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.     

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.     

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).     

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.     

温州沿海小型海岛植物丰富度和β多样性及其影响因子

于2012-2015年调查了温州沿海20个小型无居民海岛的植物组成,共记录到维管束植物366种,隶属于95科244属,其中草本植物226种木本植物140种。拟合了5个种-面积关系模型,采用赤池信息量AIC对模型进行选择,发现种-面积-生境类型关系模型SAH_nR权重系数最大,为40.26%,两种断点回归种-面积关系模型BR-SAR权重系数分别仅为6.94%和0.43%,表明基于这20个海岛拟合的种-面积关系不存在小岛屿效应。岛屿植物物种丰富度主要受面积A影响,离大陆距离,I_m对丰富度无显著作用;偏相关分析表明除A外,周长/面积比PAR和岛屿生境多样性指数H_d显著影响了植物丰富度,其逐步回归方程分别为:植物总丰富度S=76.714+1.696A-0.046PAR,R~2=0.839;木本植物丰富度S_(-woody)=6.525+0.455A+24.544H_d,R~2=0.697;草本植物丰富度S_(-herbaceous)=66.899+1.285A-0.04PAR-23.434H_d,R~2=0.865。偏最小二乘回归PLS分析中岛屿空间特征参数对岛屿物种相似性指数重要性排序为:I_m(0.61)I_i(0.56)PAR(0.49)A(0.20)岸线长度Per(0.14)生境类型H(0.072)岛屿高程E(0.065)岛屿形状指数SI(0.05)。由此可见,近岸的小型海岛植物丰富度并不总是由岛屿面积来决定;隔离度对岛屿植物β多样性影响较大。     

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.

     

Determinants of alpha and beta vascular plant diversity in Mediterranean island systems: the Ionian islands,Greece

The Ionian archipelago is the second largest Greek archipelago after the Aegean, but the factors driving plant species diversity in the Ionian islands are still barely known. We used stepwise multiple regressions to investigate the factors affecting plant species diversity in 17 Ionian islands. Generalized dissimilarity modelling was applied to examine variation in the magnitude and rate of species turnover along environmental gradients, as well as to assess the relative importance of geographical and climatic factors in explaining species turnover. The values of the residuals from the ISAR log₁₀‐transfomed models of native and endemic taxa were used as a measure of island floristic diversity. Area was confirmed to be the most powerful single explanatory predictor of all diversity metrics. Mean annual precipitation and temperature, as well as shortest distance to the nearest island are also significant predictors of vascular plant diversity. The island of Kalamos constitutes an important plant diversity hotspot in the Ionian archipelago. The recent formation of the islands, the close proximity to the mainland source and the relatively low dispersal filtering of the Ionian archipelago has resulted in islands with a flora principally comprising common species and a low proportion of endemics. Small islands keep a key role in conservation of plant priority sites.     

Floristic biogeography of the Hawaiian Islands: influences of area, environment and paleogeography

Aim A detailed database of distributions and phylogenetic relationships of native Hawaiian flowering plant species is used to weigh the relative influences of environmental and historical factors on species numbers and endemism. Location The Hawaiian Islands are isolated in the North Pacific Ocean nearly 4000 km from the nearest continent and nearly as distant from the closest high islands, the Marquesas. The range of island sizes, environments, and geological histories within an extremely isolated archipelago make the Hawaiian Islands an ideal system in which to study spatial variation in species distributions and diversity. Because the biota is derived from colonization followed by extensive speciation, the role of evolution in shaping the regional species assemblage can be readily examined. Methods For whole islands and regions of each major habitat, species–area relationships were assessed. Residuals of species–area relationships were subjected to correlation analysis with measures of endemism, isolation, elevation and island age. Putative groups of descendents of each colonist from outside the Hawaiian Islands were considered phylogenetic lineages whose distributions were included in analyses. Results The species–area relationship is a prominent pattern among islands and among regions of each given habitat. Species number in each case correlates positively with number of endemics, number of lineages and number of species per lineage. For mesic and wet habitat regions, island age is more influential than area on species numbers, with older islands having more species, more single‐island endemics, and higher species : lineage ratios than their areas alone would predict. Main conclusions Because species numbers and endemism are closely tied to speciation in the Hawaiian flora, particularly in the most species‐rich phylogenetic lineages, individual islands’ histories are central in shaping their biota. The Maui Nui complex of islands (Maui, Moloka‘i, Lāna‘i and Kaho‘olawe), which formed a single large landmass during most of its history, is best viewed in terms of either the age or area of the complex as a whole, rather than the individual islands existing today.     

On the general dynamic model of oceanic island biogeography

Aim To investigate the biological meaning of equations used to apply the general dynamic model (GDM) of oceanic island biogeography proposed by R. J. Whittaker, K. A. Triantis and R. J. Ladle. Location Analyses are presented for 17 animal groups living on the Aeolian Islands, a volcanic archipelago in the central Mediterranean, near Sicily. Methods In addition to the mathematical implementation of the GDM proposed by Whittaker, Triantis and Ladle, and termed here logATT² (, where S is species number or any other diversity metric, t is island age, A is island area, and a, b, c and d are fitted parameters), a new implementation based on the Arrhenius equation of the species–area relationship (SAR) is investigated. The new model (termed powerATT²) is: . For logATT² and powerATT² models, equations were developed to calculate (1) the expected number of species at equilibrium (i.e. when the island has reached maturity) per unit area (S_eq), and (2) the time required to obtain this value (t_eq). Whereas the intercept in the Gleason model (S = C + z log A) or the coefficient of the Arrhenius power model (S = CA^z) of the SAR can be considered measures of the expected number of species per unit area, this is not the case for the parameter a of the ATT² models. However, values of S_eq can be used for this purpose. The index of ‘colonization ability’ (CAB), calculated as the ratio , may provide a measure of the mean number of species added per unit area per unit time. Results Both ATT² models fitted most of the data well, but the powerATT² model was in most cases superior. Equilibrial values of species richness (S_eq) varied from c. 3 species km^?2 (reptiles) to 100 species km^?2 (mites). The fitted curves for the powerATT² model showed large variations in d, from 0.03 to 3. However, most groups had values of d around 0.2–0.4, as commonly observed for the z‐values of SARs modelled by a power function. Equilibration times ranged from about 170,000 years to 400,000 years. Mites and springtails had very high values of CAB, thus adding many more species per unit area per unit time than others. Reptiles and phytophagous scarabs showed very low values, being the groups that added fewest species per unit area per unit time. Main conclusions Values of equilibrial species richness per unit area are influenced by species biology (e.g. body size and ecological specialization). Theoretical and empirical evidence suggests that higher immigration rates should increase the z‐values of the Arrhenius model. Thus, in the same archipelago, groups with larger z‐values should be characterized by higher dispersal ability. Results obtained here for the parameter d conform to this prediction.     

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.     

Flea (Siphonaptera) species richness in the Great Basin Desert and island biogeography theory

Numbers of flea (Siphonaptera) species (flea species richness) on individual mammals should be higher on large mammals, mammals with dense populations, and mammals with large geographic ranges, if mammals are islands for fleas. I tested the first two predictions with regressions of H. J. Egoscue's trapping data on flea species richness collected from individual mammals against mammal size and population density from the literature. Mammal size and population density did not correlate with flea species richness. Mammal geographic range did, in earlier studies. The intermediate‐sized (31 g), moderately dense (0.004 individuals/m²) Peromyscus truei (Shufeldt) had the highest richness with eight flea species on one individual. Overall, island biogeography theory does not describe the distribution of flea species on mammals in the Great Basin Desert, based on H. J. Egoscue's collections. Alternatively, epidemiological or metapopulation theories may explain flea 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.     

Not as the crow flies: assessing effective isolation for island biogeographical analysis

Aim To evaluate the role of island isolation in explaining the distribution of vascular plant species in a dense freshwater archipelago, specifically comparing conventional measures of island isolation with landscape measures of island isolation. Location Data were collected from 35 islands within Massasauga Provincial Park on the eastern shores of the Georgian Bay, Ontario, Canada. Methods Sampled islands were located using stratified random selection based on location and size variation. The number of species was recorded along stratified random transects. Island isolation variables included distance to the mainland, distance to the nearest island, largest gap in a stepping‐stone sequence, distance to the closest upwind point of land, and a landscape measure of island isolation. The landscape measure of isolation was quantified as the percentage of the land area within 100, 250, 500, 1000, 1500 and 2000 m of each island’s perimeter. The isolation variables were calculated within a geographical information system (GIS). Dependent variables in the regression analyses included species richness, the logarithm of species richness and residuals of the species–area relationship. Independent variables included island isolation variables and their logarithmic transformations. Results Isolation plays a role, albeit small, in explaining species richness in the study area. In the regression analyses, the landscape measure of isolation provided a better fit than conventional measures of island isolation. Islands with less land than water within a 250‐m buffer were more effectively isolated and had fewer species present than islands surrounded by a greater proportion of water. Main conclusions Consistent with the species–isolation relationship, fewer species were present on more isolated islands within the Massasauga study area, as elucidated using a series of island buffers in a GIS. Applying a landscape measure of isolation to similar dense, freshwater archipelagos may elucidate species–isolation patterns not evident through conventional, straight‐line distance measurements of island isolation. The low value of the regression coefficients as well as the isolation history and high density of the Massasauga islands suggests caution in extending the results, especially to dissimilar archipelagos.     

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.     

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.