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.     

A global model of island biogeography

Aim The goal of our study was to build a global model of island biogeography explaining bird species richness that combines MacArthur and Wilson's area–isolation theory with the species–energy theory. Location Global. Methods We assembled a global data set of 346 marine islands representing all types of climate, topography and degree of isolation on our planet, ranging in size from 10 ha to 800,000 km². We built a multiple regression model with the number of non‐marine breeding bird species as the dependent variable. Results We found that about 85–90% of the global variance in insular bird species richness can be explained by simple, contemporary abiotic factors. On a global scale, the three major predictors — area, average annual temperature and the distance separating the islands from the nearest continent — all have constraining (i.e. triangular rather than linear) relationships with insular bird species richness. We found that the slope of the species–area curve depends on both average annual temperature and total annual precipitation, but not on isolation. Insular isolation depends not only on the distance of an island from the continent, but also on the presence or absence of other neighbouring islands. Range in elevation — a surrogate for diversity of habitats — showed a positive correlation with bird diversity in warmer regions of the world, while its effect was negative in colder regions. We also propose a global statistical model to quantify the isolation‐reducing effect of neighbouring islands. Main conclusions The variation in avian richness among islands worldwide can be statistically explained by contemporary environmental variables. The equilibrium theory of island biogeography of MacArthur and Wilson and the species–energy theory are both only partly correct. Global variation in richness depends about equally upon area, climate (temperature and precipitation) and isolation. The slope of the species richness–area curve depends upon climate, but not on isolation, in contrast to MacArthur and Wilson's theory.     

Global diversity of island floras from a macroecological perspective

Islands harbour a significant portion of all plant species worldwide. Their biota are often characterized by narrow distributions and are particularly susceptible to biological invasions and climate change. To date, the global richness pattern of islands is only poorly documented and factors causing differences in species numbers remain controversial. Here, we present the first global analysis of 488 island and 970 mainland floras. We test the relationship between island characteristics (area, isolation, topography, climate and geology) and species richness using traditional and spatial models. Area is the strongest determinant of island species numbers ( R ² = 0.66) but a weaker predictor for mainlands ( R ² = 0.25). Multivariate analyses reveal that all investigated variables significantly contribute to insular species richness with area being the strongest followed by isolation, temperature and precipitation with about equally strong effects. Elevation and island geology show relatively weak yet significant effects. Together these variables account for 85% of the global variation in species richness.     

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.     

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.     

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).     

Multiple source pools for Galápagos plant species richness: a critical analysis of the line of sight connectivity index

An index (C_i*E) combining the number of line‐of‐sight islands (C_i) within a radius i and target island elevation (E) has been proposed as an improved predictive model of plant species richness (S_t) in the Galápagos Archipelago. We examined this index critically and found that several major flaws preclude it from being a useful predictive tool for the archipelago. Although the number of collecting trips to an island was reported over 20 years ago to have substantial predictive value for reported plant species richness in the Galápagos Islands, this relationship was ignored in multiple regression analyses of the index. When we included the number of collecting trips in different multiple regression analyses of the index, C_i*E had less predictive power than collecting trips or ceased to be significant at all. Additionally, the strong significant relationship between elevation and area in the Galápagos Archipelago results in area having a major confounding influence on the C_i*E index. When elevation is removed from the C_i*E index, the predictive power of C_i is far less than area alone. Finally, the data used to construct and correlate the C_i*E index with (S_t) were based only on a subset of the islands and species lists that were incomplete or out of date. Species richness on islands can be related to the interaction of different factors, so development and testing of indices like C_i*E is not inappropriate. However, it is important to examine the interrelationships among the components of these indices thoroughly, and not ignore the effect of factors already known to have high predictive power. We propose several ways in which more meaningful indices of source pool(s) capacity can be constructed.     

Not seeing the ocean for the islands: the mediating influence of matrix-based processes on forest fragmentation effects

The pervasive influence of island biogeography theory on forest fragmentation research has often led to a misleading conceptualization of landscapes as areas of forest/habitat and 'non-forest/non-habitat' and an overriding focus on processes within forest remnants at the expense of research in the human-modified matrix. The matrix, however, may be neither uniformly unsuitable as habitat nor serve as a fully–absorbing barrier to the dispersal of forest taxa. In this paper, we present a conceptual model that addresses how forest habitat loss and fragmentation affect biodiversity through reduction of the resource base, subdivision of populations, alterations of species interactions and disturbance regimes, modifications of microclimate and increases in the presence of invasive species and human pressures on remnants. While we acknowledge the importance of changes associated with the forest remnants themselves (e.g. decreased forest area and increased isolation of forest patches), we stress that the extent, intensity and permanence of alterations to the matrix will have an overriding influence on area and isolation effects and emphasize the potential roles of the matrix as not only a barrier but also as habitat, source and conduit. Our intention is to argue for shifting the examination of forest fragmentation effects away from a patch-based perspective focused on factors such as patch area and distance metrics to a landscape mosaic perspective that recognizes the importance of gradients in habitat conditions.     

Levels of endemism are not necessarily biased by the co-presence of species with different range sizes: a case study of Vilenkin and Chikatunov's models

Aim  To illustrate problems in the methods proposed by B. Vilenkin and V. Chikatunov to study levels of endemism and species–area relationships.
Location  The study used data on the distribution of tenebrionid beetles (Coleoptera, Tenebrionidae) on the Aegean Islands (Greece).
Methods  A total of 32 islands and 170 taxa (species and subspecies) were included in this study. Levels of endemism were evaluated both as the proportion of endemic taxa, and according to the methods proposed by Vilenkin and Chikatunov, which are based on the number of non-endemic taxa and various relationships with area. A model of the species–area relationship proposed by these authors was also analysed.
Results  The number of endemic taxa was positively correlated with the number of taxa with different distribution types, but this positive correlation did not influence the estimation of the level of endemism. In fact, the commonly used estimate of endemicity as a percentage was strongly correlated with the endemism values calculated according to the method of Vilenkin and Chikatunov. The usual power function fitted the species–area relationship as well as the most complicated method of Vilenkin and Chikatunov.
Main conclusions  As hypothesized by Vilenkin and Chikatunov, the number of endemic taxa was influenced both by the number of taxa of other biogeographical ranks, and by an island's area. However, explanations for the positive relationship between the number of endemic taxa and taxa of different biogeographical ranks are equivocal. Importantly, this relationship did not necessarily influence the level of endemism, which could be expressed adequately by percentages. The method proposed by Vilenkin and Chikatunov to estimate the species–area relationship cannot be clearly justified on theoretical grounds and is of questionable practical utility.     

Effects of seasonality on the species–area relationship: a case study with birds in urban parks

Aim To examine how parameters of the species–area curve and factors determining bird species richness are affected by seasonality. Location One hundred and thirty urban parks of the city of Valencia (Spain) ranging between 0.03 and 18.6 ha. Methods Bird censuses were conducted monthly during 1998 and 2004. For each park, 27 variables were recorded as measures of size, structure and isolation. Linear regression was used to test for patterns in species richness relative to independent variables. The results of each season were compared. Results Curves corresponding to the breeding period (spring–summer) had significantly higher intercepts, but slopes did not change across seasons. Turnover rates of resident species were dependent on patch size and isolation considering spring, but not autumn, from different years. Additionally, turnover rates in spring were lower in parks in close proximity to others than in isolated ones. In all seasons, the number of bird species showed a strong relationship with park area, which explained c. 60% of the variation in species numbers. Habitat variables and park isolation explained a small amount of the variation in species numbers. The data did not give support to the random placement hypothesis. Main conclusions The overall conclusions of this study give little support to the possible influence of seasonality on species–area relationship or on the three main hypotheses explaining it. This lack of influence of seasonality could be explained by the seasonal stability of the bird populations of the parks within the study area as a consequence of different factors, mainly the characteristics of the species pool, similarity in the features of the parks irrespective of their size, and stability of resources and conditions across seasons. The question of whether this lack of influence of seasonality on the species–area relationship occurs in those ecological systems that have a larger seasonal variation than the urban parks of the study area remains unresolved. Closer examination of the seasonal patterns reported in this study is likely to be useful in increasing our understanding of the species–area relationship.