Towards a more general species–area relationship: diversity on all islands, great and small

Aim

To demonstrate a new and more general model of the species–area relationship that builds on traditional models, but includes the provision that richness may vary independently of island area on relatively small islands (the small island effect).

Location

We analysed species–area patterns for a broad diversity of insular biotas from aquatic and terrestrial archipelagoes.

Methods

We used breakpoint or piecewise regression methods by adding an additional term (the breakpoint transformation) to traditional species–area models. The resultant, more general, species–area model has three readily interpretable, biologically relevant parameters: (1) the upper limit of the small island effect (SIE), (2) an estimate of richness for relatively small islands and (3) the slope of the species–area relationship (in semi‐log or log–log space) for relatively large islands.

Results

The SIE, albeit of varying magnitude depending on the biotas in question, appeared to be a relatively common feature of the data sets we studied. The upper limit of the SIE tended to be highest for species groups with relatively high resource requirements and low dispersal abilities, and for biotas of more isolated archipelagoes.

Main conclusions

The breakpoint species–area model can be used to test for the significance, and to explore patterns of variation in small island effects, and to estimate slopes of the species–area (semi‐log or log–log) relationship after adjusting for SIE. Moreover, the breakpoint species–area model can be expanded to investigate three fundamentally different realms of the species–area relationship: (1) small islands where species richness varies independent of area, but with idiosyncratic differences among islands and with catastrophic events such as hurricanes, (2) islands beyond the upper limit of SIE where richness varies in a more deterministic and predictable manner with island area and associated, ecological factors and (3) islands large enough to provide the internal geographical isolation (large rivers, mountains and other barriers within islands) necessary for in situ speciation.

     

Vascular plant species richness in relation to habitat diversity and island area in the Finnish Archipelago

Aim The aim of this study is to explore the interrelationships between island area, species number and habitat diversity in two archipelago areas. Location The study areas, Brunskär and Getskär, are located in an archipelago in south‐western Finland. Methods The study areas, 82 islands in Brunskär and 78 in Getskär, were classified into nine habitat types based on land cover. In the Brunskär area, the flora (351 species) was surveyed separately for each individual habitat on the islands. In the Getskär area, the flora (302 species) was surveyed on a whole‐island basis. We used standard techniques to analyse the species–area relationship on a whole‐island and a habitat level. We also tested our data for the small island effect (SIE) using breakpoint and path analysis models. Results Species richness was significantly associated with both island area and habitat diversity. Vegetated area in particular, defined as island area with the rock habitat subtracted, proved to be a strong predictor of species richness. Species number had a greater association with island area multiplied by the number of habitats than with island area or habitat number separately. The tests for a SIE in the species–area relationship showed the existence of a SIE in one of the island groups. No SIE could be detected for the species–vegetated area relationship in either of the island groups. The strength of the species–area relationship differed considerably between the habitats. Main conclusions The general principles of island biogeography apply well to the 160 islands in this study. Vascular plant diversity for small islands is strongly influenced by physiographic factors. For the small islands with thin and varying soil cover, vegetated area was the most powerful predictor of species richness. The species–area curves of various habitats showed large variations, suggesting that the measurement of habitat areas and establishment of habitat‐based species lists are needed to better understand species richness on islands. We found some evidence of a SIE, but it is debatable whether this is a ‘true’ SIE or a soil cover/habitat characteristics feature.     

Small island biogeography in the Gulf of California: lizards, the subsidized island biogeography hypothesis, and the small island effect

Aim We used insular lizard communities to test the predictions of two hypotheses that attempt to explain patterns of species richness on small islands. We first address the subsidized island biogeography (SIB) hypothesis, which predicts that spatial subsidies may cause insular species richness to deviate from species–area predictions, especially on small islands. Next, we examine the small island effect (SIE), which suggests small islands may not fit the traditional log‐linear species–area curve. Location Islands with arthropodivorous lizard communities throughout the Gulf of California. Methods To evaluate the SIB hypothesis, we first identified subsidized and unsubsidized islands based on surrogate measures of allochthonous productivity (i.e. island size and bird presence). Subsequently, we created species–area curves from previously published lizard species richness and island area data. We used the residuals and slopes from these analyses to compare species richness on subsidized and unsubsidized islands. To test for an SIE, we used breakpoint regression to model the relationship between lizard species richness and island area. We compared results from this model to results from the log‐linear regression model. Results Subsidized islands had a lower slope than unsubsidized islands, and the difference between these groups was significant when small islands were defined as < 1 km². In addition to comparing slopes, we tested for differences in the magnitude of the residuals (from the species–area regression of all islands) for subsidized vs. unsubsidized islands. We found no significant patterns in the residual values for small vs. large islands, or between islands with and without seabirds. The SIE was found to be a slightly better predictor of lizard species richness than the traditional log‐linear model. Main conclusions Predictions of the SIB hypothesis were partially supported by the data. The absence of a significant SIE may be a result of spatial subsidies as explained by the SIB hypothesis and data presented here. We conclude by suggesting potential scenarios to test for interactions between these two small island hypotheses. Future studies considering factors affecting species richness should examine the possible role of spatial subsidies, an SIE, or a synergistic effect of the two in data sets with small islands.     

Habitat diversity, ecological requirements of species and the Small Island Effect

Aim  To explore the causal factors leading to a significant Small Island Effect (SIE), that is, the absence of the commonly found species–area relationships below an island size, on the terrestrial isopod communities from a large number of islands.
Location  Ninety islands of the Aegean Sea (Greece).
Methods  The detection of a significant SIE is assessed through the application of all three methods available in the literature. Species are divided into generalists and specialists. We tested if the minimum area and the area range of each species' occurrences differ between generalists and specialists. Next, we searched for differences in the ratios of specialists to generalists above and below the SIE threshold, and tested their cumulative ratios when islands are arranged according to increasing area, altitude or habitat diversity in order to identify the threshold where they become statistically indistinguishable from the ratio of the total set of islands.
Results  Our results indicate a strong effect of habitat availability on the SIE. Communities of islands within the SIE range, host a higher percentage of generalists. An analysis of the specific habitat requirements shows that, for isopods, the crucial factor is the lack of habitats related to inland waters from small islands.
Main conclusions  The distribution of habitats on islands of different size is of major importance for the occurrence of a SIE. The relative representation of specialist and generalist species on islands of different size plays an important role in shaping SIE-related patterns. Conservation efforts should pay special attention on freshwater habitats, especially on small Aegean islands. Identifying the causal factors of SIE, combined with a thorough knowledge of the ecological requirements of species can offer insights into identifying habitat types and groups of species that are more vulnerable to alterations of the environment.     

Patterns of species richness on very small islands: the plants of the Aegean archipelago

Aim To investigate the species–area relationship (SAR) of plants on very small islands, to examine the effect of other factors on species richness, and to check for a possible Small Island Effect (SIE). Location The study used data on the floral composition of 86 very small islands (all < 0.050 km²) of the Aegean archipelago (Greece). Methods We used standard techniques for linear and nonlinear regression in order to check several models of the SAR, and stepwise multiple regression to check for the effects of factors other than area on species richness (‘habitat diversity’, elevation, and distance from nearest large island), as well as the performance of the Choros model. We also checked for the SAR of certain taxonomic and ecological plant groups that are of special importance in eastern Mediterranean islands, such as halophytes, therophytes, Leguminosae and Gramineae. We used one‐way anova to check for differences in richness between grazed and non‐grazed islands, and we explored possible effects of nesting seabirds on the islands’ flora. Results Area explained a small percentage of total species richness variance in all cases. The linearized power model of the SAR provided the best fit for the total species list and several subgroups of species, while the semi‐log model provided better fits for grazed islands, grasses and therophytes. None of the nonlinear models explained more variance. The slope of the SAR was very high, mainly due to the contribution of non‐grazed islands. No significant SIE could be detected. The Choros model explained more variance than all SARs, although a large amount of variance of species richness still remained unexplained. Elevation was found to be the only important factor, other than area, to influence species richness. Habitat diversity did not seem important, although there were serious methodological problems in properly defining it, especially for plants. Grazing was an important factor influencing the flora of small islands. Grazed islands were richer than non‐grazed, but the response of their species richness to area was particularly low, indicating decreased floral heterogeneity among islands. We did not detect any important effects of the presence of nesting seabird colonies. Main conclusions Species richness on small islands may behave idiosyncratically, but this does not always lead to a typical SIE. Plants of Aegean islets conform to the classical Arrhenius model of the SAR, a result mainly due to the contribution of non‐grazed islands. At the same time, the factors examined explain a small portion of total variance in species richness, indicating the possible contribution of other, non‐standard factors, or even of stochastic effects. The proper definition of habitat diversity as pertaining to the taxon examined in each case is a recurrent problem in such studies. Nevertheless, the combined effect of area and a proxy for environmental heterogeneity is once again superior to area alone in explaining species richness.     

Island,archipelago and taxon effects: mixed models as a means of dealing with the imperfect design of nature's experiments

A major aim of island biogeography has been to describe general patterns of species richness across islands and to identify the processes responsible. Data are often collected across many islands; with larger datasets providing increased statistical power and more accurate parameter estimates. However, there is often structure in observational data, violating an assumption of linear models that each datum is independent. In island biogeography this structure may take the form of an island, archipelago or taxon being represented by multiple data points. We survey recent papers in this field and find that these forms of non‐independence are a common feature. Most authors addressed this problem by conducting separate analyses for each archipelago, taxon or combination of the two, but a better tool for dealing with non‐independence and structure in data, the mixed model, already exists. We demonstrate the advantages of a mixed model approach by applying it to a well‐known dataset that spans 134 observations of single island endemic (SIE) richness across 39 islands, four archipelagos and four taxa. Taking island area and age into account, SIE richness varies substantially more among archipelagos than it does among islands or taxa. We find that SIE richness rises with island age on the Azores and Galapagos, while on the Canaries and Hawaii SIE richness initially rises with age but later declines on older islands. Our analyses demonstrate three advantages to island biogeography of applying a mixed modelling approach: 1) structure in the data is controlled for; 2) the variance among islands, archipelagos and taxa is estimated; 3) all the data can be included in a single model, making it possible to test whether trends are general across all archipelagos and taxa or are idiosyncratic.     

Robust methods for detecting a small island effect

Aim The small island effect (SIE), i.e. the hypothesis that species richness below a certain threshold area varies independently of island size, has become a widely accepted part of the theory of island biogeography. However, there are doubts whether the findings of SIEs were based on appropriate methods. The aim of this study was thus to provide a statistically sound methodology for the detection of SIEs and to show this by re‐analysing data in which an SIE has recently been claimed ( Sfenthourakis & Triantis, 2009 , Diversity and Distributions, 15 , 131–140). Location Ninety islands of the Aegean Sea (Greece). Methods First, I reviewed publications on SIEs and evaluated their methodology. Then, I fitted different species–area models to the published data of area (A) and species richness (S) of terrestrial isopods (Oniscidea), with log A as predictor and both S (logarithm function) and log S (power function) as response variables: (i) linear; (ii) quadratic; (iii) cubic; (iv) breakpoint with zero slope to the left (SIE model); (v) breakpoint with zero slope to the right; (vi) two‐slope model. I used non‐linear regression with R²_adj., AIC_c and BIC as goodness‐of‐fit measures. Results Many different methods have been applied for detecting SIEs, all of them with serious shortcomings. Contrary to the claim of the original study, no SIE occurs in this particular dataset as the two‐slope variants performed better than the SIE variants for both the logarithm and power functions. Main conclusions For the unambiguous detection of SIEs, one needs to (i) include islands with no species; (ii) compare all relevant models; and (iii) account for different model complexities. As none of the reviewed SIE studies met all these criteria, their findings are dubious and SIEs may be less common than reported. Thus, conservation‐related predictions based on the assumption of SIEs may be unreliable.     

Species–area relationships in the Andaman and Nicobar Islands emerge because rarer species are disproportionately favored on larger islands

The island species–area relationship (ISAR) describes how the number of species increases with increasing size of an island (or island‐like habitat), and is of fundamental importance in island biogeography and conservation. Here, we use a framework based on individual‐based rarefaction to infer whether ISARs result from passive sampling, or whether some processes are acting beyond sampling (e.g., disproportionate effects and/or habitat heterogeneity). Using data on total and relative abundances of four taxa (birds, butterflies, amphibians, and reptiles) from multiple islands in the Andaman and Nicobar archipelago, we examine how different metrics of biodiversity (total species richness, rarefied species richness, and abundance‐weighted effective numbers of species emphasizing common species) vary with island area. Total species richness increased for all taxa, as did rarefied species richness controlling for a given sampling effort. This indicates that the ISAR did not result because of passive sampling, but that instead, some species were disproportionately favored on larger islands. For birds, frogs, and lizards, this disproportionate effect was only associated with species that were rarer in the samples, but for butterflies, both more common and rarer species were affected. Furthermore, for the two taxa for which we had plot‐level data (reptiles and amphibians), within‐island β‐diversity did not increase with island size, suggesting that within‐island compositional effects were unlikely to be driving these ISARs. Overall, our results indicate that the ISARs of these taxa are most likely driven by disproportionate effects, that is, where larger islands are important sources of biodiversity beyond a simple sampling expectation, especially through their influence on rarer species, thus emphasizing their role in the preservation and conservation of species.     

Island biogeography of the Mediterranean sea: the species–area relationship for terrestrial isopods

Aim We looked at the biogeographical patterns of Oniscidean fauna from the small islands of the Mediterranean Sea in order to investigate the species–area relationship and to test for area‐range effects. Location The Mediterranean Sea. Methods We compiled from the literature a data set of 176 species of Oniscidea (terrestrial isopods) distributed over 124 Mediterranean islands. Jaccard's index was used as input for a UPGMA cluster analysis. The species–area relationship was investigated by applying linear, semi‐logarithmic, logarithmic and sigmoid models. We also investigated a possible ‘small island effect’ (SIE) by performing breakpoint regression. We used a cumulative and a sliding‐window approach to evaluate scale‐dependent area‐range effects on the log S/log A regression parameters. Results Based on similarity indexes, results indicated that small islands of the Mediterranean Sea can be divided into two major groups: eastern and western. In general, islands from eastern archipelagos were linked together at similarity values higher than those observed for western Mediterranean islands. This is consistent with a more even distribution of species in the eastern Mediterranean islands. Separate archipelagos in the western Mediterranean could be discriminated, with the exception of islets, which tended to group together at the lowest similarity values regardless of the archipelago to which they belong. Islets were characterized by a few common species with large ranges. The species–area logarithmic model did not always provide the best fit. Most continental archipelagos showed very similar intercepts, higher than the intercept for the Canary island oceanic archipelago. Sigmoid regression returned convex curves. Evidence for a SIE was found, whereas area‐range effects that are dependent on larger scale analyses were not unambiguously supported. Main conclusions The Oniscidea fauna from small islands of the Mediterranean Sea is highly structured, with major and minor geographical patterns being identifiable. Some but not all of the biogeographical complexity can be explained by interpreting the different shapes of species–area curves. Despite its flexibility, the sigmoid model tested did not always provide the best fit. Moreover, when the model did provide a good fit the curves looked convex, not sigmoid. We found evidence for a SIE, and minor support for scale‐dependent area‐range effects.     

Island biogeography is not a single‐variable discipline: the small island effect debate

In some island systems, an ‘anomalous’ feature of species richness on smaller islands, in comparison with larger ones, has been observed. This has been described as the small island effect (SIE). The precise meaning of the term remains unresolved, as does the explanation for the phenomenon and even whether it exists. Dengler (2010 ; Diversity Distrib, 16 , 256–266.) addresses a number of conceptual and methodological issues concerning the nature and the detection of the SIE but fails to settle conclusively most of the issues he raises. We contend that his approach is theoretically flawed, especially in its treatment of habitat diversity. We offer a few suggestions of what is needed to advance understanding of the SIE.     

Plants on small islands revisited: the effects of spatial scale and habitat quality on the species–area relationship

Understanding how species diversity is related to sampling area and spatial scale is central to ecology and biogeography. Small islands and small sampling units support fewer species than larger ones. However, the factors influencing species richness may not be consistent across scales. Richness at local scales is primarily affected by small‐scale environmental factors, stochasticity and the richness at the island scale. Richness at whole‐island scale, however, is usually strongly related to island area, isolation and habitat diversity. Despite these contrasting drivers at local and island scales, island species–area relationships (SARs) are often constructed based on richness sampled at the local scale. Whether local scale samples adequately predict richness at the island scale and how local scale samples influence the island SAR remains poorly understood. We investigated the effects of different sampling scales on the SAR of trees on 60 small islands in the Raja Ampat archipelago (Indonesia) using standardised transects and a hierarchically nested sampling design. We compared species richness at different grain sizes ranging from single (sub)transects to whole islands and tested whether the shape of the SAR changed with sampling scale. We then determined the importance of island area, isolation, shape and habitat quality at each scale on species richness. We found strong support for scale dependency of the SAR. The SAR changed from exponential shape at local sampling scales to sigmoidal shape at the island scale indicating variation of species richness independent of area for small islands and hence the presence of a small‐island effect. Island area was the most important variable explaining species richness at all scales, but habitat quality was also important at local scales. We conclude that the SAR and drivers of species richness are influenced by sampling scale, and that the sampling design for assessing the island SARs therefore requires careful consideration.     

Lost and found: Short-term dynamics of the flora on 100 small islands in the White Sea

The aim was to uncover factors that influence short-term (decade) flora dynamics and species richness of northern marine islets characterized by poor flora and weak anthropogenic pressure. The study used presence–absence data of vascular plant species on 100 small uprising islets of the Kandalaksha Gulf of White Sea (Northern Karelia, Russia). We investigated the influence of islands' attributes on species richness and rates of flora dynamics. Two island types were analyzed separately: younger, stone-like and older, islet-like (which generally are larger and have higher diversity of habitats). Sampled islands were studied via classical biogeographical per island approach and metapopulation per species approach. Stone-like islands had noticeably poorer flora with higher rates of immigration and extinction when compared to those of islet-like islands. The species number for islet-like islands correlated positively with number of habitats, abundance of different habitat types and island area. Species richness of stone-like islands correlated positively only with number of habitat types. Plant species associated with birds, crowberry thickets and coastal rocks were the most stable, and the species of disturbed habitats were significantly less stable. Floristic changes that have occurred have been caused by the massive establishment of new species rather than the extinction of pre-existing taxa. Thus, most of these islands are still in the colonization (assortative) stage. While we found no relationship between island area and species number for stone-like islands, this relationship was seen on islet-like islands.     

Drivers of diversity in Macaronesian spiders and the role of species extinctions

Aim To identify the biogeographical factors underlying spider species richness in the Macaronesian region and assess the importance of species extinctions in shaping the current diversity. Location The European archipelagos of Macaronesia with an emphasis on the Azores and Canary Islands. Methods Seven variables were tested as predictors of single‐island endemics (SIE), archipelago endemics and indigenous spider species richness in the Azores, Canary Islands and Macaronesia as a whole: island area; geological age; maximum elevation; distance from mainland; distance from the closest island; distance from an older island; and natural forest area remaining per island – a measure of deforestation (the latter only in the Azores). Different mathematical formulations of the general dynamic model of oceanic island biogeography (GDM) were also tested. Results Island area and the proportion of remaining natural forest were the best predictors of species richness in the Azores. In the Canary Islands, area alone did not explain the richness of spiders. However, a hump‐shaped relationship between richness and time was apparent in these islands. The island richness in Macaronesia was correlated with island area, geological age, maximum elevation and distance to mainland. Main conclusions In Macaronesia as a whole, area, island age, the large distance that separates the Azores from the mainland, and the recent disappearance of native habitats with subsequent unrecorded extinctions seem to be the most probable explanations for the current observed richness. In the Canary Islands, the GDM model is strongly supported by many genera that radiated early, reached a peak at intermediate island ages, and have gone extinct on older, eroded islands. In the Azores, the unrecorded extinctions of many species in the oldest, most disturbed islands seem to be one of the main drivers of the current richness patterns. Spiders, the most important terrestrial predators on these islands, may be acting as early indicators for the future disappearance of other insular taxa.     

Dung beetle assemblages on tropical land‐bridge islands: small island effect and vulnerable species

Aim Using dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) in a tropical land‐bridge island system, we test for the small island effect (SIE) in the species–area relationship and evaluate its effects on species richness and community composition. We also examine the determinants of species richness across island size and investigate the traits of dung beetle species in relation to their local extinction vulnerability following forest fragmentation. Location Lake Kenyir, a hydroelectric reservoir in north‐eastern Peninsular Malaysia. Methods We sampled dung beetles using human dung baited pitfall traps on 24 land‐bridge islands and three mainland sites. We used regression tree analyses to test for the SIE, as well as species traits related to local rarity, as an indication of extinction vulnerability. We employed generalized linear models (GLMs) to examine determinants for species richness at different scales and compared the results with those from conventional linear and breakpoint regressions. Community analyses included non‐metric multidimensional scaling, partial Mantel tests, nestedness analysis and abundance spectra. Results Regression tree analysis revealed an area threshold at 35.8 ha indicating an SIE. Tree basal area was the most important predictor of species richness on small islands (<35.8 ha). Results from GLMs supported these findings, with isolation and edge index also being important for small islands. The SIE also manifested in patterns of dung beetle community composition where communities on small islands (<35.8 ha) departed from those on the mainland and larger islands, and were highly variable with no significant nestedness, probably as a result of unexpected species occurrences on several small islands. The communities exhibited a low degree of spatial autocorrelation, suggesting that dispersal limitation plays a part in structuring dung beetle assemblages. Species with lower baseline density and an inability to forage on the forest edge were found to be rarer among sites and hence more prone to local extinction. Main conclusions We highlight the stochastic nature of dung beetle community composition on small islands and argue that this results in reduced ecosystem functionality. A better understanding of the minimum fragment size required for retaining functional ecological communities will be important for effective conservation management and the maintenance of tropical forest ecosystem stability.     

No evidence for the small‐island effect in avian communities on islands of an inundated lake

The small‐island effect (SIE), i.e. the hypothesis that species richness on islands below a certain threshold area varies independently of area, has become more and more part of the theoretical framework of biogeography and biodiversity research. However, existing SIE studies are extraordinarily biased taxonomically: plants and other animal taxonomic groups are predominantly studied, while birds are almost completely overlooked. Furthermore, previous methods for the detection of SIE are flawed in one or another way, including not accounting for model complexity, not comparing all relevant models, not including islands with no species, and ignoring the effects of logarithmic data transformations and habitat diversity in generating SIE. Therefore, the existence and the prevalence of the SIE may be dubious. In this study, after controlling for all these methodological shortcomings in detecting the SIE, we test for the existence of the SIE using bird data collected on islands in the Thousand Island Lake, China. We used the line‐transect method to survey bird occupancy and abundance on 42 islands from 2007 to 2011. We used three broad sets of analyses, regression‐based analyses, path analyses and null model analyses, to overcome potential methodological problems in detecting the SIE. We found no evidence for an SIE in avian communities in the Thousand Island Lake. Model selection based on AIC_c identified the simple power model without SIE as the most parsimonious model. In contrast, there was little support for the three breakpoint regression models with SIE. Path analyses and null model analyses also did not detect an SIE. We conclude that, for the robust detection of SIE, future study should carefully take all these methodological pitfalls into account.     

Testing island biogeography theory with visitation rates of birds to British islands

Aim We consider three hypotheses – MacArthur and Wilson’s island biogeography theory (IBT), Lack’s habitat diversity idea and the ‘target effect’– that explain the pattern of decreased species richness on small and distant islands. Location We evaluate these hypotheses using a detailed dataset on the occurrence and abundance of terrestrial birds on nine islands off the coast of Britain and the Republic of Ireland. Methods  Unlike previous studies, we compile data on species that visit the islands, rather than just those that breed on them. We divided the species into five mutually exclusive categories based upon their migratory status and where they regularly breed: British residents, summer visitors to Britain, winter visitors to Britain, and vagrants from Europe or beyond Europe. For each species group on each island we calculated the average number of species visiting each year. We then regressed the average number of species against island area and distance to the mainland (all variables were log‐transformed). We also compared the average number of species visiting each island with the average number of species breeding on each island. Results  Average number of visiting British residents decreased significantly with increasing island distance, but showed no relationship with island area. There was no significant relationship between island area or island distance and average number of summer or winter visitors. European and non‐European vagrants likewise showed no relationship between numbers of species visiting and island distance. However, the relationship between island area and number of visiting species was significant for both these categories; as island area increases so too does the number of visiting species. Main conclusions  As predicted by IBT, there were fewer visiting species on more distant islands. There were substantially more visitors to each island than breeding species, supporting Lack’s argument that lower bird richness is not a result of varying immigration rates (as predicted by IBT) but rather a result of some other island property, e.g. fewer resources. Birds make a decision to either leave an island or stay and breed. The target effect was also clearly demonstrated by the increase in European and non‐European breeders with increasing island size.     

Orchids of the West Indies: predictability of diversity and endemism

Aim We examined phytogeographical patterns of West Indian orchids, and related island area and maximum elevation with orchid species richness and endemism. We expected strong species–area relationships, but that these would differ between low and montane island groups. In so far as maximum island elevation is a surrogate for habitat diversity, we anticipated a strong relationship with maximum elevation and both species richness and endemism for montane islands. Location The West Indies. Methods Our data included 49 islands and 728 species. Islands were classified as either montane (≥ 300 m elevation) or low (< 300 m). Linear and multivariate regression analyses were run to detect relationships between either area or maximum island elevation and species richness or the number of island endemic species. Results For all 49 islands, the species–area relationship was strong, producing a z‐value of 0.47 (slope of the regression line) and explaining 46% of the variation. For 18 relatively homogeneous, low islands we found a non‐significant slope of z = −0.01 that explained only 0.1% of the variation. The 31 montane islands had a highly significant species–area relationship, with z = 0.49 and accounting for 65% of the variation. Species numbers were also strongly related to maximum island elevation. For all islands < 750 km², we found a small‐island effect, which reduced the species–area relationship to a non‐significant z = 0.16, with only 5% of the variation explained by the model. Species–area relationships for montane islands of at least 750 km² were strong and significant, but maximum elevation was the best predictor of species richness and accounted for 79% of the variation. The frequency of single‐island endemics was high (42%) but nearly all occurred on just nine montane islands (300 species). The taxonomic distribution of endemics was also skewed, suggesting that seed dispersability, while remarkable in some taxa, is very limited in others. Montane island endemics showed strong species–area and species–elevation relationships. Main conclusions Area and elevation are good predictors of orchid species diversity and endemism in the West Indies, but these associations are driven by the extraordinarily strong relationships of large, montane islands. The species richness of low islands showed no significant relationship with either variable. A small‐island effect exists, but the montane islands had a significant relationship between species diversity and maximum elevation. Thus, patterns of Caribbean orchid diversity are dependent on an interplay between area and topographic diversity.     

Mediterranean island biogeography: Analysis of fern species distribution in the system of islets around Sicily

Abstract

The aim of this article was to test the way in which geographical factors influence island floras in the Mediterranean basin, using ferns as target organisms, and the islands surrounding Sicily as location. A matrix with presence/absence data concerning fern taxa in the 16 islands studied was compiled. Cluster analysis, principal co-ordinates analysis (PCoA), principal components analysis (PCA) and a Bayesian analysis were performed. For each island, the total number of fern taxa was regressed against three factors: island area, island elevation and isolation. All the analyses pointed to affinities between islands according to their different geological composition, independently from their geographic position. A clear positive island species/area relationship (ISAR) was shown only for the volcanic islands. The island species/(area×elevation) relationship (ISAER), on the contrary, was unsatisfactory. The main features of interest are the following: (1) the clear division of the islands into two groups, volcanic vs. sedimentary; (2) the floristic richness of the volcanic compared to sedimentary islands and (3) the uniqueness of the pteridophyte flora of Pantelleria. This seems to demonstrate that the lower number of taxa in the islands farthest away from the “mainland” (Sicily, Tunisia) is not due to isolation, but due to another factor, probably habitat availability.     

Human activities alter biogeographical patterns of reptiles on Mediterranean islands

Aim The theory of island biogeography predicts species richness based on geographical factors that influence the extinction–colonization balance, such as area and isolation. However, human influence is the major cause of present biotic changes, and may therefore modify biogeographical patterns by increasing extinctions and colonizations. Our aim was to evaluate the effect of human activities on the species richness of reptiles on islands. Location Islands in the Mediterranean Sea and Macaronesia. Methods Using a large data set (n = 212 islands) compiled from the literature, we built spatial regression models to compare the effect of geographical (area, isolation, topography) and human (population, airports) factors on native and alien species. We also used piecewise regression to evaluate whether human activities cause deviation of the species–area relationship from the linear (on log–log axes) pattern, and path analysis to reveal the relationships among multiple potential predictors. Results The richness of both native and alien species was best explained by models combining geographical and human factors. The richness of native species was negatively related to human influence, while that of alien species was positively related, with the overall balance being negative. In models that did not take into account human factors, the relationship between island area and species richness was not linear. Large islands hosted fewer native species than expected from a linear (on log–log axes) species–area relationship, because they were more strongly affected by human influence than were small islands. Path analysis showed that island size has a direct positive effect on reptile richness. However, area also had a positive relationship with human impact, which in turn mediated a negative effect on richness. Main conclusion Anthropogenic factors can strongly modify the biogeographical pattern of islands, probably because they are major drivers of present‐day extinctions and colonizations and can displace island biodiversity from the equilibrium points expected by theory on the basis of geographical features.     

Island biogeography of bats in Baja California, Mexico: patterns of bat species richness in a near-shore archipelago

Aim We studied the relationship between the size and isolation of islands and bat species richness in a near‐shore archipelago to determine whether communities of vagile mammals conform to predictions of island biogeography theory. We compared patterns of species richness in two subarchipelagos to determine whether area per se or differences in habitat diversity explain variations in bat species richness. Location Islands in the Gulf of California and adjacent coastal habitats on the Baja California peninsula in northwest Mexico. Methods Presence–absence surveys for bats were conducted on 32 islands in the Gulf of California using acoustic and mist‐net surveys. We sampled for bats in coastal habitats of four regions of the Baja peninsula to characterize the source pool of potential colonizing species. We fitted a semi‐log model of species richness and multiple linear regression and used Akaike information criterion model selection to assess the possible influence of log₁₀ area, isolation, and island group (two subarchipelagos) on the species richness of bats. We compared the species richness of bats on islands with greater vegetation densities in the southern gulf (n = 20) with that on drier islands with less vegetation in the northern gulf (n = 12) to investigate the relationship between habitat diversity and the species richness of bats. Results Twelve species of bats were detected on islands in the Gulf of California, and 15 species were detected in coastal habitats on the Baja peninsula. Bat species richness was related to both area and isolation of islands, and was higher in the southern subarchipelago, which has denser vegetation. Log₁₀ area was positively related to bat species richness, which increased by one species for every 5.4‐fold increase in island area. On average, richness declined by one species per 6.25 km increase in isolation from the Baja peninsula. Main conclusions Our results demonstrate that patterns of bat species richness in a near‐shore archipelago are consistent with patterns predicted by the equilibrium theory of island biogeography. Despite their vagility, bats may be more sensitive to moderate levels of isolation than previously expected in near‐shore archipelagos. Differences in vegetation and habitat xericity appear to be associated with richness of bat communities in this desert ecosystem. Although observed patterns of species richness were consistent with those predicted by the equilibrium theory, similar relationships between species richness and size and isolation of islands may arise from patch‐use decision making by individuals (optimal foraging strategies).