its fundamental niche. At the same time, effects of dispersal limitation may mean species are not always present when niche requirements are met (see e.g., Cainet al. 1998). Finally, metapopulation theory posits that local populations frequently go extinct and, even at equilibrium, only a fraction of suitable habitat will be occupied (Hanski & Gilpin 1997).

The central-peripheral model

It is widely assumed that habitat suitability declines from the centre of a species range towards the edge (Hengeveld & Haeck 1982; Brown 1984; Lawton 1993; Guoet al. 2005). In an important paper on distribution patterns of species abundance, Brown (1984) argued that local abundance reflects how well a particular site meets a species’ particular physiological and ecological requirements. Brown suggested that spatial autocorrelation in these axes (representing dominant dimensions of the niche) results in the probability of sites having similar combinations of environmental variables being an inverse function of the distance between them. Thus, increasing the distance from the optimal site should decrease the probability of a site fulfilling the niche requirements of that species. There will be a decreasing number of local sites where a species can occur at all and, even within these patches, population densities will tend to be lower because resources are scarce and/or conditions approach the limits that can be tolerated (Brown 1984). In addition, the performance of species at the range periphery is commonly assumed to decline due to the scarcity of resources and suboptimal conditions (Lawton

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 1993). Peripheral populations are also expected to have less genetic variation than central populations, due to genetic drift, founder effects, bottlenecks and inbreeding, and diminished sexuality (Levin 1970; Lawton 1993; Lesica & Allendorf 1995).

Here we review the main factors considered to limit species ranges and how populations of species respond to these limiting factors, in terms of abundance, demography, genetic diversity and developmental instability. In addition, we highlight the potential for different factors limiting distribution and consequent species responses, at different parts of the range edge as well as potential confounding effects of scale. We point out the paucity of empirical results supporting the central-peripheral model in plants and suggest other models may be needed to explain variation in the responses of species at range edges.

The limits to species’ ranges

Most discussions concerning range edges focus on the role of broadscale abiotic gradients, and/or interspecific interactions, in limiting species, and many studies have shown significant correlations between the distribution or abundance of a species and certain biotic or abiotic factors (for a review see Brownet al. 1996). Few studies however have pursued which traits are actually responsible for determining the range border (Garcia & Arroyo 2001). Caughleyet al. (1988) proposed that by examining how particular features of populations (e.g., density, growth rate, body condition) differ at peripheral versus central sites, and whether any such change is related to a decrease in habitat or environmental suitability, one may reasonably infer causation for range limitation.

One of the major sources of environmental variation at large scales is climate (and a related array of meteorological factors). Solar irradiance and mean annual temperature, for example, both decrease with increasing latitude, while summer photoperiod increases (Santamaria et al. 2003). These variables are well known to influence many aspects of plant life (Pigott 1981; Woodward 1997). Climate likely affects a suite of ecological, physical and physiological factors that all may act on population processes (rates of birth,

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. death, dispersal, etc.) to shape the dynamics of species borders (Carter & Prince 1981; Brown et al. 1996; Zacherl et al. 2003).

Other ecological factors are also important. The interface between two strongly competing species can limit the range of both (Bull & Possingham 1995; Case & Taper 2000). Competition can be direct or indirect; for example when one species hosts pathogens or parasites that are highly virulent to another species, indirect competition becomes expanded (Holt & Lawton 1994). Similarly, the distribution of a particular prey species may also set the boundary of its predator in the absence of alternative prey (Hochberg & van Baalen 1998). Bullocket al. (2000) showed that there are strong negative associations between occurrences of two closely related species of dwarf gorse, Ulex minor and U. gallii, in Britain and France. However the scale of analysis proved important to the conclusions reached. Thus, while apparent co-occurrences were detected at coarse spatial resolution, the pattern disappeared at finer resolution. These authors concluded that distributions of the two species were not independent, that they could not coexist, and that their ranges were limited by competition.

Other mechanisms for limiting species distribution include several important elements of dispersal ecology. In traditional plant ecology (particularly phytosociology), a prevailing notion was that plant species occupied all suitable habitat within a landscape (e.g., Sauer 1988). By this reasoning, absence of a species from a community has been taken as identifying a site as having ‘unsuitable’ habitat. However, simple experiments involving the introduction of seed into such unoccupied sites within the range have shown that populations often establish (e.g., Primack & Miao 1992; Tilman 1997; Ehrlen & Eriksson 2000). Moreover experimental introduction of species to sites beyond their natural range have also had success (Carter & Prince 1981; Levin & Clay 1984). Furthermore, much literature from the emergent field of invasions ecology illustrates that when human actions disperse seeds adventively, some species can go on to survive, reproduce and increase in abundance well outside their native range (see e.g., Mack et al. 2000; Higgins et al. 2003).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Phylogenetic methods and increasingly reliable techniques for paleogeographic reconstruction are helping to resolve the relative important of dispersal and vicariance to explain similarities in the floras and faunas of widely separated regions, such as the southern hemisphere landmasses. Sanmartin and Ronquist (2004) recently determined a distinction between the biogeographic histories of southern hemisphere plants and animals. Animal distributions and phylogenies largely match the sequence of the breakup of the Gondwana landmasses that produced the modem continents, with evidence for limited dispersal between Australia and South America. In contrast, the plant data indicate a predominant role for dispersal.

The likelihood that a given species is found in any particular site, therefore, clearly does not depend simply on the ‘suitability’ of that habitat. Metapopulation models often describe the dependency of populations on the overall level of occupancy of habitats at broader spatial scales; this defines a regional pool o f‘source’ populations available for colonizing suitable empty sites (Hanski & Gilpin 1997). The key indicators of metapopulation dynamics are: (1) the periodic extinction of populations; (2) habitat patches must not be so isolated as to prevent recolonisation; and (3) local populations do not have completely synchronous dynamics. At the regional scale, some species may exist as metapopulations in this classic sense, where regional persistence is governed by processes of patch colonization, extinction and recolonization. Recent reviews of evidence for plant metapopulation prevalence in nature have concluded that most species appear not to be arranged as metapopulations —hence other frameworks may be necessary for understanding large-scale, regional dynamics in plants (Murphy & Lovett- Doust 2004a). Functional landscape mosaic approaches treating structural and functional features of the landscape appear promising, in particular those utilizing techniques of gradient analysis.

Lennon et al. (1997) developed a landscape metapopulation model demonstrating how relatively sharp limits to species distributions may arise along otherwise smooth environmental gradients. More recently Holt and Keitt (2000) used a simple interpretation of the Levins metapopulation model to show that, for species whose

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. persistence depends upon a balance between colonisation and extinction, range limits could arise because of either a single gradient, or a combination of gradients in habitat availability, local extinction and interpatch colonisation rates. Their results indicated that the nature of the spatio-temporal patterns of patch occupancy towards the edge of a species range depended upon which of these factors (habitat availability, colonisation rate and extinction rate) had a latitudinal gradient.

Thus far, processes at population peripheries have generally been described in terms of the behaviour of ‘one-dimensional’ transect-like, mean field models using either ‘connected lattice’ models in one dimension (such as stepping-stone models in population genetics), or partial differential equations describing spatial change in abundance or gene frequency over one or two dimensions (Antonovicset al. 2001). Antonovicset al. (2001) recently used spatially explicit individual-based models to study the patterns and dynamics that develop in population edges as they expand into regions that become more and more unsuitable; at the same time they probed effects of plant pathogens. At the range front, local, short-lived, ‘flame-like’ population patterns developed. While the local density of individuals at population edges initially prevented the invasion of disease into these areas, in the long term marginal populations and disease seemed to be sustained by complex colonization-extinction dynamics, where there was no clear gradient in pathogen abundance at the margin (Antonovics et al. 2001).

Source-sink theory predicts that organisms often occur and sometimes may be common in unsuitable (sink) habitat, if immigration from productive source areas is sufficiently large (Holt 1985; Pulliam 1986). Defining suitable habitat is difficult, particularly for plants and other organisms which respond typically to gradients of resource quality, so other methods are often used to determine the presence of species in unsuitable habitat. The absence of reproduction, coupled with the observation of frequent immigration into an area has been used as indirect evidence of the presence of a species in sink habitat. Kadmon and Schmida (1990) measured survival and reproduction rates of the desert annual Stipa capensis in three habitats (slope, depression, wadi) in Israel. The authors demonstrated that although only 10% of the plants occurred in the moister wadis and

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. depression habitats, 75-99% of the seeds were produced there. Net reproduction in the slope habitat was negative, while net gain from dispersal (immigration minus emigration) was positive.

Keitt et al. (2001) demonstrated that for species having strong Allee effects (in which population growth rates get depressed at low population densities), a range may be both stable against contraction and prevented against expansion in the absence of any broadscale environmental gradient, so long as there exists some form of fme-scale environmental patchiness. Thus Allee effects can readily constrain and circumscribe range limits, because if reproduction does not match mortality when local density is below a threshold size, the population will decline in abundance, despite being in a basically favorable habitat. Keitt and colleagues suggested this phenomenon magnifies the importance of historical accidents in defining range limits.

History and temporal dynamics of ranges

History is also important in determining species distributions and range limits. The majority of temperate zone (including plant) species for example, have been periodically subject to large-scale climatic fluctuations that have affected ranges asymmetrically (e.g., Webb & Bartlein 1992; Veith et al. 2003). Range expansions and contractions have generally followed a north-south gradient and/or have been channelled along routes determined by the distribution of aquatic or alpine regions (Hewitt 1999). Thus the location of a population relative to the expansion source may be a more important factor in determining its response than its location relative to the core of the overall range (Gamer et al. 2004). The effect of human impacts on spatial patterns of species distribution may be of overriding importance in some places, obscuring any biogeographic ‘rules’ (Murray & Dickman 2000; Parmesanet al. 2005). For example, Channell and Lomolino (2000), in their study on range contractions concluded that understanding patterns of recent extinctions and predicting those of future ones, depends to a large degree on reconstructing and predicting the spatial dynamics of humans and associated extinction forces.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. An important general feature of range boundaries is that they are temporally dynamic. While some boundaries, such as those corresponding to coastlines and other major, relatively permanent geographic features, may appear to remain relatively constant, other boundaries are constantly shifting (Brownet al. 1996). Both the fossil and the historical records document several sorts of range shifts. One is the relatively gradual, incremental expansion or contraction of a species’ distribution along an existing range boundary, for example range shifts that accompanied the global changes in glacial geology, climate, and vegetation during the Pleistocene, and in particular within the most recent 10,000 years following the retreat of the last continental ice sheets (Brownet al. 1996).

Pollen records spanning the 16,000 years since the last glacial maximum in Britain indicate that tree species spread northward from refugia at different rates (Birks 1989). North American palynologic records show eastern hemlock(Tsuga canadensis) slowly expanding from the east over the past 2,500 years to its current western range limit in Wisconsin, in response to cooler wetter climate (Parshall 2002). More recent records have been used to look for similar range expansions (primarily at the northern range limits) and contractions (at the southern range limits) in response to climate change over the last century. Lesica and McCune (2004) monitored the abundance from 1989 through 2002 of seven plant species at or near the southern limits of their ranges, at three sites in Glacier National Park, Montana. Four of these arctic-alpine indicator species showed a significant decline during the past decade, while none increased.

Another sort of shift involves the long-distance dispersal of one or a few individuals across a biogeographic barrier to found a new and isolated population (Brownet al. 1996). For example, it is estimated that as many as 291 long-distance colonists gave rise to the current native flowering plants of Hawaii, which is located approximately 4,000 km from the nearest large land mass of North America (Sakai et al. 1995). Today these events are typically considered ‘invasions’ and are well known to often have been aided by human activities (Kolar & Lodge 2001).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Finally, there are collapses of ranges: rapid contractions of once widespread species to one or a small number of isolated sites due to, for example, demographic effects or human disturbance (Brown et al. 1996). For example, red spruce (Picea rubens) has historically been an important and characteristic component of the Acadian Forest Region of eastern Canada (Rowe 1972). However the species has declined to a point where it is becoming increasingly uncommon across large portions of its former range. Current site occupancy has been estimated at between one-tenth and one-fifth of its former extent, in terms of population sizes, numbers and densities, and geographical distribution (Mosseler et al. 2000). The historical range of American chaffseed (Schwalbea americana), a federally listed endangered species, once extended from Florida to New York. The plant is currently known from about 20 populations mostly in South Carolina. The most significant threat to this species is fire suppression, which allows plant succession to proceed to the point where there is not enough light for the plant to compete successfully (Kirkman et al. 1999).

Both abiotic- and biotically-controlled range edges imply some equilibrium between the current range and the particular biotic or abiotic factors that limit a species’ distribution (Box 1981). Yet, historically-determined range edges do not represent an equilibrium (Davis 1986). Rather they exist as artifacts of historical events and may be in the process of either expansion or contraction (Jacobson 1979; Pigott 1989). A historically- determined range edge is also subject to biotic and abiotic influences, and ultimately the balance of these three kinds of factors and the evolutionary potential of expanding populations should determine the eventual limits to the species distribution (Brauer & Geber 2002).

Species responses at edges

A note on terminology

We note some confusion in use of the word ‘marginal’ in the literature; it is sometimes used to refer to areas at the periphery of a species’ range (e.g., Lennonet al. 2002), and other times for sites that are ecologically suboptimal (e.g., Hochberg & van Baalen 1998). Sometimes the two meanings appear to be used interchangeably, with the

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. assumption that sites that are spatially peripheral in the range are also ecologically suboptimal (see e.g., Antonovicset al. 2001; Kawecki 2003; Castro et al. 2004). However as we show, habitats at the periphery of the range are not necessarily ecologically marginal and marginal habitats are not always peripheral in the range. Brassard (1984) suggested use of the terms optimal, favourable or marginal when referring to ecological suitability and central, intermediate and peripheral (or edge) when referring to spatial location within the range; we support the use of these terms in order to avoid the assumption that peripheral areas are necessarily marginal habitats.

Abundance

It has been long known that species abundances are not evenly distributed across the range; rather, population abundances follow complex spatial patterns (Hengeveld & Haeck 1982; Brown 1984). When abundance distributions for significant numbers of species have been examined over entire (or nearly entire) geographic ranges, it has generally been observed that most species exist in low abundance at most sites and at high abundance in only a few sites (Brown et al. 1995). For example, 85% of eastern North American trees are considered ‘somewhere-abundanf within their geographic range while the remainder are ‘everywhere-sparse’ throughout their range, never reaching high abundance (Murphy et al. 2005). Moreover, many sites exist within the range where the species is absent altogether. The eastern North American tree Honey Locust (Gleditsia triacanthos) is absent from over 70% of the area within the boundaries of its mapped geographic range. Ehrlen and Eriksson (2001) found that patch occupancy t 2 varied considerably among seven herb species in suitable habitat patches within a 5 km Swedish study area. Thus, two of the study species occupied only a fifth of the available suitable habitat, whereas another two exhibited almost total patch occupancy.

The ‘abundant-centre distribution’ describes the widely held assumption in ecology and biogeography that species abundances tend to be greater toward the centre of the geographical range, and lower at the periphery (Sagarin & Gaines 2002; Murray & Lepschi 2004). Belief in the abundant-centre distribution is persistent and widespread in the literature and is included in many introductory textbooks. It also forms the basis of

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. numerous ecological and evolutionary theories and models (see Sagarin & Gaines 2002), and has implications for some of the most fundamental and emerging issues in ecology (e.g., the genetic structure of populations, climate change predictions and conservation strategies). Thus ecologists and conservation biologists interested in range contraction leading to extinction have predicted that if ranges implode, final populations of a species should persist near the centre of the historical range (Lawton 1995; Wolfet al. 1996).

On the other hand, some researchers have noted a tendency for species to persist in the isolated portions of the range, frequently at the periphery, where they are protected from anthropogenic disturbances that spread rapidly through more contiguous (and typically more central) populations (Lomolino & Channell 1995; Channell & Lomolino 2000). For example, Channell and Lomolino (2000) observed patterns of range contraction of 245 species from a broad range of taxonomic groups (including plants) and found that 98% of the species maintained populations in at least a portion of their historical geographic range. In fact, remnant populations of 91 species occurred exclusively in the periphery of their historical range.

There are three main predictions of the abundant-centre distribution: (1) abundance should be higher in sites closer to the centre of the range; (2) species should occupy more sites towards the centre of the range; and (3) abundance and occupancy should decline linearly towards the edge of the range. Sagarin and Gaines (2002) recently reviewed 145 separate tests of the abundant-centre distribution in 22 empirical studies. Among the striking results, these authors found that only 39% of the tests supported the hypothesis, and that all but two studies inadequately sampled the species range. Most studies relied on a small number of points and severely undersampled the range edges.

In a recent analysis of the distribution of abundance across the entire or nearly entire ranges of 134 eastern North American tree species, Murphyet al. (2005) also found that the abundant-centre distribution was in fact not well supported for most species. The results showed that although species were never more abundant at edges, it was also true that areas closest to the centre were not always themost abundant areas within the range.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Rather, peaks of abundance often occurred at intermediate areas between the centre and edge of range, though these peaks usually occurred closer to the centre than to the edge. Furthermore, these tree species often reached relatively high abundances at peripheral parts of the range, though their occurrence there was less frequent. Other researchers working with herbaceous plants observed that neither abundance nor density decreased towards the edge of the range (e.g., Lactuca serriola, Prince et al. 1985; Coccoloba cereifera, Ribeiro & Fernandes 2000).

Source-sink dynamics (Pulliam 1988) has been invoked to understand mechanisms that might lead to lower abundances at the edge of a range. Thus peripheral populations likely represent some combination of sources (where births exceed deaths and emigration exceeds immigration) and of sinks (where the opposite demographic conditions prevail). Whether a population is a source or sink should depend upon local environmental conditions, as well as the proximity to, and rate of exchange of dispersing individuals with, other populations.

Populations of highly vagile organisms at range boundaries may be predominantly sinks, while peripheral populations of more sedentary organisms, such as some plants, may occur in local patches of favorable environment. This framework leads to the prediction that peripheral populations are more likely to have higher turnover (i.e., extinction and colonization events). Unfortunately there are few studies that allow for reliable turnover rate estimation at a sufficient number of points across the range. However, Dohertyet al. (2003) did find increased turnover rates at species’ range edges using the data from the U.S. Breeding Bird Survey.

Demography

Many differences in plant performance have been reported between central and peripheral populations of species, some indicating a decline in performance towards edges and some suggesting increased performance in edge populations. For example, Stokes et al. (2004) found population growth rates were greater inUlex gallii and U. minor populations at the periphery of the species’ ranges. Population density U.of minor

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. was also greater at the range edge (Stokes et al. 2004). Clones and ramets ofCocciloba cereifera produced more leaves and inflorescences towards the edge of its range compared with the central parts of the range, and aggregations of the species lacking any flowering individuals were concentrated in the centre of the range (Ribeiro & Fernandes 2000). Carter and Prince (1985) reported that while the prickly lettuce, Lactuca serriola became rarer towards range edges in Britain, it showed no loss of vigour in the edge populations. On the other hand, seed production has been shown to decrease in northernmost populations of some tree species, due to climatic stress (Pigott 1989; Despland & Houle 1997; Garcia et al. 2000). Smaller, peripheral populations ofLloydia serotina produced fewer flowers and seeds than larger ones (Jones & Gliddon 1999). A significant decline in population density and seed production occurs toward the range edges in Cirsium acaule and C. heterophyllum (Jump & Woodward 2003).

Small, isolated, peripheral populations of a widespread plant species may vary in their reproductive strategies from those of large populations in the centre of a range. This may be due to the size and isolation of peripheral populations, resulting in restricted cross fertilisation and gene flow (Jones & Gliddon 1999). For example, peripheral populations ofArenaria uniflora are more self-compatible than central populations (Wyatt 1986). In plants, the balance between sexual and asexual reproductive modes is thought to be affected by biotic or abiotic factors limiting sexual reproduction, possibly via pollinator visitation rates, temperature, or availability of suitable safe sites for seed germination and establishment (Barrett 1980; McKee & Richards 1996; Garcfa et al. 2000).

In environments such as those at the northern limit of any northern hemisphere species, the maintenance of populations of long-lived perennials may depend more on survival rates of mature plants and on vegetative propagation than on sexual reproduction (see e.g., Eckert 2001). These demographic characteristics of peripheral populations surely contribute to long-term persistence. For example, northern peripheral populations of Tilia cordata appear to have persisted in Europe, despite little or no regeneration from seed in most populations (Pigott and Huntley 1981). Dorken and Eckert (2001) determined that populations ofDecodon verticillatus at the northern periphery of the

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. range in New England produced little if any seed, while most populations about 300 km further south set abundant seed. The spread of dioecious Elodea canadensis (Canada pondweed) across Europe involved only female plants and clonal growth (Sculthorpe 1967).

Several authors have proposed the use of size-frequency distribution patterns to narrow determination of the range of factors that could be most significant in limiting species distributions (e.g., Kelly et al. 2001). Thus if populations at the edge of the range are biased toward smaller size classes, or younger ages, this suggests survivorship rates are lower, or that edge populations may be ephemeral and suffer frequent local extinctions. Alternatively, if peripheral populations tend to have an irregular size structure, and sporadic waves of discrete cohorts, or if they are biased towards older size classes, then recruitment failure likely plays a more important role in limiting the edge of the range (Zacherl et al. 2003). Infrequent recruitment at range margins could be caused by a number of factors, including low adult fecundity, irregular delivery of propagules, or low juvenile survival. Any feature that varies temporally and targets recruitment success could cause irregular size structure. Examination of age and size structures inTsuga canadensis showed that recruitment of stems to the canopy was more continuous at the species’ range centre than at northern peripheral sites (Kavanagh & Kellman 1986). A recent comparison of pairs of closely-related, ecologically similar tree species showed the more abundant species to have ‘smoother’ population size profiles than the less abundant species at the same site (Kelly et al. 2001), suggesting greater recruitment fluctuation in the less abundant species.

Genetic diversity and adaptation

In theory, because of their presumed small size and isolation, peripheral populations are expected to have less genetic variation than central populations due to genetic drift, founder effects, bottlenecks and inbreeding and diminished sexuality (Levin 1970; Lawton 1993; Lesica & Allendorf 1995). Empirical studies have shown that edge populations sometimes have less genetic diversity than central populations (e.g.,Betula spp., Coyleet al. 1982; Gleditsia triacanthos, Schnabel & Hamrick 1990; Lychnis

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. viscaria, Lammi et al. 1999), and sometimes more genetic diversity Phlox( spp., Levin 1977,1978; Pinus edulis, Betancourt et al. 1991). Gapare et al. (2005) found gene flow in peripheral populations of Sitka spruce Picea( sitchensis) was three times lower than in core populations. A lack of genetic diversity may or may not affect fitness. Lammiet al. (1999) reported that, although peripheral populations ofLychnis viscaria had less genetic diversity than central populations, fitness components of germination, seedling mass and seed yield were not reduced.

Several authors have highlighted the potential evolutionary significance of peripheral populations. Mayr (1982, p 602) pointed out that peripheral populations often diverge from central populations. He noted “Aberrant populations of species almost invariably are peripherally isolated and, more often than not, the most aberrant population is the most distant one.” In peripheral parts of the range, ecological conditions are likely to be different, even if they are not less optimal. Natural selection is thus likely to affect gene frequencies, and unique genotypes may be formed and favoured. Studies of gene frequencies have detected such differences in many plant species (reviewed in Lesica & Allendorf 1992, 1995 and see examples therein). Environmental variation in phenotype, phenotypic plasticity and reaction norms, are also expected to play a significant role in determining patterns of adaptation and distribution at range limits (Antonovics 1976; Sultan 2000,2001).

Holt (2003) speculated that since most ranges span large spatial scales relative to the spatial domain of individual mobility, it is unlikely that dispersal over short time-frames links all populations in a geographic range into a relatively seamless evolutionary unit. Instead, ranges likely comprise many local evolutionary ‘arenas’ and the range as a whole evolves because of the accumulated impact of evolution at local scales. This may involve either an adaptation arising in one arena and spreading throughout a species distribution, or localized adaptation leading to range shifts. When local adaptation occurs in different parts of a species’ range, each locally-adapted population may have a distinct niche (Holt 2003). Thus, a map showing habitat availability or suitability for a population adapted to conditions in the core area of a range may be very different from that for a population

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. existing at one of the margins (Travis & Dytham 2004). The adaptation of peripheral populations to local conditions may be prevented by gene flow from central, more densely populated parts of the range (Holt & Gomulkiewicz 1997). However, significant isolation may preclude this interchange and allow local differentiation of peripheral populations (Garcia-Ramos & Kirkpatrick 1997; Hoffman & Blows 1994).

Seemingly inherent to any traditional sense of a descriptive, species-based ecology is the situation when two completely different species (i.e., different in a phylogenetic sense), may have effectively thesame general ecologies. Thus the classic example of those plants found in the various Mediterranean environments of the world —in California, in Australia, in the Mediterranean itself —these habitats are replete with unrelated species all sharing an array of ecological features, all having been shaped by common selection pressures (o f‘warm, wet, westerly winds in winter’, as elementary geography classes customarily characterized aspects of the environment of the Mediterranean climate).

Common and widespread plant species may perform well in a wide range of environmental conditions, however the capacity of individual genotypes to perform well across the full range of conditions is often limited (Joshiet al. 2001; DeWitt et al. 1998). Instead, common plant species may be characterized by both phenotypic plasticity and large genetic variation (Bazzaz 1986). Any single species may therefore contain a great variety of ecologies, in the different populations. Thus a statement of distribution like “The range of habitats of the grass Agrostis tenuis is very wide” may mean either thatan individual is capable of wide success (phenotypically labile, etc.) or, alternatively, thatA. tenuis has got a wide range of genetic polymorphisms that vary regionally. As it happens this is a species having wide ecological tolerance that is associated with narrow individual tolerances; it is an array of specialized ecotypes (Harper 1977).

Transplant experiments provide a simple and powerful way to evaluate distinctions between ecotypic specialization and phenotypic lability, and to further probe the causes of range limits (Antonovics 1976; Sultan 2001). Such experiments enable characterization of phenotypic variation and adaptation among populations along

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. environmental gradients (e.g., Rice & Mack 1991; Nagy & Rice 1997; Donohueet al. 2000; Galloway & Fenster 2000; Donohueet al. 2001). Reciprocal transplant experiments with plants often show home versus away advantages over both relatively small scales (Lovett-Doust 1981; Waser & Price 1985; Sorket al. 1993) and over larger scales (Galloway & Fenster 2000; Joshiet al. 2001), although local adaptation is not ubiquitous (e.g., Antonovics & Primack 1982; Rice & Mack 1991).

Local adaptation is also likely scale dependent. For example, Galloway and Fenster (2000) found that populations ofChamaecrista fasciculata showed reduced performance in long-distance (>1000km) transplants, enhanced performance in intermediate-distance (10-100 km) transplants, and performance similar to the home population for short- distance transplants (1-10 km). Galloway and Fenster suggested that the contribution of metapopulation processes to the evolutionary dynamicsC. of fasciculata result in populations that may not be adapted to particular sites but rather to a range of environments, determined by the scale of colonization.Chamaecrista fasciculata appears to be able to adjust to the environmental variation found within a 100-km area. However, individuals were less able to adjust to the environmental extremes represented by the long-distance transplants.

Developmental instability

Patterns of developmental instability have been proposed as a useful tool for quantifying the degree of environmental and genetic stress that individuals experience during their development (Kark et al. 2004). Developmental instability is usually measured as patterns of fluctuating asymmetry (FA) in morphology, and formation of phenodeviants (that is, deviant, additional, or missing morphological characters on one side of a bilaterally symmetrical organism or structure) (Moller & Swaddle 1997). Studies on levels of fluctuating asymmetry related to range effects are rare, particularly in plants.

The first study to examine the effects of range location on fluctuating asymmetry was Moller’s (1995) study of museum specimens of bird species from peripheral and central populations. Moller measured FA in extravagant feather ornaments and found that the

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. level of fluctuating asymmetry in males was almost 75% higher in peripheral populations than in central ones. Peripheral populations also had increased FA in female wing length and female short tail measures. Moller attributed the increased FA in peripheral populations to several factors, including the likelihood of strong selection against heterozygotes in enzyme loci, increased homozygosity in peripheral populations due to inbreeding, and extreme environmental conditions.

More recent studies have also demonstrated increased FA in peripheral populations of bird species. Kark (2001) demonstrated a sharp decrease in asymmetry of the third toe in chukar partridge populations, the further they were away from the core of the species range. Carbonell and Telleria (1998) also recorded increased asymmetry of tarsus length in blackcap ( Sylvia atricapilia) populations close to the range boundary compared to central locations. None of these studies determined FA across the entire range of a species, rather only one aspect of the range edge was measured. Gonzalez-Guzman and Mehlman (2001) measured FA in the scissor-tailed flycatcher (Tyrannus forficatus) across its entire breeding distribution and found FA tended to increase in male birds towards the centre of the range.

Siikamaki and Lammi (1998) found that patterns of flower FA increased in peripheral populations of the herbLychnis viscaria. However the authors found that in common garden experiments the levels of FA did not differ between plants from the central and peripheral populations, suggesting a strong role for local environmental conditions in regulating FA. Murphy and Lovett-Doust (2004b) measured leaf FA parameters in 18 populations across the geographic range ofGleditsia triacanthos, including at the northern, southern and western peripheries, as well as in the centre of the range. Fluctuating asymmetry was not higher in populations at peripheral parts of the range. Lowest levels of FA were recorded in northern and southern populations and highest FA levels were recorded in the central populations.

Levin (1970) highlighted the potential evolutionary importance of developmental instability in peripheral populations. Levin proposed that peripheral isolates may have a

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. source of phenotypic variation not immediately exploitable by most central populations, whose genetic and physical environments are less harsh, and also that such intra organism variation may serve as a substrate for selection leading to the stabilization and fixation of a novel expression.

Different edges - different responses

Brown et al. (1995) reported that the distribution of abundance values exhibited a distinctive bimodal pattern of spatial variation within the geographic ranges of four passerine birds. Spatial autocorrelation analysis produced two peaks, one at short distances (corresponding to proximal sites within the geographic range), and one at the maximum distance (corresponding to sites at opposite ends of the geographic range). Thus sites close together were more likely to have similar abundances, irrespective of where they were located in the range, and sites at the range periphery tended to have consistently low abundance, producing the second peak at maximum distances. This second peak in spatial autocorrelation at maximum distances implies that passerine bird species respond similarly to all aspects of the range edge. However, a recent spatial autocorrelation analysis of the distribution of abundances in 134 eastern North American trees suggested, in contrast, that plant species may not respond to all range edges in the same way, as abundance values at opposite directions of the range tended to be relatively dissimilar to each other (Murphyet al. 2005).

Many studies that report species responses to edges only examine one aspect of the range edge. However it is likely that different aspects of the range are constrained by different factors; consequently species responses in terms of abundance, performance, etc., will also be different. Studies that do compare individual species response to, or performance at, more than one aspect of a range edge often find differences. For example, for many northern hemisphere tree species, the best growth is achieved at the southern range limit (Schenk 1996). Loehle (1998) proposed that northern and southern range limits for North American trees result from a tradeoff between cold hardiness and maximum height growth rate. In the Mediterranean region, boreo-alpine tree species are mostly restricted in the southern parts of their ranges to refugia at high altitude, facing conditions very

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. different from those in the northern limits of their distribution (Castroet a l 2004). Valiente-Banuet et al. (2004) found evidence of latitudinal variation in the pollination system of a columnar cactus(Pachycereus pecten-aboriginum) that is clearly linked to predictability of the nectar-feeding bat (Leptonycteris curasoae), which is resident in the southern part of the cactus range and migratory in the northern part of the range. Thus, in southern populations the cactus exhibits a specialized pollination system while in northern populations flowers are pollinated by a range of animal vectors.

The relative importance of biotic (usually density-dependent) and abiotic (usually density-independent) factors in limiting populations is likely to change depending on the position of the population within the species’ range (Garcia & Arroyo 2001) and on the aspect of the range edge. Mac Arthur (1972) suggested that biotic interactions tended to limit distribution and abundance at lower latitudes due to increasing numbers of potentially competing species. In contrast abiotic factors were more likely to be limiting at higher latitudes. For example, in northern hemisphere plants low temperatures may limit poleward spread through their effects on both the vegetative (Woodward 1990, 1997) and reproductive phases of plant growth (Pigott & Huntley 1981; Woodward 1990). Loehle (1998) found a tradeoff between growth rate and freezing tolerance for 22 species of North American trees and suggested that, as a result, northern hemisphere trees are out-competed by trees with faster growth rates at their southern range limits.

The ‘steepness’ of the edge likely also affects the response. Species limited in their distribution by, say, an ocean edge (i.e., a very steep edge) may not show reductions in abundance or performance in the same way that the species would if the distribution limit was more gradual, and related to a particular climatic or other environmental gradient.

Edge of range effects: a matter of scale?

Species distributions within a regional landscape result from temporally dynamic processes operating at both local and regional spatial scales. A comprehensive analysis of the factors that most influence species distributions ideally should include habitat characteristics measured at multiple scales, because species-environment relationships

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. are well known to vary with the scale of observation (Wiens 1989; Kotliar & Wiens 1990; Levin 1992). For example, Munzbergova (2004) performed a sowing experiment and studied seedling recruitment over three years, using eight dry grassland species at 22 localities. Recruitment success was compared at three spatial scales (between localities occupied and unoccupied by focal species; between blocks occupied and unoccupied by focal species within occupied localities; and between plots with and without seed addition within occupied blocks). Comparisons among scales demonstrated that conclusions about importance of limitation by seed, and site availability for species distribution depend upon the spatial scale used, with limitation by site availability becoming increasingly important with decreasing spatial scale.

The lower limit to which an organism responds to the environment (i.e., its grain) tends to be constrained by that organism’s physiological, perceptual and behavioral phenotypes, while the upper limit (its extent), is set by the lifetime range of the individual (Wiens 1989; Kotliar & Wiens 1990). Studying a system at an inappropriate scale is likely to obscure detection of true patterns. Thus, the large spatial scale required for the study of geographic ranges renders it very difficult to obtain sufficient high quality data to answer important questions about the characteristics of populations within them. In addition, the estimated area of the geographic range itself is inherently dependent on the spatial resolution at which the occurrence of individuals is mapped (Fortinet al. 2005). The finer the resolution, the smaller the area over which a species is perceived to occur (Gaston 2003). For example, ranges mapped at coarse resolution often do not depict holes, or breaks in range boundaries (where a species does not occur) or patch islands around the perimeter, where isolated populations are found. Somewhat more precision is afforded by “dot maps” that plot each location where a species has been recorded (Brown et al. 1996).

The actual scales at which individual studies are carried out can differ substantially (Blackburn & Gaston 1998). Although in theory the physical edge of range of a species is relatively simple to define (it is where the abundance of a species declines to zero), in most studies that describe characteristics of populations at the edge of the range the

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. populations concerned are usually at some distance from this literal edge. Thus, edge may include any site that is located closer to the range limit than to the geographic centre of the range (see e.g., Channell & Lomolino 2000), making comparison of effects at range edges confounded and complex. This problem relates to another major problem in studies of core-peripheral responses, that is, the greater proportion of a species range that is edge compared with the proportion that is centre. For example, in the most conservative case, if the geographic range is a circle, 75% of the area of the circle is located closer to the edge than to the centre; the average distance to the centre of a circular range is two-thirds of the radius (i.e., closer to the edge than the centre). Thus, given that most species are sparse throughout most of their geographic range reaching peaks in abundance at only a relatively few sites (e.g., Murray & Lepschi 2004; Murphy et al. 2005), the probability of finding a low abundance site at the edge of the range is higher than it is at the centre of the range simply because there is more of them and more area in which to find them. In an analysis of distributions of peaks of abundances in birds (based on the data from the North American Breeding Bird Survey), McGill (2005) found the average distance of the highest observed abundance from the center of the range, rescaled into units of percentage of the radius of the range, was 0.81 (compared with the average distance from centre to edge of range of 0.667). Thus, there was no tendency for abundances to be greater in the centre of the range, in fact, abundances tended to be higher toward the periphery.

Similarly, all else being equal the number of unsuitable or suboptimal sites at the edge of a range is also likely to be greater, given the greater overall area. The existence of so many more unsuitable or suboptimal sites at range edges has a number of implications for the analyses of distribution and performance of species existing there. Thus, more propagules should tend, by chance, to be introduced into suboptimal sites (Sax & Brown 2000) at peripheral areas of the range; moreover field studies, particularly those involving transects, are more likely, by chance, to include suboptimal sites at range edges than in central parts of the range.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The greater area of edge compared with centre of range also means that the edge of the range requires greater sampling than the centre to adequately characterize any patterns there. Pseudoreplication as a result of unrepresentative or uneven sampling is a common logistical or design problem (Lammi et al. 1999; Sagarin & Gaines 2002) in central- peripheral studies. Finally, most studies that examine edge effects only examine one part of the species’ overall range (Sagarin & Gaines 2002); these are usually termed ‘partial’ studies, as opposed to ‘comprehensive’ studies that embrace all or a very large proportion of the extent of the geographic range (Blackburn & Gaston 1998). Partial studies concluding, for example, that abundances are lower at the range edge would tend to misrepresent a distribution in which abundances decline from a southern to northern range limit (Sagarin & Gaines 2002).

Living on the edge - not as ‘stressful’ as it sounds?

Given that peripheral populations of species are not always less abundant, do not always show reduced performance indicators, and often do not have diminished genetic diversity or increased developmental instability, it is not clear whether these populations actually experience higher levels of environmental or genetic stress. Individuals that persist at the edge of the range may be adapted to suboptimal environments. Either phenotypic plasticity or population differentiation would allow peripheral populations to adapt (Hoffmann & Blows 1984). It bears repeating that what may be stressful to some (human) perspectives represents, simply, home to another perspective, in particular if a product of evolution there.

While peripheral populations may experience lower abundances, it is also possible that detrimental density-dependent effects on individual fitness components and development are reduced to an extent that makes peripheral habitats comparable with central ones (Kiflawi et al. 2000; Murphy & Lovett-Doust 2004b). Reduced density dependence can offset density-independent effects of mortality and fecundity, resulting in stable populations of relatively low abundance.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Selection in marginal environments may favour higher quality individuals. Potential tautology notwithstanding, individuals of higher genotypic quality are thought to be more developmentally stable, even in conditions of high stress (Palmer 1994). If selection is stronger in edge-of-range environments, we would expect to see lower abundance, but comparable performance and developmental stability (Gonzalez-Guzman & Mehlman 2001) in populations at the edge of the range (Kiflawiet al. 2000). Alternatively, populations may only persist in relatively high-quality sites at the periphery of the range. Griggs (1914) early noted that . ..whereas a species may be ubiquitous in the centre of its range, occurring in all sorts of habitats because highly favoured, at its areal limits it will be closely limited to those conditions which are most favourable to it.” Lennonet al (2002) reported that several tree species in Alaska tended to occur in especially favourable sites within peripheral areas. In core areas, most slope types were occupied, although shallower-sloped sites were preferred. Importantly, these authors demonstrated that in the peripheral areas it is only the most favourable, shallower-sloped sites that are occupied. We have shown for many eastern North American tree species, populations reach comparable abundances in peripheral and central parts of the species’ range, however these occur at peripheral sites with less frequency than in central and intermediate parts of the range (Murphy et al. 2005).

Given the relatively broad physiological limits of tolerance for many plant species, it is possible that site quality does not decline gradually towards the edge of the range; rather species may exist in a mosaic of relatively low- and high-quality sites across the range (see too Murphy & Lovett-Doust 2004a, b). We have also shown that although the proportion of sites unoccupied by eastern North American tree species was greater at the peripheral parts of a species range, some 40% of the area in the core of the range was also unoccupied (Murphy et al. 2005). Since it is unlikely that only high quality sites are occupied and only unsuitable sites are unoccupied in eastern North American forested landscapes, this result suggests that there is a gradient of habitat suitability in the core, with occupied areas including both high-quality and low-quality sites, though low-quality sites may be more common in peripheral areas (given the higher proportion of unoccupied sites there).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Alternatives to the central-peripheral model

The central-peripheral model assumes that responses to environmental gradients are unimodal and symmetric (Oksanen & Minchin 2002). However, empirical evidence supporting these assumptions are scarce (McGill 2005) and species interactions, human impact and disturbance, and historical factors may change the response shape even if the fundamental response were symmetric. Furthermore, the response shape may vary depending on regional or habitat-specific conditions, and an alternative model needs to incorporate regional differences in species responses along gradients (for example, as noted above the potential relative importance of biotic versus abiotic limitations at low and high latitudes).

McGill (2005) has recently begun to develop a more quantitative description of the distribution of abundances about a range. He recognized such structural measures of a distribution as its continuity (abundances varying in a smooth continuous fashion); the pattern of peaks, drops and tails; occurrences of unimodality of peak abundances; and centeredness of the peak, as well as other estimates of symmetry. Based on North American breeding bird distributions, McGill rejects the relatively long-standing claim that species distributions of abundance have a Gaussian (normal) distribution pattern across the range. He argues compellingly for a consensus position that he describes as the peak-and-tail structure of abundance across a species range. The major departure from a Gaussian model involves the absence of any peak abundance centeredness, symmetry and unimodality, all of which he shows to be unsupported by the distributions of avian abundance data. McGill suggested a ‘tradeoff model as a mechanism to explain the peak-and-tail structure of abundance. This model allows for the importance of biotic factors (interspecific interactions) as they change along an abiotic gradient. Thus the model trades off survival due to environmental tolerance, against fecundity due to competitive dominance and the resulting greater resource intake.

From a plants’ perspective, the landscape within which it exists is a mosaic, both in a structural context (in terms of, for example, barriers, conduits, sources and sinks, landforms and disturbance elements) as well as in a functional context (in terms of

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. nutrient resources, grazing and predation risks, density of pollinators and dispersal agents, successional status, etc.) (Murphy and Lovett-Doust 2004a) —all elements of which are likely to have consequences for distribution, abundance and performance. Several authors have recently described gradient-based approaches to understanding landscape ecology (McIntyre & Hobbs 1999; Lindenmayeret al. 2003) in the context of understanding effects of habitat fragmentation on plant species.

Gradients of habitat quality, which may be composed of several landscape structural and functional components get mapped in a grid-based structure, giving a framework for interpreting a species’ response (e.g., abundance, reproductive success) to the landscape (see e.g., McIntyre & Hobbs 1999). This type of approach could equally well be applied to interpreting responses of species to range edges. In a grid-based model, each cell within the grid can be described in terms of the response function (e.g., abundance) and its structural and functional context. Treatment of the landscape as a mosaic, utilizing relevant gradients of abiotic, biotic, and historical and human impact based parameters of importance to the particular species, is more likely to portray the fate of plant populations.

Conclusion

It is clear that abundance and performance of a species is not simply related to proximity to the edge of the range. Rather, responses at range limits vary depending on the species’ ecology and evolutionary history, as well as the type and aspect of the edge, and historical elements of changing climates and anthropogenic effects. Theoretical approaches that rely on the assumptions of the central-peripheral model probably fail to depict important variation and may lead to erroneous conclusions.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter 3 - Context and connectivity in plant metapopulations and landscape mosaics: does the matrix matter?1

Introduction

Ecological analysis at large spatial scales has emerged over the past decade as the subject of two, quite distinct sub-disciplines: metapopulation ecology and landscape ecology. The former provides one framework for understanding population dynamics —as consequences of migration, colonization, and extinction events in spatially structured habitats (Hanski & Gilpin 1997). In theory, metapopulations represent the organisms inhabiting regional landscapes — the reef fishes, grizzly bears, buttercups and butterflies, each experiencing its environment at unique, species-specific scales. At the same time, the study of landscape ecology considers a variety of subjects, including population dynamics, however its general goal is often summarized as the effects of landscape structure and spatial configuration on ecological processes (Tischendorf & Fahrig 2001, Turner et al. 2001).

The major theoretical models of both landscape ecology and metapopulation ecology assume a binary landscape, composed of “habitat” and “matrix” (i.e., the nonhabitat surrounding native habitat patches) (Wiens 1997). Metapopulation models have focussed almost exclusively on the habitat patch component, rather than the matrix (Ricketts 2001). An important distinction between the metapopulation approach and the spatially- explicit population approach of landscape ecology is that metapopulation models essentially ignore the characteristics of the non-habitat, or matrix portion of the landscape (e.g., Ims & Yoccoz 1997). In contrast, landscape models often assume that movement between patches depends on attributes of the matrix, which may influence dispersal mortality and/or movement direction (e.g., Tischendorf & Fahrig 2000a, 2001). At the same time, too much research in landscape ecology seems to focus more upon elements of spatial explicitness than on the biology of living organisms. Each of these emergent

1 This chapter was published in 2004 (Murphy, H.T. & Lovett-Doust, J. (2004) Context and connectivity in

plant metapopulations and landscape mosaics: does the matrix matter? Oikos, 105, 1-14)

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ecological sub-disciplines would seem to benefit from the integration of some of the approach of the other.

Plants differ from animals in several fundamental features of life history. In this paper we focus upon these features in plants to better understand patterns of regional variation. At all spatial scales, suitable environments are interspersed in a matrix of more or less inhospitable space (Eriksson & Ehrlen 2001). This means that for most plant species a fragmented habitat is the physical arena within which population dynamics, ecological processes, adaptation and evolution occur. As Eriksson and Ehrlen (2001) have shown, persistence of plants over the long term requires coping with temporally and spatially unpredictable resources. Many plant life-history features, including dispersal structures, seed dormancy, seed size and clonal propagation can be interpreted in this context - in conjunction with the rootedness of plants, necessary for capturing the diffuse water and

mineral resources in the soil and of CO2 in leaves. For example, the existence of long- lived life cycle stages (seeds, vegetative ramets) means that local populations may persist for a long time even though a patch has become unsuitable. Ehrlen and Eriksson (2003) argue that successful dispersal and recruitment in plant populations may be very sporadic and therefore recolonization is unlikely after local population extinction. In plants, dispersal over long distances may be governed by significant stochasticity. Moreover, while the definition of long distance may differ between species it is only infrequently more than a few hundred metres (e.g., Cain et al. 2000).

The over-riding importance of dispersal has long been recognized in influencing large scale patterns of distribution and geographic ranges in terrestrial plants (see Reed et al. 2000). For plants, the mobility of the recruitment stage occurs primarily through dispersal of seeds or propagules, and via pollen movement (Bullocket al. 2002, Thompsonet al. 2002). It is inherently difficult to track individual seeds as they disperse from a parent plant to their final site of deposition, and especially the rare, longer- distance events which are generally required for colonization of new habitat (Greene & Calogeropoulos 2002, Wang & Smith 2002). Such difficulties are doubly true for tracking pollen-mediated dispersal events (Dow & Ashley 1998; Waser et al. 2000).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Understanding landscape matrix effects on connectivity, as it relates to large scale population dynamics, requires understanding the movements of those animals which disperse seeds, most commonly birds, mammals and ants (Chambers & McMahon 1994), as well as the agents which move pollen.

As Raybould et al. (2002) have described, progeny fitness tends to be dependant on the distance between parents, so classical metapopulation biology may be sufficient. However, the extent of outcrossing may be an important confounding factor (e.g., Byers 1998; Waser et al. 2000; Greene & Calogeropoulos 2002). In animal-pollinated plants, reproductive success may be negatively related to the distance between flowering patches; several studies have documented lower success in isolated or fragmented populations (Aizen & Feinsinger 1994; Groom 2001). Furthermore, even when pollinators successfully travel long distances between patches, the quality of the pollen transferred may decline. For example, generalist pollinators may visit a variety of species when travelling longer distances, and heterospecific pollen may clog stigmas and lower reproductive success (Groom 2001).

There is an obvious acknowledgement of the importance of concepts of landscape ecology in the metapopulation literature (see e.g., Hanski & Gilpin 1997). Yet it must also be reckoned that the major elements characterizing landscape ecology remain absent from metapopulation models, which are typically focused on idealized habitat in a featureless landscape (Wiens 1997). Wiens (1997) gave two reasons for the lack of integration. First, metapopulation theory continues to be tied to a simplistic patch-matrix view of the landscape. Second, due to the challenges in quantifying complex spatial patterns, landscape ecology has not developed theoretically to a point that enables a body of metapopulation theory, which is already relatively complex, to encompass it. Here we will suggest that an integrative, landscape perspective promotes understanding of large- scale spatial dynamics in plants.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The matrix from a plant perspective

From a plant’s perspective, there are several reasons why an integrated perspective on the landscape mosaic is important. When the distinction between habitat and non-habitat (matrix) is fairly clear, definition of distinct habitat patches is relatively uncomplicated and species dynamics may be described in terms of the properties of those patches (Thomas & Kunin 1999). For example, suitable habitat may be relatively easily defined for obligate epiphytes growing on tree trunks, or for hemiparasitic mistletoes growing in tree canopies. However for many other species, in particular those having relatively broad limits of physiological tolerance, there is often no clear distinction between habitat and matrix, and defining distinct habitat patches becomes difficult or impossible (see Freckleton & Watkinson 2002). Assessment o f‘empty’ but suitable patches is even more difficult, and there are still only a few studies that have used experiments to estimate occupancy in plants (Ehrlen & Eriksson 2000).

Most plants probably respond to gradients of resource quality (Withet al. 1997). For these species, suitable habitat lies along some environmental continuum, from optimal habitat, through suitable-, and suboptimal-, with many biotic and abiotic parameters contributing toward suitability. Where a species does not perceive sharp and distinct boundaries, patch properties become less important and the nature of the overall landscape mosaic becomes increasingly significant in species’ dynamics (Thomas & Kunin 1999).

It is perhaps not surprising then that a major conclusion from the several recent reviews of plant metapopulation prevalence in nature - by Husband and Barrett (1996), Bullock et al. (2002), and Freckleton and Watkinson (2002) - was that many plants appear not to be arranged as metapopulations. Hence other frameworks may be necessary to understand large-scale, regional dynamics in plants (and perhaps also other organisms, sharing relevant life history features). As Freckleton and Watkinson (2002) have described for plants, at the regional scale some species appear to exist asmetapopulations in the classic sense, where regional persistence is governed by the processes of patch colonization, extinction and recolonization. However according to Freckleton and

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Watkinson other species exist asregional ensembles, systems of essentially unconnected local populations persisting in an ill-defined mosaic of suitable and unsuitable habitat; while still others exist asspatially extended populations, essentially a single, extended population occupying large tracts of suitable habitat, but whose regional dynamics exist as a simple extension of local dynamics. We note that Ehrlen and Eriksson (2003) have recently argued that the typology of Freckleton and Watkinson (2002) may be interpreted as if local processes alone are sufficient to understand regional dynamics for most plant populations. Ehrlen and Eriksson (2003) state that the available evidence indicates local processes are insufficient for understanding regional dynamics in most plant species and suggest that metapopulation theory should be developed further as a tool for studies of plants, rather than being replaced by a new typology. Pannell and Obbard (2003) point out that the metapopulation terminology has been successfully adopted in evolutionary and population-genetic analysis of species that do not occupy readily identifiable habitat patches. In these analyses it is the discrete nature of the groups of organisms involved, rather than the discrete nature of the habitat patches, that affects important aspects of population genetics.

We support the conclusion of Freckleton and Watkinson (2002) that most plant populations appear not to be organised as metapopulations. However, like Ehrlen and Eriksson (2003), we do not find the new typology necessarily useful and suggest that the landscape mosaic approach we present here for understanding regional dynamics of plant populations benefits little from this pre-characterization of the nature of the regional dynamics. Rather, as Thomas and Kunin (1999) noted, many such labels might better be considered as points on continua, and in fact populations may exhibit elements of several categories, or their definition may be dependant on a particular spatial or temporal scale.

The assumption in metapopulation ecology that properties of the matrix are unimportant is probably only really true for terrestrial organisms inhabiting oceanic islands. This situation sits at one extreme of a continuum extending from situations such as these true islands, where the marine matrix is completely inhospitable and quite homogenous (e.g., Gilpin & Diamond 1980), through the (paradigmatic) metapopulation landscape where

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. discrete habitat is separated by a homogenous matrix that is not suitable for colonization but is also not fatal to dispersers (Ims & Yoccoz 1997), and finally to continuous habitat in which the matrix nature is indistinguishable from the patch (see Vandermeer & Carvajal 2001). One feature that distinguishes terrestrial habitat fragmentation from the true island model (of MacArthur & Wilson 1967) is that the matrix may, for some species, actually be hospitable to varying degrees. In this case the matrix should have a strong influence on the between-patch processes of dispersal and colonization, as well as the within-patch processes of extinction, population growth and density dependence (Davies et al. 2001). The matrix has at least three potential roles in between-patch processes: (1) reducing or enhancing dispersal and colonization rates; (2) providing alternative, though possibly suboptimal, habitat; and (3) as a source of novel invading species competing for patch space (Davieset al. 2001; Cooket al. 2002).

Characterizing the matrix: toward a functional mosaic approach

In principle, the matrix begins at the edge of a patch and is composed of an array of natural and anthropogenically-derived features which tend to act as barriers to, or conduits for, biotic movement. Researchers have sought to characterize and quantify the matrix in various ways. Of the many structural features of the landscape, corridors have received the greatest attention, mostly from conservation biologists (Wiens 2002b). Corridors through the matrix are thought generally to facilitate movement between patches within fragmented landscapes, and thus impact regional population dynamics by increasing gene flow, enabling re-establishment of locally extinct populations and increasing species diversity within otherwise isolated areas (Tewksbury et al. 2002). Contrary arguments have been raised, based primarily on the role that corridors may play in facilitating the spread of disease or disturbance, or the movements of predators or species of concern (see Wiens 2002b). Characterizing the structure and function of corridors in the landscape is problematic (Beier & Noss 1998). Lidicker (1999) pointed out that difficulties arise due to an unclear definition of corridors, and proposed that corridors should be viewed functionally, as any narrowly delimited place in the environment that facilitates movement of organisms between patches, relative to the matrix. According to Lidicker, corridors should not be construed as linear strips of

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. habitat independently supporting breeding populations of focal species, and they need not necessarily be of the same habitat quality as the patches they connect.

Regardless of whether corridors are effective or not as conduits for species movement in fragmented landscapes, a focus on corridors as the defining element of connectedness in a matrix tends to perpetuate the simplistic patch-matrix view of landscapes and obscures some of the richness of detail that characterises landscape mosaics (Wiens 2002b). “Connectivity” (in a general landscape ecology sense) is an aggregate property of the structural configuration and composition of elements in a landscape mosaic; it is the relative permeability of their boundaries to species (Wiens 2002b), and the success with which focal organisms move between particular patches without starving, being preyed upon or otherwise suffering mortality in the process of moving. Connectivity is a functional measure of landscape structure - the degree to which the landscape facilitates or impedes the movement of individuals among patches (Tayloret al. 1993). When a landscape is composed of habitat patches embedded in a matrix used only for dispersal of a particular species, the connectivity of that landscape is a combined result of landscape composition, landscape configuration and the ease of movement of individuals through the matrix (Tayloret al. 1993).

Although a boundary, or ecotone, may have properties of its own, the nature of a boundary is largely contextual, determined by the surrounding environment (Wiens 2002a). To capture this, the term ‘landscape context’ is becoming common in the literature, especially in studies of habitat fragmentation, although the meaning and method of characterization are not yet standard. “Context” determines the rate of immigration into a patch, through (1) the amount of occupied habitat in the area around the patch that is within the dispersal range of the organism; and (2) the quality of the intervening nonhabitat area - the matrix - for survival and dispersing individuals (see Fahrig 2001). Landscape context has been used in general to refer to the composition, and sometimes the configuration or arrangement, of landscape elements surrounding a particular focal habitat type (Forman 1995). Some authors (e.g., Gustafson 1998; Steffan-Dewenter et al. 2002) contend that the simple proportion of a habitat type in a

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. landscape is nearly as important as many other, more complex, measures of heterogeneity since this compositional characteristic effectively determines the probable range of many configuration characteristics, including patch size and isolation distances (both of which are essential parameters in metapopulation ecology).

Lindenmayer et al. (1999) have used landscape context to characterize the contrast in the composition of the landscape that was included in, and surrounded, habitat patches of interest. Landscape context has also been categorized variously by the proportion of habitat types, and by the diversity of habitat types at a given spatial scale (Steffan- Dewenter et al. 2002), by the proportion of forest cover alone (Donovanet al. 1997), total cover of focal habitat type, and configuration, or spatial arrangement of focal habitat type (Mazerolle & Villard 1999).

Similarly, ‘patch context’ and ‘gap context’ have been used variously to describe the components of variability in surroundings, as an attribute of a habitat patch or gap. ‘Gap context’ seems to be an important determinant in the species composition of colonized gaps. Bullock et al. (2002) investigated gap colonization capacity in seven grassland species and showed that the number of seedlings colonizing a gap was correlated with the abundance of the species in the immediate neighbourhood of the gap. Dalling et al. (1998) reported a similar relationship where, in forest gaps, composition was determined by the proximity of parents.

Several authors have drawn comparisons between ecological edges and cellular membranes or filters, noting that edges may be differentially permeable to ecological flows (Fagan et al. 1999). Habitat proximal to a patch may be more important in determining dispersal rates than habitat farther away, since proximal habitat must be crossed in order to migrate, whereas more distal habitat is less likely to lie within the realised migration route of any particular individual (Moilanen & Hanski 1998). Thus a further context-related variable having potentially important influences on movement of organisms or propagules is ‘edge context’. Furthermore, the permeability of the edge itself may be just as important as the permeability of the environment between two

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. patches in determining the probability of success of emigration or immigration. Patches may be bounded by an impenetrable boundary that dispersing individuals virtually never cross (i.e., a ‘hard edge’, such as the boundary between an urban subdivision and a remnant mature woodland), or a barrier that is very permeable to dispersers (i.e., a ‘soft edge’) (Stamps et al. 1987), such as that between a mature forest patch and regrowth forest.

Effects of scale

Most ecological processes and interactions depend on spatial scales much larger than that of a single patch, and ecologists have become increasingly aware of the importance of linking spatial patterns with ecological processes at various scales (e.g., Thieset al. 2003). Problems of spatial scale generally pertain to issues of extent, grain and resolution of data collection or observation (Gustafson 1998). In practice, ecological studies tend to treat scale simplistically, prefacing it variously by patch-, landscape-, local-, regional-, small-, medium-, large-, fine-, individual-, population- or habitat-, for example, and rarely with reference to whether the scale is based on biological properties of the organisms, physical properties of the landscape or some interaction of the two.

Clearly, relevant spatial scale is species specific. Different species perceive a landscape at different scales (Keitt et al. 1997), and even related species respond to processes operating at different spatial scales. For example, landscape context influenced the abundance and distribution of solitary wild bees, bumble bees and honey bees at different spatial scales (Steffan-Dewenter et al. 2002). Furthermore, the same species might perceive its environment at different scales during different life stages. Plants of most species live parts of their lives at two different spatial scales: the relatively broad, dispersal scale of the seed and pollen grain, and the relatively fine scale of the sessile adult. For adults, day-to-day growth may depend only on immediate microsite conditions, such as light, water and soil nutrient levels. But reproductive success may depend on processes operating at broader scales, for example, pollen production of nearby males, for outcrossing plants, and movement of pollinators in the surrounding landscape (Kollmann 2000). Hence spatial scale is also process-specific. At the fine end

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. of the spatial scale continuum, a fundamentally different set of processes (e.g., microsite selection) may be involved than at broader scales (e.g., dispersal capacity and colonization, abundance, and range of distribution) (Bowers & Dooley 1999).

In terms of connectivity, where we are mostly concerned with problems of movement and mobility, scale must generally be defined by both the degree of vagility of the species in question, and the scale at which the species responds to landscape patterns. Proper analysis requires that the scale of measurement of the physical landscape and that of the organism’s response fall within the same scale domain, or the region of the scale continuum over which patterns either do not change, or change monotonically with changes in scale (Wiens 1989).

Measuring connectivity

At present there is no commonly accepted measure of connectivity (Tischendorf & Fahrig 2000a). Metapopulation ecologists measure connectivity mostly at the patch scale, while landscape ecologists measure connectivity as a species-specific attribute of the landscape, and both camps use these measures in different ways. Yet as mentioned, the underlying process is the same: movement of individuals (here as ramets, seeds, or pollen) across a landscape (Tischendorf & Fahrig 2001). Despite the fact that terrestrial habitat patches tend to be surrounded by a complex mosaic of other landcover types (see e.g., Forman 1995), which may differ in their resistance to the movement of individuals among patches, the landscape matrix has mostly been assumed to be uniform, and most connectivity measures in the literature of population ecology are based on simple nearest- neighbour distances (Moilanen & Neiminen 2002), or negative exponential distances with population size or area as weighting functions (Hanski 1999).

In metapopulation theory, movement success depends on the distance between patches and the inherent “dispersal ability” of an organism (as captured in the colonization rate parameter) (Gustafson & Gardner 1996; Moilanen & Hanski 2001). Goodwin and Fahrig (2002) cogently showed that dispersal success is not only a function of an organism’s dispersal ability but also depends on particular attributes of the landscape, which may

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. differentially impede movement and/or increase dispersal mortality. In fact, although it is widely held that species having high mobilities are more tolerant of habitat loss and fragmentation (due to the potential for increased colonization rates), the high emigration rates in these species may also increase the overall population mortality rate, by placing such individuals in a perilous matrix more frequently. Therefore, as Fahrig (2001) argued, the concept of dispersal ability may only be applicable in a species’ optimal environment and not necessarily in a human-altered, fragmented landscape.

In landscape ecology models, movement through the landscape is assumed to depend on the interaction between characteristics of the matrix and the movement behaviour of the organism (e.g., Tischendorf & Fahrig 2000a). Tischendorf and Fahrig (2000a) examined the use and measurement of the term connectivity (in conjunction with either landscape, patch or habitat) in the literature and found a significant lack of consistency. In particular, connectivity was sometimes measured in a structural manner and sometimes in a functional manner; and it was sometimes simply equated with corridors, or with patch isolation, both of which the authors considered are only components of connectivity. In theoretical studies, connectivity has been estimated as dispersal success, i.e., the number of successful immigrants into habitat patches in a landscape, or as search time, the number of movement steps individuals require to find a new habitat (Tischendorf & Fahrig 2000b). More recently, Tischendorf and Fahrig (2000b) have proposed using the rate of immigration into equal-sized habitat cells in a landscape, as a measure for landscape connectivity that accounts for both within- and between-patch movement.

incorporating the matrix in measures of connectivity

Movement between patches has been mostly thought of in terms of corridors (Tischendorf & Fahrig 2000a), however it is perhaps more usefully envisioned as a complex product of particular patch qualities (e.g., resistance to movement, or patch residence time), boundary properties, and context (Wiens 2002a). Ricketts (2001) conducted a mark-recapture study of a butterfly community inhabiting meadows in a naturally patchy landscape. Ricketts used a maximum likelihood technique to estimate the relative resistances of the two major matrix types (willow thicket and conifer forest)

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. to butterfly taxa — thus for example, conifer was 3-12 times more resistant than willow to movement, for four of the six butterfly taxa studied. Ricketts’s results suggest that the surrounding matrix may significantly influence the effective isolation of habitat patches, rendering them more or less isolated than simple distance would indicate.

For mobile organisms which tend to migrate only short distances between patches, resistance parameters may be relatively straight-forward to calculate and incorporate into metapopulation models. However measures of the effect of a heterogeneous matrix on migration or dispersal are not so easy to estimate for organisms such as plants — which rely on a variety of other organisms and agents (water, wind), to disperse propagules and gametes between patches.

Landscape ecologists have given considerable effort to quantifying the spatial composition and configuration of landscapes (Gustafson 1998). Patch-based measures portray features of particular patches, independent of their surroundings. Adjacency and contrast measures, for example, deal with what lies directly across the boundary of a given patch type. Indices such as semivariance, lacunarity and fractal dimension, characterise features of the landscape mosaic as a whole (Gustafson 1998). In terms of connectivity, measures of landscape spatial structure alone are not synonymous with measures of connectivity, although they are clearly related. Together with spatially- referenced records of biotic inventories or ecological variables of interest (e.g., population abundance, species richness values, species diversity), these measures can serve as probes to assess how landscapes affect ecological processes (Wiens 2002b). Landscape indices continue to be refined for different species in different circumstances at different scales, and there now exists a large array of metrics that have been used to relate landscape structure with ecological variables - with mixed success (Gustafson 1998). Ecologists have had some success in the prediction of ecological patterns such as abundance and diversity, from landscape and patch indices (e.g., see Mazerolle & Villard 1999). However the difficulties associated with predicting the response of ecological entities to spatial pattern has led to few definitive tests, at the level of ecological processes (Gustafson 1998).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Clearly the incorporation of matrix effects into measures of connectivity is not straightforward and, despite the efforts of both metapopulation and landscape ecologists, there is still much to be accomplished before any benefit is realised in terms of the outcomes of theoretical models in these fields, and ultimately for predictions of regional dynamics and persistence of a species in fragmented landscapes. Landscape context, boundary effects and the matrix all importantly influence connectivity and ultimately individual success, as we try to show in the following.

Effect of landscape context on connectivity

Laurance et al. (2002) recently synthesized key findings over 22 years from the Biological Dynamics of Forest Fragments Project, in central Amazonia. Fragments surrounded by regrowth forest 5-10 m tall experienced less intensive changes in microclimate and had lower edge-related tree mortality than did similar fragments adjoined by cattle pastures. Edge avoidance by mixed-species bird flocks was also reduced when fragments were surrounded by regrowth rather than cattle pasture. Laurance et al. point out that several species of primates, antbirds, obligate flocking birds, and euglossine bees, all of which had disappeared soon after fragment isolation, recolonized fragments when regrowth regenerated in the surrounding landscape. Furthermore, some of the Amazonian matrix habitats were more suitable for rainforest fauna than others. Thus regrowth dominated by Cecropia trees, which tends to be tall and floristically diverse with a relatively closed canopy, was used by more rainforest bird, frog, and ant species than was more open Fw/w'a-dominated regrowth (see Laurance et al. 2002). In general, the more closely the matrix approximated the structure and microclimate of the primary forests, the more likely that fragmentation-sensitive species could use it. Fahrig (2001) estimated that under certain circumstances up to 58% less habitat was required for population persistence if a matrix of very low quality was converted to one of very high quality. These results indicate that the composition of the matrix can have a significant influence on fragment connectivity and functioning.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Many authors have demonstrated the effects of landscape context and connectivity in community structure (Pearson 1993; Holt 1997; Sisket al. 1997). MacArthur and Wilson (1967) used surface area combined with age as the principle factors predicting species richness on oceanic islands. In terrestrial ecosystems, species diversity is also significantly affected by other landscape-level factors, beyond patch size (e.g., Lovett- Doust & Kuntz 2001; Lovett-Doustet al. 2003). The notion of “mass effect” has been used at the community level to describe how neighbouring communities influence species composition of a target community (see e.g., Canteroet al. 1999). Similarly the “rescue effect” describes how occupied patches on the brink of extinction are rescued by immigrating dispersers from other occupied patches (Gottelli 1991). This occurs in a manner analogous to the way in which ‘sink’ populations are maintained at the population level, through dispersal from ‘source’ populations (Pulliam 1988), and how species presence is maintained in sub-optimal habitat in metapopulations (Holt 1997). Holt (1997) used variants of the Levins metapopulation model to examine the effect of spatial heterogeneity on community structure. Holt’s theoretical results suggested that species having high occupancies in the abundant habitat (the matrix) had the potential to contribute disproportionately to species composition in the more sparse habitat (the patches), via a spillover effect. This effect has important implications for determining the effect of the matrix on biodiversity in fragmented landscapes.

Forest fragments are susceptible to “bombardment” of propagules from weedy plant species in the matrix vegetation, which may then be incorporated into the fragments community (Janzen 1986). Many authors have documented invasion of forest habitats from plant species in the matrix (e.g., Janzen 1983; Tabarelliet al. 1999; Cooket al. 2002). Coinciding with an increase in exotic species in Atlantic forest fragments, Tabarelli et al. (1999) described a decline in the relative number of species from plant families considered most important for vertebrate frugivores (e.g., Myrtaceae, Lauraceae, Rubiaceae and Sapotaceae). Although this study provided no data on abundance of these vertebrates, it is likely that decreases in the abundance and diversity of fleshy fruits will ultimately lead to an impoverished vertebrate community (Tabarelli et al. 1999). Changes in the abundance of seed predators can have significant impacts on plant

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. populations in patches. For example, Currenet al. (1999) found that recruitment of canopy trees, mostly from the family Dipterocarpaceae, collapsed in the Gunung Palung National Park in western Borneo. During a masting event in 1998, dipterocarp recruitment in the park fell drastically because of an increase in seed predation by vertebrates that had moved into the park from surrounding degraded areas.

Effect of corridors and “stepping-stones” on connectivity

Corridors linking patches in fragmented landscapes may improve connectivity between patches and hence dispersal success for some species. The use of corridors enabling movement in the matrix habitat has received considerable attention, in particular for butterflies (Haddad 2000, 1999; Dover & Fry 2001), other insects (Hill 1995; Nichollset al. 2001) and small mammals (Downes et al. 1997; Bolger et al. 2001; Coffman et al. 2001). These studies typically demonstrate that for some species in certain landscape contexts, corridors facilitated movement between patches, but were often not essential. Furthermore, the disparate response of species, even closely related taxa, is noteworthy (Bolger et al. 2001; Dover & Fry 2001).

Tewksbury et al. (2002) recently conducted a study linking the effects of corridors across an array of plant-animal interactions. They tested hypotheses of corridor function in an experimental landscape, by studying movements of butterflies and pollen and bird- dispersed seeds. Corridors were found to facilitate the movement of butterflies between connected patches. Pollen movement mirrored the movement of the butterflies, and a significantly greater proportion of flowers produced fruit in connected patches than in unconnected patches. Seeds of the two species studied (large, fruiting shrubs, Ilex vomitoria and wax myrtle,Myrica cerifera) were more likely to be found in connected patches than unconnected ones. The study also demonstrated increases in fruit set and seed movement in connected patches across diverse sets of pollinators and seed dispersers, suggesting a potentially wide application.

Highly mobile species, such as birds and many insects, can move rapidly over extensive areas of fragmented landscapes, and for these species even small remnant patches of

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. habitat may act as ‘stepping stones’ across the landscape and enhance movement (Fischer & Lindenmayer 2002; Lovett-Doustet al. 2003). Nason and Hamrick (1997) reported that small fragments and even single, lone trees may serve as important stepping stones for pollinator movement between larger patches of tropical forest. Solitary and isolated paddock trees in fragmented landscapes in Australia have been shown to serve as connecting landscape elements for a range of bird species (Fischer & Lindenmayer 2002), while several authors (Guevara & Laborde 1993; Luck & Daily 2003) have demonstrated the importance of free-standing trees in the surrounding matrix of tropical rain forest patches, as foci for seed deposition by birds. Thus connectivity may be improved between patches without necessity for a continual corridor between patches; rather, remnant habitat between patches may suffice to improve connectivity for relatively mobile species.

For particularly long-lived species, such as trees (where old age for many species may mean many decades, even centuries), the traditional definition of the matrix in the metapopulation paradigm (namely, habitat suitable for traversing but unsuitable for supporting breeding individuals [Wiens 1997]) is often not appropriate. Levin (1995) reviewed the importance in highly modified habitats of isolated trees, or “reproductive outliers,” to within-patch population dynamics. Levin suggested that these trees may serve as bridges between populations and concluded that, although isolated individuals may produce fewer seeds than do individuals located within inhabited patches, they may be a major source for pollen and seeds to nearby populations, retarding the divergence of local populations and forming nuclei for new populations. Few empirical studies have considered the importance to regional dynamics of trees residing in matrix habitat. Where these individuals have been considered, the results support the conclusion of Levin (1995), that they may contribute in a number of important ways to regional dynamics. For example, adult trees ofSymponia globulifera in pasture habitat have been shown to contribute most of the seedlings in nearby remnant forest patches, whereas remnant forest adults produced very few of the seedlings residing in their own patch (Aldrich & Hamrick 1998).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Effect of edges on connectivity

Edge effects are closely related to both landscape context and corridor effects on connectivity. Sisket al. (1997) suggested that many matrix effects may actually manifest as edge effects. For example, landscape context should not be expected to have much impact on emigration for patches with relatively hard (impermeable) edges (as in, e.g., a forested patch adjacent to an industrial, or developed area). Consequences of disruptions to dispersal via edge permeabilities have long been linked to plant pollination and seed dispersal in fragmented landscapes. By disrupting or impeding movement of pollinators, edges having relatively high impermeability may restrict pollen flow and seed dispersal among plants in patches (Fagan et al. 1999).

Edge-mediated effects on seed dispersal and seed mortality may also be important in determining species composition, and successional patterns in patches (Faganet al. 1999). In regions of remnant tropical forest surrounded by a harsher, modified environment, edge-related seed mortality may impede germination of native tree flora at the expense of more edge-tolerant weedy species, so altering successional patterns and making fragmented forest even less similar to unfragmented forest (Janzen 1983). In temperate forests, extinction likelihoods may be greater due to decreased population sizes near habitat edges, as Jules (1998) concluded from his study of fragmentation effects on demography of the understory herbTrillium ovatum. The mechanism for the demographic change was likely a combination of reduced seed set and diminished survivorship of seeds and seedlings near edges.

Edge-related gradients in physical and biotic variables are likely to be less pronounced when the matrix is more similar in structure to that of the fragment (Gasconet al. 1999). Mesquita et al. (1999) reported that Amazonian forest fragments surrounded by pasture had significantly higher tree mortality than fragments adjoined by Vismia spp regrowth forest. Laurance et al. (2000) also reported disproportionate mortality of large canopy and emergent trees in Amazonian forest fragments following fragmentation.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Thus far, processes at population margins and zones of contact have generally been described in terms of the behaviour o f‘one-dimensional’ transect-like, mean field models (see Antonovicset al. 2001). Such studies have used either ‘connected lattice’ models in one dimension (such as stepping-stone models in population genetics), or partial differential equations describing spatial change in abundance or gene frequency over one or two dimensions. Antonovicset al. (2001) recently used spatially explicit individual- based models to study the patterns and dynamics that develop in population margins as they expand into regions that become more and more unsuitable; at the same time they probed effects of plant pathogens. At the margins, local, short-lived, ‘flame-like’ population patterns developed. While the local density of individuals at population margins initially prevented the invasion of disease into these margins, in the long term marginal populations and disease seemed to be sustained by complex colonization- extinction dynamics, where there was no clear gradient in pathogen abundance at the margin (Antonovicset al. 2001).

Effects of matrix land use

Biemacki et al. (2003) investigated effects of land-use in the matrix surrounding a reserve of nearly a hundred designated natural areas along the 735 km Niagara Escarpment, a regional biodiversity hotspot in southern Ontario, Canada. Seven land-use categories were mapped in the matrix surrounding each natural area. Stepwise logistic regression techniques were used to identify factors influencing presence/absence, and size of the major biotic groups (including plants). Results showed that both the types of land-use and their proportions at different distances from the edge of each patch of natural area (at 0, 100, 250 and 500m from the perimeter) had highly significant effects on species richness of biota.

In another study, Lovett-Doustet al. (2003) compared three general classes of ownership of natural area patches in Ontario, Canada —private, public, and mixed —in terms of both numbers and kinds of rare species measured for global and regional rarity. Land ownership had highly significant effects on rare species richness, including plants, with, in this case, more rare species occurring in publicly-owned patches than in privately

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. owned ones, even after other factors, such as the size of the patch, were controlled statistically.

A functional mosaic approach

Plant population dynamics are often influenced by more than a patch/matrix model can account for. Many plants exist in situations where individuals are not clustered in their distribution and definition of distinct populations is problematic, and where suitable habitat patches are not easily delineated, but rather where gradients of habitat suitability more appropriately characterize the region. Here description of the landscape in terms of suitable patches and a homogenous matrix greatly oversimplifies reality, and an integrative, landscape-based approach to understanding regional scale dynamics is likely to be more valuable. Such a large, layered situation seems to lend itself to Forman’s (2002) notion of a ‘functional mosaic model’ in which the landscape is composed of places influencing movement and flow of organisms.

To date, landscape context has been limited generally to the inclusion of such spatial parameters as habitat composition and configuration (Mazerolle & Villard 1999). Presumably, for plants, physical factors such as light intensity and moisture availability are important parameters. Other authors have taken a more functional approach (e.g., Forys & Humphrey 1999). For plants, the distribution of “safe sites” (sensu Harper 1977) for seed germination and seedling recruitment should be very important. Furthermore, populations of pollinators and seed dispersers will likely be necessary; factors associated with pathogens/parasites and competitors will all also likely be important functional variables. We suggest landscape connectivity be viewed as a composite of parameters occurring via structural context - including both physical and

spatial parameters - as well as an array of functional context parameters (at both community and population levels). Table 3.1 outlines components of a landscape mosaic approach, and general parameters which should be considered in the parsing of landscape connectivity. Naturally the most important consideration is how the organism of interest perceives its environment, and at what scales, with suitable weighting of the most relevant components and metrics.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. In order to further develop the mosaic approach, we support Thomas and Kunin’s (1999) suggestion of a grid-based approach to mapping spatially structured populations that do not adhere neatly to habitat/non-habitat delineations. This approach has several advantages when dealing with plant populations at regional scales. Employment of a spatial grid system avoids the need for a subjective definition of suitable habitat patches, and allows for an evaluation of the relative significance of different components of the landscape. This approach is also amenable to grid-based modeling and allows plant distributional data to be related to Geographic Information System datasets. Many authors have demonstrated advantages of spatially explicit or spatially realistic grid-based models for assessing aspects of plant population and community dynamics (e.g., successional patterns: Hovestadt et al. 2000; tree species diversity patterns: Liu & Ashton 1999; competition: Coomeset al. 2002). Several other authors have recently described a gradient-based approach to viewing landscapes (McIntyre & Hobbs 1999; Theobald & Hobbs 1999; Lindenmayer et al. 2003). Gradients of habitat quality, which may be composed of several landscape structural and functional components, are mapped in a grid-based structure, giving a framework for interpreting a species response (e.g., dispersal, reproductive success) to the landscape (see e.g., McIntyre & Hobbs 1999).

The matrix clearly js important in its effect on connectivity and population dynamics of species living in fragmented habitats. However we have argued that, for many plant species, patches of suitable habitat are not readily defined and, furthermore that plants likely respond to gradients of habitat suitability. Thus, by default, the matrix, or unsuitable habitat (as traditionally defined), is also difficult to discern, and nebulous. The advantage of the functional mosaic approach, when combined with division of the landscape into a grid, is that each cell within the grid can be described simply in terms of local population size and its structural and functional context, without the need to define explicitly patch and matrix habitat. In this sense, the answer to the question we pose in the title of this paper is, strictly, no - the matrix is not important, but neither is the patch

- rather the nature of the composite landscape mosaic is the key determinant of the fate of plant populations.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Conclusions

A landscape is always heterogeneous at some spatial or temporal scale. Structurally it is a mosaic, with multiple sources, barriers, conduits, attractors, repellents, sinks, avoidance spots, and comfort places (Forman 2002). From an individual’s perspective, it is a mosaic of food resources, grazing and predation risks, confrontations and competitions, and structural conditions. It is also a mosaic of land use, land ownership, management and jurisdiction. Treatment of the landscape as a mosaic, with attention given to dominant features of the landscape context and how they interact, to determine the fate of populations has been eloquently advocated by landscape ecologists (see in particular Wiens 1997,2002b; and Forman 2002). As we have demonstrated, the empirical evidence continues to urge a more integrative perspective when considering regional population persistence, compared to that mostly employed in current metapopulation and landscape ecological approaches. Thus, in the words of Forman (2002): “We can now move beyond the stage of patches-in-an-inhospitable matrix, source and sink, and corridor-connecting-two-patches Why couldn’t the patch-corridor-matrix model be enriched or even replaced by a functional mosaic model, in which the landscape is composed of such places portraying movements and flows?”

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 3.1 - Components of a landscape mosaic approach to connectivity: parameters of structural (physical and spatial) and functional (community level and population level) contexts in a landscape. Structural Functional Physical context Spatial context Community level Population level

• Habitat nature and quality, • Habitat composition • Species richness • Density of conspecifics extent of disturbance • Habitat configuration • Fraction of habitat • Nearest neighbour • Resource availability: • Habitat diversity: specialists distances, nearest mineral nutrients, light, richness, evenness, • Invasibility potential mate distances water, etc dominance, similarity, • Soil mineralization • Plant sizes and size • Climatic parameters etc. • Successional trends distribution • Soil types • Habitat dispersion, • Biomass • Density of pollinators, • Physical elements: contagion • Overall dynamics dispersal agents, landforms, waterbodies, • Edge extent (turnover of individuals) predators, prey roads, urban development • Resilience • Local extinctions, • Land use/land cover colonizations • Pollen availability • Seed production

• Seedling recruitment Chapter 4 - Effects of different edges versus central sites on patch- and landscape-level population biologyGleditsia in triacanthos (Honey Locust)

Introduction

The particular habitats a species occupies across its geographic range occur in a variety of regional landscape contexts, incorporating both structural (physical and spatial) and functional (community and population level) parameters, interacting to determine the performance of individual plants, and consequently population structure, abundance and distribution (Murphy & Lovett-Doust 2004a). Presumably, for plants, physical factors such as light intensity and moisture availability are major parameters, as well as the distribution of “safe sites” (sensu Harper 1977) for seed germination and seedling recruitment. Populations of pollinators and seed dispersers will often be necessary; factors associated with pathogens/parasites and competitors will all also likely be important functional variables determining plant performance. While large-scale geographic patterns of plant performance have commonly been studied, and range-wide patterns of abundance are an important focus of biogeography and macroecology (Brown et al., 1996; Murphy et al. 2005), few studies have explored the geographic variability of populations and their respective environments beyond local or regional levels (see Murphy & Lovett-Doust 2005a). Consequently, while it is widely accepted that the ability of species to cope with their environment determines their performance, little attention has been paid to the question of how this ability may vary across species’ ranges, whether all edges of the range are the same, and whether theory suggests some population performance results ought to be expected over others.

The central-peripheral model

permission of the copyright owner. Further reproduction prohibited without permission. autocorrelation in these axes (representing dominant dimensions of the niche) results in the probability of sites having similar combinations of environmental variables being an inverse function of the distance between them. Thus, increasing the distance from the optimal site should decrease the probability of a site fulfilling the niche requirements of that species. There should be a decreasing number of local sites where a species can occur at all and, even within such patches, population densities will tend to be lower because resources are scarce and/or conditions approach the limits that can be tolerated (Brown 1984). Peripheral populations are also expected to have less genetic variation than central ones, due to genetic drift, founder effects, bottlenecks and inbreeding, and diminished sexuality (Levin 1970; Lawton 1993; Lesica & Allendorf 1995).

Many differences in plant performance have been reported between central and peripheral populations of species, some indicating a decline in performance towards edges and others suggesting increased performance in edge populations. For example, Stokes et al. (2004) found growth rates were greater in Ulex gallii and U. minor populations at the periphery of their ranges. Density ofU. minor was also greater at the range edge (Stokes et al. 2004). In the narrow endemic shrub Cocciloba cereifera, both clones and ramets produced more leaves and more inflorescences towards the edge of its range compared with the central parts of the range, and aggregations of the species lacking any flowering individuals were concentrated in the centre of the range (Ribeiro & Fernandes 2000). Carter and Prince (1985) reported that while the prickly lettuce, Lactuca serriola became rarer towards range edges in Britain, it showed no loss of vigour in the edge populations. On the other hand, seed production has been shown to decrease in northernmost populations of some tree species, due to climatic stress (Pigott 1989; Despland & Houle 1997; Garcia et al. 2000). Smaller, peripheral populations ofLloydia serotina produced fewer flowers and seeds than larger ones (Jones & Gliddon 1999). A significant decline in population density and seed production occurs toward the range edges in Cirsium acaule and C. heterophyllum (Jump & Woodward 2003). Caughley et al. (1988) proposed that by examining how particular features of populations (e.g., density, growth rate, reproductive performance) differ between peripheral and central

permission of the copyright owner. Further reproduction prohibited without permission. sites, and whether any such change is related to a decrease in habitat or environmental suitability, one may reasonably infer causation for range limitation.

Population size distribution carries a wealth of demographic information and is frequently the most unequivocal and accessible attribute available for a population, particularly when the species is long-lived. Several authors have proposed the use of size-frequency distribution patterns to narrow determination of the range of factors that could be most significant in limiting species distributions (e.g., Kelly et al. 2001). In plants the size distribution of a population represents the demographic attributes of recruitment, mortality and individual growth rates over time (Enright & Watson 1991; Fox & Gurevitch 2000; Kellyet al. 2001). Thus if populations at the edge of the range are biased toward smaller size classes, or younger ages, this suggests survivorship rates are lower, or that edge populations may be ephemeral and suffer frequent local extinctions. Alternatively, if peripheral populations tend to have an irregular size structure, and sporadic waves of discrete cohorts, or if they are biased towards older size classes, then recruitment failure likely plays a more important role in limiting the edge of the range (Zacherl et al. 2003).

Any feature that varies temporally and targets recruitment success could cause irregular size structure. Studies of age and size structures in Tsuga canadensis showed that recruitment of stems to the canopy was more continuous at the species’ range centre than at more northern, geographically peripheral sites (Kavanagh & Kellman 1986). A recent comparison of twelve pairs of closely-related, ecologically similar tree species concluded the more abundant species had typically ‘smoother’ population size profiles than the less abundant species at the same site (Kelly et al. 2001), suggesting greater recruitment fluctuation in the less abundant species.

For dioecious species, the sex ratio constitutes a fundamental structural parameter of a population and is related to reproductive strategies, growth patterns and survival rates (Freeman et al. 1976, Banuelos & Obeso 2004). In many dioecious species, male and female individuals are known to respond differently to different types of stress; numerous

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. reports exist for spatial segregation between the sexes along environmental gradients (see e.g., Lovett-Doustet al. 1987; Bertiller et al. 2002). In dioecious species, females are expected to be more sensitive to environmental stress than males because female reproductive effort requires the largest input of resources (Freemanet al. 1976). If peripheral populations were environmentally stressed, we would expect to see male- biased sex ratios due to: (a) higher mortality of females (Banuelos & Obeso 2004); (b) greater clonal growth by males (Lovett-Doust & Lovett-Doust 1988); and/or (c) male reproduction starting earlier in life and being more frequent than in females (Nicotra 1998).

Landscape fragmentation and plant population dynamics

Most North American landscapes have been heavily influenced by human land use. The resulting landscape mosaic is a mixture of remnant natural and human-managed patches that vary in size, shape and arrangement. This spatial patterning is a unique arrangement that emerges at the landscape level, and changes in species composition of patches in fragmented landscapes, as well as demographic effects on individual species have been observed (Jules 1998; Gascon et al. 1999; Murphy & Lovett-Doust 2004a). For example, small and isolated plant populations seem less likely to attract pollinators than large ones and as a result individual plants in small populations may receive less pollen from pollinators. In dioecious plants, pollinator limitation may result in reduced levels of seed set (Steffan-Dewenter & Tscharntke 1999). Tree populations occurring on smaller fragments have been shown to suffer reductions in fruit production and seed germination, relative to populations in larger fragments and more continuous forest populations (Nason & Hamrick 1997). Resultant decreases in both long- and short-term population viability and average individual fitness are expected to result from the effects of habitat fragmentation (Lande 1988; Ouborg & Van Treuren 1995).

Here we present results from field work over two years, measuring population performance parameters in 22 populations ofG. triacanthos across its geographic range. Populations are located in northern, southern and western peripheral parts, as well as in geographically central parts of the range. We analyse size-distribution frequency

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. patterns, sex ratios, and variation in male and female reproductive output inG. (riacanthos populations in relation to position in the range, population size and density, and measures of the surrounding landscape structure, at several spatial scales.

Methods

Gleditsia triacanthos, Honey Locust

Throughout its North American range (Figure 4.1),G. triacanthos occurs as a relatively minor component of natural forest stands and is generally considered early successional and fairly intolerant of shade (Sullivan 1994). The species is found typically on moist bottomland and in abandoned fields and pastures (Blair 1990) in the eastern and central United States; it is a minor, rare component of the tree flora of southern Ontario. The species attains its maximum height in the valleys of small streams in southern Indiana and Illinois (Gordon 1966). Honey Locust is tolerant of low temperatures in the north (- 29 to -34°C) and, although ample soil moisture is necessary for optimal growth, the species appears resistant to seasonal drought (Blair 1990). The species seems to depend on mesic conditions of soil humidity for germination and seedling survival (Burton and Bazzaz 1991). Although the species has been characterized as presenting low frequency of regeneration beneath closed canopy (Grime & Jeffrey 1965), its own canopy offers adequate germination conditions (Burton & Bazzaz 1991). Over its rangeG. triacanthos grows naturally to a maximum elevation of 610-760 m (Blair 1990).

Gleditsia triacanthos is a frequent invader of disturbed areas in the eastern deciduous forests of North America (Burton & Bazzaz 1995). In Kansas (at the western edge of the natural geographic range), the species frequently invades abandoned fields from low- lying wooded areas or from hedgerows (Schnabel & Hamrick 1995). The species is also considered invasive in several areas well outside its natural geographic range, for example, in the montane forests of Argentina (Marco & Paez 2000), in Queensland and New South Wales, Australia (Csurhes & Kriticos 1994) and in South Africa (Wellset al. 1986).

permission of the copyright owner. Further reproduction prohibited without permission. Honey Locust is a fairly strictly canalised, dioecious tree. Close inspection of many plants indicated a small fraction of individuals (<1%) may produce some perfect flowers (Schnabel & Hamrick 1995; personal observations). The inflorescence in both sexes is an unbranched raceme, where males tend to have more flowers per inflorescence than females (Tucker 1991). The female inflorescence has few, stalked carpellate flowers; whereas male inflorescences make more abundant staminate flowers, with triatic clusters of three flowers per stalk near the base (Tucker 1991).

Male trees tend to flower every year, while in females flower and fruit production is more sporadic (Schnabel et al. 1991). Female trees in some parts of the range are reported to flower and fruit most years punctuated by occasional mast years, while in other, typically more northern parts, females may flower and fruit only every two to three years (Schnabel & Hamrick 1990). Flowering is initiated in late April in the southern part of the range and mid-June in the northern part, although flowering has occurred earlier due to yearly climatic variation (personal observations for 2002 and 2003 flowering seasons, for trees located from Louisiana to southern Ontario). Females produce long, indehiscent pods, ranging mostly between 15-25 cm, and containing 10-30 bean-like seeds (Waldron 2003).

G. triacanthos is considered to have a highly outcrossing mating system and maintains high genetic diversity within populations and low, but significant genetic differentiation between populations (Schnabel & Hamrick 1990). The low levels of genetic diversity between populations suggest that gene flow among populations is relatively high. The species is pollinated by a variety of insects, including bees, moths and butterflies (Schnabel 1988). Schnabel (1988) has shown that 15-50 per cent of the effective pollinations at three sites in Kansas occurred by pollen originating outside the sites, and were comparable to estimates of pollen migration for wind-pollinated species. Schnabel and Hamrick’s (1995) direct estimates of pollen-mediated gene flow in Kansas populations suggest that pollen is widespread over areas as large as 25 to 100 hectares (or c. 200-500 m in any direction from a site), despite the highly discontinuous and irregular nature of the distribution ofG. triacanthos populations in their study area. These authors

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. suggest that pollen-mediated gene flow has historically played a significant role in preventing high levels of genetic differentiation in G. triacanthos populations over scales of tens to hundreds of kilometers.

G. triacanthos reproduces both sexually and vegetatively. Clonal growth may be more associated with males than females (Schnabel et al. 1991) and may not be as extensive as might be suggested by.the strongly aggregated spatial distribution of the species in some populations. Schnabel et al. (1991) compared the genotypes among stems in 50G. triacanthos clumps in a population in Kansas, and found that 48 of the 50 clumps contained more than one individual and 44 clumps consisted solely of genetically unique stems.

Seed dispersal is highly localized, most fruits falling directly beneath the maternal tree. Longer-distance dispersers include deer, cattle, horses and small mammals, all of which likely contribute to the rapid spread of the species into open fields (Schnabel et al. 1991). Schnabel et al. (1998) used a maximum-likelihood maternity analyses model to estimate individual female fertility for maternal trees across a large number of naturally- established seedlings and saplings, at two sites in Kansas. Maximum-likelihood fertility estimates at the two sites showed that the three highest-fertility females accounted for 58% of the progeny at the first site, and 46% of progeny at the second, whereas 18 of 34 and 16 of 35 females, respectively, had fertility estimates that did not exceed 1%. Estimates of seed dispersal distances indicated that this was highly localized at the first site but nearly random at the second site. Seven of the 320 juveniles at one site, and 14 of 665 juveniles at the second site, had genotypes that were not compatible with any of the possible maternal parents within the sites, suggesting that both sites received a minimum 2.1% seed from distances >100-200 m away. The results demonstrate that effective seed dispersal distances may vary significantly from population to population, based, most likely, on the behaviour of secondary seed dispersers. Early North American megafaunal dispersers of Honey Locust have been extinct for some several thousands of years now, likely impacting the importance today of seed dispersal in the species (Barlow 2002).

permission of the copyright owner. Further reproduction prohibited without permission. Study site locations

Twenty-two sites in eastern North America were selected to represent the northern, southern, western and central parts of the native range of Honey Locust (Figure 1). At sites with large populations, a subset of all trees (within a portion of the site) was identified and mapped, using a Trimble AgGPS differential global positioning system (GPS). At sites with relatively small populations, all trees were included. Each tree was sexed, based on flower observations in the spring of each year (2002-2003). Table 4.1 identifies populations and their locations, and gives the number of males, females and trees that did not flower over the study period. For each tree, the height and circumference at breast height (cbh) were measured.

Vegetative and reproductive output

On each tree sampled, all primary and secondary branches were counted, then five secondary branches were subsampled and counted from the third to the maximum branch order. The total number of branches at each order was estimated for the tree by multiplying the mean number of an order’s branches by the total number of estimated- (i.e., third to maximum order) or known- (i.e., first and second order) branches at the previous order. Total number of branches per order were summed to provide an estimate of the total number of branches for the entire tree. The overall total number of inflorescences was estimated for the whole tree by multiplying the mean number of inflorescences per branch order by the estimated number of branches at that order. Total inflorescences per branch order were summed across the tree. In the fall of each year the total number of fruits per female tree was estimated using the same method as for inflorescences.

Population parameters

Latitude and elevation above sea-level were recorded on-site with a Trimble AG132 GPS. Distance from the approximate centre of the range to each site was measured using ArcGIS 9 (ESRI, Redlands CA). Boundaries of the area within which population sizes were estimated were defined either by abrupt changes in land use or vegetation type (e.g., water, agriculture, topography), or by distance of >300 m to the next nearest tree in any direction. This distance is beyond the upper limit of seed dispersal distances reported for

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the species (Schnabel et al. 1998). Average nearest neighbour distance was determined using ArcGIS 9 software.

Several performance parameters were calculated for each population. The proportion of the population in each of five size classes (size class 0 = < 5cm cbh; 1 = 6-15cm cbh; 2 - 16-50cm cbh; 3 = 51-100cm cbh; 4 = > 100cm cbh) was calculated. Population sex-ratio was calculated as the number of flowering males divided by the number of flowering females (i.e., values >1 are male-biased). Mean male and female inflorescence production and mean female fruit production were estimated for each population. The proportion of males and females flowering in each year and the proportion of females fruiting in each year was also derived. Mean number of branches per tree was also calculated.

Landscape parameters

GAP analysis (U.S.G.S. 2005) land cover layers were obtained for the States of Kansas (43 classes of landcover), Illinois (30 classes), Kentucky (48 classes), Tennessee (11 classes), Louisiana (23 classes) and Mississippi (16 classes). GAP landcover data was not available for Ohio, so the seven northern sites were not included in this analysis. The landcover data is derived from an assessment of the vegetation cover of each state. Vegetation is generally identified to the alliance level (groups of plants sharing dominant species), based on the National Vegetative Classification Scheme (NVCI) (Federal Geographic Data Committee 1996,1997; Grossmanet al. 1994). Remotely sensed Landsat TM satellite data is used as the basis for determining vegetative alliance distributions at a resolution of 30 m (Bara 1994). For consistency, each State’s landcover classes were reclassified to a common 19 classes (shown in Table 4.2).

Landscapes at four buffer distances (100,450,1000 and 5000 m) around the 15 sites (for which GAP landcover data were available) were ‘clipped’ from the GAP landcover layers for measurement of fragmentation statistics in FRAGSTATS (McGarigal & Marks 1995) (n = 60 landscapes). In order to reduce the number of parameters, only landcover types typically associated with the species (based on its ecological characteristics and known

permission of the copyright owner. Further reproduction prohibited without permission. occurrences) were included in the model. Thus, landcover classes 1 (agricultural and cultivated land), 3 (water), 6 (upland deciduous forest), 10 (pasture, abandoned fields, grassland and herbaceous cover) and 13 (bottomland deciduous forest) were included. The proportion of the landscape comprised of each of the five landcover types was calculated for each of the four buffer distances around each site. In addition, the total number of patches (including patches of all landcover types) was extracted from each landscape.

Statistical analyses

All statistics were conducted using SPSS Ver 13 for Windows (SPPS Inc, Chicago IL). Pearson correlation analyses were used to determine if population performance parameters were related to population- and landscape-level factors. Pearson correlation analysis was also used to determine the relationship between flower and fruit production and tree size. Chi-squared analysis was used to test the departure of sex ratios from unity. One-way ANOVA was used to determine whether population flower and fruit production varied significantly between years, and whether male and female vegetative production varied within sites.

Results

Population size-frequency distributions

The most striking difference in population size-frequency distributions was between the southern and western sites (Figure 4.2). Southern population sizes were heavily skewed toward larger size classes whereas western populations had the opposite trend, having higher proportions of trees in the smaller size classes. Southern populations contained greatest proportions of individuals in the largest size class (cbh > 100 cm) of any of the regions, with up to 50% of individuals in this class. In central and northern sites the largest three size classes (trees >15cm cbh) contained relatively similar proportions of individuals. Central and northern sites differ from each other primarily in the proportion of the population in the second size class (i.e., 6-15 cm), with northern sites generally containing more individuals in this size class.

permission of the copyright owner. Further reproduction prohibited without permission. Within each size class, individual sites were ranked from 1 to 22, according to the proportion of individuals per class. Thus the site having the highest proportion of individuals in a size class would be ranked 22, for that size class. Figure 4.3 shows the site rankings for each site, across all size classes. All southern sites ranked highest in either size class 3 or 4 (the two largest), whereas all western sites ranked highest for one of the two smallest size classes. Both central and northern sites showed a mix of classes with the higher rankings; six of the seven central sites ranked lowest in either size class 0 or 1.

Population sex-ratios

Population sex-ratios ranged between 0.96 (at western site 1) and 4.67 (at southern site 3) (Table 4.3). Western population 1 was the only site having a marginally female biased sex-ratio. Sex-ratios at six sites (two northern, two central and two southern sites) deviated significantly from a 1:1 ratio (Table 4.3). No western sites had a population sex-ratio deviating significantly from 1:1. Sex-ratios and results of chi-square tests for deviation from a 1:1 ratio in the three largest size classes are also shown in Table 4.3 (size class 0 had only vegetative individuals, size class 1 only vegetative or male individuals). In five populations only males in size class 2 flowered. Sex ratios in 11 populations in this size class deviated significantly from a 1:1 ratio; only one of these populations was female biased. Similarly, 11 populations deviated significantly from a 1:1 ratio in size class 3; again only one population was significantly female biased in this size class.

Three of the four western populations, one southern, one northern and two central populations had a significantly female biased sex-ratio in the largest size class. Three of the four southern populations and six of the seven northern populations showed male- biased sex ratios in all size classes (though not always significantly different from a unity).

Reproductive output

Female size (measured as cbh) was significantly positively correlated with both average flower (r = 0.261, P<0.001, n = 388) and fruit production (r = 0.317, PO.OOl, n = 226).

permission of the copyright owner. Further reproduction prohibited without permission. Similarly, there was a significant positive correlation between male size and average flower production (r = 0.367, p <0.001, n = 479).

Male trees generally reproduced at a smaller size than female trees (Figure 4.4), whereas the proportion of males and females at the largest size class was not significantly different, except for in the western region, where females pre-dominated at the largest size class. At western and northern sites nearly all trees in the largest size class flowered at least once in the two years (Figure 4.4). However, in the southern and central parts of the range, there still remained up to a third of the individuals in size class four that did not flower in either year.

In general, most male trees flowered in each population each year (Figure 4.5a). More than 60% of males flowered at all sites in both years, with one exception. At southern site 3 only 40% of males flowered in year 2. Mean population inflorescence production by males was significantly positively correlated between years (Table 4.4) although the proportion of males flowering was not.

The proportion of males and females flowering at a site in a given year was positively correlated (Table 4.4). The proportion of females flowering in each population was more variable, ranging from 27% (central site 7 in year 2) to 100% (in one northern, one southern and two western sites, all in year 1). On average, western sites had the highest proportions of females flowering and central sites the lowest (Figure 4.5b); the values were significantly positively correlated between years (Table 4.4). The proportion of females fruiting in each population ranged from 21% (northern site 1, year 2) to 91% (northern site 7, both years), and values between years were positively correlated (Table 4.4). On average, the proportion of females producing fruits in a population was highest in western sites and lowest in southern sites (Figure 4.5c). Fruit production was significantly positively correlated with female flower production in year 1, but not in year 2 (though the proportion of females fruiting in year 2 was positively correlated with the proportion of females flowering in year 2) (Table 4.4).

permission of the copyright owner. Further reproduction prohibited without permission. The mean number of male inflorescences at each site in both years is shown in Figure 4.6. Male inflorescence production did not vary significantly between years (p > 0.05) at any site. Female inflorescence production varied significantly between years at only one site (northern site 7, t = -2.963, df = 18, p <0.05); fruit production varied significantly between years at three sites (central site 6, t = 3.322, df = 25, p < 0.01; northern site 6, t = 2.319, df = 15, p < 0.05; and northern site 7, t = 3.264, df = 18, p < 0.05).

Both male and female flower production was generally more variable between trees at central and northern sites (Figure 4.6,4.7a) whereas inflorescence production at southern and western sites remained lower and less variable. Fruit production in southern and western sites was very low in both years (Figure 4.7b). Very little fruit was produced in the northern sites in the second year of the study, except for in northern site 3, where fruit production was similar to that produced in the first year. The very high variation in fruit production at central site 7 was driven primarily by very large numbers of fruits being produced by two females at the site (the range in fruit production at this site was 182/tree to 16,385/tree).

Vegetative output

Mean number of branches per tree varied regionally. Western sites had among the lowest total number of branches per tree while southern sites had the highest (Figure 4.8). Central and northern site vegetative production was variable. The three northernmost sites had relatively low branch production, while mean number of branches in the remaining four sites was higher. Mean number of branches per tree did not vary significantly between males and females within sites (p > 0.05 for all sites).

Landscape spatial structure

The landscape surrounding sites in the central region was dominated by upland deciduous forest and pasture; whereas in the southern region these landcover types were in very low proportions in the landscape, and up to 50% of the landscape was composed of bottomland deciduous forest (Figure 4.9). The landcover comprising the highest proportion of the landscape in the western region was also bottomland deciduous forest;

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. however, the proportions of all landcover types in the western region were relatively more similar (Figure 4.9).

Landscape structure parameters were significantly, and generally highly correlated, at all four scales at which they were measured (see Appendix 4.1). Thus the results of correlation analysis with population performance parameters are only shown with landscape parameters at the 500 m scale (Table 4.5). There were few significant correlations between parameters of reproductive performance and landscape factors that held for both years (Table 4.5). The exception was female flower production, which was significantly positively correlated with the amount of pasture/grassland in the landscape, in both years 1 and 2. Female fruit production showed a similar trend however the correlation in year 1 was not significant. Male flower production in year 2 was positively correlated with the amount of upland deciduous forest in the landscape and negatively correlated with the amount of bottomland deciduous forest in the landscape.

The proportion of individuals in the largest two size classes (3 and 4) decreased with latitude and increased with increasing nearest-neighbour distance (i.e., decreasing density) (Table 4.5). In contrast, the proportion of individuals in size class 0 (seedlings < 5cm cbh) increased with latitude and distance from the centre of the range. Mean number of branches decreased with increasing latitude and increased with increasing nearest neighbour distance and the proportion of the landscape comprised of agriculture.

Nearest neighbour distance was positively correlated with the proportion of females flowering, though the correlation was only significant in year 2 (Table 4.5). Population size showed no correlation with any of the population performance parameters (Table 4.5).

Discussion

Population demographic parameters

Demographic structure in G. triacanthos populations exhibits marked regional differentiation. Sites at the western edge of the range are biased toward juvenile size

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. classes whereas sites in the south are dominated by large adult trees. Central and northern sites contain few juveniles but relatively similar proportions in the three larger size classes. The differences in size-frequency distributions suggest regional variation in recruitment and/or survivorship.

There are a number of possible explanations for skewed size-frequency distributions. The relative successional stage of the community containing each population is a difficult factor to quantify, however it is likely to be important in its influence on size-frequency distributions, given that G. triacanthos exists mostly as an early successional forest species and is intolerant of shade (Blair 1990). Southern populations have the lowest density of individuals (mean nearest-neighbour distance is 21 m compared with 8.1 m, 7.2 m, and 7.8 m in central, northern and western sites, respectively). Southern populations also reproduce less and are both less fertile and show diminished fecundity, having fewer individuals in the populations fruiting and few fruits produced per individual, despite having a higher proportion of large individuals. The near absence of recruitment and low density of the populations in the south suggests these populations are at a much later stage in the successional process than the highly recruiting and dense western populations.

Infrequent or very low levels of recruitment could be caused by a number of factors, including low adult fecundity, irregular delivery of propagules, or low juvenile survival. Our results suggest low adult fecundity in the southern sites could be an important limiting factor. In addition, a very high proportion of the landscape surrounding the southern sites is composed of bottomland deciduous forest. Indeed the sites themselves occur almost exclusively in bottomland deciduous forest (see Table 4.1). This forest type has a high canopy coverage (Grossman 1994), hence sites for germination and juvenile survival may be limited for this shade-intolerant species. Sites in western, northern and central parts of the range tend to occur on a broader range of landcover types, including more open vegetation such as pasture/grassland, and landcover classified as sparse, woody vegetation. Sites containing more open vegetation likely provide more opportunities for germination and survival of juvenile stages. In fact, the only southern

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. site (S2) that includes some individuals located in land classified as pasture/grassland also has 12% of the population in size class 1 (5-15cm cbh), as compared with 2%, 0%, and 4% for sites SI, S3 and S4, respectively.

Sites in Kansas experience active recruitment as evidenced by the ‘young’ stage structure of the populations there. This stage structure is probably a result of both high recruitment and high mortality, since there are few large adults in the population. The proportions of individuals flowering and fruiting in the western sites are among the highest of all sites, which is somewhat surprising given the generally smaller mean tree size there, though flower and fruit production per individual is quite low. In the western sites almost all individuals in the largest size class flowered in both years, and almost all females in this class fruited in both years. In contrast, in the southern sites up to 25% of trees in the largest size category did not flower in either year and the proportion of females fruiting was relatively low compared to the other regions. Schnabelet al. (1995) found that female reproductive success (measured as contribution to established juveniles) at two populations in Kansas was dominated by a very small number of individuals producing the majority of the established juveniles. Approximately 50% of the females in each population were effectively reproductively inactive despite producing pistillate flowers and, often, fruits. This suggests that even if mean population fruit production is low, recruitment could still be relatively high if the most reproductively successful females regularly produce viable fruits. Levin (1995) has shown the significance of such reproductive outliers to regional ecological genetics.

Central and northern regions of the range appear to contain a mix of populations at various successional stages. Three of the central sites ranked highest in the intermediate size class, two ranked highest in the lowest class and the remaining two in the largest two size classes. Similarly at the northern sites, three sites ranked highest in the two smallest size classes, three in the second largest size class, and one in the intermediate size class. The amount of flowers produced was also variable between sites and within some sites. Northern sites tended to produce very little fruit, although approximately half the females

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. in each site produced some fruit. Fruit production in central sites was relatively low, but highly variable between trees in some sites.

The six populations with male-biased sex ratios were located in southern (2), central (2) and northern (2) areas of the geographic range. Higher mortality among females is not likely to be causing the male-biased population sex-ratios in most of our study populations since over half of the populations have sex ratios that are either female biased or exactly 1:1 in the oldest size class. Our data suggest the most likely reason for male- biased population sex ratios is earlier onset of reproduction in males (see Figure 4.4) and less frequent flowering in females. If one sex is able to reproduce at an earlier age, or is sexually active more frequently or for a longer time, then the sex ratio may become skewed, particularly if only currently-flowering individuals are being enumerated. In addition, Schnabel et al. (1991) suggested males of G. triacanthos may be more associated with clonal growth than females, so this may also contribute to male-biased sex ratios. In western populations where the proportions of individuals flowering in the populations is highest, population sex ratios were not significantly different from 1:1.

Landscape spatial structure

Landscape parameters did not tend to have consistent correlations with population performance parameters. Since landscape parameters were highly correlated at all scales (from 100 - 10000 m), this suggests neither site-level nor regional level landscape structure has much influence on population performance. The exception was the significant positive correlation in both years between the proportion of the landscape composed of pasture/grassland and female flower production. Using U.S. Forest Service data, Murphy and Lovett-Doust (2005b) showed that abundance (in the form of phytosociological ‘importance values’) and density ofG. triacanthos populations peaked in the north-west region of the range. However, results of a multiple linear regression analysis showed that landscape parameters (similar to those used in this analysis) accounted for only 20% of the variation in abundance there, compared with up to 37% in other parts of the range.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The relatively high abundance values recorded for G. triacanthos in the northwestern part of the range suggest that either abiotic conditions are more favourable for the species, or perhaps that the species experiences lowered competition there. The relative importance of biotic (usually density-dependent) and abiotic (usually density-independent) factors in limiting populations is likely to change depending on the position of the population within the species’ range (Garcia & Arroyo 2001), and on the aspect of the range edge. MacArthur (1972) suggested that biotic interactions tended to limit distribution and abundance at lower latitudes, due to increasing numbers of potentially competing species. In contrast abiotic factors were more likely to be limiting at higher latitudes. For example, in northern hemisphere plants low temperatures may limit poleward spread through their effects on both the vegetative (Woodward 1990, 1997) and reproductive phases of plant growth (Pigott & Huntley 1981; Woodward 1990). Loehle (1998) reported a tradeoff between growth rate and freezing tolerance for 22 species of North American trees and suggested that, as a result, northern hemisphere trees are out- competed by trees with faster growth rates at their southern range limits.

Our data suggest this might to some extent also be the case withG. triacanthos. Climatic conditions are probably not limiting recruitment and growth of populations in the south, since G. triacanthos grows well and even becomes invasive in areas far south of its southern North American range limit (e.g., in Argentina [Marco & Paez 2000], and in Mexico [Estrada-Castillonet al. 2002]). Overall vegetative growth is also relatively high for trees in the southern part of the range. Finally, Murphy and Lovett-Doust (2004b) found that estimates of fluctuating asymmetry (FA), often used as a measure of environmental or genetic ‘stress’ incurred during individual development, in leaves of G. triacanthos was lowest in trees from southern populations (i.e., the same individuals studied here). Fluctuating asymmetry was highest in western sites, suggesting abiotic conditions may be more ‘stressful’ there. Tree density in the southern sites is very low compared with other parts of the range, and abundance values are lower generally in the south compared with the north-west of the range (Murphy & Lovett-Doust 2005b), also suggesting increased competition.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Invasion ofG. triacanthos in the San Lorenzo mountain forest of north-west Argentina is associated with the colonization of forest gaps (>100 m2) cleared for cattle grazing. De Viana and Speroni (2003) studied the seed bank associated with different stages of succession there and found seeds were more abundant in transitional and colonizing stages (mean o f 1.23 and 1.55 seeds/800 cm3, respectively) than in mature stages (0.03 seeds). In addition, seeds had more than 78% viability in all stages. G. triacanthos may also present a seedling bank, with juveniles aged up to 22 years recorded in populations in Kansas (Schnabel & Hamrick 1995). Given the potential role that a seed or seedling bank could play in buffering the effects of variation in seed and seedling availability, and noting the tendency in the species toward mast fruiting, it is likely that our short-term estimates of reproductive output fail to capture all of the processes leading to the differences in size-frequency distribution between the sites.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. References

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

114

Landcover type (minor) Upland deciduous forest and prairie Deciduous forest and Mix ofdeciduous forest and young, Deciduous forest Mix ofdeciduous forest and young, Deciduous forest and Mix ofupland and bottomland deciduous forest Upland deciduous forest and prairie Mix ofdeciduous forest and pasture/abandoned fields Deciduous forest pasture/grassland (minor) sparse, woody vegetation. sparse, woody vegetation. pasture/grassland (minor) 803 594 853 830 706 700 598 739 655 647 5/3/7 14/11/6 12/8/17 17/11/11 21/15/41 23/24/37 24/19/20 95/60/52 35/17/28 41/28/36 15 37 39 150 160 155 500 207 210 200 size of vegetatives from centre of Approx. Males/females/ Approximate distance population sampled geographic range (km)

site Latitude/ Longitude at approximate middle of 39°08'04" / 95°25'59" 39°18'37" / 96°39'19" 41°32'42" / 82°49'00 41°55'21" / 82°30'45" 41°47'19" / 82o41'10"

OH Deer Creek State Park, 39°37'47" / 83°13'43" Park, OH Delaware State Park, 40o22'12" / 83°03'08" East Harbour State OH Pelee Island, ON AW Marian, OH 39°38,40" / 82°52'50" 6 Site Site Location, State W3 Melvem Lake, KS 38029'59" / 95°43'01" geographic range and the landcovertype in whichindicates the sampled only atrees few individualsoccur at the occur site (‘minor’ in that landcovernext to a landcovertype at thetype site). W2 Perry Lake, KS TablePopulation 4.1 — locations, arranged from most northerly to most southerly latitudes,with number ofmale, female and W1 Tuttle Creek, KS vegetative trees sampled, total population size (including unsampled individuals), approximate distance from centre of N1 Point Pelee, ON N3 N 4 N 7 Deer Creek Marina, OH 39° 37'11" / 83° 15'08" N 5 N N 2

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

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Landcovertype Bottomland deciduous forest Mix ofupland deciduous forest and Upland deciduous forest Mix oupland f and bottomland deciduous forest and pasture/grassland (minor) Mix ofupland and bottomland deciduous forest and pasture/grassland Mix ofupland and bottomland deciduous forest and forested wetland Mix ofupland and bottomland Bottomland deciduous forest (minor) pasture/grassland (minor) (minor) Pasture/grassland deciduous forest and pasture/grassland (minor)

49 148 124 113 160 127 137 501 670 from centre of Approximate distance geographic range (km)

18/16/37 19/16/15 14/11/20 14/11/29 27/26/11 31/20/12 35/15/16 29/26/34 24/10/86 65 64 54 89 66 180 125 120 500 size of vegetatives Approx. Males/females/ population sampled

site / 89°11'24" ” ” / 89°17'24" 4 8 , approximate middle of 3T3A'\2" 38°27'23" / 97°09'44" 3 7 0 3 7 37°34'12" / 89°11'24" 37°25’27" / 88o40’0r'

IL Park, TN Lakes, TN Park, IL Land Between the Big Cypress Tree 36°24'36"State / 87°55'48" 36°15'36" / 89°01T2" 6 Site Site Location, State Latitude/ Longitude at SI Tensas River, LA 32°18'12" / 91°22'44" W4 Milford Lake, KS Cl Murphysboro State C2 C3 Cedar Lake, IL Giant City State Park, C4 Lake Glendale, IL C C l C5 Rushing Creek, KY 36°42'00" / 88o03'00'

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116 Landcovertype Bottomland deciduous forest Bottomland deciduous forest and pasture/grassland

581 632 from centre of Approximate distance geographic range (km) 28/6/41 sampled 18/14/10 567 Mix ofbottomland deciduous forest 24/11/15 vegetatives Males/females/

75 42 175 size o f Approx. population

site approximate middle of 31°0i,27" / 9i°50'32" Site Site Location, State Latitude/ Longitude at S4 Pomme de Terre, LA S2 Natchez State Park, MS 31°35'58" S3 / 91°12'27" Bayou Cocodrie, LA 31°34'44" / 91°36'27"

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 4.2 - Landcover classifications used in analysis. Landcover classes in bold were used in the correlation analysis. Class Number Description

1 Agricultural land, cultivated land 2 Urban 3 Water 4 Undefined, clouds, cloud shadow 5 Barren, mined bare ground. Beaches, strip mine, quarries, gravel pits, non vegetated 6 Upland forest deciduous 7 Upland forest evergreen 8 Upland forest mixed 9 Dense pine, planted pine 10 Pasture, grassland, herbaceous 11 Non-forested wetland, marsh, non forested swamp 12 Coniferous forest 13 Bottomland forest deciduous 14 Bottomland forest evergreen 15 Bottomland forest mixed 16 Upland scrub, scrub rangeland 17 Prairie 18 Revegetated deciduous forest, mined deciduous 19 Floodplain forest, frequently flooded wetland forest

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 4.3 - Sex ratio and results of Chi Squared tests of departure from a 1:1 sex ratio, for the overall population and for size classes 2,3 and 4. (sex ratios > 1 indicate male bias) y2 values in grey shading are significant at p < 0.05 Population Size class 2 Size class 3 Size class 4 code Sex-ratio X2 Sex-ratio X2 Sex-ratio x2 Sex-ratio X2

Cl 1.04 0.02 all male 33.33 1.15 0.43 0.63 > 4 .9 5 ;

C2 1.13 0.12 1.50 1.90 0.75 1.06 — C3 1.27 0.36 all male 16.67 3.00 7.69 1.20 0.61 C4 1.55 2.37 3.33 23.56 1.00 0.00 1.14 0.44

C5 2.33 ; 8.oo i1 3.20 19.87 1.78 7.84, 3.00 16.67 ; C6 1.12 ^0.16 1.60 3.46 1.18 0.48 0.70 2.30

C l 2.40 >' 5.76 5.00 8.47 2.40 , 6.70 0.50 11.11 N1 1.46 2.45 all male 36.11 . 2.00 9.80 1.00 0.00

N2 1.67 0.50 — — 1.00 0.00 1.00 0.00 N3 1.58 . 7.90 ’ 4.00 22.91 6.17 ■ 27.59 1.15 0.49 N4 1.50 0.80 1.67 2.78 1.00 0.00 all male 100.00 N5 1.55 1.29 all male 33.33 > 1.50 2.35 1.43 2.94 N6 2.06 6.23; 1.44 2.42 1.50 4.00 , 1.00 0.00

N7 1.27 ... 0.36 0.83 0.70 2.67 17.48 0.50 1 1 .1 1 .: SI 1.19 0.26 0.50 ii.ii, 1.40 '''2.22 0.64 ;, 4.68 ■ S2 1.29 0.50 2.00 8.33 , 1.14 0.42 1.00 0.00 S3 4.67 -.14.24 all male 77.78 5.00 31.37; 2.75 7.42 ,< S4 2.18 ,4.83 ; 1.00 0.00 ’ 4.00 25.71 1.67 5.26. W1 0.96 0.02 1.11 0.20 0.50 11.11 0.67 ,4.00, W2 1.26 0.58 1.50 3.16 1.25 0.93 0.60 v,6:25i':

W3 1.40 1.00 1.00 0.00 1.67 ——

W4 1.27 0.36 1.50 1.33 1.20 0 .3 4 all female 100.00,.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. o f branches Mean # 1 119 0.102 1 Sex- ratio

1 -0.19 -0.24 females fruiting 2) (yr % 1 0.058 -0.307 (yr 2) (yr Female 0.035 -0.136 0.096 -0.301 .485(*) females fruiting 1) (yr % 1 0.017 -0.276 -0.002 1 .588(**) Female (yri) ) 1 2 0.034 Male (yr 1 0.18 -.575(**) 0.07 -0.198 -0.14 0.293 0.014 Male 1) (yr ) 1 2 -0.35 -0.389 -0.41 -0.079 -0.202 .599(**) -0.05 Female (yr 1 0.126 -0.167 -0.402 •503(*) ;.521(*) 0.246 0.297 -0.199 -.534(*) •631(**) - Female 1) (yr ) 2 0.37 0.274 0.171 0.225 -0.109 -0.142 -0.252 -0.233 -0.245 0.169 Male (yr 1 0.248 0.122 -0.022 581(**) 1) Male (yr 1 0.18 -0.29 -0.136 0.211 0.006 -0.084 0.015 547(**> -0.359 -0.388 .466(*) 0.236 -0.053 -0.018 0.162 -0.166 0.133 -0.144 -0.124 -0.216 .434(*) Flowerproduction Proportion ofindividualsflowering fruit Female production Female 2) (yr 1 0.02 0.011 0.034 533(*) -0.043 -0.341 -0.416 -0.115 .432(*). Female i) (yr ) 2 Male (yr 1) Male (yr2) Female (yr2) -0.073 Female (yr 1) Female (yr2) - .465(*).'> Female (yr 1) -0.045 -0.252 -0.033 -0.239 (yr Male (yr 1) Male (yr2) 0.403Female (yr2) 0.167 Female (yr 1) shading are significant at (*) p<0.05 and (**) p < 0.01 Parameter Flowerproduction Proportion oindividualsflowering f % females fruiting Female fruitFemale production % females fruiting I) (yr Mean # obranches f Sex-ratio Table 4.4 - Results ofcorrelation analysis between parameters ofpopulation performance. Correlation coefficients in grey

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 488(*) 0.007 0.318 0.199 0.194 0.121 -0.392 -,490(*) 120

0.187 -0.252 -0.016 -0.392 -0.231 0.314 -0.411 0.298 -0.142 0.045 .500(*) ,633(**) 0.2070.032 -0.149 0.041 0.094 .599(**) 0.326 0.243 -0.298 0.0760.233 0.179 -0.045 -0.096 0.253 -0.085 -0.114 -0.113 -0.083 -0.05 -0.131 -0.123 Pop. Size NN Dist Elevation

0.02 -0.26 0.097 0.293 0.018 -437(*) -0.037 -0.106 -0.137 0.208 0.14 0.02 -0.262 •458(*) .481(*) -.527(*) Distance from centre -0.08 -0.15 0.085 0.3880.109 0.268 -0.034 -0.072 -0.386 -0.388 -0.259 -0.309 -0.115 .4 7 0 0 -.4 4 7 0 Longitude 4 5 1 0 0.147 0.102 0.212 0.272 0.192 -0.409 -0.201 -0.178 -0.312 -.6 8 5 (0 Latitude 5 1 9 0 0.014 .4 5 4 0 -0.098 0.349 -0.245 -0.277 -0.025 0.106 .5 7 7 0 7 4 4 ( 0 ' -•541 0 0.069 -0.013 -0.417 -0.056 .572(*) 0.071 0.301 -0.258 -0.402 -0.183 0.257 -0.266 -0.068 630(*) -0.126 -0.242 0.269 -0.143 -0.156 0.086 -0.194 0.257 0.062 0.113 0.235 -0.108 -0.145 •548(*) -0.402 .541(**) -0.26 -0.31 -0.012 -0.321 0.373 4 6 9 0 -0.053 -0.097 -0.33 0.2 0.336 -0.355 0.418 -0.194 -0.433 0.075 Proportion ofthe landscape -0.387 -0.409 -0.018-0.162 0.303 0.177 0.263 -0.491 0.181 0.149 -0.315 -0.158 1 3 6 10 13 0.45 -0.15 0.257 0.101 0.177 0.174 0.272 -0.238 0.229 -0.133 -0.172 0.227 -0.216 0.183 -0.038 -0.136 -0.309 -0.249 -0.06 -0.377 •644(**) -0.35 0.362 0.141 0.423 0.135 -0.435 -0.1 0.476 0.507 0.003 -0.113 -0.114 Size class 4 0.272 Size class 1Size class 2Size class 3 0.161 0.492 Size class 0 -0.259 Mean number obranches f Populationsex-ratio coefficients in grey shading are significant at (*) p<0.05 and (**) p <0.01 Population Parameters Patch density Female (yr 1) -0.306 0.327 0.419 -0.402 -,692(**) Female (yr 1) -0.12 -0.245 -0.251 -0.18 .814(**) -0.385 0.284 -0.17 -0.122 0.125 -0.312 -0.105 %females fruiting (yr 1)Female (yr2) % females fruiting (yr2) 0.05 Populationsize class distribution -0.242 -0.066 0.035 -0.104 -0.022 -0.028 0.138 0.328 Female (yr 1) Female (yr2) Male (yr 1)Male (yr 2) -0.024 -0.068 0.27 -0.091 0.474 Table 4.5 - Results ofcorrelation analysis between population performance parameters and landscape factors. Correlation Femalefruit production Female (yr 2) -0.204 -0.121 0.2 Male (yr 1) Male (yr2) 0.042 -0.132 0 Proportion oindividualsflowering f Flowerproduction

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 104°W 100°W 96°W 92°W 88°W 84°W 80°W 76°W

MN SD ON!

42°N' NY -42°N — IA—

MD, 38° N' KS ■38° N .C 3j VA

NC o AR •34°N

•30°N

125 250 500 750 1,000, Ki|ometei 26° N' ■26°N

100°W 96°W 92°W 88°W 84°W 80°W

Figure 4.1 - Range of Gleditsia triacanthos (in grey shading) (from Prasad and Iverson 2003) and site locations

121

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (a) Central (b) Northern

Size class Size class

Figure 4.2 - Mean proportion of individuals in each CBH size class (0 = <5 cm; 1 = <15 cm; 2 = 16-50 cm; 3 = 51-100 cm; 4 = > 100 cm) in (a) central, (b) northern, (c) southern, and (d) western populations.

122

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2 5 - ■ Rank of 0 0 Rank of 1 ^ Rank of 2 • O Rank of 3 20 - □ Rank of 4 < ► < ► 1 5 - C (0 < ►

10- < >

5 -

4 0 -

CCCCCCCNNNNNNNSSSSWWWW 123456712345671234 1234 Site

Figure 4.3 - Site rankings by proportion of individuals in the size class for classes 0 (< 5cm cbh), 1 (5-15 cm cbh), 2 (15-50cm cbh), 3 (15-50 cm cbh) and 4 (>100 cm cbh). The site with the highest proportion of individuals in a size class would be ranked 22, for that size class.

123

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. (a) Central (b) Northern 100-1— I X

RJ u 0) .a (A **■O c o r a s CL

O .aa (A **•O e o € o a o

ii i i i iii ii i iii i i ii ii ii i i iii i ii u0 mO fO u1 ml f1 u2 m2 f2 u3 m3 f3 u4 m4 f4 uO mO fO u1 m l f1 u2 m2 f2 u3 m3 f3 u4 m4 f4 Sex/Size class Sex/Size class

Figure 4.4 - Proportion of individuals of each sex (vegetative/unknown [u], male [m], female [f]) in each size class (0 = <5 cm; 1 = <15 cm; 2 = 16-50 cm; 3 = 51-100 cm; 4 = > 100cm) in (a) central, (b) northern, (c) southern, and (d) western populations.

124

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 100—

Em ori t- W 8 0 - c ■ O + ■-E o) 6 0 - Qo . k..E o | a o 4 0 - c 5=

20-

m 100- (0 O T" + 8 0 - ■ I ? O 'C a a> Ha | 6 0 - S 8 4 0 - = 1

12 20 -

100-

<4- m so — oio ou

6 0 - a c

20 -

CN S W Region

Figure 4.5 - Mean regional proportions of individual (a) males flowering, (b) females flowering and (c) females fruiting in year one (filled squares) and year two (unfilled squares)

125

Mean number of Inflorescences (+/- 1SE) 12000 12000 -j 16000 10000 10000 - 14000 6000 6000 4000 4000 2000 8000 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 1 2 3 W4 W3 W2 W1 S4 S3 S2 S1 N7 N6 N5 N4 N3 N2 N1 C7 C6 C5 C4 C3 C2 1 ------1 ------1 ------1 ------1 ------1 ------1 ------1 ------M 1 ------Site 1 ------r ------r~ ■Year 2 □1 Year 126 9000 ■ Year 1 (a) □ Year 2 8000 m “ 7000

« 6000 0

fe 3000 a E = 2000 c I I 1000 f [ i I ai 1------1------1------i------1------1------1------1------1------1------[------1------1------1------1------1------1------1------1------1------1------1 C1 C2 C3 C4 C5 C6 C7 N1 N2 N3 N4 N5 N6 N7 S1 S2 S3 S4 W1 W2 W3W4

20000 ■ Y ear 1 (b) a Year 2 18000

tu 16000 CO 14000

12000

10000

8000

6000 **

4000 * 2000 j * * t MS1 x I | 0 5 I C1 C2 C3 C4 C5 C6 C7 N1 N2 N3 N4 N5 N6 N7 S1 S2 S3 S4 W1 W2 W3 W4 S ite

Figure 4.7 - Mean number of female (a) inflorescences and (b) fruits at each site in year 1 and year 2. ** indicates difference is significant at p < 0.01 or * p < 0.05

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Mean total number of branches (+/-1 SE) 3000 4000 2000 2500 3500 1500 1000 500 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 1 2 3 W4 W3 W2 W1 S4 S3 S2 S1 N7 N6 N5 N4 N3 N2 N1 C7 C6 C5 C4 C3 C2 C1 f i .-j.... i i i i ...... | j . - i i i Hi i — ------i Site 1 ------1 ------1 ------1 ------1 ------1 ------i 1 ------{ 1 ------1 ------i i 1 ------1 ------128 1 at a. uIV 6 0 - ■olA nc 5 0 -

4 0 -

3 0 -

20 -

10-

0 -

Central Southern W estern Region

Figure 4.9 - Mean proportion of the landscape composed of (o) agricultural land, (•) water, (□) upland deciduous forest, (■) pasture/grassland and (0) bottomland deciduous forest.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter 5 - Landscape-level effects on developmental instability: fluctuating asymmetry across the range of Honey Locust, Gleditsia triacanthos (Fabaceae)2

Introduction

Developmental instability is produced by local disturbances during development that lead to the inability of an individual to undergo identical development on both sides of a plane of symmetry (Moller & Swaddle 1997). As developmental disturbances are random, average expression of a trait is symmetric, and asymmetry shows a normal distribution of right to left differences whose mean is zero. This is termed ‘fluctuating asymmetry’ (FA) (Van Valen 1962). In plants, various kinds of morphological symmetry (e.g., radial, bilateral) may be used as a basis for estimating developmental instability (Palmer & Strobeck 2003). Furthermore individual plants have many repeated parts (leaf, flower, branch, thorn, etc.) enabling within-individual replication and allowing for increased rigor in between-individual comparisons. Studies of plant developmental instability have lagged behind those in animals, despite apparent advantages.

Genetic and molecular mechanisms underpinning developmental instability remain unclear (Houle 1998), however most models envision that “stressors” disrupt physiology during ontogeny, altering development (Freemanet al. 1993). At the same time, major genetic perturbation may disturb normal physiological processes, leading to phenotypic effects (Parsons 1991).

Associations between environmental stressors and developmental instability are also unclear. For example, Bjorkstenet al. (2000) reviewed twenty-one plant and animal studies published since 1997, and testing patterns of FA produced by controlled variation of environmental factors. Seven studies reported increased FA with environmental stress

2 This chapter was published in 2004 (Murphy, H.T. & Lovett-Doust, J. (2004) Landscape-level effects on

developmental instability: fluctuating asymmetry across the range ofGleditsia triacanthos (Fabaceae).

International Journal of Plant Sciences, 165, 795-803)

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. whereas another seven did not, and seven studies showed an increase in FA with stress that was either trait- or stressor-specific (Bjorkstenet al. 2000).

Studies of FA in plants grown in natural gradients of particular stressors do provide useful and consistent evidence of responses to environmental factors, at least within species. For example, leaf FA inBetula pubescens increased with elevation, indicating a plant response to higher wind speed, lower temperature, or lower soil nitrogen, or a combination of these (Wilseyet al. 1998). An example o f trait- or stressor-specific results is shown by Roy and Stanton (1999), who examined experimentally the effect of particular stressors (levels of boron, water, salt, nutrients and light) on FA in four traits (petal, leaf, fruit and cotyledon) in wild mustard Sinapsis arvensis. Roy and Stanton reported increased asymmetry in all of the stress environments. However, the particular trait that responded varied according to stressor, and there was no concordance for FA among traits, within individuals.

Ecologists have increasingly been concerned with populations existing in fragmented habitats, or at the edge of the range of a species distribution (Hanski 1994; Siikamaki & Lammi 1998; Gastonet al. 2000). Remnant patches in a fragmented landscape tend to contain populations of generally smaller size, which are less connected to each other than in continuous habitat. Such populations are likely to have elevated extinction risk, due to demographic and genetic effects associated with the changes in landscape structure (Ouberg 1993; Widen 1993; Hanski 1994; Murphy & Lovett-Doust 2004). Near the extremes of its range of distribution, a species is likely to decline in abundance and in the number of sites it occupies (Gastonet al. 2000). Populations tend to be smaller and more distant from each other (Lawton 1993), exacerbating any effects of human fragmentation. Marginal populations are also more likely to occur in ecologically stressful conditions (Parsons 1991; Siikamaki & Lammi 1998).

Here we report patterns of leaf FA in populations across the geographic range of Gleditsia triacanthos (Honey Locust). Specifically, we aimed (1) to assess between-trait consistency of FA measures; (2) to determine if developmental instability is increased in

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. small or peripheral populations; and (3) to determine if developmental instability is related to varying environmental conditions across the geographic range.

Methods

Study organism

Throughout its range (Figure 5.1),Gleditsia triacanthos occurs as a minor component of natural forest stands and is generally considered early successional and fairly intolerant of shade (Sullivan 1994). The species is found typically on moist bottomland and in abandoned fields and pastures of the eastern and central United States; it attains its maximum growth in the valleys of small streams in southern Indiana and Illinois (Bums & Honkala 1990). Honey Locust is tolerant of low temperatures in the north (-29 °C to - 34°C) and appears resistant to seasonal drought. Over its range G. triacanthos grows naturally to a maximum elevation of 610-760 m (Bums & Honkala 1990). Leaves are pinnate (sometimes bipinnate at shoot apices) with 5-20 leaflets, arranged opposite each other (Ghent 1994).

Six sites were chosen in each of the northern and central areas of the species range, and three sites each in the western and southern parts of the range (Figure 5.1, Table 5.1). Several parameters were measured for each location, including latitude, elevation, distance from the geographic centre of the range, population size and average nearest- neighbour distance, and a range of climatic variables.

Latitude and elevation above sea-level were recorded on-site with a Trimble AG 132 GPS. Distance from the approximate centre of the range to each site was measured using ArcMap (ESRI, Redmond CA). Boundaries of the area within which population sizes were estimated were defined either by abrupt changes in land use or vegetation type (e.g., water, agriculture, topography), or by distance of >300m to the next nearest tree in any given direction. This distance is beyond the upper limit of pollen and seed dispersal distances reported for the species (range of 85-240m for pollen [Schnabel and Hamrick 1995]; range of 0-180m for seed [Schnabel et al. 1998]). Population size was estimated as the number of mature (i.e., >5m in height) individuals within the site area. Average

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. nearest-neighbour (NN) distances for trees at each site were calculated using Arclnfo (ESRI, Redmond CA) (Table 5.1).

Climatic variables were obtained from the nearest weather station to each site (Appendix 5.1). Variables extracted included (1) mean annual precipitation, (2) mean annual temperature, (3) mean minimum temperature, (4) mean maximum temperature, (5) mean spring temperature, (6) minimum spring temperature, (7) maximum spring temperature.

Twenty leaves were collected from each of between 5 and 10 mature trees (>5m in height) at each site (Table 5.1). Leaves were sampled from the outermost reaches of the first primary branch of trees at all sites in August 2002; they were scanned at high resolution and images were analysed using SigmaScan Pro 5 software (SPSS Inc, Chicago IL).

Fluctuating asymmetry

Measures of two different FA traits were taken for every leaf (Figure 5.2). Leaflet length was determined for each leaflet on either side of the rachis, and leaflet length FA (LFA) was calculated as |R-L| leaflet lengths. Intemodal distance was determined as distance from the rachis base to each leaflet, on each side of the rachis; intemodal distance FA (DFA) was calculated as the |R-L| intemodal distances. For each leaf, FA was calculated as

Leaf FA = 2|Rj-Lj|/N

where Rj is the value for the right side, Li is the value for the left side, and N is the number of leaflet pairs on each leaf. For each population, FA was calculated as

Population FA = £(|Leaf FA|)/N

where N is the number of leaves sampled in the population.

Following Palmer (1994), statistical properties of FA were evaluated. Thus average leaf FA values were tested for normality of R-L values around a mean of zero. A Kolmogorov-Smimov test (K-S test) was computed separately for each population,

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. followed by a t-test for the hypothesis Ho: p(R-L) = 0. Kurtosis and skew statistics were also calculated for each population (Appendix.5.2).

Results of the K-S test indicated significant deviations from a normal distribution at five sites for LFA and at two sites for DFA (Appendix 5.2). Kurtosis values indicate the extent of leptokurtic distributions for these sites. Leptokurtic distributions have been observed in several investigations (Waldmann 2002) and are usually attributed to the mixing of distributions with different variances. Using a mathematical model, Leung and Forbes (1997) demonstrated that both normal and leptokurtic distributions may represent FA. Mean signed FA values differed significantly from 0 in only three of the 18 populations for LFA (Appendix 5.2). Furthermore there is no consistent skew in the data. In general, although optimum statistical conditions for FA cannot be met in all populations, the results provide no evidence for the occurrence of either antisymmetry or directional asymmetry and thus are suitable for analysis of FA.

FA and trait size

The relationship between average leaf FA and leaf size was examined via correlation analysis. There were significant but weak positive correlations between both LFA and DFA and leaf width and length (Table 5.2). However when absolute FA values were scaled by size (Leaf LFA/leaf width = LFAR, and Leaf DFA/leaf length = DFAR), the result was a significant negative correlation between the adjusted FA value and trait size. Furthermore, adjusting absolute LFA and DFA values for trait size did not affect the general pattern of results. Similarly, a transformation of the type ln(R) - ln(L) did not affect the general pattern of results. Hence, values unadjusted for trait size were used in all analyses.

Measurement error

Potential measurement error was assessed by measuring 20 randomly selected leaves a second time (N = 380 leaflets). Repeat measurements were all made by one person not involved in the original measuring. A mixed-model ANOVA with factors node (leaflet number), side (right or left) and repeat (first or second trial) was used to provide an F-test

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. of the repeatability of the derived asymmetry values (Swaddle et al. 1994). Repeated measures were highly correlated (r = 0.999, p<0.0001) and asymmetry estimates were

significantly repeatable (F 13,2 6 = 6016, p< 0 .0 0 0 1 ).

Statistical analysis

Pearson correlation coefficients were calculated for the two FA traits, at leaf-, tree- and population levels. A nested ANOVA design was used to test for significant differences between regions and sites (nested within region). ANOVA was performed on log- transformed absolute FA values (to correct for a non-normal truncated distribution). Least significant difference (LSD) pair-wise post-hoc tests were performed on ANOVA results.

Principal component analysis was conducted on the seven climatic parameters. Pearson correlation analysis was used to test for effects of various environmental and landscape- level parameters on population FA values.

LFA and DFA ‘distances’ were calculated between each site and every other site. (For

example, the LFA distance between site 1 and site 2 = LFA| - LFA 2, and between site 1

and site 3 = LFAi - LFA 3 etc.). Similarly, latitudinal distances and geographic distances were calculated for all combinations of site values, to determine whether closer sites (latitudinally or geographically) were more similar to each other than more distant sites. Pearson correlation analysis was then used to determine if LFA or DFA distance was related to latitudinal or geographic distance.

All statistics were conducted using SPSS Ver 10 for Windows (SPPS Inc, Chicago IL).

Results

There was a weak but significant positive correlation between the two FA measures, both within a leaf (n = 3062, r = 0.134, p < 0.001) and at the tree level (n = 162, r = 0.389, p < 0.001). At the population level the two parameters were not significantly correlated (n= 18, r = 0.291, p> 0.05).

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. There was a significant difference in both LFA and DFA among regions (north, west, central, south) (Table 5.3, Figure 5.3) and sites (nested within region) (Table 5.3, Figure 5.4). At the regional level, LFA values differed significantly between all regions. DFA values were significantly higher in the central region than in the northern, western and southern regions, which did not differ significantly from each other. The site displaying highest levels of asymmetry in LFA was site 12, in the central region of the range, while lowest levels occurred at site 17, in the southern edge of the range (Figure 5.4). Levels of DFA were lowest at the most northern site (site 1) and highest at site 12, in the central region of the range (Figure 5.4).

There were significant positive correlations between all climatic parameters (all combinations p < 0.001). Principal component analysis reduced the number of variables to one component (Table 5.4) which explained 91% of the variance (PCA 1 in Table 5.5). There was no significant correlation between LFA or most DFA values and any of the environmental or landscape-level parameters (Table 5.5). Only NN distance correlated significantly with DFA (r = -0.481, p < 0.05); DFA decreased with increasing NN distance (NN distance and LFA also showed a negative correlation, although it was not significant). However, this correlation did not remain significant after Bonferroni adjustment for multiple comparisons (p > 0.05).

There were significant correlations between LFA and DFA distance and both geographic and latitudinal distances (Table 5.6). Thus sites closer together, latitudinally and geographically, tended to have more similar LFA values. In contrast, the correlation between both latitudinal distance and geographic distance with DFA values was negative.

Discussion

Correlations between FA characters

Incorporating more than one estimate of deviation from symmetry in a trait should yield greater confidence in the estimate of developmental instability of an individual, if developmental instability affects all traits in an individual similarly (Palmer and Strobeck 2003). However, many studies have failed to find correlations in asymmetry between

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. traits in the same individual (Soule & Cuzin-Roudy 1982; Jennions 1996; Sherry & Lord 1996a, b; Waldmann 1999; Andalo et al. 2000). Moreover, correlations between FA measures in the same trait of an individual are often weak (Sherry and Lord 1996a; Heard et al. 1999). Palmer and Strobeck (2003) suggested that leaves in studies of FA may be less reliable indicators because they are known to exhibit phenotypic plasticity, and deviations from symmetry may arise due to direct effects of the environment, along with the random effect of developmental instability. Phenotypic plasticity, despite “canalized” development (sensu Waddington 1942), is well known in leaves - e.g., apomictic Taraxacum leaf shapes, heterophylly in aquatic species such asRanunculus, orSagittaria (see Silvertown & Lovett-Doust 1993; West-Eberhard 2003).

The two traits measured in this study may be developmentally correlated. However, significant correlations between the two measures of FA within leaves and within trees in this study probably are due to large samples sizes, as the correlations themselves were weak. Analysis of developmentally independent traits (e.g., from leaves and flowers) would provide a more robust estimate of developmental instability among samples (Palmer 1994). Many studies also measure concordance in asymmetries for different characters at the population level, even where correlations are not found at the individual level (Palmer & Strobeck, 1986; Sherry & Lord, 1996a), however, although there was a correlation between the two measures at the population level in the present study, it was not significant.

FA and landscape-level parameters

FA was not correlated with latitude or climatic conditions. Moreover, there was no apparent trend of increasing FA toward range margins in Honey Locust. The six lowest levels of LFA were recorded from sites in the northern (3 sites) and southern (3 sites) regions of the species range. The two highest levels of LFA were recorded from central sites. Similarly, the two highest levels of DFA were recorded from central sites while the lowest level was recorded in the most northerly site (site 1). In the only other study of FA across the range of a plant species of which we are aware, Siikamaki and Lammi (1998) found flower FA increased in marginal populations ofLychnis viscaria. However,

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the authors found that in common garden experiments the level of FA did not differ between plants from the central and marginal populations, implying a strong role for local environmental conditions in regulating FA. Several recent studies in birds have also reported increased FA in marginal populations (Moller 1995; Carbonell & Tellaria 1998; Kark 2001; but see Gonzalez-Guzman & Mehlman 2001), in keeping with the general notion that populations in marginal areas of a range are exposed to higher levels of environmental and/or genetic stress. None of these studies measured FA across the entire range of a species, rather only one aspect of the range edge was measured.

Nearest-neighbour distance was significantly negatively correlated with DFA before Bonferroni adjustment. The increase in FA with decreasing NN distance might be attributed to higher levels of intraspecific competition at high densities. Elevation of the site had no effect on FA. Given that all sites were well below the elevation level regarded as the maximum for the species (Bums & Honkala 1990), and that the sites at lowest elevation (15-16 m, in the southern edge of range) had amongst the lowest FA values, it is possible that elevation would have some effect on FA as the maximum was approached.

Plants in smaller populations also failed to show increased levels of FA. Three of the smaller populations (i.e., <100 individuals) in the study (sites 4, n = 38; 10, n = 64; and 14, n = 89) abutted agricultural land and were most likely part of larger, continuous populations prior to fragmentation. It is possible that the genetic effects of smaller population sizes are yet to manifest in these populations (we envision this as a kind of ‘fragmentation debt’). Following fragmentation, genetic deterioration is expected to have greatest effect in small and isolated populations relying on frequent sexual reproduction. Plants with long generation times, and a means of clonal reproduction, are likely to suffer less in the short term (Fischer & Matthies 1998).

Few studies have examined directly the relationship between developmental instability and small population size in plants, and results appear mixed. Siikamaki and Lammi (1998) found FA was significantly negatively correlated with population size in the

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. perennial herb Lychnis viscaria. Heard et al. (1999) measured FA in flowers and leaves in 13 fragmented populations of prairie phlox(Phlox pilosa). For three of the four parameters, a strong positive correlation was found between population size and developmental instability, i.e. the opposite trend to what is expected if small populations were subject to greater genetic or environmental stress (Heard et al. 1999).

There are few other sources for comparison of FA in plants across a species range and in relation to fragmentation. However, a few examples from the animal literature are relevant (Sarre 1996; Wauters et al. 1996; Anciaes & Marini 2000). Generally FA has been shown to increase in smaller, and geographically marginal populations; however the results are not conclusive. Where increases in FA are reported in smaller populations, it is sometimes unclear as to whether genetic or environmental stressors are responsible (e.g., Siikamaki & Lammi 1998).

Conclusions

This study represents the first landscape-scale analysis of fluctuating asymmetry in natural populations of a tree species. The results suggest either that Honey Locust trees do not experience greater ‘stress’ in marginal or fragmented populations, or that FA is not a particularly effective indicator of environmental or genetic stress in Honey Locust populations. Although FA varied significantly between sites across the range, no landscape-level or climatic parameters we measured were responsible for the variation. Furthermore, the contrasting correlations between LFA distance and latitudinal and geographic distance (positive correlation) and DFA distance and latitudinal and geographic distance (negative correlation), and the lack of a strong correlation between the two measures at the population level indicates the potential for erroneous conclusions in the use of FA as an indicator of potential stress in plant populations.

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) 6 . 8 8.7 ( 3.0 (2.8) 7.9 (5.6) 5.6 (4.9) 3.8 (4.9) 7.0 (23.0) 6.3 (10.5) 10.9 (12.3) 16.0(18.2) 44.0 (29.2) 175 175 129 175 349 332 289 257 246 306 17 64 38 150 160 150 500 200 209 200 148 853 830 803 594 706 655 647 700 598 (km)

4 7 h . 82°30'45" 82°41'10" 82°49'00 83°03'08" 83°13'43" 83° 15'08" 38°29'59" 95°43'01" 37° 48' 00" 3 9 0 3 7 3 9 ° 3 7 'ir 39°18'37" 96°39'19" 39°08'04" 95°25'59" 41055'21" 41°47'19" 40°22'12" 41°32'42" 10 10 10 10 10 10 10 sampled Longitude ofgeographic range population n (m) (m) and (SD) Melvem Lake, KS Delaware State Park, OH Deer Creek State Park, OH Pelee Island, ON East Harbour State Park, OH Deer Creek Marina, OH Perry Lake, KS Site Name, State # oftrees Latitude/ Distance from centre Approx. size of Elevatio NN distance 10 Murphysboro State Park, IL 1 1 Point Pelee, ON site parameters, including nearest-neighbour (NN) distances between trees 6 8 Table 5.1 -Population locations arranged from most northerly to mostsoutherly latitudes,with numberof trees sampled, and 7 Tuttle Creek, KS

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 147

) 8 . 1 0 ( 6 . 8 9.9 (13.9) NN distance (m) and (SD) 99 6.7 (7.7) 140 6.1 (4.8) 144 18.2(30.4) n (m ) Elevatio

60 126 32 1 2 0 population Approx. size of

49 137 89 129 5.0 (7.6) 127 113 501 500 15 567581 38 75 16 15 2 1.4(11 .8 ) 14.8 (10.4) (km ) ofgeographic range Distance from centre

" 2 4 ° 03' 00' Latitude/ Longitude 87° 55' 48" 89° 01' 12" 89° 25' 12 89° I?- 37° 37' 48" 124 180 8 8 36° 15' 36" 89° 11'24" 36° 24' 36" 36° 42’ 00" 32°18'12" 37° 34* 12" 91°22'44" 31°35'58" 91°12'27" 31°34'44" 91°36'27"

7 1 0 1 0 1 0 1 0 1 0 1 0 1 0 sampled # o ftrees Name, State Cedar Lake, IL Land Between the Lakes, TN Giant City State Park, IL Bayou Cocodrie, LA Natchez State Park, MS Site 15 Big Cypress Tree State Park, TN 16 Tensas River, LA 1 1 17 1 2 14 13 Rushing Creek, KY 18

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 5.2 - Pearson correlation coefficients between leaflet length FA (LFA), intemodal distance FA (DFA), size-adjusted LFA (LFAR) and DFA (DFAR) and leaf length and leaf width (n = 3062) FA Measure leaf length leaf width LFA 0.061** 0.157** LFAR -0.114** -0.079** DFA 0.176** 0.113** DFAR -0.130** -0.071** ** p<0.01

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 5.3 - Nested ANOVA results for the effects of landscape (region) and site on log leaflet length FA (logLFA) and intemodal distance FA (logDFA)

Source d f Mean square F P LFA Region 3 12.059 40.274 <0.001 Site within Region 14 3.560 11.888 <0.001 DFA Region 3 6.713 11.000 <0.001 Site within Region 14 3.408 5.585 <0.001

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 5.4 - Result of principal component analysis of climatic factors - amount of variance explained by component 1 for each climatic parameter. Climatic Parameters Component 1

Annual rainfall 0.861 Mean temperature 0.995 Mean max. temperature 0.946 Mean min. temperature 0.984 Spring mean temperature 0.942 Spring min. temperature 0.955 Spring max. temperature 0.986

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r P r P Latitude 0.334 0.175 0.010 0.968 Site elevation 0.367 0.134 -0.067 0.792 Distance from centre of range -0.278 0.263 -0.388 0.112 Population size 0.191 0.448 -0.147 0.560 NN distance -0.414 0.087 -0.481 0.043* PCA 1 -0.218 0.385 0.057 0.823 * p < 0.05

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 5.6 - Results of Pearson correlation analysis between leaflet length FA (LFA) and intemodal distance FA (DFA) distance measures, and latitude and geographic distance measures (N = 306) FA Measure Latitudinal distance Geographic distance

r P r P LFA distance 0.194 0.001** 0.138 0.016* DFA distance -0.116 0.042* -0.196 0.001** ** p < 0.01, * p<0.05

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. -60° N a m A

46° N- -46° N

42° N -42° N

38° N -38° N

34°N- -34° N

30° N- •30° N

26° N- ■26° N

500 250 500 Kilometers

1-22° N 100°W 96°W 92° W 88°W &4°W 80°W

Figure 5.1 - Range of Gleditsia triacanthos (in grey shading) (from Prasad and Iverson 2003) and site locations

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Leaflet length

Intemodal distance

Figure 5.2 - Leaflet length FA (LFA) measured along the length of the midvein of each leaflet, and intemodal distance FA (DFA) measured along the leaf rachis from the base of the leaf to each leaflet

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 0.27 -i

0.25 -

iu 0.23 - to

7 0.21 - 2 _l 0.19 -

0.17 -

0.15 North West Central South

0.25 -<

0.23 -

Ui w 0.21 - V" I + 5 0.19 - a

0.17 -

0.15 North West Central South Region

Figure 5.3 - (a) Mean regional leaflet length FA (LFA) and (b) intemodal distance FA (DFA) and results of LSD post-hoc multiple comparison test. The same letters above regions indicate no significant difference in mean values (NB Post-hoc comparisons were calculated on absolute log-transformed FA values).

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 0.35 - ♦ Northern (a) □ Western 0.3 - • Central

a Southern 0.25

ill ? w 0.2 H

gj 0.15

0.1 -

0.05

--1 30 32 34 36 38 40 42 44

04 -i

0.35 -

03 -

iu 0.25 - (0

T+ 02 - 5 Q 0.15 -

0.05 -

3032 34 38 40 42 44 Latitude

Figure 5.4 - Mean (a) leaflet length FA (LFA) and (b) intemodal distance FA (DFA) values at each site arranged by latitude

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter 6 - Distribution of landscape spatial structure and abundance across the range ofGleditsia triacanthos (Honey Locust)

Introduction

Increased recognition of the importance of landscape- and regional-scale processes in understanding the distribution and abundance of organisms has led to the relatively recent emergence of macroecology (Gaston & Blackburn 2000), and an awareness of patterns and processes that are otherwise indistinguishable at smaller scales. Macroecology involves the ecology of “wide expanses of space, long periods of time and large numbers of taxa” (Blackburn & Gaston 2003); it addresses patterns of distribution and abundance of species at geographical spatial scales and evolutionary time scales (Brown 1995). Thus complex relationships between variables, such as species range size and individual abundances, and location in the range and abundance, underpin most macroecological theories.

The distribution of abundance and central-peripheral models

When abundance distributions have been examined over entire geographic ranges (or nearly entire ranges) it has generally been observed that species exist in low abundance at most sites and at high abundance in only a few sites (Brown et al. 1995); this has been termed by Murray et al. (1999) as a ‘somewhere abundant’ distribution. Murray et al. (1999) described a smaller proportion of species as having an ‘everywhere sparse’ distribution; these species occur in low abundance throughout their geographic ranges and thus are in the tail of rank-abundance curves wherever they occur.

The ‘abundant-centre’ distribution describes the widely held assumption in biogeography and macroecology that in general species abundances tend to be greater toward the centre of the geographical range, and lower in the periphery (Murray & Lepschi 2004). In an important paper, Brown (1984) argued that local abundance reflects how well a particular site meets a species’ particular physiological and ecological requirements. Brown suggested that spatial autocorrelation in these axes, representing dominant dimensions of

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the niche, could underpin the abundant-centre distribution. Brown’s central-peripheral model of the distribution of abundance assumes that responses to dominant niche axes are unimodal and symmetric. Increasing the distance from the optimal site should decrease the probability of a site meeting the multidimensional needs of that species (Brown 1984). At increasing distances from the optimal location, there will be a decreasing number of local sites where a species can occur. Even within these patches, population densities will tend to be lower because of resource limitation and/or as conditions approach the limits that can be tolerated physiologically (Brown 1984).

Thus there are three main predictions of the abundant-centre distribution: (1) abundance should be greater in sites closer to the centre of the range; (2) species should occupy more sites towards the centre of the range; and (3) abundance and occupancy should decline linearly towards the edge of the range. The striking result of a recent review of 145 separate tests of the abundant-centre distribution found only 39% of the results actually supported the abundant centre hypothesis and, moreover, that all but two studies (of 22) inadequately sampled species’ ranges (Sagarin & Gaines 2002).

Brown et al. (1995) reported that abundance exhibited a distinctive bimodal pattern of spatial variation within the geographic ranges of four passerine birds. Spatial autocorrelation analysis produced two peaks, one at short distances (corresponding to proximal sites within the geographic range), and one at the maximum distance (corresponding to sites at opposite ends of the geographic range). Thus sites close together were more likely to have similar abundances irrespective of where they were located in the range, and sites at the range periphery tended to have consistently low abundance, producing the second peak at maximum distances. This second peak in spatial autocorrelation at maximum distances implies that species respond similarly to all aspects of the range edge.

Here I use an available spatial dataset of abundance (in the form of phytosociological ‘importance values’ [see Prasad & Iverson 2003]) across the geographic range of the eastern North American tree Gleditsia triacanthos (Honey Locust) to test three

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. macroecological hypotheses: (a)G. triacanthos occurs in low abundance across most of its geographic range; (b) G. triacanthos is more abundant in the centre of its range than at the periphery; and (c) there is a bimodal distribution of spatial autocorrelation in abundance across the range.

In the past, critical examination of macroecological theories has suffered from a paucity of good quality datasets (Sagarin & Gaines 2002; Mathiuset al. 2004), and analyses have often been based on inadequate range size information, restricted spatial coverage of abundance data, and/or limited taxonomic extent (Kotze 2003; Sagarin & Gaines 2002; Blackburn et al. 2004). Fortunately a growing availability of large scale geographic information system (GIS) datasets, and sophisticated tools for spatial analysis has enabled patterns of distribution of abundance within species’ ranges to be examined in more detail. The spatial data on both abundance and distribution which we use here has been collected and compiled in a consistent way across the entire geographic range. Thus many of the problems and biases relating to limited, inadequate or unreliable data previously identified in macroecology studies are avoided (see e.g., Sagarin & Gaines 2002; Blackburn et al. 2004).

Effects of landscape spatial structure on abundance

Most North American landscapes have been influenced by human land use and the resulting landscape mosaic is a mixture of natural and human-managed patches that vary in size, shape and arrangement. This spatial patterning is a unique arrangement that emerges at the landscape level (Turner 1989), and changes in species composition of patches in fragmented landscapes, as well as demographic effects on individual species have been implicated (Jules 1998; Gascon et al. 1999; Murphy & Lovett-Doust 2004). For example, small and isolated plant populations seem less likely to attract pollinators than large ones and as a result individual plants in small populations may receive less pollen from pollinators (Steffan-Dewenter & Tschamtke 1999). In dioecious plants, pollinator limitation may result in reduced levels of seed set (Costinet al. 2001). Tree populations occurring on smaller fragments have been shown to suffer reductions in fruit

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. production and seed germination, relative to populations in larger fragments and continuous forest populations (Nason & Hamrick 1997).

Habitat fragmentation may also generate important changes in the physical environment of habitat patches. Converting continuous habitat into smaller discrete patches increases the amount of edge habitat, even if the total area of habitat remaining is unchanged. Effects of edges on the physical environment of patches are relatively well documented (e.g., Murcia 1995). For example, edge-mediated effects on seed dispersal and seed mortality may be significant in determining species composition, and successional patterns in patches (Fagan et al. 1999). Laurance et al. (2000) reported disproportionate mortality of large canopy and emergent trees in Amazonian forest fragments following fragmentation.

Landscape ecologists have developed an array of metrics that can be used to quantify the spatial structure of landscapes. Some authors (e.g., Gustafson 1998; Steffan-Dewenteret al. 2002) contend that the simple extent or proportion of a habitat type in a landscape, is nearly as important as many other, more complex indices of heterogeneity, since this effectively determines the probable range of many configuration characteristics, including patch size and isolation distances, both of which are essential parameters in population and metapopulation ecology.

The relative importance of position in the geographic range and landscape spatial structure in determining distribution and abundance of plant species is still poorly understood (Ehrlen & Eriksson 2000). The effect of human impacts on spatial patterns of species distribution may be of overriding importance in some places, obscuring any macroecological ‘rules’ (Murray & Dickman 2000; Parmesanet al. 2005). Here I use GIS datasets of landcover to extract measures of the spatial structure of landscapes across the range ofG. triacanthos. I determine the extent to which variation in abundance can be explained by landscape spatial structure, and whether this is consistent across the range.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Methods

Study Species - Gleditsia triacanthos (Honey Locust)

Throughout its North American range (Figure 6.1),G. triacanthos occurs as a relatively minor component of natural forest stands and is generally considered early successional and fairly intolerant of shade (Sullivan 1994). The species is found typically on moist bottomland and in abandoned fields and pastures (Bums & Honkala 1990), in the eastern and central United States; it is a minor, rare component of the flora of southern Ontario. The species attains its maximum height in the valleys of small streams in southern Indiana and Illinois (Gordon 1966). Honey Locust is tolerant of low temperatures in the north (-29 to -34°C) and, although ample soil moisture is necessary for optimal growth, the species appears resistant to seasonal drought. Over its rangeG. triacanthos grows naturally to a maximum elevation of 610-760 m (Bums & Honkala 1990).

Abundance estimates

Abundance estimates in the form of phytosociological importance values were available at the Eastwide Database from U.S.D.A. Forest Service Forest Inventory and Analysis data (Prasad & Iverson 2003), comprising >100,000 plots and records for c. 3 million trees in 37 states. Importance values effectively reduce detailed datasets for individual species in local stands to standardized values depending on the total number of tree species present; they are composite values representing both a species’ density and relative dominance, compared to other species in that community. Importance values are calculated for each species as:

IV(x) = 50*BA(x) / BA(all species) + 50*NS(x) / NS(all species)

where x is a particular species at a plot, BA is basal area, and NS is number of stems (summed for overstory and understory individuals). In monotypic stands, the IV would reach the maximum of 100. Importance value scores are averaged for 20 x 20 km cells and are available for the U.S. east of the 100th meridian (total of 8407 cells, 3.36 million km2) (Figure 6.1).

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Between 1971 and 1977, Elbert Little, Chief Dendrologist with the U.S.D.A. Forest Service published a series of maps of tree species ranges based on inventory lists, detailed forest surveys, field notes and herbarium specimens (Prasad & Iverson 2003). These published (and now digitized) maps have become the standard reference for most U.S. and Canadian tree species ranges. Little’s range map for G. triacanthos is used here.

‘Centre of range’ may be taken to mean the point from which the species originated and dispersed to all other points, or it may mean the geographical centre of the current distribution. Either way, it is a difficult point (both figuratively and literally) to identify precisely for many species. We do nota priori define the centre of a species range. Instead we measure the distance from each cell to the nearest edge of range. In this case ‘edge of range’ is the nearest boundary as per Little’s range maps. The total range size for G. triacanthos was separated into ten even distance-from-edge classes to allow for comparison of mean abundance values at various distance classes. Thus distance class 1 represents cells in the highest 10% of the range of distances from the edge (i.e. points closest to the ‘centre’), and class 10 represents those in the lowest 10% of distances from the edge (i.e., points closest to the edge). We separated the geographic range ofG. triacanthos into four ‘quadrants’ in order to assess different aspects of the range edge (see Figure 6.1)

The distance between the centre of each occupied cell and every other occupied cell was calculated, and we produced a spatial autocorrelogram (Brown et al. 1995) of Moran’s / (the covariance between points at a given distance divided by variance of all points) for all pairs of points at ten spatial ‘lags’ (i.e., the maximum distance between occupied cells divided into ten even-distance increments). The value of Moran’s / is an approximate analogue to the Pearson correlation coefficient and generally varies between 1 and -1. Positive autocorrelation in the data translates into positive values ofI, negative autocorrelation into negative values and values close to zero represent no spatial autocorrelation (Legendre & Legendre 1998).

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Data were extracted from the IV database and range map using ArcGIS 9.0 (ESRI Inc, Redlands CA). All statistics were conducted using SPSS 13 for Windows (SPSS Inc, Chicago IL). One-way ANOVAs with Tukey’s post-hoc tests were used to test for significant differences between average IV values (log transformed) at increasing distance classes from the centre to the edge of range.

Landscape spatial structure

GAP analysis (U.S.G.S. 2005) land cover layers were obtained for the States of Kansas (43 classes of landcover), Illinois (30 classes), Kentucky (48 classes), Tennessee (11 classes), Louisiana (23 classes), Iowa (29 classes), Missouri (40 classes) and Mississippi (16 classes). The landcover data is derived from an assessment of the vegetation cover of each state. Vegetation is generally identified to the alliance level (groups of plants sharing dominant species) based on the National Vegetation Classification Scheme (NVCI) (Federal Geographic Data Committee 1996, 1997; Grossman et al. 1994). Remotely sensed Landsat TM satellite data is used as the basis for determining vegetation alliance distributions at a resolution of 30 m (Bara 1994). For consistency, each State’s landcover classes were reclassified to a common 19 classes using the classification shown in Table 6.1.

Two hundred and eighteen 20 x 20 km cells, or ‘landscapes’, for which an IV was available (including IV = 0) were clipped from the landcover layer for analysis of fragmentation statistics in FRAGSTATS (McGarigal & Marks 1995). All cells with an IV > 9 in the eight States (and within the geographic range ofG. triacanthos) were included (n = 82). The remaining cells were chosen randomly and included IV = 0 values (n = 31).

Backwards stepwise regression analysis was conducted with SPSS 13.0 (SPSS Inc, Chicago IL) to determine if fragmentation parameters accounted for the variation in IV between cells. In order to reduce the number of parameters, only landcover types typically associated with the species (based on its ecological characteristics and known occurrences) were included in the model. Thus, landcover classes 1 (agricultural and

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. cultivated land), 3 (water), 6 (upland deciduous forest), 10 (pasture, abandoned fields, grassland and herbaceous cover) and 13 (bottomland deciduous forest) were included. A variety of fragmentation statistics was calculated for each of these landcover classes in each cell. A correlation matrix was constructed and highly correlated indices were eliminated. Parameters remaining in the analyses are shown in Table 6.2 (n = 16). In order to normalize a positive skew in the dependent variable (IV) a square root transformation was used. Only six cells contained an IV > 40 (43, 56, 61, 64,66,100) and these were also eliminated from the analyses to normalize the data.

Results

Gleditsia triacanthos is absent from over 70% of the cells in its range (Table 6.3). The vast majority of the remaining cells (23%) had IV values between 1 and 5. Less than 0.5% of the total cells had an IV larger than 25 (Table 6.3). The number of occupied cells peaked at distance category six (38%) and gradually declined toward both the centre (19%) and the edge (13%) (Figure 6.2).

The highest average importance value was recorded in distance category 8 which was significantly higher than catgory three (p< 0.01) and ten (p < 0.05) (Figure 6.3a). This pattern in overall abundance was driven primarily by the pattern in cells in the north western part of the range (quadrant 1) (Figure 6.4a). Abundance in the other three quadrants remained relatively constant from centre to edge-of-range (Figure 6.4b-d). In the dataset, unoccupied cells within the range are recorded as a zero IV. All the unoccupied cells were removed to determine whether the peak in abundance shifted when only occupied cells were considered. Distance category 10 had the highest average abundance with unoccupied cells removed (Figure 6.3b). Categories 7-10 were all significantly higher than categories 1-5 (p < 0.05). Again, this pattern is driven primarily by the distribution of abundance in quadrant 1 and, to a lesser extent, quadrant 2 (the north eastern quadrant). The mean number of co-occurring species in each distance class for G. triacanthos peaked at category 5 and declined toward both the centre and edge of range (Figure 6.5).

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The pattern of distribution of abundance from centre to edge-of-range in G. triacanthos was compared with that for four other species in the taxonomic family Fabaceae (i.e., Cercis canadensis, Gleditsia aquatica, Gymnocladus dioicus, Robinia pseudoacacia) (Figure 6.6). These other fabaceous species generally show a decline in abundance from the centre toward the edge of range (Figure 6.6a). However, when only occupied cells were considered, these species show the opposite trend, i.e., an increase in abundance from centre to edge-of-range (Figure 6.6b).

A further comparison was made between the pattern of distribution of abundance in G. triacanthos and species regarded as having similar ecological characteristics, i.e., those which exist ecologically as pioneer and/or bottomland shade intolerant species (as per Barnes & Wagner 1981) (see Appendix 6.1). These species showed a distinct peak in abundance at intermediate distances between the centre and edge-of-range, both when all cells were considered (Figure 6.7a) and when only occupied cells were considered (Figure 6.7b).

Gleditsia triacanthos achieves a peak in abundance at relatively high latitude (40°N) (Figure 6.8a). This point also corresponds to the latitude where the species occupies the greatest number of cells (Figure 6.8b). In fact, at this latitude the number of occupied cells is almost as high as the number of unoccupied cells (39°N is the only point where the number of occupied cells is higher). There is a gradual decline in abundance from western to eastern longitudes (except for the western-most longitude, -98°W, which included only a small number of cells) (Figure 6.8c). Similarly the number of occupied cells generally declined from west to east (Figure 6.8d).

Spatial autocorrelation in abundance

Abundance values were spatially autocorrelated for cells close together, i.e., the first spatial lag (Figure 6.9). Moran’s / remained close to zero or slightly negative for spatial lags 2, 3 and 4, while 5-8 showed quite strong negative autocorrelation.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Landscape spatial structure and abundance

Results of the regression analysis show that when all cells in the range were considered

(n = 2 1 2 ), seven parameters remaining in the model predicted 26% of the variance in abundance (Table 6.4). There was a significant linear relationship between IV and these

predictor variables (F( 7 , 170) = 10.22, p < 0.001). IV increased with a decrease in the area and number of patches of deciduous forest and with the total number of patches in the landscape. An increase in the amount of deciduous forest and pasture edge and the area of agricultural land in the landscape were associated with increases in abundance.

There were regional differences in the effects of the landscape parameters on abundance (Table 6.4). Landscape parameters only accounted for 20% of the variation in IV in

quadrant 1 (north-west) (F^, 83 ) = 4.27, p < 0.001). Decreasing the number of patches of agricultural land, deciduous forest and pasture, and the area of deciduous forest, and increasing agriculture and pasture edge and the total number of patches was associated with an increase in IV. In quadrant 2, landscape parameters accounted for 37% of the

variation in abundance (F( 8 ,so) = 5.359, p < 0.001). Decreasing the area of deciduous forest, agriculture and pasture resulted in an increase in IV. Increasing the number of patches of deciduous forest, agriculture and pasture as well as the total number of patches was associated with an increase in IV. In quadrants 3 and 4 only four landscape parameters were retained, accounting for 31% of the variation in IV ( F ^ ) = 4.025, p < 0.05). Thus, decreasing the area of bottomland deciduous forest, edge of pasture and total number of patches, and increasing the area of pasture resulted in an increase in IV.

Since G. triacanthos behaves as an early successional species and fairly intolerant of shade, the amount of edge and in turn the area of the landscape under forest, could be expected to contribute to variation in abundance. Decreasing the area of deciduous forest was associated with an increase in IV in the overall model and in each of the regional models. In the overall model and in the north-western part of the range, increasing the total amount of deciduous forest and pasture edge tended to cause an increase in abundance. The patchiness of the landscape was an important component of the overall model as well as each of the regional models, however the effect of the total number of

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. patches in the landscape varied. Whereas in the overall model and in the north-eastern and southern parts of the range decreasing the total number of patches in the landscape was associated with an increase in IV, in the north-western part of the range IV increased with an increase in total number of patches.

Correlation analyses show that the area of land under agriculture, pasture and upland deciduous forest, and the ‘patchiness’ of the landscape (i.e., total number of patches) increased significantly with latitude (i.e., to the north) while the area of land under bottomland deciduous forest and water decreased (Table 6.5). The positive correlation of pasture land and latitude is deceptive, however, because Louisiana does not have any land classified as pasture (and when Louisiana cells were excluded, the correlation was lost and indeed became negative [n = 183, r = -0.196, p < 0.001]). The patchiness of the landscape and the area of land under upland deciduous forest cover decreased significantly with increasing longitude (i.e., to the west); the north-east quadrant (quadrant 2) contains the most-patchy landscapes. To the south, bottomland deciduous forest, and aquatic cover were greater components of the landscape.

Discussion

Gleditsia triacanthos is sparse throughout its range, reaching peaks in abundance at very few cells, i.e., the species shows a ‘somewhere-abundant’ pattern of distribution. The proportion of cells where it is absent altogether (71%) is higher than the average for other eastern North American trees (approximately 60%) (Murphyet al. 2005). Murphy et al. (2005) showed that most eastern North American trees have a ‘somewhere-abundant’ distribution while only 15% of species have an ‘everywhere-sparse’ distribution.

The central-peripheral model of distribution of abundance

Gleditsia triacanthos does not exhibit any of the characteristics predicted by the central- peripheral model’s ‘abundant-centre’ distribution; neither abundance nor number of sites occupied is highest in the centre of its range, and abundance and occupancy do not decline linearly towards the edge of the range. The species reaches greatest abundance and occupancy in the north-western to north-central parts of its range (Figure 6.1). This results in a peak of abundance at distance category 8 when all cells are considered and

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. category 10 when only occupied cells are considered. In both cases cells closest to the centre of the range show among the lowest abundance.

Murphy et al. (2005) showed that the abundant-centre distribution was not well supported for the majority of 134 eastern North American tree species, rather an ‘abundant-core’ distribution better described the pattern of abundance, where ‘core’ in some cases ranged out to beyond the midpoint between centre and edge-of-range. That is, peaks of abundance often occurred at intermediate areas between the centre and edge of range, though these peaks usually occurred closer to the centre than to the edge. Such a pattern is also in contrast to the pattern found inG. triacanthos-, where the abundance peak is closer to the edge than to the centre. Gleditsia triacanthos also exhibits a fairly distinct pattern of abundance across the range when compared with the other Fabaceae species and with the pioneer and/or bottomland species as a whole.

The central-peripheral model assumes that responses to environmental gradients are unimodal and symmetric (Oksanen & Minchin 2002). However, empirical evidence supporting these assumptions are scarce (McGill 2005) and species interactions, human impact and disturbance and historical factors may change the response shape even if the fundamental response were symmetric (Murphy & Lovett-Doust, 2005). For example, the relative importance of biotic versus abiotic limitations on plant distribution and abundance at low and high latitudes may vary.

The distribution of abundance and landscape spatial structure

Spatial structure of the landscape contributes significantly to the variation in abundance throughout the range, however, regional effects are evident. Landscape structure appears to have a greater effect on abundance in the north-eastern and southern parts of the range. Thus, in the area where variation in abundance is greatest (the north-west), landscape structure appears to have little effect on that variation, and other parameters are likely more important. Alternatively, landscape parameters we have not measured might affect abundance in this region. For example, other habitat types may be important. Prairie habitat is a component of the Kansas landscape that is rare in other parts of the range (and

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. thus not included as a measure of landscape structure in this analysis). Considering that the area of pasture habitat is a significant component of both the overall model and the region in quadrant 1, it is possible that prairie would also be significant here, given the likely ecological similarities between the two landcover types.

The scarcity of the effects of current habitat structure may be due to the non-equilibrium status of the present landscape, which is strongly affected by ongoing human impact (Dupre & Ehrlen 2002). Species respond to landscape changes with a certain time-lag (Eriksson 1996) and plants with long generation times, and a means of clonal reproduction, seem likely to suffer less in the short term (Fischer & Matthies 1998).

Implications of scale

The size of the cells for which abundance data are available (20 km x 20 km) is relatively large compared with that of the average forested plot in some parts of the Honey Locust range. For example, the vast majority of forest parcels in the south-central Illinois region (approximately in the centre of the geographic range ofG. triacanthos) are less than 1 acre (0.4 ha) (Illinois Department of Energy and Natural Resources 1994). Thus, each 20 km x 20 km cell may comprise numerous individual surveyed forest plots.Gleditsia triacanthos may reach relatively high abundance in one or a few plots within the cell however when averaged over all plots in the cell, the IV may be low if the species is absent or in very low abundance in the majority of plots. If there is only one, or very few surveyed forest plots within the cell and G. triacanthos is in very high abundance in the plot, the entire cell is recorded as a high abundance regardless of how small a proportion that plot makes up of the cell. In this case, measurement of landscape parameters for the entire 20 km x 20 km cell probably has little relationship with abundance.

Conclusions

Clearly, many factors influence the distribution of abundance across the range in Honey Locust, and these factors appear to differ regionally and latitudinally. The species shows the predicted ‘somewhere abundant’ distribution, however, Honey Locust does not conform to range theory that predicts an abundant centre distribution. Similarly, the species does not show the predicted bimodal distribution in spatial autocorrelation (but

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. neither do most of the other trees which have been examined). In fact, spatial autocorrelation of sites at the most distant spatial lags tends to be negative, suggesting that the species does not respond in the same way to all range edges.

The spatial distribution in abundance in the four range quadrants illustrates that the pattern of abundance in the north-western part of the range is quite different from that at other range aspects. However, landscape structure explains only 20% of the variation in abundance in this area, compared with 37% in the north-eastern and 30% in the southern part of the range. The significant effects of landscape spatial structure on abundance in G. triacanthos lend further emphasis to the notion that models of distribution and abundance need to take into account regional differences in human impact and disturbance and in species responses to these differences. Models built on one small area may not apply to any other, and one model built over a large area may have weak local predictive power, because of differences in the landscape spatial structure and habitat availability, or in the species response.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. References

Bara, T. J. (1994) Multi-resolution land characteristics consortium documentation notebook. EMAP-Landscape Characterization, EPA Office of Research and Development, Research Triangle Park, NC.

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Gordon, D. (1966) A revision o f the genus Gleditsia (Leguminosae). PhD Thesis, Indiana University, Bloomington, IN.

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Laurance, W.F., Delamonica, P., Laurance, S.G., Vasconcelos, H.L. & Lovejoy, T.E. (2000) Rainforest fragmentation kills big trees. Nature, 404, 836-836.

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Murphy, H.T., VanDerWal, J. & Lovett-Doust, J. (2005) The distribution of abundance across the range in eastern North American trees. Global Ecology and Biogeography, in revision.

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Murray, B.R. & Lepschi, B.J. (2004) Are locally rare species abundant elsewhere in their geographical range? Austral Ecology, 29,287-293.

Murray, B.R., Rice, B.L., Keith, D.A., Myerscough, P.J., Howell, J., Floyd, A.G., Mills, K. & Westoby, M. (1999) Species in the tail of rank-abundance curves. Ecology, 80, 1806-1816.

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Oksanen, J. & Minchin, P.R. (2002) Continuum theory revisited: what shape are species

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Prasad, A.M. & Iverson, L.R. (2003). Little's range andFIA Importance value database for 135 eastern US tree species, U.S.D.A. Forest Service, DE, URL http://www.fs.fed.us/ne/delaware/4153/global/IittIefia/index.htmI.

Sagarin, R.D. & Gaines, S.D. (2002) The ‘abundant-centre’ distribution: to what extent is it a biogeographical rule? Ecology Letters, 5,137-147.

Steffan-Dewenter, I., Munzenberg, U., Burger, C., Thies, C. & Tschamtke, T. (2002) Scale-dependent effects of landscape context on three pollinator guilds.Ecology, 83, 1421-1432.

Steffan-Dewenter, I. & Tschamtke, T. (1999) Effects of habitat isolation on pollinator communities and seed set. Oecologia, 121,432-440

Sullivan J. (1994) Gleditsia triacanthos. In Fire Effects Information System, [Online]. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory. URL http://www.fs.fed.us/database/feis/[].

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U.S.G.S. (2005) GAP Analysis program URL http://www.gap.uidaho.edu/)

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 6.1 - Landcover classifications used in analysis. Landcover classes in bold were used in the regression analysis. Class Number Description

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 6.2 - Landscape parameters used in linear regression analysis Parameters Abbreviation

Classes 1, 3, 6, 10, 13 Class area (ha) CA Number of patches NP Total edge (m) TE Per cell Total number of patches TNP

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 6.3 - Number and proportion of cells in each importance value (IV) category within the range of G. triacanthos. IV Number of cells Proportion of cells (%)

0 2679 71.27 1-5 848 22.56 6-10 130 3.46 11-15 46 1.22 16-20 25 0.67 21-25 14 0.37 26-30 6 0.16 31-40 2 0.05 41-60 4 0.11 61-80 3 0.08 81-100 2 0.05 Total 3759 100

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 6.4 - Results of linear regression analysis of landscape parameters and importance value for all cells and for three of the range quadrants. (NB. Quadrant 3 contained only six cells so these were included in the analysis with Quadrant 4). In Landscape Parameters column, number denotes the landcover class; letters denote the landscape parameter abbreviation (see Table 6.2). Landscape SE Parameter b SE beta t significance R2 estimate

ALL (n = 212) Constant 2.620 0.344 7.619 0.000 0.267 1.107 1CA 0.000 0.000 0.209 2.412 0.017 6CA 0.000 0.000 -0.284 -3.566 0.000 6TE 0.000 0.000 0.192 1.901 0.059 13CA 0.000 0.000 -0.163 -2.085 0.039 13NP 0.000 0.000 -0.145 -2.027 0.044 10TE 0.000 0.000 0.317 4.193 0.000 TNP 0.000 0.000 -0.434 -4.309 0.000 Quadrant 1 (n = 96) Constant 2.747 0.433 6.345 0.000 0.203 1.079 1NP -0.001 0.000 -0.439 -2.384 0.019 1TE 0.000 0.000 0.393 2.028 0.046 6CA 0.000 0.000 -0.303 -2.258 0.027 13NP -0.001 0.001 -0.207 -1.685 0.096 10NP 0.000 0.000 -1.117 -2.752 0.007 10TE 0.000 0.000 0.453 3.179 0.002 TNP 0.000 0.000 0.754 1.803 0.075 Quadrant 2 (n = 66) Constant 12.214 3.108 3.930 0.000 0.375 1.069 1CA 0.000 0.000 -1.490 -2.619 0.012 1NP 0.001 0.000 1.267 4.043 0.000 6CA 0.000 0.000 -1.656 -3.698 0.001 13CA -0.001 0.000 -0.588 -3.214 0.002

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Landscape SE P aram eter b SE beta t significance R2 estimate 13NP 0.001 0.000 0.496 2.720 0.009 10CA 0.000 0.000 -0.921 -2.642 0.011 10NP 0.001 0.000 1.768 3.383 0.001 TNP -0.001 0.000 -2.375 -3.729 0.000 Quadrant 3 and 4 (n = 50) Constant 3.138 0.475 6.603 0.000 0.309 0.585 13CA 0.000 0.000 -0.586 -2.654 0.014 10CA 0.000 0.000 1.773 2.754 0.011 10TE 0.000 0.000 -2.003 -2.970 0.007 TNP 0.000 0.000 -0.765 -3.109 0.005

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 6.5 - Correlation matrix of landscape structure parameters and latitude and longitude (n = 212). In Landscape Parameters column, number denotes the landcover class; letters denote the landscape parameter abbreviation (see Table 6.2). Landscape Parameter Latitude Longitude

1CA 0.480 *** 0.034 1NP -0.044 -0 441*** 1TE 0.413 *** -0.167* 3CA -0.562 *** -0.113 3NP 0.201 ** -0.039 3TE -0.634 *** -0.104 6CA 0.052 -0.345*** 6NP 0.482 *** -0.192** 6TE 0.309 *** -0.218** 10CA 0.166* -0.071 10NP 0.528 *** -0.116 10TE 0.441 *** -0.063 13CA -0.621 *** -0.092 13NP -0.349 *** -0.326*** 13TE -0.503 *** 0.004 TNP 0.371 *** -0.252*** *** p < 0.001 ** p< 0.01 * p < 0.05

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•42° N

36°N'

•36°N

Importance •30°N Value [=□» (^Qo.1-10 H I 10. 1 - 2 0 H i 20.1 - 4 0 250 500 1,000 Kilometsrs H I 40.1 -6 0 60 .1 -1 0 0 96°W 90°W 84°W 72°W78“W I | Littles range boundary

Figure 6.1 - Importance values across the geographic range ofG. triacanthos. Quadrant 1 contains those cells with latitude >36°N and longitude >92°W (i.e. north western part of range), n = 1212 Quadrant 2 contains those cells with latitude >36°N and longitude <92°W (i.e., north eastern part of range), n = 1231 Quadrant 3 contains those cells with latitude <36°N and longitude <92°W (i.e., south eastern part of range), n = 383 Quadrant 4 contains those cells with latitude <36°N and longitude >92°W (i.e., south western part of range) n = 933

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80- w o o 60 - c o ■■e 0 1 4 0 - IL

20 -

0 T i nr Hr If i " 1 123456789 10 Distance category

Figure 6.2 - Proportion of unoccupied (solid bars) and occupied (hollow bars) cells at each distance category forG. triacanthos.

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8 .00 -

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Figure 6.3 - Mean importance value (IV) from the closest to the centre (1) to the edge of range (10) in 10% increments of the total range for G. triacanthos for (a) all cells and (b) occupied cells only. Note the different y-axis scales.

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+ 2: re

123456789 10 123456789 10 Distance category Distance category

Figure 6.4 - Mean importance value (IV) from the closest to the centre (1) to the edge of range (10) in 10% increments of the total range for G. triacanthos for (a) quadrant 1 (b) quadrant 2, (c) quadrant 3 and (d) quadrant 4 cells.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 2 4 -

V 2 2 -

20 -

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Figure 6.5 - Mean number of co-occurring species from the closest to the centre (1) to the edge of range (10) in 10% increments of the total range forG. triacanthos.

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S 0.6-

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Figure 6.6 - Mean importance value (IV) for four species of Fabaceae from the centre (1) to the edge of range (10) in 10% increments of the total range (a) all cells and (b) occupied cells only. Note the different y-axis scales.

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7.00- 3.5-

6.50-

7 3.0- 6.00-

S 2.5— 5.50-

5.00- 2.0- 4.50-

123456789 10 123456789 10 Distance category Distance category

Figure 6.7 - Mean importance value (IV) from the centre (1) to the edge of range (10) in 10% increments of the total range for all pioneer species (n = 27; see Appendix 6.1) for (a) all cells and (b) occupied cells only. Note the different y-axis scales.

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oa> 60- *5 6°H c o o ■e 0 o 1 40H 1 4 ° - CL tL

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1 r m i i r I I I I I I I I I I I I I I I I I I I TT 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 Latitude L o ngitude

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Figure 6.8 - Proportion of cells unoccupied (solid bars) and occupied (hollow bars) from (a) southern to northern latitudes and (b) western to eastern longitudes; and mean importance value from (a) southern to northern latitudes and (b) western to eastern longitudes for G. triacanthos.

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Figure 6.9 - Spatial autocorrelogram showing Moran’s / at increasing distances between cells. One unit on the horizontal axis indicates one interval of the maximum distance between cells, with 1 representing cells in the closest 10 % of the range of distances.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Chapter 7 - Niche differentiation and habitat suitability across the range in Gleditsia triacanthos (Honey Locust)

Introduction

It is widely assumed that habitat suitability declines from the centre of a species’ range towards the edge (Hengeveld & Haeck 1982; Brown 1984; Lawton 1993; Guoet al. 2005; but see Murphy & Lovett-Doust 2005a). Brown (1984) suggested that spatial autocorrelation in axes representing dominant dimensions of the niche results in the probability of sites having similar combinations of environmental variables being an inverse function of the distance between them. The central-peripheral model of species distributions assumes that responses to environmental gradients are unimodal and symmetric (Oksanen & Minchin 2002), and that increasing the distance from the optimal site decreases the probability of a site fulfilling the niche requirements of that species. There will be a decreasing number of local sites where a species can occur at all and, even within these patches, population densities will tend to be lower because resources are scarce and/or conditions approach the limits that can be tolerated physiologically (Brown 1984).

Hutchinson (1957) classically defined the ‘fundamental niche’ of a species as an ‘n- dimensional hypervolume’ in which every point corresponds to a combination of environmental factors allowing a species to persist. The simplest interpretation of this view of the niche is that a species should occur everywhere that conditions are suitable and never where conditions are unsuitable. Hutchinson defined the smaller ‘realized niche’ as that portion of the fundamental niche actually occupied by a species. According to Hutchinson, as a result of competitive exclusion a species may frequently be absent from portions of its fundamental niche. It is now well recognised that statistical models of species distributions provide a description of the realised niche of a species but can say little about the fundamental niche (Austin 2002). Furthermore, environmental variables typically examined in such modeling efforts represent only relatively few of the possible ecological-niche dimensions (Hutchinson, 1957). Nevertheless, currently

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. available digital environmental data coverages encompass many variables that influence species’ macrodistributions.

In peripheral parts of the range, ecological conditions are likely to be different (even if not less optimal). Selection is thus likely to affect gene frequencies, and unique genotypes may be formed and favoured. Studies of gene frequencies have detected such differences in many plant species (reviewed in Lesica & Allendorf 1992, 1995). Environmental variation in phenotype due to phenotypic plasticity and reaction norms is also expected to play a significant role in determining patterns of adaptation and distribution at range limits (Antonovics 1976; Sultan 2000,2001).

Several authors have highlighted the potential evolutionary significance of peripheral populations. Mayr (1982, p. 602) pointed out that peripheral populations often diverge from central populations, noting “Aberrant populations of species almost invariably are peripherally isolated and, more often than not, the most aberrant population is the most distant one.” Holt (2003) speculated that ranges comprise many local evolutionary ‘arenas’ and that a range evolves because of the (overall) accumulated impact of evolution at local scales. This may involve either an adaptation arising in one arena and spreading throughout a species distribution, or localized adaptation leading to range shifts.

When local adaptation occurs in different parts of a range, each locally-adapted population may have a distinct niche (Holt 2003). Thus a map showing habitat availability or suitability for a population adapted to conditions in the core area of a range may be very different from that for a population existing at one of the margins (Travis & Dytham 2004). Suitability models built on one particular area may not apply to another, and a model built over a large area may have comparatively weak local predictive power, because of subtle differences in the niche space occupied (Osborne & Suarez-Seoane 2002). Spatial data partitioning is in general believed to improve distribution models because it better accounts for regional variation in the data set (Osborne & Suarez-Seoane 2002). Traditionally, this has been ascribed to geographic heterogeneity in the predictor

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. variables, such as systematic changes in geology, or vegetation, etc., that become apparent at large spatial scales (e.g., Unwin & Unwin 1998).

Here we develop a gradient-based habitat-suitability model for Gleditsia triacanthos (Honey Locust) based on known occurrences of the species and reported patterns of landcover, an array of soil parameters, and elevation. Suitability models based on occurrence in particular regional conditions, as well as conditions across the geographic range as a whole, are built and compared to determine how abiotic axes of the species’ niche space vary regionally across the geographic range.

Methods

Study species

Throughout its North American range (Figure 7.1),Gleditsia triacanthos (Honey Locust) occurs as a relatively minor component of natural forest stands and is generally considered early successional and fairly intolerant of shade (Sullivan 1994). The species is found typically on moist bottomland and in abandoned fields and pastures (Blair 1990) in the eastern and central United States; it is a rare component of the flora of southern Ontario in Canada. The species attains its maximum height in the valleys of small streams in southern Indiana and Illinois (Gordon 1966). Honey Locust is tolerant of low temperatures in the north (-29 to -34°C) and, although ample soil moisture is necessary for optimal growth, the species appears resistant to seasonal drought. Over its rangeG. triacanthos grows naturally to a maximum elevation of 610-760 m (Blair 1990).

Land cover

GAP analysis (USGS 2005a) land cover layers were obtained for Kansas (43 classes), Illinois (30 classes), Kentucky (48 classes), Tennessee (11 classes) and Louisiana (23 classes). Landcover data was derived from an assessment of the vegetation cover of each state. Vegetation is generally identified to the alliance level (groups of plants sharing dominant species) based on the National Vegetation Classification Scheme (NVCI). Remotely sensed Landsat TM satellite data is used as the basis for determining vegetation

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. alliance distributions at a resolution of 30 m (Bara 1994). We reclassified each State’s landcover classes to a set of 19 classes for analysis, shown in Appendix 7.1.

Soil variables

Data layers for soil parameters were obtained from the State Soil Geographic (STATSGO) database (USDA-NRCS 2005) for Kansas, Illinois, Kentucky, Tennessee and Louisiana. This data set consists of geo-referenced digital map data and attribute data, with an approximate minimum area delineation of 625 hectares. The STATSGO maps were compiled by generalizing more detailed soil-survey maps into soil associations in a 1:250,000 scale. Ten soil variables were extracted from the attribute data associated with the maps (Appendix 7.2).

Digital elevation

Elevation data was derived from the USGS National Elevation Dataset (NED) (USGS 2005b). NED is the result of the USGS effort to provide 1:24,000-scale Digital Elevation Model (DEM) data for the continental US at a consistent projection (Geographic), resolution (1 arc second), and elevation unit (meters).

GARP ecological niche model

GARP (Genetic Algorithm for Rule-set Production) (GARP: http://biodi.sdsc.edu/; see http://beta.lifemapper.org/desktopgarp/ for software download) is a desktop program that uses a genetic algorithm to create an ecological niche model for a species. Genetic algorithms constitute one class of artificial intelligence applications and were inspired by models of genetics and evolution (Andersonet al. 2003).

The output model represents the environmental conditions where focal species would be able to maintain populations (Stockwell & Peters 1999). GARP uses as input a set of point localities where the species is known to occur and a set of GIS layers representing the environmental variables that might limit the species' ability to persist at a location. GARP searches for non-random associations between environmental characteristics of localities of known occurrence versus those of the overall study region. It works in an iterative process of rule selection, evaluation, testing, and incorporation or rejection to

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. produce a heterogeneous rule-set characterizing the species’ ecological requirements (Anderson et al. 2003).

Data layers were compiled for three general regions, representing southern (Louisiana) western (Kansas) and central (Illinois, Kentucky and Tennessee) parts of the range ofG. triacanthos. Due to GARP’s data limitations, analysis was restricted to eight layers. Pearson correlation coefficients were calculated for all combinations of the environmental layers. We eliminated highly correlated layers; all layers remaining in the analysis had values correlated at < 50%. Layers remaining in the analysis are shown in Table 7.1.

All individual tree locations in the three regions were input as presence points (n = 906). All non-presence points are considered in GARP to be absence data. The model was set to regard 50% of the available occurrence points as training points (i.e., used in model construction), and the remaining points are used for testing. Twenty runs were conducted for each region (at up to 20 hours Pentium 4 CPU time per cycle) with all points input as presence points (the ‘all-tree’ model). In addition, presence points for each region were run individually in each region (i.e., the ‘southem-tree’, ‘westem-tree’ and ‘central-tree’ models). For each run, GARP produces a binary ESRI grid file for the analysis area with 0 representing a predicted absence and 1 a predicted presence. Grids for each run were summed to give a composite map where each value in a pixel equals the number of runs (of twenty) predicting presence in that cell. Thus a total of four composite grids was generated for each region. For example, for the southern region, the all-tree model is run, then the southem-tree model, the westem-tree and central-tree models, individually. Data values (0 to 20) in each grid were reclassified from unsuitable to high suitability according to Table 7.2.

Results

Perhaps not surprisingly the lowest proportions of unsuitable habitat were predicted in a region when using only occurrence point values for that region (Figure 7.2). Thus when only central occurrence points were used to predict suitability in the central region of the

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. range, the proportion of habitat predicted as unsuitable (51%) was lower than when using either southern only (98%), or western only (98%) occurrence points (Figure 7.2a). The proportion of unsuitable habitat in the western region predicted using the westem-tree model was the lowest (23%), while using the southem-tree model 95% of the landscape was predicted to be unsuitable and using the central-tree model 63% was predicted to be unsuitable (Figure 7.2b). Similarly, using the southern-tree model only in the south, the proportion of habitat that was unsuitable was 52%, compared with using the westem-tree (99%) or central-tree models (91%) (Figure 7.2c).

In central parts of the range, 30% of the habitat was predicted to be of high suitability, when considering solely the central occurrence points (Figure 7.2a), whereas for both the westem-tree model in the western region and southem-tree model in the southern region, the proportion of habitat predicted to have high suitability was only 6% (Figures 7.2b, c).

The model for the southern trees failed to predict the occurrence of any trees in the central region of the range, and only predicted the occurrence of 20% of the trees in the western region of the range (i.e., it predicted unsuitable habitat at the locations of 80% of the trees in the western region) (Table 7.3). The model for the western trees failed to predict the occurrence of suitable habitat for any trees in either the central or southern parts of the range. The model created using the central trees failed to predict the occurrence of suitable habitat for any trees in the southern part of the range, but did predict suitable habitat for 68% of the trees in the western region. The all-tree model (including locations for all 906 individual trees from 15 populations) predicted suitable habitat for all locations of trees in the western region, 92% of trees in the central region and only 62% of trees in the southern region (Table 7.3).

When the all-tree model (n = 906) was used to predict habitat suitability in each of the three regions, the central region had the lowest proportion of unsuitable habitat (24%), compared with the southern (68%) and western (47%) regions (Figure 7.3). The proportion of highly suitable habitat in the central region was also highest (21%) in the central region and lowest in the southern region (1%).

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Over all the regions, habitat suitability as predicted by the southem-tree model only was lowest with only 20% of habitat predicted as suitable, and with 15% of that being in the very low and low suitability categories (Table 7.4). Habitat suitability predicted by the westem-tree model was 30% and by the central-tree model 27%. In contrast, when all points were considered in all regions, the proportion of the total region predicted as suitable habitat was 51% (Table 7.4).

Discussion

These results suggest that the niche space occupied by G. triacanthos varies regionally and that between some regions in particular there may be significant niche differentiation. In particular, while there is some overlap between the niche space occupied by trees in the western and central regions of the range, and, to a lesser extent, between the southern and western regions of the range, there appears to be virtually no overlap between the niche space occupied by central and southern trees. Thus, the central-tree model successfully predicted the occurrence of over two-thirds of the trees in the west but none of the trees in the south, and the southem-tree model was able to predict the occurrence of 20% of the trees in the west but none of the trees in the central part of the range.

Clearly, the better the regional coverage of known tree locations, the better the model is at predicting occurrences across the entire region. Similarly, regional tree models performed very well at predicting the occurrence of suitable habitat across known locations of trees in their particular region. Moreover, the all-tree model predicted suitable habitat at the locations of all the trees in the western part of the range and most of the trees in the central part of the range, although it did not perform so well in the south. In addition, when the southern-tree model was applied to the entire study region only 20% of the habitat was predicted as constituting suitable habitat, compared with 27% using the central-tree model, 30% when using the westem-tree model and 51% using the all-tree model. This result, combined with absence of an overlap in niche space between the southern and central trees, suggests that trees in the southern region occupy the narrowest niche breadth. This is in keeping with the widely held theory that northern

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. hemisphere species are limited more by competition at lower latitudes and by abiotic conditions at higher latitudes (MacArthur 1972; Loehle 1998). Competitive displacement may be responsible for limiting the niche breadth of G. triacanthos in the southern part of its range. Murphy and Lovett-Doust (2005b) showed tree density in these southern sites was very low compared with other parts of the range, suggesting increased competition.

G. triacanthos appears to occupy its broadest niche space in the western region of its range; the westem-tree model predicts the highest proportion of suitable habitat across the entire study area, and westem-tree niche space overlaps to some degree with both central and southern tree niche space. We have previously shown thatG. triacanthos does not conform to the central-peripheral model prediction of an abundant centre distribution (Murphy & Lovett-Doust 2005b). Indeed the species reaches greatest abundance and occupancy in the north-western to north-central parts of its range suggesting that either abiotic conditions are more favourable, or perhaps that the species experiences lowered competition there. Murphyet al. (2005) showed that just 35% of some 134 eastern tree species had an abundant-centre distribution.

There are few other studies that have examined niche overlap and niche breadth at the scale of geographic ranges. Choler & Michalet (2002) reported a narrower ecological amplitude for the alpine tundra sedge Carex curvula ssp. rosae in the northern part of its range, whereas for C. c. ssp. curvula the niche breadth of range-margin populations was not reduced compared to that of range-centre populations.

The relationship between habitat and the niche

Habitat in both landscape and metapopulation ecology is envisioned as occurring in compact units, i.e., patches. A primary difference between the two disciplines is that in landscape ecology habitat quality is regarded as continuous rather than discrete as it is in metapopulation ecology (Dennis 2003). Notions of habitat and the niche are closely coupled and as Dennis (2003) noted “accurate recognition of the habitat is a prerequisite for the determination of the niche which otherwise can only be notional”. We have noted that there is often no clear distinction between habitat and non-habitat for plants having

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. relatively broad physiological tolerances (such as many trees), and defining distinct habitat patches is difficult or impossible (see Murphy & Lovett-Doust 2004). Rather, many plants respond to gradients of resource quality and suitable habitat lies along some ecological continuum, from optimal habitat, through merely suitable-, to suboptimal-, with many biotic and abiotic parameters contributing toward suitability. Clearly then, ‘on the ground’ plant’s-eye-view delineation of the niche of many plant species is equally, if not more, complicated.

Recent concepts of source-sink dynamics, metapopulation dynamics and dispersal limitation complicate any relationship between a species’ niche and its occurrence in suitable habitat (Pulliam 2000). Dispersal may relegate a species to habitats in which its niche requirements are not fully met (‘sink’ populations, Pulliam 2000) or dispersal limitation may mean species are not always present when niche requirements are met (see e.g., Cain et al. 1998). Finally, metapopulation theory posits that local populations frequently go extinct and, even at equilibrium, only a fraction of suitable habitat will be occupied (Hanski & Gilpin 1997). Clearly the distribution of environmental conditions in space and time, the regional landscape structure, and biotic competition and dispersal all play some role in determining species distributions in relation to the distribution of suitable habitat.

Gleditsia triacanthos is sparsely distributed throughout its range, reaching peaks in abundance at very few locations i.e., the species shows a ‘somewhere-abundant’ pattern of distribution (Murphy & Lovett-Doust 2005b). In fact Murphy and Lovett-Doust (2005b) showed, using USDA Forest Service data, that the species is absent from c. 70% of its geographic range. Our all-tree suitability model indicates unsuitable habitat constitutes nearly half of the study area, suggesting 20% of potentially suitable habitat is unoccupied. A central ecological assumption in the use of habitat suitability models is that vegetation is in a state of equilibrium with the environment. This is unlikely to be the case where history and disturbance are important in structuring the distribution of the species under study (Austin 2002), as they are particularly likely to be for an early successional tree such as G. triacanthos. There are, therefore, limits to the degree of

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. success that can be achieved by using current environmental predictors and adult distributions which may reflect past, but not present, habitat suitability. An additional issue is that unmodelled processes may dominate patterns of species distributions at the local scale, leading to locally poor performance of large-scale models (Osbome & Suarez-Seoane 2005). Nevertheless, GARP gave excellent predictions about habitat suitabilities across the range and warrants further investigation for conservation purposes.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. References

Anderson, R.P., Lew, D. & Peterson, A.T. (2003) Evaluating predictive models of species' distributions: Criteria for selecting optimal models. Ecological Modelling, 162, 211 232.

Antonovics, J. (1976) Nature of limits to natural selection.Annals o f the Missouri Botanical Garden, 63, 224-247.

Austin, M.P. (2002) Spatial prediction of species distributions: an interface between ecological theory and statistical modelling.Ecological Modelling, 157,101-118.

Bara, T. J. (1994) Multi-resolution land characteristics consortium documentation notebook. EMAP-Landscape Characterization, EPA Office of Research and Development, NC.

Blair, R.M. (1990) Gleditsia triacanthos L., honeylocust. In:Silvics o f North America: Vol 2. Hardwoods, (eds. R. M. Bums & B.H. Honkala). United States Department of Agriculture, Agriculture Handbook 654. Washington, DC.

Brown, J.H. (1984) On the relationship between abundance and distribution of species. American Naturalist, 124,255-279.

Cain, M.L., Damman, H. & Muir, A. (1998) Seed dispersal and the Holocene migration of woodland herbs. Ecological Monographs, 68, 325-347.

Choler, P. & Michalet, R. (2002) Niche differentiation and distribution of Carex curvula along a bioclimatic gradient in the southwestern Alps. Journal o f Vegetation Science, 13, 851-858.

Dennis, R.H.L., Shreeve, T.G. & Van Dyck, H. (2003) Towards a functional resource- based concept for habitat: a butterfly biology viewpoint. Oikos, 102,417-426.

Gordon, D. (1966) A revision o f the genus Gleditsia (Leguminosae). PhD Thesis, Indiana University, Bloomington, IN.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Guo, Q.F., Taper, M., Schoenberger, M. & Brandle, J. (2005) Spatial-temporal population dynamics across species range: from centre to margin.Oikos , 108,47-57.

Hanski, I. & Gilpin, M.E. (1997) Metapopulation biology: ecology, genetics and evolution. Academic Press, New York, NY.

Hengeveld, R. & Haeck, J. (1982) The distribution of abundance. 1. Measurements. Journal o f Biogeography, 9, 303-316.

Holt, R.D. (2003) On the evolutionary ecology of species' ranges.Evolutionary Ecology Research, 5, 159-178.

Hutchinson, G.E. (1957) Concluding remarks. Cold Spring Harbour Symposium Quantitative Biology, 22, 415-427.

Lawton, J.H. (1993) Range, population abundance and conservation.Trends in Ecology & Evolution, 8,409-413.

Lesica, P. & Allendorf, F.W. (1992) Are small populations of plants worth preserving? Conservation Biology, 6, 135-139.

Lesica, P. & Allendorf, F.W. (1995) When are peripheral-populations valuable for conservation?Conservation Biology, 9, 753-760.

Loehle, C. (1998) Height-growth rate tradeoffs determine northern and southern range limits for trees. Journal o f Biogeography, 25, 735-742.

Mac Arthur, R. (1972) Geographical ecology. Harper and Row, New York, NY.

Mayr, E. (1982)The Growth o f biological thought: diversity, evolution, and inheritance. Harvard University Press, Cambridge, MA.

Murphy, H.T. & Lovett-Doust, J. (2004) Context and connectivity in plant metapopulations and landscape mosaics: does the matrix matter?Oikos, 105, 3-14.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Murphy, H.T. & Lovett-Doust, J. (2005a) Living on the edge vs life at the centre: a review of plant population ecology across the geographic range. In review.

Murphy, H.T. & Lovett-Doust, J. (2005b) The distribution of landscape spatial structure and abundance across the range in Gleditsia triacanthos. In review.

Murphy, H.T., VanDerWal, J. & Lovett-Doust, J. (2005) Distribution across the range in eastern North American trees. Global Ecology and Biogeography. In revision.

Osbome, P.E. & Suarez-Seoane, S. (2002) Should data be portioned spatially before building large-scale distribution models. Ecological Modelling, 157,249-259.

Pulliam, H.R. (2000) On the relationship between niche and distribution. Ecology Letters, 3, 349-361.

Stockwell, D.R.B. & Peters, D.P. (1999) The GARP modelling system: Problems and solutions to automated spatial prediction.International Journal o f Geographic Information Systems, 13,143-158.

Sullivan, J. (1994) Gleditsia triacanthos. Fire Effects Information System [Online]. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory, Fort Collins, CO. URL http://www.fs.fed.us/database/feis/.

Sultan, S.E. (2000) Phenotypic plasticity for plant development, function and life history. Trends in Plant Science, 5, 537-542.

Sultan, S.E. (2001) Phenotypic plasticity for fitness componentsPolygonum in species of contrasting ecological breadth. Ecology, 82, 328-343.

Travis, J.M.J. & Dytham, C. (2004) A method for simulating patterns of habitat availability at static and dynamic range margins. Oikos, 104,410-416.

Unwin, A. & Unwin, D. (1998) Exploratory spatial data analysis with local statistics.The Statistician, 47,415-421.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. USDA, Natural Resources Conservation Service (2005) State Soil Survey Geographic database. URL http://www.ncgc.nrcs.usda.gov/products/datasets/statsgo/index.html.

USGS (2005a) GAP Analysis program URL http://www.gap.uidaho.edu/

USGS (2005b) National Elevation Dataset URL http://ned.usgs.gov/.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 7.1 - Parameters used in GARP habitat suitability modelling Param eter Unit Source

Landcover Categorical classification 1 GAP analysis - 19 (see Appendix 7.1) Soil available water inches STATSGO soil survey capacity Drainage Categorical classification 0 STATSGO soil survey (very poor) to 6 (excessive) Annual flood frequency Categorical classification 1 STATSGO soil survey (none) to 5 (water) Soil permeability Inches/hour STATSGO soil survey Slope % STATSGO soil survey Soil Texture (N04) % passing sieve Number 4 STATSGO soil survey (course grained) Elevation metres USGS National Elevation Dataset (NED)

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 7.2 - Suitability classifications based on the composite output of 20 GARP runs (e.g., grid cell value of 0 indicates 0 of 20 runs predicted presence in that cell). Suitability Grid cell values Unsuitable 0 Very low 1-5 Low 6-10 Medium 11 -1 5 High 16-20

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 7.3 - Proportion of trees in each region classified as occurring in any class of suitable habitat using the individual regional tree-models and the all-tree model. Model Southern Region Western Region Central Region Southem-tree model 100 20 0 Westem-tree model 0 93 0 Central-tree model 0 68 100 All-tree 63 100 92

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 7.4 - Proportion of each class of predicted habitat suitability summed over the study area, using the individual regional tree-models and the all-tree model. Southern- Western-tree Central-tree A ll-tree Suitability tree model model model model

Unsuitable 80.4 70.5 72.85 49.0 Total suitable 19.6 29.5 27.15 51.0 Very low 11.7 17.5 10.56 22.5 Low 3.7 6.1 5.34 7.6 Medium 1.8 3.5 1.16 8.7 High 2.4 2.4 10.08 12.2

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 100’W 96*W 92*W 88'W 84'W 80*W 70'W

SD MN

3 8 ’N - -38*N VA

1 2 5 2 5 0 5 0 0 7 5 0 1,000 Kilometers

9 6 ’W 92*W ‘W 8 4 ’W88

Figure 7.1 - Native geographic range of Gleditsia triacanthos (in stipple) and location of regions for which GARP habitat-suitability models were generated. Shown is the GARP all-tree habitat suitability model for the western (Kansas), southern (Louisiana), and central (southern Illinois, Kentucky and Tennessee) regions. Darker shading indicates higher habitat suitability, as predicted by GARP.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 100- cd Suitability ■ unsuitable 8 0 - m □ v. low o g 6 0 —| CD B low □ m ed

50s * 40i □ high

20 -

0 [

100- L L i (b) 80—| m S 6 0 —I ro< (o <0 4 0 - «■ 2

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Figure 7.2 - Proportion of unsuitable and very low- to high-suitability cells in (a) central, (b) western, and (c) southern regions, generated using the individual regional tree models.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. without prohibited reproduction Further owner. copyright the of permission with Reproduced Figure 7.3 - Proportion of unsuitable and very low- to high-suitability cells in each in cells high-suitability to low- very and unsuitable of Proportion - 7.3 Figure region, generated using the all-tree model. all-tree the using generated region, % % of cells - 0 4 - 0 6 - 0 7 20 - 0 3 - 0 5 10 - - 1 ot est W South T Region Central E3 v. low low v. E3 ed m □ low B unsuitable ■ high □ Suitability 211

Chapter 8 - Scaling problems in plant ecology and the importance of regionality and the landscape mosaic.

Introduction

Metapopulation theory and landscape ecology have contributed enormously in recent years to our understanding of the distribution of organisms in heterogeneous landscapes. We now understand that local populations of species are subject to relatively frequent extinction and colonizations in nature, and that questions of dynamics and viability of natural populations should be addressed at a larger scale than that of the local population (Hanski 1999). At the same time, large-scale ecology or macro-ecology encompasses all of the elements that are included in population biology, thus knowledge of the population biology of a species underpins understanding of population dynamics at the larger-scale. Increasing empirical and theoretical evidence points to the importance of scale-dependent variability in parameters determining the distribution, abundance and performance of organisms (e.g., Keitt et al. 1997; Steffan-Dewenter et al. 2002; Thies et al. 2003). Yet, perhaps not surprisingly given the complexities involved, there is a relative paucity of ecological studies taking a multi-scale approach to examining the response of species to their surroundings (but see Bowers & Dooley 1999; Kollmann 2000; Steffan-Dewenteret al. 2002; Munzbergova 2004).

Plants of most species live parts of their lives at two different spatial scales: the relatively broad, dispersal scale of the seed and pollen grain, and the relatively fine scale of the sessile adult. Reproductive success may depend on processes operating at broader scales, for example, pollen production of nearby males, for outcrossing plants, and movement of pollinators in the surrounding landscape (Kollmann 2000). At the fine end of the spatial scale continuum, a fundamentally different set of processes (involving e.g., microsite selection) may be involved than at broader scales (e.g., dispersal capacity and colonization, abundance, and abiotic factors associated with the range of distribution) (Bowers & Dooley 1999).

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. In this thesis I have distinguished between factors and processes influencing abundance and performance of the dioecious treeGleditsia triacanthos (Honey Locust) at the scale of individuals, populations, landscapes and regions across its geographic range. I have utilized available databases of phytosociological ‘importance values’ and digital environmental data-layers, as well as field measurements of individual and population performance values to address questions about the role of landscape fragmentation and aspects of geographic range location in regional plant population dynamics.

General results

The central - peripheral model

Gleditsia triacanthos is sparse throughout its range, reaching peaks in abundance at relatively few sites within the range, i.e., the species shows a ‘somewhere-abundant’ pattern of distribution (Table 6.3) The species does not exhibit any of the characteristics predicted by the central-peripheral model’s ‘abundant-centre’ distribution (see e.g., Sagarin & Gaines 2002); neither relative abundance nor the number of sites occupied is highest in the centre of its range, and abundance and occupancy do not decline linearly towards the edge of the range (Figure 6.4; Figure 6.8). In addition, G. triacanthos does not show the predicted bimodal distribution in spatial autocorrelation (Figure 6.9) (but neither do most of the eastern North American trees which have been examined [Murphy et al. 2005]). In fact, spatial autocorrelation of sites at the most distant spatial lags tends to be negative, suggesting that the species does not respond in the same way to all range edges. As it happens, the species achieves greatest relative abundance and occupancy in the north-western to north-central parts of its range (Figure 6.1).

All aspects of population performance inG. triacanthos exhibit marked regional variation. Populations at the western periphery of the range experienced relatively high reproductive output, low vegetative output, and active recruitment, but apparently low survivorship (Chapter 4, Figures 4.3,4.5) and high levels of developmental instability (Figure 5.4). In contrast, populations in the southern part of the range show very little recruitment (Figure 4.3), low density, low levels of developmental instability (Figure 5.4) and low reproductive output but high vegetative output (Chapter 4, Figure 4.5).

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Populations in the northern peripheral parts of the range, as well as in geographically central parts of the range had a mix of size-frequency distributions, variable reproductive and vegetative outputs (Chapter 4, Figures 4.2,4.5,4.6,4.7) and variable levels of developmental instability (Figure 5.4).

Landscape spatial structure

Many factors influence the distribution of abundance and population performance across the range in Honey Locust, and effects of these factors appear to differ regionally and latitudinally. Spatial structure of the landscape contributes significantly to the variation in abundance throughout the range. Landscape structure (particularly area and number of patches of deciduous forest and overall landscape ‘patchiness’) appears to have a greater effect on abundance values in the north-eastern and southern parts of the range (Table 6.4). Thus, in the area where variation in abundance is greatest (the north-west), landscape structure appears to have little effect on that variation, and other parameters are likely more important. Landscape structure also appeared to have little effect on population performance parameters (Table 4.5). In addition, population size had no effect on demographic structure, or reproductive- or vegetative-output inG. triacanthos (Table 4.5). Plants in smaller populations also failed to show increased levels of FA (Table 5.5) (and see Murphy & Lovett-Doust 2004a).

Range limitation and niche differentiation

The relatively high abundance values and active recruitment recorded for G. triacanthos in the northwestern part of the range suggest that either abiotic conditions are more favourable for the species, or perhaps that the species experiences lower competition there. The relative importance of biotic (usually density-dependent) and abiotic (usually density-independent) factors in limiting populations is likely to change depending on the position of the population within a species’ range (Garcia & Arroyo 2001), and on the aspect of the range edge (Loehle 1998). MacArthur (1972) suggested that biotic interactions tended to limit distribution and abundance at lower latitudes, due to increasing numbers of potentially competing species. In contrast, abiotic factors were more likely to be limiting at higher latitudes (and see Loehle 1998).

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Our data suggest this might to some extent also be the case withG. triacanthos. Climatic conditions are probably not limiting recruitment, growth and abundance of populations in the south, since G. triacanthos grows well and even becomes invasive in areas far south of its southern North American range limit (e.g., in Argentina [Marco & Paez 2000], and in Mexico [Estrada-Castillonet al. 2002]). Overall vegetative growth was relatively high for trees in the southern part of the range (Figure 4.8) and estimates of fluctuating asymmetry in leaves ofG. triacanthos were lowest in trees from southern populations (Figure 5.4) (Murphy & Lovett-Doust 2004a). Tree density in the southern sites was also very low compared with other parts of the range (Table 5.1) and trees in the southern region appear to have the narrowest niche breadth (Chapter 7). These results suggest increased competition may indeed be limiting recruitment and population growth, and competitive displacement may be responsible for limiting niche breadth of G. triacanthos in the southern part of its range.

Low survivorship (Figure 4.3) and high levels of developmental instability (Figure 5.4) suggest abiotic conditions may be more ‘stressful’ in western sites, however high abundance (Chapter 6, Figure 6.1), population density and reproductive output (Figure 4.5) all imply otherwise. In addition, G. triacanthos appears to occupy its broadest niche space in the western region of its range (Chapter 7). It seems likely that while abiotic conditions may be limiting to some extent in the western region, lowered competition allows the species to reach high abundance. However, in abiotically stressful environments, there could be high temporal variance in demographic parameters, or catastrophes, leading to elevated extinction risk (Holtet al. 2005) which might limit the range boundary in this region.

Northern, and geographically central populations show variable performance (Chapter 4, Figures 4.2,4.5,4.6, 4.7), which is likely related to the successional stage of the site occupied. It is somewhat surprising that northern populations do not seem to show an overall reduction in abundance and population performance in response to abiotic conditions at the most northerly latitudinal range boundary. The only two populations persisting in Ontario (at the most northern part of the range) are located on Pelee Island in

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Lake Erie (c. 15 km from mainland Ontario) and Point Pelee, a relatively isolated patch of forest habitat in an otherwise heavily modified agricultural and urban landscape. These two populations are separated from the next nearest populations by Lake Erie. Dispersal limitation may limit recruitment further north as sites suitable for recruitment become more sparse and existing populations are relatively small (Table 4.1) with low fecundity (Figure 4.5). A lack of habitat availability may limit ranges, even when extinction and colonization is relatively constant over space (Holtet al. 2005). Thus individuals within suitable patches at range limits may experience environments no different at all from those experienced by individuals in the range center (Carter & Prince 1981).

Regionality versus the central-peripheral model

Abundance and performance of G. triacanthos populations can not simply be related to position in the range as predicted by the central-peripheral model. However, there is distinct regional differentiation in population parameters, abundance and distribution of populations, and the apparent niche space occupied. There are likely several reasons for the absence of a central-peripheral pattern in plant population responses. The central- peripheral model assumes that responses to environmental gradients are unimodal and symmetric (Oksanen & Minchin 2002). However, empirical evidence supporting these assumptions are scarce (McGill 2005) and species interactions, human impact and disturbance, and historical factors all may change the response shape even if the fundamental response were symmetric. Furthermore, the response shape may vary depending on regional or habitat-specific conditions, and an alternative model needs to incorporate regional differences in species responses along gradients (for example, as noted above the potential relative importance of biotic versus abiotic limitations at low and high latitudes). Indeed, theory predicts that response shapes should differ among gradient types (Austin & Smith 1989) or gradient locations (Austin & Gay wood 1994). The analysis of species response shapes is of great ecological and theoretical interest (Austin 1999; Oksanen & Minchin 2002; Rydgren et al. 2003) and is a topic that warrants further empirical investigation.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Another potential reason for the absence of a central-peripheral pattern of species responses is that the geographic centre of the range today may not be the centre of the ecological niche optima. Rather, this centroid may be on the move due to changing environmental conditions or there may be several ecological centroid optima due to adaptation. Furthermore, changes in the landscape as a result of fragmentation may result in inclusion of new or shifted points of optima along certain niche axes, particularly for early successional species such asG. triacanthos which before human alteration of the landscape may have been reliant on recruitment in natural forest gaps for population growth. Alternatively the centroid may have remained in place but dispersal limitation may be skewing the distribution of the species around it, so that the distribution range is lopsided relative to the centre. In any case, future empirical investigations of patterns in species distribution, abundance and performance needs to take a multi-scale approach in order to determine the relationship between ecological niche optima and geographic location.

Environmental parameters likely constituting key environmental niche axes are also dynamic over the time scales involved in population dynamics of long-lived species. Over the last century some parts of the range ofG. triacanthos have experienced cooling while others have experienced warming. For example, in the southern Midwest region, including southern Indiana, Illinois and Missouri, the average temperature has cooled by approximately 0.6°C and there has been a 10-20% increase in precipitation, while in the southeast region of the range (including Mississippi and Louisiana) both temperature and precipitation have generally increased (National Assessment Synthesis Team 2000). Historical analogs of shifts in the distribution of plant species suggest that rates of change in range distribution may be slow relative to the current and predicted rates of climatic change (Gear & Huntley 1991).

Regional differences in population performance, distribution and abundance thus could be related to shifting optima in axes of ecological niche dimensions, but could still be viewed as ultimately tied to principles of the central-peripheral model. Alternatively, a species’ ‘regionality’ might be viewed as an ecological attribute entirely independent of

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the central-peripheral m odel, functionally integrating individual and population performance across regional environmental and ecological gradients. A species regionality would encompass notions of regional niche differentiation, habitat-specific demography, and scale-specific responses to ecological and environmental gradients. The generation and depiction of a species regionality factor lends itself well to the grid- based functional mosaic approach we have described earlier (Murphy & Lovett-Doust 2004b) and expand on further below. A regionality model makes no assumptions about unimodality, symmetry or centeredness along ecological niche axes; but at the same time spatial autocorrelation in species responses can be incorporated at any scale at which it occurs.

Fragmentation from the perspective of an early successional tree

We (Murphy & Lovett-Doust 2004b) and others (e.g., McIntyre & Hobbs 1999; Forman 2002; Wiens 2002) have recognized that the binary view of a landscape, as containing either suitable habitat or non-suitable matrix is too simplistic and does not capture the complex and varied nature of landscapes. McIntyre and Hobbs (1999) suggested that nearly all the current fragmentation models reflected an overly anthropocentric view of the world and failed to account for organism-perceptions of landscapes. But how to characterise fragmentation from a plant’s eye view of the landscape, especially when that plant is an early successional and relatively long-lived tree?

Certain aspects of fragmentation potentially benefit an early successional tree (e.g., increased edges) even if the ultimate effect is negative (due to a decrease in overall quantity of habitat available). For example, Ferreira and Laurance (1997) showed that in forest fragments of 1000 ha, 22-42% of the area is actually influenced by edges. Moreover, for particularly long-lived tree species, very small fragments and even individual trees may persist and contribute to regional population dynamics for many years after being isolated in a landscape. Levin (1995) reviewed the importance in highly modified habitats of isolated trees, or “reproductive outliers,” to within-patch population dynamics. Levin suggested that these trees may serve as bridges between populations and concluded that, although isolated individuals may produce fewer seeds than do

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. individuals located within inhabited patches, they may be a major source of pollen and seeds for nearby populations, retarding the divergence of local populations and forming foci for new populations.

The landscape ‘continuum model’ was proposed by McIntyre and Hobbs (1999) as an alternative to the traditional fragmentation model. In this model the authors categorise landscapes along a continuum from intact to variegated, to fragmented and relictual. Although Mclntrye and Hobbs highlight the importance of individual species perceptions of the landscape, their landscape continuum model still uses a categorical typology to describe landscapes. This model is also essentially pattern-based and does not make a distinction between spatial and environmental continuum (Manning et al. 2004). The ‘continuum concept’ of plant science deals with abstract environmental continua. The concept states that vegetation has gradually changing species composition along environmental gradients with each species having an individualistic and independent distribution (Austin 1999; and see Whittaker 1975). As Austin (1985) noted general discussion of the continuum concept provoked considerable confusion because those unfamiliar with the concept equated position on an environmental gradient with physical location on a transect. However, there is no necessary spatial relationship between sites with similar values on a gradient. The gradients are the abstract dimensions of an ecological space, where the relative positions of sites reflect their similar environments or floristic composition.

A key challenge for much of the research in human-impacted landscapes is to determine how individual organisms perceive and respond to the various landscape continuum. Recently Manning and colleagues (2004) described an integration of landscape and environmental continua models in the concept ofUmwelt - individual species perception and response. Umwelt, developed by the Estonian theoretical biologist Jakob von Uexkiill in 1926, is described as the ‘phenomenal world’ or ‘self-world’ of an organism. This continua-Umwelt model provides a useful basis for directing landscape ecological research and conceptualizing landscapes, and lends itself well to the functional mosaic approach and incorporation of regionality effects.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Landscape connectivity and the functional mosaic approach

A species’ sensitivity to habitat fragmentation is generally related to its ability to persist in local patches and to recolonize patches by moving across a landscape. The over-riding importance of dispersal has long been recognized in influencing large-scale patterns of distribution and geographic ranges in terrestrial plants (see Reed et al. 2000). Limited reproduction combined with low dispersal can result in a species being absent from a large proportion of seemingly suitable habitat. Such recruitment limitation can occur at multiple scales; from that of the microhabitat, to that of successional stages across a landscape to geographical regions across a species range (Levin & Clay 1984; Primack & Miao 1992; Pulliam 2000). Connectivity measures the degree to which the landscape facilitates or impedes movement. Metapopulation ecologists measure connectivity mostly at the patch scale, while landscape ecologists measure connectivity as a species- specific attribute of the landscape, and both camps use these measures in different ways. Yet as we have emphasized (Murphy & Lovett-Doust 2004b), the underlying process is the same: movement of individuals (as ramets, seeds, or pollen) across a landscape (Tischendorf & Fahrig 2001).

Concepts of permeability, percolation, friction and resistance have been used in various contexts in an attempt to quantify connectivity of the landscape. An understanding of landscape factors affecting these types of parameters has been informed generally from grid lattice models in which individual cells are either habitat or non-habitat (e.g., Urban & Kiett 2001; Sondgerath & Schroder 2002). We suggest a functional mosaic approach is most suited to understanding regional population dynamics, where plants exist in situations where individuals are not clustered in their distribution and where suitable habitat patches are not easily delineated, but rather where gradients of habitat suitability more appropriately characterize regions. The functional mosaic approach could equally well be applied to interpreting responses of species to range edges while incorporating indices of regionality. The grid-based functional mosaic model includes no preconceived expectations about how a species should respond in terms of how fragmented the landscape is or how far the cell is from the edge of range; each cell within the grid can be

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. described in terms of the response function (e.g., abundance or performance) and its structural and functional context in the landscape.

Treatment of the landscape as a mosaic, utilizing relevant scale- and regional-specific gradients of abiotic, biotic, and historical and human impact based parameters of importance to the particular species, is required to portray the fate of plant populations.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. References

Austin, M.P. (1985) Continuum concept, ordination methods and niche theory.Annual Review o f Ecology and Systematics, 16, 39-61.

Austin, M.P. (1999) On silent clash of paradigms: some inconsistencies in community ecology.Oikos, 86, 170-178.

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Ferreira, L.V. & Laurance, W.F. (1997) Effects of forest fragmentation on mortality and damage of selected trees in central Amazonia. Conservation Biology, 11, 797-801.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 226 -0.51 -0.353 -0.358 -0.297 1 M I Kasai ^ S d l l mm P i

0.156 0.048

-0.218 -0.343 -0.431 -0.474 -0.122 -0.189 -0.405

isi wmt mmi

IS® Pi 0.018 0.432 -0.221 mm s i r a i SSI SMI -0.23 0.053 -0.025 -0.448 IgMBB 0.438 -0.059 -0.427 -0.258 -0.088 -0.493 -0.037 -0.371 -0.403 0.173 •mmt -0.18 -0.465 0.037 0.418 -0.08 0.266 -0.367 0.231 0.022 0.108 -0.382 -0.284 0.002 0.296 -0.213 -0.373 0.464 -0.021 -0.365 -0.422 -0.499 -0.032 -0.254 LPD LPD 1 3 7 11 14 500m (?)J 32 -0.493 -0.216 -0.283 SI! mm g s mm PBSSBS a-;S -0.41 0.333 0.228 -0.314 -0.022 -0.192 -0.193 -0.019 0.369 0.078 -0.203 -0.253-0.031 0.195 -0.501 0.358 -0.495 0.091 -0.248 -0.013 -0.072 m m §886(SJ)1 MS m m 0.4 -0.043 -0.139 -0.105 -0.009 0.218 0.076 -0.4 -0.404 -0.37 0.159 0.413 0.036 -0.234 -0.367 -0.399 0.379 -0.133 mmt SIS i i a i i HU 0.15 0.339 0.382 0.437 0.261 0.473 0.143 0.19 -0.214 -0.214 -0.135 -0.231 -0.169 -0.229 -0.125 -0.146 -0.179 -0.238 -0.388 -0.416 0.457 KS3S& 0.293 -0.24 0.048 0.352 -0.263 -0.157 -0.054 -0.212 -0.178 -0.144 -0.211 -0.174 -0.093 -0.229 -0.205 s i s 1 0.2 -0.167 0.08 0.304 -0.199 0.108 0.397 0.307 -0.178 0.296 -0.297 0.196 -0.236 -0.444 0.26 -0.063 -0.038 -0.263 -0.232 -0.403 100m LPD LPD 1 3 7 11 14 ggzogiii class 1 0.229 %class 3 % class 6 % class 6 % class 1 % class 6 % class 3 0.267 % class 1% class 3 -0.119 % class 6 % % % class 1% class -0.345 3 % class 13 0.005 % class 10 0.129 % class 13 % class 10 % class 10 0.037 % class 10 % class 13 -0.431 0.049 -0.178 -0.306 % class 13 LPD Parameter LPD 100 500 LPD 0.053 0.015 0.499 1000 LPD 5000 Appendix 4.1 - Correlations between landscape fragmentation indices at four scales. LPD = Total landscape patch density Scale APPENDICES

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 227 14 -0.202 7 11 -0.025 P P i i 0.325 -0.472 -0.233 0.219 5000 0.022 0.225 0.352 -0.362 -0.403 -0.229 -0.393 LPD 1 3 14 !?555

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27.3 Max 27.5 21.3 21.3 24.3 Spring

Temp (°C) 228

12.3 12.4 25.1 14.8 14.5 26.8 12.5 10.9 15.4 14.114.1 27.4 27.4 Min (°C) Temp Spring

16.8 21.1 20.8 21.5 20.8 Mean Spring Temp (°C)

13.1 13.7 17.9 13.1 21.9 16.8 18.8 19.3 18.8 20.7 19.2 19.2 Max Mean Temp (°C)

6.5 19.1 5.6 7.5 6.1 Mean Min Temp (°C) 9.5 5.7 13.2 17.9 9.1 5.0 9.6 3.5 15.7 17.6 10.910.9 4.9 4.9 16.8 18.8 12.4 25.1 12.8 12.7 6.3 12.7 6.3 10.0 (°C) Mean Temp (mm) 804.6 Mean 894.4 993.9 993.9 927.9 13.4 1127.7 Precip.

5 820.1 5 929.7 2 2 14 14 837.9 15 1127.7 25 25 40 873.9 12.4 20 25 21 (km) Approx. distance from site Manhattan, KS Carbondale, IL Carbondale, IL Deer Creek Lake, OH* Circleville OH Lawrence, KS Emporia, KS Pelee Island, ON Delaware, OH Circleville OH Cedar Lake, IL IL OH Tuttle Creek, KS Perry Lake, KS Melvem Lake, KS Murphysboro State Park, Pelee Island, ON Point Pelee, ON Kingsville ON East Harbour State Park, Put-in-Bay, OH Deer Creek State Park, OH Deer Creek Lake, OH* Deer Creek Marina, OH 10 11 1 Site Name, State Weather Station 7 8 9 3 5 6 2 4 Delaware State Park, OH data derived from The Global Historical Climatology Network(GHCN1 1889-1987); all US station data derived from the Appendix 5.1 -Location ofnearest weather station to each site and historical climaticvariables National Climatic Data Centre (NCDC TD 9641 Clim 81 1961-1990 Normals) -Kingsville station data from Environment Canada Weather Office (1971-2000 Canadian Normals Data), Pelee Island station

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Max 30.5 Spring Temp (°C) 229 18.5 14.1 27.4 Min 14.215.4 27.5 28.1 (°C) Temp Spring

20.8 20.9 21.8 24.124.5 18.2 30.1 Mean Spring Temp (°C)

19.2 Max 23.9 25.1 Mean Temp (°C)

11.6 Mean Min Temp (°C) 19.0 12.9 12.7 6.3 14.2 7.9 20.5 17.7 (°C) Mean Temp Mean (mm) 1369.0 1431.0 1114.3 1345.1 13.7 7.1 20.3 1345.1 13.7 7.1 20.3 20.9 14.2 27.5 1348.7 Precip.

2328 1542.0 19.0 12.9 25.1 24.5 18.5 30.5 20 20 23 (km) distance Approx. from site Weather Station Carbondale, IL Dover, TN 5 Martin, TN Natchez, MS Giant City State Park, IL Makanda, IL* 5 Rushing Creek, KY Dover, TN TN Park, TN Tensas River, LA Tallulah, LA 16 Natchez State Park, MS Natchez, MS 15 12 13 14 Land Between the Lakes, 15 Big Cypress Tree State 16 17 18 Bayou Cocodrie, LA Vidalia, LA* Site Name, State * Rainfall only

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230 1.118 1.136 1.359 0 .8 8 8 0 .9 7 9 0.9 2 0 K -S Z 2 0 .9 5 8 0.9 8 4 2.127*** 0.1170 0.608 0 .2 6 2 0 0.2880 0.751 0 .3 2 5 0 -0 .3 7 1 0 -0 .2 1 4 0 -0.1660 0.821 0 .0 8 2 0 -0.0220 0.680 -0.3570 0.810 DFA 0 .5 2 2 0 0 .5 5 2 0 2 .4 1 1 0 Kurtosis Skew 0.01710.0174 0.4380 3.0690 0.0214 0.1630 0.0176 1.0480 0.0770 0 .02 97 0 .02 85 0.0181 0.9910 0.01700.0180 -0.07700.0199 1.1870 -0.0500 1.0290 0.400 0.0171 2.0390 -0.3980 0.0155 1.3830 0.0367 11.9650 -1.6410 0.0181 6.6180 -0.9830 0.0327 -0.2670 0.1980 0.513 0.0113 0.7110 -0.2170 0.362 -0 .0 1 2 5 -0 .0 1 7 0 -0 .0 19 7 -0 .0 1 7 2 1.0 4 6 1.326 0.0283 K-SZ2 Mean1 SE 1.411* -0.0271 0.0226 6.9160 1.0880 1.622* 0 .7 7 4 0.578 -0.0303 0 .4 6 4 0 0.6890 1.1250.5300 0.920 0.0261 -0.0031 0.0126 0 .1 2 7 0 0.9770 2.210*** 0.0145 2.2330 1.780** 0.0104 -0 .0 2 0 0 -0.1590 1.604* -0.0518 -0.3730 1.433* 0.0188 -1.2730 0.827 -0.0243 LFA 1 .41 70 1.7730 -0.4710 0.743 1.9930 0.0050 0.765 0.0151 Kurtosis Skew 3 .0 8 1 0 2 .4 6 9 0 3.2 6 8 0 5.2300 -0.8580 0.944 8.06406 .7 3 6 0 -1.4340 4.0720 -0.42002 7 .4 6 6 0 0.47605.2560 -0.2060 0.4560 0.647 1.175 -0.0083 0.0025 20.6040 2.3280 1.084 -0.0148 0.0073 2.0410 0 .0 1 5 9 0 .0 1 4 3 0 .0 1 0 8 SE 0 .0 2 0 3 0 .0 1 7 6 0.0027 0.0091 0 .0 0 7 7 0.0106 0.0172 -0 .0 0 9 7 -0 .0 3 0 7 -0.0084 0.0122-0.0387*** 4.5630 -0.0154 0.0162 5.2510 -0.0229 0.0192 5.0100 -0.0615** -0.0231* 0.0106 -0.0152-0.0097 0.0090 0.0164 185 -0.0171 183 -0.0166 0.0119 N M e a n 1 1617 166 174 18 136 12 187 14 15 187 11 190 13 198 1 2 0 0 10 95 5 191 S ite 3 100 -0.02568 0.0217 9 189 67 195 192 0.0180 0.0216 2 199 4 95 -0.0137 0.0130 1 - 1 The significance ofthe result for at-test of the hypothesis Ho: p(R-L) = 0 is shown (*** p < 0.001, ** p < 0.01, * p < 0.05) 2 - *** p < 0.001, ** p < 0.01, * p < 0.05 FA (DFA) at each site. Appendix 5.2 -Mean, standard error, kurtosis and skew statistics forsigned leaflet length FA (LFA) and internodal distance

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Appendix 6.1 - Species considered to be pioneer and/or shade intolerant bottomland species. Species Family Order

Betula papyrifera Betulaceae Fagales Fraxinus nigra Oleaceae Scrophulariales Fraxinus pennsylvanica Oleaceae Scrophulariales Gleditsia triacanthos Fabaceae Fabales Juglans nigra Juglandaceae Fagales Juniperus virginiana Cupressaceae Pinales Liquidambar styraciflua Hamamelidaceae Hamamelidales Maclura pomifera Moraceae Urticales Nyssa aquatica Nyssaceae Comales Nyssa sylvatica var. biflora Nyssaceae Comales Pinus echinata Pinaceae Pinales Pinus elliottii Pinaceae Pinales Pinus palustris Pinaceae Pinales Pinus resinosa Pinaceae Pinales Pinus strobus Pinaceae Pinales Pinus taeda Pinaceae Pinales Pinus virginiana Pinaceae Pinales Platanus occidentalis Platanaceae Hamamelidales Populus deltoides Salicaceae Salicales Populus grandidentata Salicaceae Salicales Populus tremuloides Salicaceae Salicales Prunus serotina Rosaceae Rosales Quercus marilandica Fagaceae Fagales Robinia pseudoacacia Fabaceae Fabales Salix amygdaloides Salicaceae Salicales Salix nigra Salicaceae Salicales Sassafras albidum Lauraceae Laurales

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Appendix 7.1 - Landcover classifications used in analysis Class Number Description 1 Agricultural land, cultivated land 2 Urban 3 Water 4 Undefined, clouds, cloud shadow 5 Barren, mined bare ground. Beaches, strip mine, quarries, gravel pits, non vegetated 7 Upland forest deciduous 8 Upland forest evergreen 9 Upland forest mixed 10 Dense pine, planted pine 11 Pasture, grassland, herbaceous 12 Non-forested wetland, marsh, non-forested swamp 13 Coniferous forest 14 Bottomland forest deciduous 15 Bottomland forest evergreen 16 Bottomland forest mixed 17 Upland scrub, scrub rangeland 18 Prairie 20 Revegetated deciduous forest, mined deciduous 21 Floodplain forest, frequently-flooded wetland forest

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Appendix 7.2 - Soil attribute data extracted from STATSGO soil survey maps Attribute Calculation Values/Units Annual For each soil unit, flood frequency was 1 = None Flood weighted by component then weighted 2 = Occasional Frequency component values were summed for the unit. 3 = Rare 4 = Frequent 5 = Water/Permanent Drainage For each soil, unit drainage class was weighted 0 = water Class by component then weighted component 1 = very poorly - poorly values were summed for the unit. 2 = somewhat poorly 3 = moderately well 4 = well 5 = somewhat excessive 6 = Excessive Available Total inches of available water in each soil Inches Water layer weighted by component and summed. Capacity Thus: Wtavg = (Laydeph - Laydepl) * (Awcl + Awch)/2 Where Wtavg = total inches of available water in each soil layer (horizon) Laydeph = ending depth of the soil layer Laydepl - beginning depth of the soil layer Awcl = low value for the range in the available water capacity for each soil layer Awch = high value for the range in the available water capacity for each soil layer

pH Averaged by layer, weighted by component pH and summed for map unit (see example above)

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Permeability Averaged by layer, weighted by component Inches/hour and summed for map unit (see example above) Texture Averaged across layers, weighted by N04 component then averaged for map unit. Percent passing sieve NO 10 Number 4 (course), NO40 Number 10, Number 40 NO200 and Number 200 (fine) Slope For each soil unit, slope was averaged and % weighted by component then weighted component values were summed for the unit.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. COPYRIGHT RELEASES

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. VITA AUCTORIS

NAME: Helen T. Murphy

PLACE OF BIRTH: Adelaide, South Australia

YEAR OF BIRTH: 1971

EDUCATION: Thomas More College, Adelaide, South Australia, Australia 1983-87

James Cook University, Townsville, Queensland, Australia 1991-1995 B. App. Sc (Hons)

Australian National University, Canberra, Australian Capital Territory, Australia 1998-2000 M. Env Law

University of Windsor, Windsor, Ontario, Canada 2001-2005 Ph.D (Biological Sciences)

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