Species–area relationships and additive partitioning of diversity of native and nonnative herpetofauna of the West Indies

To evaluate the regional biogeographical patterns of West Indian native and nonnative herpetofauna, we derived and updated data on the presence/absence of all herpetofauna in this region from the recently published reviews. We divided the records into 24 taxonomic groups and classified each species as native or nonnative at each locality. For each taxonomic group and in aggregate, we then assessed the following: (1) multiple species–area relationship (SAR) models; (2) C‐ and Z‐values, typically interpreted to represent insularity or dispersal ability; and (3) the average diversity of islands, among‐island heterogeneity, γ‐diversity, and the contribution of area effect toward explaining among‐island heterogeneity using additive diversity partitioning approach. We found the following: (1) SARs were best modeled using the Cumulative Weibull and Lomolino relationships; (2) the Cumulative Weibull and Lomolino regressions displayed both convex and sigmoid curves; and (3) the Cumulative Weibull regressions were more conservative than Lomolino at displaying sigmoid curves within the range of island size studied. The Z‐value of all herpetofauna was overestimated by Darlington (Zoogeography: The geographic distribution of animals, John Wiley, New York, 1957), and Z‐values were ranked: (1) native > nonnative; (2) reptiles > amphibians; (3) snake > lizard > frog > turtle > crocodilian; and (4) increased from lower‐ to higher‐level taxonomic groups. Additive diversity partitioning showed that area had a weaker effect on explaining the among‐island heterogeneity for nonnative species than for native species. Our findings imply that the flexibility of Cumulative Weibull and Lomolino has been underappreciated in the literature. Z‐value is an average of different slopes from different scales and could be artificially overestimated due to oversampling islands of intermediate to large size. Lower extinction rate, higher colonization, and more in situ speciation could contribute to high richness of native species on large islands, enlarging area effect on explaining the between‐island heterogeneity for native species, whereas economic isolation on large islands could decrease the predicted richness, lowering the area effect for nonnative species. For most of the small islands less affected by human activities, extinction and dispersal limitation are the primary processes producing low species richness pattern, which decreases the overall average diversity with a large among‐island heterogeneity corresponding to the high value of this region as a biodiversity hotspot.     

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.     

Richness and composition of plants and birds on land‐bridge islands: effects of island attributes and differential responses of species groups

Aim To test relationships between the richness and composition of vascular plants and birds and attributes of habitat fragments using a model land‐bridge island system, and to investigate whether the effects of fragmentation differ depending on species natural history traits. Location Thousand Island Lake, China. Methods We compiled presence/absence data of vascular plant and bird species through exhaustive surveys of 41 islands. Plant species were assigned to two categories: shade‐intolerant and shade‐tolerant species; bird species were assigned to three categories: edge, interior, and generalist species. We analysed the relationships between island attributes (area, isolation, elevation, shape complexity, and perimeter to area ratio) and species richness using generalized linear models (GLMs). We also investigated patterns of composition in relation to island attributes using ordination (redundancy analysis). Results We found that island area explained a high degree of variation in the species richness of all species groups. The slope of the species–area relationship (z) was 0.16 for all plant species and 0.11 for all bird species. The lowest z‐value was for generalist birds (0.04). The species richness of the three plant species groups was associated with island area per se, while that of all, generalist, and interior birds was explained mainly by elevation, and that of edge bird species was associated primarily with island shape. Patterns of species composition were most strongly related to elevation, island shape complexity, and perimeter to area ratio rather than to island area per se. Species richness had no significant relationship with isolation, but species composition did. We also found differential responses among the species groups to changes in island attributes. Main conclusions Within the Thousand Island Lake system, the effects of fragmentation on both bird and plant species appear to be scale‐dependent and taxon‐specific. The number of plant species occurring on an island is strongly correlated with island area, and the richness of birds and the species composition of plants and birds are associated with variables related to habitat heterogeneity. We conclude that the effects of fragmentation on species diversity and composition depend not only on the degree of habitat loss but also on the specific patterns of habitat fragmentation.     

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.     

sars: an R package for fitting,evaluating and comparing species–area relationship models

The species–area relationship (SAR) constitutes one of the most general ecological patterns globally. A number of different SAR models have been proposed. Recent work has shown that no single model universally provides the best fit to empirical SAR datasets: multiple models may be of practical and theoretical interest. However, there are no software packages available that a) allow users to fit the full range of published SAR models, or b) provide functions to undertake a range of additional SAR‐related analyses. To address these needs, we have developed the R package ‘sars’ that provides a wide variety of SAR‐related functionality. The package provides functions to: a) fit 20 SAR models using non‐linear and linear regression, b) calculate multi‐model averaged curves using various information criteria, and c) generate confidence intervals using bootstrapping. Plotting functions allow users to depict and scrutinize the fits of individual models and multi‐model averaged curves. The package also provides additional SAR functionality, including functions to fit, plot and evaluate the random placement model using a species–sites abundance matrix, and to fit the general dynamic model of oceanic island biogeography. The ‘sars’ R package will aid future SAR research by providing a comprehensive set of simple to use tools that enable in‐depth exploration of SARs and SAR‐related patterns. The package has been designed to allow other researchers to add new functions and models in the future and thus the package represents a resource for future SAR work that can be built on and expanded by workers in the field.     

Re‐approaching the small island effect

Aim To propose a new approach to the small island effect (SIE) and a simple mathematical procedure for the estimation of its upper limit. The main feature of the SIE is that below an upper size threshold an increase of species number with increase of area in small islands is not observed. Location Species richness patterns from different taxa and insular systems are analysed. Methods Sixteen different data sets from 12 studies are analysed. Path analysis was used for the estimation of the upper limit of the SIE. We studied each data set in order to detect whether there was a certain island size under which the direct effects of area were eliminated. This detection was carried out through the sequential exclusion of islands from the largest to the smallest. For the cases where an SIE was detected, a log‐log plot of species number against area is presented. The relationships between habitat diversity, species number and area are studied within the limits of the SIE. In previous studies only area was used for the detection of the SIE, whereas we also encompass habitat diversity, a parameter with well documented influence on species richness, especially at small scales. Results An SIE was detected in six out of the 16 studied cases. The upper limit of the SIE varies, depending on the characteristics of the taxon and the archipelago under study. In general, the values of the upper limit of the SIE calculated according to the approach undertaken in our study differ from the values calculated in previous studies. Main conclusions Although the classical species–area models have been used to estimate the upper limit of the SIE, we propose that the detection of this phenomenon should be undertaken independently from the species–area relationship, so that the net effects of area are calculated excluding the surrogate action of area on other variables, such as environmental heterogeneity. The SIE appears when and where area ceases to influence species richness directly. There are two distinct SIE patterns: (1) the classical SIE where both the direct and indirect effects of area are eliminated and (2) the cryptic SIE where area affects species richness indirectly. Our approach offers the opportunity of studying the different factors influencing biodiversity on small scales more accurately. The SIE cannot be considered a general pattern with fixed behaviour that can be described by the same model for different island groups and taxa. The SIE should be recognized as a genuine but idiosyncratic phenomenon.     

From plots to islands: species diversity at different scales

Aim To investigate how plant diversity of whole islands (‘gamma’) is related to alpha and beta diversity patterns among sampling plots within each island, thus exploring aspects of diversity patterns across scales. Location Nineteen islands of the Aegean Sea, Greece. Methods Plant species were recorded at both the whole‐island scale and in small 100 m² plots on each island. Mean plot species richness was considered as a measure of alpha diversity, and six indices of the ‘variation’‐type beta diversity were also applied. In addition, we partitioned beta diversity into a ‘nestedness’ and a ‘replacement’ component, using the total species richness recorded in all plots of each island as a measure of ‘gamma’ diversity. We also applied 10 species–area models to predict the total observed richness of each island from accumulated plot species richness. Results Mean alpha diversity was not significantly correlated with the overall island species richness or island area. The range of plot species richness for each island was significantly correlated with both overall species richness and area. Alpha diversity was not correlated with most indices of beta diversity. The majority of beta diversity indices were correlated with whole‐island species richness, and this was also true for the ‘replacement’ component of beta diversity. The rational function model provided the best prediction of observed island species richness, with Monod’s and the exponential models following closely. Inaccuracy of predictions was positively correlated with the number of plots and with most indices of beta diversity. Main conclusions Diversity at the broader scale (whole islands) is shaped mainly by variation among small local samples (beta diversity), while local alpha diversity is not a good predictor of species diversity at broader scales. In this system, all results support the crucial role of habitat diversity in determining the species–area relationship.     

How island size and isolation affect bee and wasp ensembles on small tropical islands: a case study from Kepulauan Seribu,Indonesia

Aim To study the effects of isolation and size of small tropical islands on species assemblages of bees (superfamily Apoidea) and wasps (superfamily Vespoidea). Location Twenty islands in the Kepulauan Seribu Archipelago off the coast of west Java, Indonesia. The size of surveyed islands ranges between 0.75 and 41.32 ha; their distance from the coast of Java varies between 3 and 62 km. Methods Field work was conducted from February to May 2005. Bees and wasps were caught with a sweep net during sampling units of 15 min, continuing until four consecutive samples revealed no new species. Total species richness was quantified by the estimators Chao 2, first‐order jackknife and Michaelis–Menten. The software binmatnest was used to test for nestedness of species assemblages. Similarities of species composition between islands were quantified by Sørensen’s similarity index. Results Eighty‐two species were recorded on the 20 surveyed islands. Species richness declined with increasing isolation of islands from the source area, Java. Although the size of the largest island exceeded that of the smallest island by a factor of almost 60, island size only very weakly affected species richness of bees; no effect of island size was found for wasps. Mean body size of species decreased with increasing island isolation. Nestedness of island faunas was only weakly developed. Species composition of both superfamilies was affected by island isolation, but not by island size. Main conclusions While the species–isolation relationship on the very small islands of Kepulauan Seribu followed the prediction of MacArthur and Wilson’s equilibrium theory, the absence of a species–area relationship indicated a weak ‘small‐island effect’, at least in wasps. The combination of an only weakly developed pattern of nested species subsets, the shift in species compositions and the decline of mean body size with increasing island isolation from the source area indicates that biotic interactions and different species traits contribute to the shaping of communities of bees and wasps within the archipelago. The potential of biotic interactions for generating distribution patterns of species within the archipelago is also emphasized by the observed restriction of some species with apparently high dispersal abilities to outer islands.     

Towards a ‘Sea‐Level Sensitive’ dynamic model: impact of island ontogeny and glacio‐eustasy on global patterns of marine island biogeography

A synthetic model is presented to enlarge the evolutionary framework of the General Dynamic Model (GDM) and the Glacial Sensitive Model (GSM) of oceanic island biogeography from the terrestrial to the marine realm. The proposed ‘Sea‐Level Sensitive’ dynamic model (SLS) of marine island biogeography integrates historical and ecological biogeography with patterns of glacio‐eustasy, merging concepts from areas as diverse as taxonomy, biogeography, marine biology, volcanology, sedimentology, stratigraphy, palaeontology, geochronology and geomorphology. Fundamental to the SLS model is the dynamic variation of the littoral area of volcanic oceanic islands (defined as the area between the intertidal and the 50‐m isobath) in response to sea‐level oscillations driven by glacial–interglacial cycles. The following questions are considered by means of this revision: (i) what was the impact of (global) glacio‐eustatic sea‐level oscillations, particularly those of the Pleistocene glacial–interglacial episodes, on the littoral marine fauna and flora of volcanic oceanic islands? (ii) What are the main factors that explain the present littoral marine biodiversity on volcanic oceanic islands? (iii) How can differences in historical and ecological biogeography be reconciled, from a marine point of view? These questions are addressed by compiling the bathymetry of 11 Atlantic archipelagos/islands to obtain quantitative data regarding changes in the littoral area based on Pleistocene sea‐level oscillations, from 150 thousand years ago (ka) to the present. Within the framework of a model sensitive to changing sea levels, we discuss the principal factors affecting the geographical range of marine species; the relationships between modes of larval development, dispersal strategies and geographical range; the relationships between times of speciation, modes of larval development, ecological zonation and geographical range; the influence of sea‐surface temperatures and latitude on littoral marine species diversity; the effect of eustatic sea‐level changes and their impact on the littoral marine biota; island marine species–area relationships; and finally, the physical effects of island ontogeny and its associated submarine topography and marine substrate on littoral biota. Based on the SLS dynamic model, we offer a number of predictions for tropical, subtropical and temperate volcanic oceanic islands on how rates of immigration, colonization, in‐situ speciation, local disappearance, and extinction interact and affect the marine biodiversity around islands during glacials and interglacials, thus allowing future testing of the theory.     

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.     

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.     

Evolutionary assembly of island faunas reverses the classic island–mainland richness difference in Anolis lizards

Aim Islands are widely considered to be species depauperate relative to mainlands but, somewhat paradoxically, are also host to many striking adaptive radiations. Here, focusing on Anolis lizards, we investigate if cladogenetic processes can reconcile these observations by determining if in situ speciation can reduce, or even reverse, the classical island–mainland richness discrepancy. Location Caribbean islands and the Neotropical mainland. Methods We constructed range maps for 203 mainland anoles from museum records and evaluated whether geographical area could account for differences in species richness between island and mainland anole faunas. We compared the island species–area relationship with total mainland anole diversity and with the richness of island‐sized mainland areas. We evaluated the role of climate in the observed differences by using Bayesian model averaging to predict island richness based on the mainland climate–richness relationship. Lastly, we used a published phylogeny and stochastic mapping of ancestral states to determine if speciation rate was greater on islands, after accounting for differences in geographical area. Results Islands dominated by in situ speciation had, on average, significantly more species than similarly sized mainland regions, but islands where in situ speciation has not occurred were species depauperate relative to mainland areas. Results were similar at the scale of the entire mainland, although marginally non‐significant. These findings held even after accounting for climate. Speciation has not been faster on islands; instead, when extinction was assumed to be low, speciation rate varied consistently with geographical area. When extinction was high, there was some evidence that mainland speciation was faster than expected based on area. Main conclusions Our results indicate that evolutionary assembly of island faunas can reverse the general pattern of reduced species richness on islands relative to mainlands.     

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.     

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.     

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.     

The influence of dispersal on macroecological patterns of Lesser Antillean birds

Aim Dispersal is often assumed to be a major force in shaping macroecological patterns, but this is rarely tested. Here I describe macroecological patterns for two groups of Lesser Antillean birds and then use population genetic data to assess if differences in dispersal ability could be responsible for the groups’ contrasting patterns. Importantly, the population genetic data are derived independently from any data used to generate the macroecological patterns. Location The Lesser Antilles, Caribbean. Methods I used data from the literature to construct species–area curves and evaluate the decline in species compositional similarity with geographic distance (hereafter distance–decay) for two sets of bird communities in the Lesser Antilles, those found in rain forest and those in dry forest. I then used mitochondrial DNA sequences from island populations to assess the dispersal ability of rain forest and dry forest species. Results Rain forest species show steeper species–area curves and greater distance–decay in community similarity than dry forest species, patterns that could be explained by rain forest species having more limited dispersal ability. Both conventional analyses of M, the number of migrants per generation between populations, and alternative analyses of D_A, the genetic distance between populations, suggest that rain forest species disperse between islands less frequently than dry forest species. Main conclusions Differences in dispersal ability are a plausible explanation for the contrasting macroecological patterns of rain forest and dry forest species. Additionally, historical factors, such as the taxon cycle and Pleistocene climate fluctuations, may have played a role in shaping the distribution patterns of Lesser Antillean birds.     

Marine island biogeography. Response to comment on ‘Island biogeography: patterns of marine shallow‐water organisms’

In this response we have incorporated data on gastropod and seaweed biodiversity referred to by Ávila et al. (2016, Journal of Biogeography, doi: 10.1111/jbi.12816 ) to allow an updated analysis on marine shallow‐water biogeography patterns. When compared to the biogeography patterns reported in Hachich et al. (2015, Journal of Biogeography, 42 , 1871–1882), we find (1) no differences in the patterns originally reported for reef fish or seaweeds, (2) minor differences in gastropod species–area and species–age patterns and (3) a significant difference for the gastropod species‐isolation pattern. In our original work, we reported that there was limited evidence that gastropod species richness was influenced by island isolation; however, our new analysis reveals a power‐model relationship between these variables. Thus, we are now able to conclude that gastropod species diversity, whose dispersal capacity is intermediate between seaweeds (lowest) and reef fish (highest), is also influenced by island isolation.     

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.     

Lizard predation alters the effect of habitat area on the species richness of insect assemblages on Bahamian isles

Aim Anolis lizard invasions are a serious threat world‐wide, and information about how this invasive predator affects the diversity of prey assemblages is important for many strategic conservation goals. It is hypothesized that these predators reduce the slope of species–area relationships (SARs) of their prey assemblages. The effects of island area and predation by anolis lizards on the species richness of insular insect assemblages were investigated. Location Twenty‐four isles around Staniel Cay, Exuma Cays, Bahamas. Methods Flying insects were sampled using half‐sized Malaise traps for three consecutive days on each island in May 2007. First, the effect of island area on the probability of lizard presence was evaluated. Then, the effects of the presence–absence of predatory lizards on SARs were analysed for the overall insect assemblage and for the assemblages of five dominant insect orders. Results Our results indicated that anolis lizards occurred primarily on larger islands. The species richness of the overall insect assemblage and five dominant insect orders significantly increased with island area. The interaction between island area and predator presence–absence significantly affected the overall insect assemblage and Diptera and Hymenoptera assemblages (but not Coleoptera, Hemiptera and Lepidoptera assemblages). The presence of predators caused decreases in the slope of the SARs. Main conclusions The presence of predatory lizards strongly affects species richness of insular insect assemblages with the island area being a crucial determinant of the species richness. Therefore, the slope of the SAR can serve as a measure of the consequence of invasive predatory species on native insect assemblages.