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 literature and collected datasets of SAR’s. Most of these datasets were collected by using journal and literature searches; this collection had been obtained before I began work with the project. I identified 11 additional usable datasets in an online database of sources used by Drakare, Lennon & Hillebrand (2006), who identified 794 datasets of SAR’s for their study. Most of their SAR datasets were unusable for this study because they didn’t fit the parameters SAR’s for this study necessitated. Specifically, I was unable to use datasets that: (i) originated from nested sampling areas, since all β diversity in nested sample areas is completely explained by changes in area; (ii) were obtained from stream systems, since there is no clear way to establish habitat areas in these datasets; (iii) failed to provide the total number of species found in the study (γ); and, (iv) datasets that encompassed very large areas, such as entire countries or continents. Additionally, some datasets were discarded because they contained a small number of sample patches. I avoided all datasets with fewer than 5 patches; most datasets had 10 or more patches. In all, 180 datasets were accumulated for this study.

2

For each dataset, I recorded the type of habitat (forest, grassland, deserts, lakes), the taxon (plants, birds, fish, terrestrial insects, mammals, herpetofaunas, or aquatic invertebrates), flying vs. non-flying, latitude (tropical, temperate, or polar), and whether the study plots were oceanic islands or habitat fragments. I also noted more specific descriptions for each of the studies (e.g., mountaintop “islands”) but these study-specific annotations were not considered any further in the statistical analysis. The value of γ (the total species richness identified in each study) was also recorded.

The nls function in the R programming language was used for statistical analysis of species-area relationships for each of the 180 datasets. Initially, species-area relationships were fit to all datasets using 6 equations that have been used in the previous literature – the power, linear, exponential, logistic, negative exponential and monod models. These 6 equations differ in whether they are monotonically decreasing (power, exponential, negative exponential, monod) or not (linear, logistic), and whether they have an asymptote (logistic, exponential, negative exponential, monod) or not (power, linear). Both the AIC value (Akaike Information Criterion) and the BIC value (Bayesian Information Criterion) were obtained for each model to test for goodness-of-fit. In many cases, particularly with the logistic and negative exponential models, the models did not converge. Despite collecting this information, I did not conduct further study on comparisons of the different model fits. This information was collected for future analyses, but is not a central component of the current study, which is primarily concerned with the partitioning of β diversity. For this study, the results of the power function – the most widely used form of the S-A relationship (Drakare, Lennon & Hillebrand, 2006) was used in all further analysis.

An R script (Appendix 1) was then used to calculate α (the mean within-sample diversity across all the samples in a study) and β (β=γ−α) for each of the 180 datasets. β was further partitioned into β(area) (area-dependent) and β(replace) (area-independent) components in each dataset using the methods described in Crist & Veech (2006). According to this partitioning method, β(area) is calculated as the difference between the species richness of the largest habitat patch predicted by least-squares regression and the mean species richness (α) of all other habitat patches in the study. β(replace) is then calculated as the remaining β that is unexplained by area: β(replace)= β −β(area). Combining these terms, we have β(replace)=γ−α−β(area).

In 8 cases, the value for β(replace) was negative because the estimated values of β(area) plus α was actually higher than γ. This results in a negative value of β(replace) since it is calculated as β(replace)=γ−β(area). In these cases, it is clear that area explains practically all the β diversity, and that α+β(area) exceeded γ because of an error in estimating β(area). Instead of keeping the negative values of β(replace), these values were originally set to 0, then adjusted to 0.1 to better allow for log-log scaling in scatter plots.

3 Additionally, the R script was used to calculate summary statistics of the patch sizes, including the minimum, maximum, range, sum and mean of each dataset.

When analyses were completed for each individual dataset, I began a meta-analysis across all the datasets, whereby each dataset, with its estimated diversity components, became a single observation. I plotted α and β, as well as partitions of β diversity into β(area) and β(replace), against γ to examine whether richness components varied systematically with overall (γ) richness The proportions of α, β(area) and β(replace) as a fraction of γ were also plotted against γ to examine how these components varied when the effect of γ was removed. Regression lines were fit for each plot to look for relationships; the power function was used in fitting all regressions and the plots were placed on log-log transformed scales.

Beginning with an analysis of diversity using additive components, the lm and manova functions in R were used to run both ANOVA and MANOVA for the three components of diversity, including α, β(area) and β(replace), as a multivariate response. Since each study differed in γ diversity, I expressed these components as a proportion of γ prior to analysis. These proportions, which sum to 1.0, were then analyzed as a multivariate response to several study factors including the taxon, latitude, habitat type and whether the organism flies to determine if these factors explained a significant amount of the variation in the proportional components.

Plots were also created, and regressions fitted, of the diversity components plotted against the minimum, maximum, mean, range, and sum of the patch sizes to attempt to determine if the scale of the study had an effect on the values of the diversity components.

Analysis was also performed by treating the β components as multiplicative expressions of α, which are denoted as β’(area) and β’(replace). With the multiplicative analysis, the value of α and γ are the same as in additive partitions – only the values of β and its decomposition into β(area) and β(replace), which are expressed as the ratios of γ and therefore represent units of turnover instead of species richness, change (Veech et al. 2002). As with the analysis of additive components, MANOVA was conducted along with regressions to determine the effects of study factors and spatial scale on the multiplicative components. For this analysis, however, MANOVA was run directly on the values of β’(area) and β’(replace) instead of the proportions of total diversity; proportions of γ cannot be calculated from multiplicative components because α and β are expressed in different units.

RESULTS

Across all 180 SAR datasets, the mean raw value for β(area) was 20.0 while the mean raw value for β(replace) was much higher, 71.5. As proportions of γ, β(area) had a mean of 0.21 while β(replace) had a mean of 0.46. Expressed as multiplicative components, β’(area) had a mean value of 1.83 across all datasets; the mean value for β’(replace) was

4 2.29. In addition to the cumulative results, β(replace) was also higher than β(area) for almost all data divisions, regardless of taxa, latitude, or habitat type (habitat fragment vs. oceanic island).

As expected, regressions for the additive components of both α and total β diversity showed a clear positive relationship with γ, especially β (R2=0.962) (Figure 1). For α, R2=0.778. β(area) and β(replace) also showed a positive relationship with γ, although the fits of the regressions were not as tight (R2=0.625 for β(replace); R2=0.366 for β(area)) (Figure 2).

Regressions for both α and β(area) as proportions of γ showed slight negative relationships with γ, which is expected when looking at the components as proportions. However these relationships were weak (R2=0.118 for α; R2=0.086 for β(area)) (Figure 3). β(replace) as a proportion of γ had a slight positive relationship with γ but, again, an R2 value of only 0.082.

As multiplicative components, β’(area) and β’(replace) did not scale as tightly with γ as did the additive components (Figure 4). β’(area) showed no relationship with γ (R2=0.003) while β’(replace) showed a slight positive relationship (R2=0.202).

Results of the MANOVA for the additive components showed statistical significance in the difference of how total species richness was partitioned across habitat type, taxa, latitude and flying vs. non-flying (Table 1). For habitat type, Figure 5 shows the component most affected was β(area); the proportion of β(area) out of γ is roughly twice as much for oceanic islands (0.302) as it is for habitat fragments (0.161) (p<0.0001). β(area) is also the component most affected by both taxon and latitude. For taxon, the proportion is higher for birds (0.287) and much higher for mammals (0.404) than for plants (0.176) or insects (0.161) (Figure 6). For latitude, the proportion of β(area) is higher for habitats in tropical settings (0.342) than for studies performed in temperate settings (0.187) (Figure 7). While MANOVA did suggest statistical significance for whether the organism flies (p=0.02578), Figure 8 does not show an obvious difference for any of the diversity components.

MANOVA results for the diversity components as multiplicative expressions were also significant (p< 0.05) for all criteria (Table 2). Of particular note, the significance test for whether the organism flies was very confident (p<0.0001). This statistical significance is surprising given that β’(area) is almost identical for flying vs. non-flying organisms and β’(replace) shows a relatively small difference, slightly higher for non-flying organisms (Figure 20).

Mean patch size (Figure 9) and the range of patch sizes (Figure 10) in each study did not have a significant effect on α, β(area) or β(replace); in both cases, there was a slight positive relationship between β(area) and the mean and range of patch sizes, while the regressions for α and β(replace) were almost flat-lined across all values of mean and range of patch sizes (R2<0.1 for all regressions).

5

Habitat type specifically had a notable effect on α for both the mean and range of patch sizes. α had a slight positive relationship with mean patch size in studies from habitat fragments but a slight negative relationship with mean patch size in studies from oceanic islands (Figure 11). Similarly, α had a slight positive relationship with the range of plot sizes in studies from habitat fragments but a slight negative relationship in studies from oceanic islands (Figure 12). Still, in both cases, these relationships were very weak with considerable variability about the regression lines.

Of note, the range of patch sizes showed no effect on β(area). Even when β(area) values were plotted against a partitioning of the larger and smaller halves of range values, there was little difference in behavior; regression for β(area) values showed a very slightly stronger positive relationship in the lower half of range values than those in the higher half (Figure 13), but there is virtually no relationship (R2=0.022 for the lower half; R2=0.002 for the upper half).

Both β(area) and β(replace) had slight positive relationships with the number of patches in the study, while α had a very slight negative relationship (Figure 14). The R2 value of 0.106 for the regression for β(area) was one of the strongest fits of all the regressions exploring the effect of study scale and extent. The taxon sampled had a notable influence on how α was affected by the number of patches sampled. Studies involving plants showed α had a somewhat strong positive relationship with the number of patches sampled (Figure 15d), studies involving birds (Figure 15a) and terrestrial insects (Figure 15b) showed α with a moderately negative relationship, and studies involving mammals (Figure 15c) showed α to have a strong negative relationship. α was also affected by the habitat type when considering number of patches in the study; it had a slightly negative relationship with the number of study patches in habitat fragments but a slightly positive relationship with number of patches on oceanic islands (Figure 16). Again, however, there was considerable variation about the regressions lines. β(replace) was also affected by the habitat type in looking at the number of patches in each study – it showed no relationship at all in studies from habitat fragments but a moderately strong positive relationship in studies from oceanic islands (Figure 17; also see Figure 5).

The multiplicative components, β’(area) and β’(replace), behaved similarly to their additive counterparts over differences in study scale and sampling effort. For both the range of patch sizes (Figure 18) and the number of sample plots (Figure 19), there was a slight positive relationship for both components. However, again, the extreme variability about the regressions indicated little relationship (R2 values were less than 0.15 for all of the multiplicative regressions of study scale).

DISCUSSION

This meta-analysis demonstrates that most of the β diversity within the species-area relationship results from factors other than habitat area. This finding follows the

6 hypothesis of Crist and Veech (2006) who suggested that partitioning of β diversity due to area might be small relative to other factors. Since mathematical comparisons of models have been used to assume that practically all β diversity results from area, it is striking that other factors, much more than accounting for some of total β diversity, accounted for a majority. It is equally important to note this holds true across multiple categories of habitat types, latitudes and taxa and across multiple study scales and sampling efforts.

This study does indicate there are statistically significant differences in the ways the diversity components behave, and these differences are certainly important to consider in any conservation or policy-making effort. But despite some effects that differences in taxa, habitat type, etc., have, it remains consistent that β(replace) constitutes most of β diversity. In fact, the findings in this study suggest that as the species richness of an area increases, β(area) comprises a decreasing proportion of total richness (Figure 3).

Recent and historical literature has often attributed β diversity in the S-A relationship entirely to changes in area (e.g., Rosenzweig, 1995; Hubbell, 2001; Scheiner, 2003) and has thus overstated the importance of area in describing β diversity within the species- area relationship. Clearly, my meta-analysis shows that total β diversity cannot be equated to the slope of the S-A relationship since most of the β diversity among isolated habitats was due to factors other than habitat area.

Conservation efforts working against species extinction that over-estimate the importance of area in explaining diversity could erroneously assume a larger protected habitat will necessarily do more to protect regional biodiversity. While there is some validity in this assumption (there is still generally a relationship between species richness and habitat area), the findings of this study suggest that, due to factors like habitat heterogeneity, simply increasing the size of a protected area may not be the most effective means of protecting biodiversity. Larger-than-estimated local habitat heterogeneity could make increasing conglomeration of protected area less relevant since a larger protected area will not necessarily contain all the species found in a number of smaller area; perhaps protection of scattered smaller areas could do much more to capture the regional biodiversity in many cases. Further research might reveal a more accurate balance between the importance of habitat area and local heterogeneity that conservation efforts can benefit from. Future research could specifically look more closely at rate of total turnover at local scales.

It should be mentioned that, while the AIC and BIC values were calculated for each of 6 models used for the SAR, I applied the power model exclusively to all datasets, despite the numerous instances where the power model was not the best fit. A more extensive meta-analysis applying the best-fitting model to each SAR dataset could reveal different results. Still, due to the magnitude of the role of β(replace) in total diversity, it would be surprising if this difference would create results considerably different.

While β(replace) is larger than β(area) across most categories, the categories do have statistically significant effects on the diversity components(Table 1). Habitat type 7 appears to have the largest effect (Figures 11, 12 & 16). Oceanic islands and habitat fragments on the mainland behave very differently, in large part because of dispersal limitations on islands. It might be interesting to consider the distance from the mainland of each of the oceanic islands from this study; if dispersal limitation is indeed responsible for some of the differences identified on oceanic islands, it would be expected that habitat type would have a lesser effect on the diversity components the closer the islands were to the mainland.

β(area) plays a larger role for mammals and birds than it does for insects or plants, and β(replace) behaves reciprocally for these taxa (Figure 6), This is likely explained in the larger areas needed for mammals and birds due to greater resource needs and territoriality. β(area) also plays a larger role in tropical settings than in temperate ones, and β(replace) behaves reciprocally here as well. Perhaps the extreme biodiversity in the tropics results in large increases in species richness with only small increases in habitat area.

These categorical differences, combined with the understanding of the dominance of β(replace) in explaining diversity patterns, are important to consider in future ecological research and in conservation and policy-making efforts.

The current study does not lend support to the idea of the necessity that the diversity components be multiplicatively expressed, as advanced by some authors (e.g., Tuomisto, 2010). It has already been noted that additive partitioning is advantageous in expressing diversity components in comparable units (Crist and Veech, 2006). My findings show that the components expressed additively have a stronger relationship with γ (Figures 1 & 2) than do multiplicatively expressed components (Figure 4). Proponents of expressing the diversity components multiplicatively often argue that this reality makes the multiplicative components more independent of γ. This can be confusing language, however. Multiplicatively-expressed diversity components are only independent in the sense that, since β’ diversity is expressed as units of turnover, these numbers do not change as γ changes. It is important to note they are not statistically independent of γ at all.

It is notable that the close relationship of additive components with total species richness (Figures 1 & 2) is in spite of comprising studies of various taxa, habitat types and study extent. This partitioning of total diversity within the species-area relationship appears quite robust against such variation.

One of the most surprising findings from this study is the apparent irrelevance of spatial scale and sampling effort on the diversity components; this finding does not fit with the findings of other similar studies (e.g., Drakare, Lennon & Hillebrand, 2006; Crist & Veech, 2006). Findings do hint at some predictable relationships, such as the positive trend of regressions of both β components with the range and number of patches in the study. What is surprising, however, is the relative weakness of these relationships (Figures 10, 14).

8 When the weak relationship between the β components and the range of patch sizes was initially realized, I partitioned the range of sizes into lower and upper halves and fit a new regression to determine if this had an effect. Still, however, the relationship remained very weak (Figure 13).

Drakare, Lennon & Hillebrand (2006) involved a much higher number of datasets (794) than the current study (180). While 180 datasets would seem sufficient to provide accurate results, it is possible more datasets would be beneficial in considering the effects of study extent and scale.

CONCLUSIONS

The findings from this study indicate the assumption that area explains all β diversity has been greatly overstated. As β(replace) is a significant component of β diversity across multiple study scales, habitat types and organisms, it is clear there are other factors constituting a majority of β diversity. These factors must be considered in SAR’s in order to make more accurate land management and conservation decisions.

It is striking that the study scale and sampling effort had so little impact on the applicability of the SAR and the partitioning of β diversity. This apparent robustness suggests the overall relevance of the SAR. Understanding the importance of stochastic factors and habitat heterogeneity in β diversity will lead to more accurate estimates of species assemblages regardless of study scale, habitat or organism.

9

Figure 1. Alpha and beta diveristy as gamma diversity increases (log-log scale).

10

Figure 2. Beta diversity partitioned into beta(area) and beta(replace) as gamma diversity increases (log-log scale).

11

Figure 3. Proportions of alpha, beta(area) and beta(replace) to gamma plotted against gamma (log-log scale).

12

Figure 4. Beta’(area) and Beta’(replace) (multiplicative components) as gamma diversity increases (log-log scale).

13 Table 1. Results of MANOVA of additive components of diversity over four categories of studies, including habitat type (ocean islands vs habitat fragments), taxon (birds, mammals, insects, plants), fly (flying or non-flying), and latitude (tropics vs temperate).

FACTOR DF TEST PILLAI OR P-VALUE F-STATISTIC Habitat 3, 176 0.1723 <0.0001 Proportion α 1,178 12.61 0.0005 Proportion β(area) 1,178 25.07 <0.0001 Proportion β(replace) 1,178 2.691 0.1027 Taxon 9, 170 0.22609 0.0014 Proportion α 3, 176 1.441 0.2017 Proportion β(area) 3, 176 5.734 <0.0001 Proportion β(replace) 3, 176 3.4 0.0034 Fly 3, 176 0.07955 0.0258 Proportion α 1, 178 5.315 0.0057 Proportion β(area) 1, 178 0.9734 0.3798 Proportion β(replace) 1, 178 5.822 0.0036 Latitude 3, 176 0.1141 0.0020 Proportion α 1, 178 0.7096 0.4932 Proportion β(area) 1, 178 10.61 <0.0001 Proportion β(replace) 1, 178 4.448 0.0130

14

Figure 5. Alpha, beta(area) and beta(replace) as proportions of gamma diversity for oceanic islands and habitat fragments.

15

Figure 6. Alpha, beta(area) and beta(replace) as proportions of gamma diversity, individually for the four most abundant taxa in the study.

16

Figure 7. Alpha, beta(area) and beta(replace) as proportions of gamma diversity, individually for studies in temperate and tropical latitudes.

17

Figure 8. Alpha, beta(area) and beta(replace) as proportions of total diversity, individually for flying and non-flying organisms sampled.

18 Table 2. Results of MANOVA of multiplicative components of diversity over four categories.

FACTOR DF TEST PILLAI OR P-VALUE F-STATISTIC Habitat 2, 177 0.1869 <0.0001 β’(area) 1, 178 36.9 <0.0001 β’(replace) 1, 178 0.7967 0.3733 Taxon 6, 173 0.1358 0.0164 β’(area) 3, 176 2.524 0.0229 β’(replace) 3, 176 1.958 0.0742 Fly 2, 177 0.3292 <0.0001 β’(area) 1, 178 0.03383 0.9667 β’(replace) 1, 178 42.62 <0.0001 Latitude 2, 177 0.1151 0.0003 β’(area) 1, 178 9.917 <0.0001 β’(replace) 1, 178 2.31 0.1023

19

Figure 9. Alpha, beta(area) and beta(replace) as the mean patch size increases (log-log scale).

20

Figure10. Alpha, beta(area) and beta(replace) as the range of patch sizes increases (log- log scale).

21

Figure 11: Alpha diversity, individually for studies on oceanic islands and in habitat fragments, as the mean size of sample patches from the stuides increases (log-log scale).

22

Figure 12: Alpha diversity, individually for studies on oceanic islands and in habitat fragments, as the range of patch sizes increases (log-log scale).

23

Figure 13. Beta(area) as the range of plot sizes increases, and partitioned into the lower and upper halves of the range of plot sizes (log-log scale).

24

Figure 14. Alpha, beta(area) and beta(replace) as the number of patches in the study increases (log-log scale).

25 a)

b)

26 c)

d)

Figure 15: Alpha diversity individually for the four most abundant taxa of a) birds, b) insects, c) mammals and d) plants as the number of patches sampled increases (log-log scales).

27

Figure 16: Alpha diversity, individually for studies conducted on oceanic islands and habitat fragments, as the number of patches increases (log-log scale).

28

Figure 17: Beta(replace), individually for studies conducted on oceanic islands and habitat fragments, as the number of sample patches increases (log-log scale).

29

Figure 18: Beta’(area) and beta’(replace) as the range of patch sizes increases (log-log scale).

30

Figure 19: Beta’(area) and beta’(replace) as the number of study patches increases (log- log scale).

31

Figure 20: Beta’(area) and Beta’(replace) for flying vs. non-flying organisms.

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33 Tuomisto, H. 2010. A diversity of beta diversities: straightening up a concept gone awry. Part 1. Defining beta diversity as a function of alpha and gamma diversity. Ecography 33: 2-22.

Tuomisto, H. 2010. A diversity of beta diversities: straightening up a concept gone awry. Part 2. Quantifying beta diversity and related phenomena. Ecography 33: 23-45.

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Whittaker, R.H. 1960. Vegetation of the Siskiyou Mountains, Oregon and California. Ecological Monographs 30: 279-338.

34 APPENDIX 1 R script used to calculate individual components for each dataset.

# load nlme package

library(nlme)

# set working directory, and read data file

setwd("/Volumes/Untitled/Practicum/txtfiles/")

sp.area.dat<-read.table("Bean(a).txt",header=T)

# make variables in data frame visible in R and list variables in data set

attach(sp.area.dat)

str(sp.area.dat)

# need to change the value of gamma for each new data set

gamma<-totspp[1]

# power function using nonlinear least squares - do on all data sets, and use to calculate alpha, beta, beta-area

power<-function(area,spp,z,k) k*area^z

f1<-nls(spp~power(area,spp,z,k),data=sp.area.dat,start=c(k=10,z=0.1))

summary(f1)

print(aic.f1<-AIC(logLik(f1)))

print(bic.f1<-BIC(logLik(f1)))

f1.fit<-fitted(f1)

35

alpha<-mean(spp)

beta<-gamma-alpha

max.fit<-max(f1.fit)

beta.area<-mean(max.fit-spp)

beta.repl<-beta-beta.area

alpha

beta

beta.area

beta.repl

#plot power function

f1.fit.2<-sort(f1.fit)

area.2<-sort(area)

plot(area,spp)

lines(area.2,f1.fit.2)

# linear function using linear least squares - compare fit to power function using AIC and plot

f2<-lm(spp~area,data=sp.area.dat)

summary(f2)

print(aic.f2<-AIC(logLik(f2)))

print(bic.f2<-BIC(logLik(f2)))

f2.fit<-sort(fitted(f2))

36 lines(area.2,f2.fit,col=2)

# exponential function using linear least squares - compare fit to power function using AIC and plot

log.area<-log(area)

f3<-lm(spp~log.area)

summary(f3)

print(aic.f3<-AIC(logLik(f3)))

print(bic.f3<-BIC(logLik(f3)))

f3.fit<-sort(fitted(f3))

lines(area.2,f3.fit,col=3)

# logistic function using nonlinear least squares - compare fit to power function using AIC and plot

logistic<-function(area,spp,a,b,c) a/(1+exp(-b*area+c))

f4<-nls(spp~logistic(area,spp,a,b,c),data=sp.area.dat,start=c(a=10,b=1,c=0.1))

summary(f4)

print(aic.f4<-AIC(logLik(f4)))

print(bic.f4<-BIC(logLik(f4)))

f4.fit<-sort(fitted(f4))

lines(area.2,f4.fit,col=4)

# negative exponential

negexp<-function(area,spp,z,k) k*(1-exp(-area/z))

37 f5<-nls(spp~negexp(area,spp,z,k),data=sp.area.dat,start=c(k=10,z=0.1)) summary(f5) print(aic.f5<-AIC(logLik(f5))) print(bic.f5<-BIC(logLik(f5))) f5.fit<-sort(fitted(f5)) lines(area.2,f5.fit,col=5)

# monod monod<-function(area,spp,z,k) (k*area)/(z+area) f6<-nls(spp~monod(area,spp,z,k),data=sp.area.dat,start=c(k=10,z=0.1)) summary(f6) print(aic.f6<-AIC(logLik(f6))) print(bic.f6<-BIC(logLik(f6))) f6.fit<-sort(fitted(f6)) lines(area.2,f6.fit,col=6)

# Null model to get AIC fnull<-lm(spp~1,data=sp.area.dat) summary(fnull) print(aic.null<-AIC(logLik(fnull))) print(bic.null<-BIC(logLik(fnull)))

38 # Compare delta AICs of models power.delta<-aic.null-aic.f1 linear.delta<-aic.null-aic.f2 exponential.delta<-aic.null-aic.f3 logistic.delta<-aic.null-aic.f4 negexp.delta<-aic.null-aic.f5 monod.delta<-aic.null-aic.f6 power.delta linear.delta exponential.delta logistic.delta negexp.delta monod.delta

39 APPENDIX 2 R script used to run ANOVA and MANOVA as meta-analysis.

# set working directory, and read data file

setwd("/Volumes/Untitled/Practicum/")

sp.area.data<-read.csv("Data for MANOVA.csv",header = TRUE)

# make variables in data frame visible in R and list variables in data set

attach(sp.area.data)

str(sp.area.data)

#Combine the components of diversity to remove dependence when running MANOVA Y1<-cbind(prop_alpha, prop_beta_reg, prop_beta_rep) Model1<-lm(Y1~island)

#Run summary of Model1 to show information from univariate (ANOVA) F- tests, then run MANOVA summary(Model1) manova_Model1<-manova(Model1) print(manova_Model1) summary(manova_Model1)

#Run summary statistics for each category prop_alpha_island_mean<-tapply(prop_alpha, island, mean) prop_alpha_island_mean

prop_alpha_island_min<-tapply(prop_alpha, island, min) prop_alpha_island_min

prop_alpha_island_max<-tapply(prop_alpha, island, max) prop_alpha_island_max

prop_alpha_island_sum<-tapply(prop_alpha, island, sum) prop_alpha_island_sum

prop_alpha_island_sd<-tapply(prop_alpha, island, sd) prop_alpha_island_sd

40 prop_beta_reg_island_mean<-tapply(prop_beta_reg, island, mean) prop_beta_reg_island_mean prop_beta_reg_island_min<-tapply(prop_beta_reg, island, min) prop_beta_reg_island_min prop_beta_reg_island_max<-tapply(prop_beta_reg, island, max) prop_beta_reg_island_max prop_beta_reg_island_sum<-tapply(prop_beta_reg, island, sum) prop_beta_reg_island_sum prop_beta_reg_island_sd<-tapply(prop_beta_reg, island, sd) prop_beta_reg_island_sd prop_beta_rep_island_mean<-tapply(prop_beta_rep, island, mean) prop_beta_rep_island_mean prop_beta_rep_island_min<-tapply(prop_beta_rep, island, min) prop_beta_rep_island_min prop_beta_rep_island_max<-tapply(prop_beta_rep, island, max) prop_beta_rep_island_max prop_beta_rep_island_sum<-tapply(prop_beta_rep, island, sum) prop_beta_rep_island_sum prop_beta_rep_island_sd<-tapply(prop_beta_rep, island, sd) prop_beta_rep_island_sd

#Repeat for other variables Model2<-lm(Y1~taxon) summary(Model2) manova_Model2<-manova(Model2) print(manova_Model2) summary(manova_Model2) prop_alpha_taxon_mean<-tapply(prop_alpha, taxon, mean) prop_alpha_taxon_mean prop_alpha_taxon_min<-tapply(prop_alpha, taxon, min) prop_alpha_taxon_min prop_alpha_taxon_max<-tapply(prop_alpha, taxon, max) prop_alpha_taxon_max

41 prop_alpha_taxon_sum<-tapply(prop_alpha, taxon, sum) prop_alpha_taxon_sum prop_alpha_taxon_sd<-tapply(prop_alpha, taxon, sd) prop_alpha_taxon_sd prop_beta_reg_taxon_mean<-tapply(prop_beta_reg, taxon, mean) prop_beta_reg_taxon_mean prop_beta_reg_taxon_min<-tapply(prop_beta_reg, taxon, min) prop_beta_reg_taxon_min prop_beta_reg_taxon_max<-tapply(prop_beta_reg, taxon, max) prop_beta_reg_taxon_max prop_beta_reg_taxon_sum<-tapply(prop_beta_reg, taxon, sum) prop_beta_reg_taxon_sum prop_beta_reg_taxon_sd<-tapply(prop_beta_reg, taxon, sd) prop_beta_reg_taxon_sd prop_beta_rep_taxon_mean<-tapply(prop_beta_rep, taxon, mean) prop_beta_rep_taxon_mean prop_beta_rep_taxon_min<-tapply(prop_beta_rep, taxon, min) prop_beta_rep_taxon_min prop_beta_rep_taxon_max<-tapply(prop_beta_rep, taxon, max) prop_beta_rep_taxon_max prop_beta_rep_taxon_sum<-tapply(prop_beta_rep, taxon, sum) prop_beta_rep_taxon_sum prop_beta_rep_taxon_sd<-tapply(prop_beta_rep, taxon, sd) prop_beta_rep_taxon_sd

#Repeat for other variables Model3<-lm(Y1~fly) summary(Model3) manova_Model3<-manova(Model3) print(manova_Model3) summary(manova_Model3) prop_alpha_fly_mean<-tapply(prop_alpha, fly, mean) prop_alpha_fly_mean

42 prop_alpha_fly_min<-tapply(prop_alpha, fly, min) prop_alpha_fly_min prop_alpha_fly_max<-tapply(prop_alpha, fly, max) prop_alpha_fly_max prop_alpha_fly_sum<-tapply(prop_alpha, fly, sum) prop_alpha_fly_sum prop_alpha_fly_sd<-tapply(prop_alpha, fly, sd) prop_alpha_fly_sd prop_beta_reg_fly_mean<-tapply(prop_beta_reg, fly, mean) prop_beta_reg_fly_mean prop_beta_reg_fly_min<-tapply(prop_beta_reg, fly, min) prop_beta_reg_fly_min prop_beta_reg_fly_max<-tapply(prop_beta_reg, fly, max) prop_beta_reg_fly_max prop_beta_reg_fly_sum<-tapply(prop_beta_reg, fly, sum) prop_beta_reg_fly_sum prop_beta_reg_fly_sd<-tapply(prop_beta_reg, fly, sd) prop_beta_reg_fly_sd prop_beta_rep_fly_mean<-tapply(prop_beta_rep, fly, mean) prop_beta_rep_fly_mean prop_beta_rep_fly_min<-tapply(prop_beta_rep, fly, min) prop_beta_rep_fly_min prop_beta_rep_fly_max<-tapply(prop_beta_rep, fly, max) prop_beta_rep_fly_max prop_beta_rep_fly_sum<-tapply(prop_beta_rep, fly, sum) prop_beta_rep_fly_sum prop_beta_rep_fly_sd<-tapply(prop_beta_rep, fly, sd) prop_beta_rep_fly_sd

#Repeat for other variables Model4<-lm(Y1~latitude) summary(Model4)

43 manova_Model4<-manova(Model4) print(manova_Model4) summary(manova_Model4) prop_alpha_latitude_mean<-tapply(prop_alpha, latitude, mean) prop_alpha_latitude_mean prop_alpha_latitude_min<-tapply(prop_alpha, latitude, min) prop_alpha_latitude_min prop_alpha_latitude_max<-tapply(prop_alpha, latitude, max) prop_alpha_latitude_max prop_alpha_latitude_sum<-tapply(prop_alpha, latitude, sum) prop_alpha_latitude_sum prop_alpha_latitude_sd<-tapply(prop_alpha, latitude, sd) prop_alpha_latitude_sd prop_beta_reg_latitude_mean<-tapply(prop_beta_reg, latitude, mean) prop_beta_reg_latitude_mean prop_beta_reg_latitude_min<-tapply(prop_beta_reg, latitude, min) prop_beta_reg_latitude_min prop_beta_reg_latitude_max<-tapply(prop_beta_reg, latitude, max) prop_beta_reg_latitude_max prop_beta_reg_latitude_sum<-tapply(prop_beta_reg, latitude, sum) prop_beta_reg_latitude_sum prop_beta_reg_latitude_sd<-tapply(prop_beta_reg, latitude, sd) prop_beta_reg_latitude_sd prop_beta_rep_latitude_mean<-tapply(prop_beta_rep, latitude, mean) prop_beta_rep_latitude_mean prop_beta_rep_latitude_min<-tapply(prop_beta_rep, latitude, min) prop_beta_rep_latitude_min prop_beta_rep_latitude_max<-tapply(prop_beta_rep, latitude, max) prop_beta_rep_latitude_max prop_beta_rep_latitude_sum<-tapply(prop_beta_rep, latitude, sum) prop_beta_rep_latitude_sum

44 prop_beta_rep_latitude_sd<-tapply(prop_beta_rep, latitude, sd) prop_beta_rep_latitude_sd

45 APPENDIX 3 Sources of species-area relationship data.

Source Taxon Gamma Alpha Beta(reg) Beta(rep) # of Range of Mean Patches Patch Sizes Patch Size Amerson, A.B. (1975). Species richness on the nondisturbed northwestern Hawaiian islands. Ecology 56: 435- 444. Bird 29 10.83 12.43 5.73 18 6.76 2.27 Amerson, A.B. (1975). Species richness on the nondisturbed northwestern Hawaiian islands. Ecology 56: 435- 444. Plant 18 3.56 6.85 7.59 18 6.76 2.27 As, S. (1984). To fly or not to fly – colonization of Baltic islands by winged and wingless carabid beetles.” Journal of Biogeography 11(5): 413-426. Insect 45 13.80 10.43 20.77 10 159.5 36.07 Azeria, E.T. (2004). Terrestrial bird community patterns on the coralline islands of the Bird 38 6.23 6.66 25.11 26 2141 288.08

46 Dahlak Archipelago, Red Sea, Eritrea. Global Ecology and Biogeography 13: 177-187. Baldi, A. and T. Kisbenedek (1999). Orthopterans in small steppe patches: an investigation for the best-fit model of the species-area curve and evidences for their non-random distribution in the patches. Oecologica 20(2): 125-132. Insect 32 5.44 8.83 17.72 27 39.982 3.14 Barrett, K., D.A. Wait, and W.B. Anderson (2003). Small island biogeography in the Gulf of California: lizards, the subsidized island biogeography hypothesis, and the small island effect. Journal of Biogeography 30: 1575-1581. Herp. 45 3.55 7.94 33.51 60 122351 4972.31 Baz, A. and A. Garcia-Boyero (1995). The effects of forest fragmentation on Insect 81 36.92 2.49 41.59 13 2111.4 306.88

47 butterfly communities in central Spain. Journal of Biogeography 22(1): 129-140, Bean and T. Crist, unpublished. Insect 49 16.65 5.30 27.05 26 1.47 0.40 Bean and T. Crist, unpublished. Plant 230 52.62 8.14 169.25 26 1.47 0.40 Behle, W. (1978). Avian biogeography of the Great Basin and intermountain region. Great Basin Naturalist Memoirs 2: 55-80. Bird 80 42.71 11.61 25.68 14 92462.575 27953.37 Blake, J.G. (1991). Nested subsets and the distribution of birds on isolated woodlots. Conservation Biology 5(1): 58-66. Bird 46 24.00 18.49 3.51 12 598.2 79.66 Bolger, D.T., A.C. Alberts and M.E. Soule (1991). Occurrence patterns of bird species in habitat fragments: sampling, extinction and nested species subsets. American Naturalist 137(2): 155-166. Bird 5 3.56 2.09 0.00 9 49.875 7.06 Bolger, D.T., A.C. Alberts, R.M. Mammal 9 2.07 3.69 3.24 28 83.975 13.54

48 Sauvajot, P. Potenza, C. McCalvin, D. Tran, S. Mazzoni and M.E. Soule (1997). Response of rodents to habitat fragmentation in coastal southern California. Ecological Applications 7(2): 552-563. Boomsma, J., A. Mabelis, M. Verbeek, E. Los (1987). Insular biogeography and distribution ecology of ants on the Frisian Islands.” Journal of Biogeography 14(1): 21-37. Insect 25 11.28 10.32 3.40 18 16176 2937.44 Brose, U. (2003). Island biogeography of temporary wetland carabid beetle communities. Journal of Biogeography 30: 879-888. Insect 136 43.07 1.21 91.72 30 0.235 0.09 Brown, J.H. (1978). The theory of insular biogeography and the distribution of boreal birds and Bird 20 8.00 6.85 5.15 17 385649.23 102502.60

49 mammals. Great Basin Naturalist Memoirs 2: 209-227. Brown, J.H. (1978). The theory of insular biogeography and the distribution of boreal birds and mammals. Great Basin Naturalist Memoirs 2: 209-227. Mammal 34 9.22 8.53 16.25 23 394714.188 105975.60 Brown, J.H. (1971). Mammals on mountaintops: nonequilibrium insular biogeography. American Naturalist 105(945): 467-478. Mammal 13 5.71 4.83 2.47 17 301992.613 85591.49 Browne, R. (1981). Lakes as islands - biogeographic distribution, turnover rates, and species composition in the lakes of central New York." Journal of Biogeography 8(1): 75-83. Fish 113 26.40 13.76 72.84 10 20410 7233.00 Browne, R. (1981). Lakes as islands - biogeographic distribution, turnover rates, and species composition in the lakes of central New York." Journal of Invert. 78 7.50 16.84 53.66 36 206.6994 20.89

50 Biogeography 8(1): 75-83. Browne, R. (1981). Lakes as islands - biogeographic distribution, turnover rates, and species composition in the lakes of central New York." Journal of Biogeography 8(1): 75-83. Invert. 36 11.92 3.95 20.13 12 204.1 62.01 Buckley, R.C. (1985). Distinguising the effects of area and habitat type on island plant species richness by separating floristic elements and substrate types and controlling for island isolation. Journal of Biogeography 12: 527-535. Plant 100 12.98 32.15 54.87 61 0.4089 0.04 Case, T.J. and M.L. Cody, editors (1983). Island biogeography in the Sea of Cortez. University of California Press, Berkley. Mammal 27 2.88 6.17 17.95 34 120760 11174.71 Case, T.J. (1975). Species numbers, density compensation, and Herp. 15 5.25 3.82 5.93 24 142940 18343.33

51 colonizing ability of lizards on islands in the Gulf of California. Ecology 56(1): 3-18. Chown, S.L., N.J.M. Gremmen and K.J. Gaston (1998). Ecological biogeography of Southern Ocean islands: species- area relationships, human impacts, and conservation. American Naturalist 152(4): 562-575. Bird 15 1.84 1.32 11.84 25 719865 86521.96 Chown, S.L., N.J.M. Gremmen and K.J. Gaston (1998). Ecological biogeography of Southern Ocean islands: species- area relationships, human impacts, and conservation. American Naturalist 152(4): 562-575. Insect 775 51.00 30.63 693.37 25 719865 86521.96 Chown, S.L., N.J.M. Gremmen and K.J. Gaston (1998). Ecological biogeography of Southern Ocean islands: species- area relationships, Insect 172 9.28 5.12 157.60 25 719865 86521.96

52 human impacts, and conservation. American Naturalist 152(4): 562-575. Chown, S.L., N.J.M. Gremmen and K.J. Gaston (1998). Ecological biogeography of Southern Ocean islands: species- area relationships, human impacts, and conservation. American Naturalist 152(4): 562-575. Mammal 15 2.64 6.28 6.08 25 719865 86521.96 Chown, S.L., N.J.M. Gremmen and K.J. Gaston (1998). Ecological biogeography of Southern Ocean islands: species- area relationships, human impacts, and conservation. American Naturalist 152(4): 562-575. Plant 471 47.96 54.21 368.83 25 719865 86521.96 Chown, S.L., N.J.M. Gremmen and K.J. Gaston (1998). Ecological biogeography of Southern Ocean islands: species- area relationships, human impacts, and Plant 348 29.28 35.03 283.69 25 719865 86521.96

53 conservation. American Naturalist 152(4): 562-575. Chown, S.L., N.J.M. Gremmen and K.J. Gaston (1998). Ecological biogeography of Southern Ocean islands: species- area relationships, human impacts, and conservation. American Naturalist 152(4): 562-575. Bird 78 21.36 8.40 48.24 25 719865 86521.96 Chown, S.L., N.J.M. Gremmen and K.J. Gaston (1998). Ecological biogeography of Southern Ocean islands: species- area relationships, human impacts, and conservation. American Naturalist 152(4): 562-575. Bird 65 5.20 16.57 43.23 25 719865 86521.96 Coleman, B.D., M.A. Mares, M.R. Willig and Y. Hsieh (1982). Randomness, area, and species richness. Ecology 63(4): 1121-1133. Bird 38 6.77 33.42 0.00 30 69.35 4.22 Conroy, C.J., J.R. Demboski and J.A. Cook (1999). Bird 23 8.29 3.54 11.17 24 576740 136277.10

54 Mammalian biogeography of the Alexander Archipelago of Alaska: a north temperate nested fauna. Journal of Biogeography 26: 343-352. Convey, P., R.I. Lewis Smith, D.A. Hodgson and H.J. Peat (2000). The flora of the South Sandwich Islands, with particular reference to the influence of geothermal heating. Journal of Biogeography 27: 1279-1295. Plant 11 2.36 5.49 3.15 11 50350 7977.27 Convey, P., R.I. Lewis Smith, D.A. Hodgson and H.J. Peat (2000). The flora of the South Sandwich Islands, with particular reference to the influence of geothermal heating. Journal of Biogeography 27: 1279-1295. Plant 41 8.27 1.24 31.48 11 50350 7977.27 Convey, P., R.I. Lewis Smith, D.A. Plant 38 10.09 13.12 14.79 11 50350 7977.27

55 Hodgson and H.J. Peat (2000). The flora of the South Sandwich Islands, with particular reference to the influence of geothermal heating. Journal of Biogeography 27: 1279-1295. Crowe, T.M. (1979). Lots of weeds: Insular phytogeography of vacant urban lots. Journal of Biogeography 6(2): 169-181. Plant 128 32.46 27.00 68.54 126 0.726 0.12 Daily, G.C. and P.R. Ehrlich (1995). Preservation of biodiversity in small rainforest patches: rapid evaluations using butterfly trapping. Biodiversity and Conservation 4: 35- 55. Insect 38 12.25 9.19 16.56 8 224 39.75 Davidar, P., K. Yoganand and T. Ganesh (2001). Distribution of forest birds in the Andaman islands: importance of key Bird 47 25.02 28.50 0.00 45 134797 8372.11

56 habitats. Journal of Biogeography 28: 663-671. Davidar, P., K. Yoganand and T. Ganesh (2001). Distribution of forest birds in the Andaman islands: importance of key habitats. Journal of Biogeography 28: 663-671. Bird 47 31.71 18.97 0.00 14 22975 3136.07 Davidar, P., K. Yoganand and T. Ganesh (2001). Distribution of forest birds in the Andaman islands: importance of key habitats. Journal of Biogeography 28: 663-671. Bird 47 26.79 24.02 0.00 14 134797 15434.86 Davidar, P., K. Yoganand and T. Ganesh (2001). Distribution of forest birds in the Andaman islands: importance of key habitats. Journal of Biogeography 28: 663-671. Bird 43 18.06 26.16 0.00 17 112794 6867.77 Davies, N. and D.S. Smith (1998). Munroe revisited: a survey of west Insect 350 37.63 160.50 151.86 68 10865970 349586.60

57 Indian butterfly faunas and their species-area relationship. Global Ecology and Biogeography Letters 7(4): 285- 294. Deshaye, J. and P. Morisset (1988). Floristic richness, area, and habitat diversity in a hemiarctic archipelago. Journal of Biogeography 15(5/6): 747-757. Plant 271 78.79 153.97 38.24 34 92.135 12.38 Dueser, R.D. and W.C. Brown (1980). Ecological correlates of insular rodent diversity. Ecology 61(1): 50-56. Insect 5 2.11 2.49 0.40 9 2168 670.78 Dexter, R.W., W.F. Hahnert and J.A. Beatty (1988). Disribution of the terrestrial isopoda on islands of western Lake Erie. In The Biography of the Island Region of Western Lake Erie. Editors J.F. Downhower, 13-23. Ohio State University Press, Invert. 12 5.50 3.30 3.20 22 4042.97 299.48

58 Columbus. Dodson, S.I. and M.Silva-Briano (1996). Crustacean zooplankton species richness and associations in reservoirs and ponds of Aguascalientes State, Mexico. Hydrobiologia 325: 163-172. Mammal 53 7.68 5.86 39.46 19 1189.9988 88.53 Fattorini, S. (2002). Biogeography of the tenebrionid beetles (Coleoptera, Tenebrionidae) on the Aegean Islands (Greece). Journal of Biogeography 29: 49-67. Insect 166 16.13 46.80 103.08 32 825620 63476.56 Fernandez-Juricic, E. (2000). Bird community composition patterns in urban parks of Madrid: the role of age, size and isolation. Ecological Research 15: 373- 383. Bird 32 11.72 6.94 13.34 25 117.2 19.44 Fournier, E. and M. Loreau (2001). Respective roles of recent hedges and forest patch Insect 40 23.17 4.83 12.00 6 19.7 7.23

59 remnants in the maintenance of ground-beetle (Coleoptera: Carabidae) diversity in an agricultural landscape. Landscape Ecology 16: 17-32. Ghandi, K.J.K., J.R. Spence, D.W. Langor and L.E. Morgantini (2001). Fire residuals as habitat reserves for epigaeic beetles (Coleoptera: Carabidae and Staphylinidae). Biological Conservation 115: 379-393. Insect 19 8.25 0.73 10.02 8 10.74 2.49 Ghandi, K.J.K., J.R. Spence, D.W. Langor and L.E. Morgantini (2001). Fire residuals as habitat reserves for epigaeic beetles (Coleoptera: Carabidae and Staphylinidae). Biological Conservation 115: 379-393. Insect 14 6.25 0.39 7.36 8 2.99 0.50 Glaser, P. (1992). Raised bogs in Plant 26 16.44 0.48 9.07 9 24810 4677.78

60 eastern North America – regional controls for species richness and floristic assemblages. Journal of Ecology 80: 535-554. Glaser, P. (1992). Raised bogs in eastern North America – regional controls for species richness and floristic assemblages. Journal of Ecology 80: 535-554. Plant 55 29.55 0.94 24.51 11 140 79.09 Glaser, P. (1992). Raised bogs in eastern North America – regional controls for species richness and floristic assemblages. Journal of Ecology 80: 535-554. Plant 59 41.17 0.61 17.22 6 290 85.00 Glaser, P. (1992). Raised bogs in eastern North America – regional controls for species richness and floristic assemblages. Journal of Ecology 80: 535-554. Plant 40 19.11 3.70 17.19 9 20110 3578.89 Glaser, P. (1992). Raised bogs in eastern North Plant 42 21.75 1.11 19.14 8 17000 2473.75

61 America – regional controls for species richness and floristic assemblages. Journal of Ecology 80: 535-554. Glaser, P. (1992). Raised bogs in eastern North America – regional controls for species richness and floristic assemblages. Journal of Ecology 80: 535-554. Plant 27 20.44 0.91 5.65 9 9690 1150.00 Golden, D.M. and T.O. Crist (1999). Experimental effects of habitat fragmentation on old-field canopy insects: community, guild and species responses. Oecologia 118(3): 371-380. Insect 189 44.06 5.13 139.81 16 0.013 0.01 Gotelli, N.J. and A.M. Ellison (2002). Biogeography at a regional scale: determinants of ant species density in New England bogs and forests. Ecology 83(6): 1604-1609. Insect 24 4.91 0.74 18.35 22 86.4657 14.45 Gotelli, N.J. and L.G. Abele (1982). Bird 213 38.16 47.38 127.47 19 44132 4441.47

62 Statistical distributions of west Indian land bird families. Journal of Biogeography 9(5): 421-435. Griffiths, T. and D. Klingener (1988). On the distribution of greater Antillean bats. Biotropica 20(3): 240-251. Mammal 38 9.62 14.46 13.92 21 11145000 1020448.00 Harcourt, A.H. (1999). Biogeographic relationships of primates on south- east Asian islands. Global Ecology and Biogeography 8(1): 55-61. Mammal 29 2.90 9.00 17.10 31 75775000 5144387.00 Harper, K.T., D.C. Freeman, W.K. Ostler and L.G. Klikoff (1978). The flora of Great Basin mountain ranges; diversity, sources, and dispersal ecology. Great Basin Naturalist Memoirs 2: 81-103. Plant 2225 298.67 156.45 1769.89 15 133902.385 46896.05 Harris, M.P. (1973). The Galapagos Avifauna. Condor 75: 265-278. Bird 23 13.87 8.59 0.55 15 582229.3271 59586.99 Haila, Y., I. Hanski Bird 20 7.23 2.60 10.17 13 3.7 2.33

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64 (1993). Turnover of breeding birds in small forest fragments: the “sampling” colonization hypothesis corroborated. Ecology 74(3): 714- 725. Hoekstra, H.E., W.F. Fagan (1998). Body size, dispersal ability and compositional disharmony: the carnivore-dominated fauna of the Kuril Islands. Diversity and Distributions 4: 135-149. Mammal 18 5.88 4.38 7.74 8 664500 146600.00 Howe, R. (1979). Distribution and behavior of birds on small islands in northern Minnesota. Journal of Biogeography 6(4): 379-390. Bird 35 13.14 5.68 16.17 7 1.06 0.39 Johansson, L., A. Andersen and A.C. Nilssen (1994). Distribution of bark insects in “island” plantations of spruce (Picea abies (L.) Karst.) in subarctic Norway. Polar Insect 12 5.09 1.01 5.90 11 71.5 18.97

65 Biology 14: 107-116. Johnson, M.P. and D.S. Simberloff (1974). Environmental determinants of island species numbers in the British Isles.” Journal of Biogeography 1: 1149-1154. Plant 1666 361.17 324.24 980.59 41 2136.8 274.02 Juvik, J.O. and A.P. Austring (1979). The Hawaiian avifauna: biogeographic theory in evolutionary time. Journal of Biogeography 6(3): 205-224. Bird 36 10.38 12.00 13.63 8 1046323 204302.10 Koh, L.P., N.S. Sodhi, H.T.W. Tan and S.H. Kelvin (2002). Factors affecting the distribution of vascular plants, springtails, butterflies and birds on small tropical islands. Journal of Biogeography 29: 93-108. Insect 37 5.71 18.68 12.62 17 1025.7 79.39 Koh, L.P., N.S. Sodhi, H.T.W. Tan and S.H. Kelvin Insect 56 9.00 16.87 30.13 39 1146.16 66.03

66 (2002). Factors affecting the distribution of vascular plants, springtails, butterflies and birds on small tropical islands. Journal of Biogeography 29: 93-108. Koh, L.P., N.S. Sodhi, H.T.W. Tan and S.H. Kelvin (2002). Factors affecting the distribution of vascular plants, springtails, butterflies and birds on small tropical islands. Journal of Biogeography 29: 93-108. Insect 23 8.24 13.90 0.86 17 1025.7 79.39 Koh, L.P., N.S. Sodhi, H.T.W. Tan and S.H. Kelvin (2002). Factors affecting the distribution of vascular plants, springtails, butterflies and birds on small tropical islands. Journal of Biogeography 29: 93-108. Plant 587 34.53 98.91 453.56 1025.7 79.39 Kohn, D.D. and D.M. Plant 81 17.68 55.78 7.54 47 99.549 6.67

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68 Colonization success of carabid beetles on Baltic islands. Journal of Biogeography 27: 807-819. Kotze, D.J., J. Niemalä and M. Nieminen (2000). Colonization success of carabid beetles on Baltic islands. Journal of Biogeography 27: 807-819. Insect 34 16.43 4.69 12.88 7 56 19.30 Kratter, A. (1991). 1st nesting record for Williamson sapsucker (Sphyrapicus- thyroideus) in Baja- California, Mexico, and comments on the biogeography of the fauna of the Sierra San Pedro Martir.” Southwestern Naturalist 36(2): 247-250. Bird 65 19.80 25.81 19.39 20 1068.29 123.90 Krauss, J., I. Steffan-Dewenter and T. Tscharntke (2003). Local species immigration, extinction, and turnover of Insect 54 27.52 12.36 14.13 31 5.1081 0.87

69 butterflies in relation to habitat area and habitat isolation. Oecologica 137: 591-602 Krauss, J., I. Steffan-Dewenter and T. Tscharntke (2003). Local species immigration, extinction, and turnover of butterflies in relation to habitat area and habitat isolation. Oecologica 137: 591-602 Insect 20 6.50 7.20 6.30 32 5.1081 0.87 Krauss, J., I. Steffan-Dewenter and T. Tscharntke (2003). Local species immigration, extinction, and turnover of butterflies in relation to habitat area and habitat isolation. Oecologica 137: 591-602 Insect 34 20.69 5.66 7.65 32 5.1081 0.87 Krauss, J., A.M. Klein, I. Steffan- Dewenter and T. Tscharntke (2004). Effects of habitat area, isolation, and landscape diversity on plant species Plant 308 88.97 26.56 192.47 31 5.1081 0.90

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77 extinction of understory birds in the eastern Usambara Mountains, Tanzania. Conservation Biology 5(1): 67-78. Newmark, W.D. (1986). Species- area relationship and its determinants for mammals in western North American national parks. Biological Journal of the Linnean Society 28: 83-98. Plant 25 6.46 4.20 14.34 24 2065300 244579.20 Niemela, J., E. Ranta and Y. Haila (1985). Carabid beetles in lush forest patches on the Aland Islands, southwest Finland – an island mainland comparison. Journal of Biogeography 12(2): 109-120. Insect 25 15.40 2.61 6.99 5 0.007 0.01 Niemela, J., E. Ranta and Y. Haila (1985). Carabid beetles in lush forest patches on the Aland Islands, southwest Finland – Insect 25 9.20 1.04 14.76 5 20.9 19.44

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80 Volume 50: 289- 307. Oosterbroek, P. (1994). Biodiversity of the Mediterranean region. In Systematics and conservation evaluation. Editors P.I. Forey, C.J. Humphries and R.I. Vane-Wright. Systematics Association Special Volume 50: 289- 307. Insect 677 238.83 7.02 431.14 6 565000 550000.00 Oosterbroek, P. (1994). Biodiversity of the Mediterranean region. In Systematics and conservation evaluation. Editors P.I. Forey, C.J. Humphries and R.I. Vane-Wright. Systematics Association Special Volume 50: 289- 307. Insect 498 116.83 11.01 370.16 6 565000 550000.00 Oosterbroek, P. (1994). Biodiversity of the Mediterranean region. In Systematics and conservation evaluation. Editors Mammal 195 89.00 2.87 103.13 6 565000 550000.00

81 P.I. Forey, C.J. Humphries and R.I. Vane-Wright. Systematics Association Special Volume 50: 289- 307. Oosterbroek, P. (1994). Biodiversity of the Mediterranean region. In Systematics and conservation evaluation. Editors P.I. Forey, C.J. Humphries and R.I. Vane-Wright. Systematics Association Special Volume 50: 289- 307. Herp. 163 53.50 4.36 105.14 6 565000 550000.00 Oosterbroek, P. (1994). Biodiversity of the Mediterranean region. In Systematics and conservation evaluation. Editors P.I. Forey, C.J. Humphries and R.I. Vane-Wright. Systematics Association Special Volume 50: 289- 307. Bird 399 263.83 5.38 129.79 6 565000 550000.00 Peintinger, M., A. Bergamini and B. Insect 64 21.09 2.45 40.46 23 14.43 5.39

82 Schmid (2003). Species-area relationships and nestedness of four taxonomic groups in fragmented wetlands. Basic and Applied Ecology 4: 385-394. Peintinger, M., A. Bergamini and B. Schmid (2003). Species-area relationships and nestedness of four taxonomic groups in fragmented wetlands. Basic and Applied Ecology 4: 385-394. Insect 16 7.61 1.18 7.21 23 14.43 5.39 Peintinger, M., A. Bergamini and B. Schmid (2003). Species-area relationships and nestedness of four taxonomic groups in fragmented wetlands. Basic and Applied Ecology 4: 385-394. Plant 316 105.14 12.81 198.05 37 17.64 6.05 Peintinger, M., A. Bergamini and B. Schmid (2003). Species-area relationships and nestedness of four Plant 126 32.97 3.30 89.72 37 17.64 6.05

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87 468-477. Rydin, H. and S.O. Borgegard (1988). Plant species richness on islands over a century of primary succession: Lake Hjalmaren. Ecology 69(4): 916- 927. Plant 240 57.86 65.69 116.45 37 2.512 0.23 Schoereder, J.H., C. Galbiati, C.R. Ribas, T.G. Sobrinho, C.F. Sperber, O. DeSouza and C. Lopes-Andrade (2004). Should we use proportional sampling for species-area studies? Journal of Biogeography 31: 1219-1226. Insect 86 28.29 48.54 9.17 17 90.16 19.86 Sepkoski, J.J. and. M.A. Rex (1974). Distribution of freshwater mussels: coastal rivers as biogeographic islands. Systematic Zoology 23(2): 165- 188. Invert. 79 11.48 8.34 59.18 45 7135676.24 1706690.00 Sfenthourakis, S., S. Giokas and E. Tzanatos (2004). From sampling stations to Insect 59 17.19 15.72 26.10 15 47615 7269.33

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93 Siikamäki, J. Suhonen and K. Virolainen (1999). Species immigration, extinction and turnover of vascular plants in boreal lakes. Ecography 22: 240-245. Virola, T., V. Kaitala, M. Kuitunen, A. Lammi, P. Siikamäki, J. Suhonen and K. Virolainen (1999). Species immigration, extinction and turnover of vascular plants in boreal lakes. Ecography 22: 240-245. Plant 44 19.76 8.04 16.20 25 139.8 29.92 Vuilleumier, F. (1970). Insular biogeography in continental regions I. the northern Andes of South America. American Naturalist 104(938): 373-388. Bird 83 23.67 23.32 36.02 15 346200 84266.67 Weaver, M. and M. Kellman (1981). The effects of forest fragmentation on woodlot tree biotas in southern Ontario. Plant 33 12.50 1.21 19.29 10 7.29 3.60

94 Journal of Biogeography 8(3): 199-210. Weissman, D.B. and D.C. Rentz, 1976. Zoogeography of the grasshoppers and their relatives (Orthoptera) on the California Channel Islands. Journal of Biogeography 3: 105-114. Insect 54 17.50 17.18 19.32 8 246.4 113.19 Welter-Schultes, F.W. and M.R. Williams (1999). History, island area and habitat availability determine land snail species richness of Aegean islands. Journal of Biogeography 26: 239-249. Invert. 135 10.52 10.70 113.78 29 2679.42 221.24 Welter-Schultes, F.W. and M.R. Williams (1999). History, island area and habitat availability determine land snail species richness of Aegean islands. Journal of Biogeography 26: 239-249. Invert. 120 8.38 8.42 103.20 29 2679.42 221.24

95 West, N.E., R.J. Tausch, K.H. Rea and P.T. Tueller (1978). Phytogeographical variation within juniper-pinyon woodlands of the Great Basin. In Intermountain biogeography: an interpretive synthesis. Editor K.T. Harper. Great Basin Natural Memoirs 2: 119-136. Plant 367 58.44 13.47 295.09 18 152100 56411.11 White, P.S. and R.I. Miller (1988). Topographic models of vascular plant richness in the southern Appalachian high peaks. Journal of Ecology 76: 192- 199. Plant 342 113.40 66.44 162.16 10 4897 1212.80 Winkler, H. and C. Kampichler (2000). Local and regional species richness in communities of surface-dwelling grassland Collembola: indication of species saturation. Ecography 23: 385- Insect 23 13.50 1.63 7.87 10 56.75 9.31

96 392. Wright, J.P., A.S. Flecker and C.G. Jones (2003). Local vs. landscape controls on plant species richness in beaver meadows. Ecology 84(12): 3162-3173. Plant 103 37.14 1.57 64.29 14 3.99 1.02 Yiming, L., J. Niemelä and L. Dianmo (1998). Nested distribution of amphibians in the Zhoushan archipelago, China: can selective extinction cause nested subsets of species? Oecologia 113: 557-564. Herp. 10 4.80 6.02 0.00 20 468.4 49.45 Zimmerman, B.L. and R.O. Bierregaard (1986). Relevance of the equilibrium theory of island biogeography and species-area relations to conservation with a case from Amazonia. Journal of Biogeography 13(2): 133-143. Herp. 39 18.29 20.65 0.06 7 499 103.14

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