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Abstract Partitioning Β-Diversity in Species-Area ABSTRACT PARTITIONING β-DIVERSITY IN SPECIES-AREA RELATIONSHIPS: IMPLICATIONS FOR BIODIVERSITY AND CONSERVATION by Jonathan Lee The species-area relationship (SAR), a vital tool in community ecology, attempts to quantify the biodiversity of an area by identifying the species richness from sample patches. Diversity within a patch is known as α-diversity while diversity among patches is known as β-diversity. Some ecologists argue that differences in area explain all β- diversity in independent sampling while others argue β-diversity partially results from other factors, such as habitat heterogeneity or stochastic factors. In this meta-analysis of SAR data, β-diversity was partitioned into area-dependent and area-independent components; it was determined factors besides area explain a large portion of β-diversity in independent SAR samples. It was surprising that neither the sampling effort nor study scale had a significant effect on the diversity components. PARTITIONING β-DIVERSITY IN SPECIES-AREA RELATIONSHIPS: IMPLICATIONS FOR BIODIVERSITY AND CONSERVATION A Practicum Report Submitted to the Faculty of Miami University in partial fulfillment of the requirements for the degree of Master of Environmental Science Institute of Environmental Sciences by Jonathan Eric Lee Miami University Oxford, Ohio 2010 Advisor_____________________________ Dr. Thomas Crist Reader______________________________ Dr. Doug Meikle Reader______________________________ Dr. Jing Zhang TABLE OF CONTENTS List of Tables iii List of Figures iv Introduction 1 Objectives 2 Methods 2 Results 4 Discussion 6 Conclusions 9 Tables and Figures 10 References 33 Appendix 1 35 Appendix 2 40 Appendix 3 46 ii LIST OF TABLES Table 1. Results of ANOVA and MANOVA of 14 additive components of diversity over four categories. Table 2. Results of ANOVA and MANOVA of 19 multiplicative components of diversity over four categories. iii LIST OF FIGURES Figure 1. Alpha and beta diveristy as 10 gamma diversity increases (log-log scale). Figure 2. Beta diversity partitioned into beta(area) and 11 beta(replace) as gamma diversity increases (log-log scale). Figure 3. Proportions of alpha, beta(area) and beta(replace) 12 to gamma plotted against gamma (log-log scale). Figure 4. Beta’(area) and Beta’(replace) (multiplicative 13 components) as gamma diversity increases (log-log scale). Figure 5. Alpha, beta(area) and beta(replace) as proportions 15 of gamma diversity for oceanic islands and habitat fragments. Figure 6. Alpha, beta(area) and beta(replace) as proportions 16 of gamma diversity, individually for the four most abundant taxa in the study. Figure 7. Alpha, beta(area) and beta(replace) as proportions 17 of gamma diversity, individually for studies in temperate and tropical latitudes. Figure 8. Alpha, beta(area) and beta(replace) as proportions 18 of total diversity, individually for flying and non-flying organisms sampled. Figure 9. Alpha, beta(area) and beta(replace) as the mean 20 patch size increases (log-log scale). Figure10. Alpha, beta(area) and beta(replace) as the range 21 of patch sizes increases (log-log scale). Figure 11. Alpha diversity, individually for studies on oceanic 22 islands and in habitat fragments, as the mean size of sample patches from the stuides increases (log-log scale). Figure 12. Alpha diversity, individually for studies on oceanic 23 islands and in habitat fragments, as the range of patch sizes increases (log-log scale). Figure 13. Beta(area) as the range of plot sizes increases, and 24 partitioned into the lower and upper halves of the range of iv plot sizes (log-log scale). Figure 14. Alpha, beta(area) and beta(replace) as the number 25 of patches in the study increases (log-log scale). Figure 15. Alpha diversity individually for the four most 26 abundant taxa of a) birds, b) insects, c) mammals and d) plants as the number of patches sampled increases (log-log scales). Figure 16. Alpha diversity, individually for studies conducted 28 on oceanic islands and habitat fragments, as the number of patches increases (log-log scale). Figure 17. Beta(replace), individually for studies conducted on 29 oceanic islands and habitat fragments, as the number of sample patches increases (log-log scale). Figure 18. Beta’(area) and beta’(replace) as the range of patch 30 sizes increases (log-log scale). Figure 19: Beta’(area) and beta’(replace) as the number of 31 study patches increases (log-log scale). Figure 20: Beta’(area) and Beta’(replace) for flying vs. 32 non-flying organisms. v INTRODUCTION Ecologists, conservation biologists, and policy-makers are increasingly concerned about habitat fragmentation, environmental change and loss of biodiversity. A fundamental principle used by ecologists is the species-area relationship (SAR), a central metric in studies of community ecology (Drakare, Lennon & Hillebrand, 2006). The relationship is generally a nonlinear increase in species richness as the size of the regional habitat, or the size of the habitat sampled, increases. SAR’s are often used to determine or predict the biodiversity of habitat fragments that differ in area, which is widely considered to be one of the most important determinants of species richness in habitat remnants. Since most natural habitats are small and isolated, the SARs sampled from a subset of habitat remnants or species distribution maps are often used to predict how future changes in land use will influence regional biodiversity (Nelson et al. 2009, Giam et al. in press). This approach therefore provides a predictive framework for understanding future changes in biodiversity. A limitation of this approach is that it focuses on the number of species found within a habitat patch but ignores the changes in species composition among patches. Ecologists have long recognized that turnover in species composition among habitats is important to understanding regional patterns of biodiversity (reviewed by Veech et al. 2002). Total diversity (γ) found in a set of samples from different habitats may be partitioned into α (within samples) and β (among samples) diversity. There are several models used to describe these separate components of diversity, including multiplicative (γ=αβ) and additive (γ=α+β) expressions (Lande, 1996; Veech et al. 2002). Historically, multiplicative partitions of species diversity have been linked to species-area relationships because the power function (S=kAz, where S=number of species and A=habitat area) is most commonly used to describe species-area relationships and log- transformation of the power function (log S=log k + z log A) is similar to the log- transformed multiplicative relationship (log γ=log α+log β). Rosenzwieg (1995) even provided mathematical expressions that equated these two relationships. This assumes, however, that all the β-diversity among habitats is a result of differences only in area, which is generally not the case for isolated habitat patches that differ in species composition due to other factors such as habitat heterogeneity and dispersal limitation (Crist and Veech 2006). On the other hand, in the case of nested sampling areas, this would be an accurate assumption; larger sample areas necessarily contain all the species diversity of the smaller samples, so area would be the only factor responsible for a difference in species richness among the samples. Even in isolated habitats, some species assemblages in smaller habitats tend to form nested subsets of those found in larger habitats, a pattern that is documented in some birds and mammals (Crist & Veech, 2006). In the more general case of non-nested patterns of species assemblages, however, there are other factors that could influence β-diversity besides area, such as among-patch heterogeneity or stochastic effects (Crist & Veech, 2006). 1 OBJECTIVES The research aim for this project is to determine how much of the β-diversity in species- area relationships is explained by differences in habitat area and how much is unexplained and due to other factors. I conducted a meta-analysis of SAR datasets from the available literature by partitioning the total amounts of β diversity into area- dependent and area-independent components. If significant parts of β diversity are unexplained by area, then estimation of the β diversity among habitats as gauged from species-area relationships may be greatly underestimated in the recent and historical literature. I quantified the partitions of β diversity in species-area relationships using both additive and multiplicative components. I also conducted statistical analyses to determine if α and β components of diversity in species-area relationships differed among taxa, between habitat and oceanic islands, between tropical and temperate environments, and across study scales and extent (i.e., mean patch size, range of patch sizes and number of sample patches). Variation in both the numbers and kinds of species present in isolated habitat remnants is an important consideration for biodiversity conservation at the regional scale because if turnover of species among habitats is considerable, then large numbers of habitats must be set aside to capture the regional biodiversity. If the effect of area underestimates the actual β diversity, we cannot expect to make accurate estimations of the amount of turnover in species composition among habitats. The shifts in the numbers and kinds of species in natural habitat remnants are crucial to sound conservation and management practices. METHODS I, and others working on this project before me, searched the primary
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