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.     

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

Aim

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

Location

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

Methods

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

Results

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

Main conclusions

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

     

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

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

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.     

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.     

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.     

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

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

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.     

A unified model of island biogeography sheds light on the zone of radiation

Islands acquire species through immigration and speciation. Models of island biogeography should capture both processes; however quantitative island biogeography theory has either neglected speciation or treated it unrealistically. We introduce a model where the dominance of immigration on small and near islands gives way to an increasing role for speciation as island area and isolation increase. We examine the contribution of immigration and speciation to the avifauna of 35 archipelagoes and find, consistent with our model, that the zone of radiation comprises two regions: endemic species diverged from mainland sister-species at intermediate isolation and from insular sister-species at higher levels of isolation. Our model also predicts species-area curves in accord with existing research and makes new predictions about species ages and abundances. We argue that a paucity of data and theory on species abundances on isolated islands highlights the need for island biogeography to be reconnected with mainstream ecology.     

陆地生境岛屿藓类植物小岛屿效应驱动因素分析——以太行山脉中段山顶为例

小岛屿效应描述了种-面积关系的一种特殊现象,是当前生物地理学和生物多样性研究理论框架的重要组成部分。随着气候变暖,山顶物种的生存受到威胁,然而以山顶生境岛屿为载体对小岛屿效应的研究还十分缺乏。该研究以太行山脉中段19个面积0.06–801.58km²的山顶生境岛屿为研究区,在2019–2021年的夏秋季对藓类进行调查。共记录到藓类131种,隶属于23科68属。采用6种种-面积关系回归模型,分别检测了所有藓和6个常见藓科是否存在小岛屿效应。根据小岛屿效应形成机制的生境多样性假说、灭亡假说和营养补给假说,选择了岛屿高度、温度年变化范围和单位面积净初级生产力作为变量,对小岛屿效应的驱动因素进行分析。在各类群组中,使用多元线性回归和变差分解分别评估上述3个变量对物种丰富度变化的线性影响。首先使用5个面积最小的岛屿进行分析,计算出3个变量对物种丰富度变化的贡献,然后以迭代的方式逐次加入面积更大的1个岛屿,并再次进行变差分解分析。最后使用广义线性回归分析了3个变量对物种丰富度变化的贡献在迭代过程中的变化趋势。结果显示,所有藓和6个常见藓科均存在小岛屿效应,其面积阈值分布在0....     

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.     

Size evolution in island lizards

Aim  The island rule, small animal gigantism and large animal dwarfism on islands, is a topic of much recent debate. While size evolution of insular lizards has been widely studied, whether or not they follow the island rule has never been investigated. I examined whether lizards show patterns consistent with the island rule.
Location  Islands worldwide.
Methods  I used literature data on the sizes of island–mainland population pairs in 59 species of lizards, spanning the entire size range of the group, and tested whether small insular lizards are larger than their mainland conspecifics and large insular lizards are smaller. I examined the influence of island area, island isolation, and dietary preferences on lizard size evolution.
Results  Using mean snout–vent length as an index of body size, I found that small lizards on islands become smaller than their mainland conspecifics, while large ones become larger still, opposite to predictions of the island rule. This was especially strong in carnivorous lizards; omnivorous and herbivorous species showed a pattern consistent with the island rule but this result was not statistically significant. No trends consistent with the island rule were found when maximum snout–vent length was used. Island area had, at best, a weak effect on body size. Using maximum snout–vent length as an index of body size resulted in most lizard populations appearing to be dwarfed on islands, but no such pattern was revealed when mean snout–vent length was used as a size index.
Main conclusions  I suggest that lizard body size is mostly influenced by resource availability, with large size allowing some lizard populations to exploit resources that are unavailable on the mainland. Lizards do not follow the island rule. Maximum snout–vent length may be biased by sampling effort, which should be taken into account when one uses this size index.     

The island biogeography of languages

Aim To examine the degree to which area, isolation, environmental conditions and time since first settlement explain variation in language richness among islands. Location Pacific islands ranging east–west from Rapa Nui to Indonesia and north–south from Hawaii to New Zealand. Methods We constructed a dataset of 264 Pacific islands that support 1640 languages (c. 24% of the world's languages). We examined possible predictors of language richness using three different types of models: linear regression models, linear mixed models that included random effects for language phylogeny and simultaneous autoregressive models. We tested whether the following variables, alone or in combination, predict language richness: island area and isolation, climate (rainfall, temperature), mean growing season, soil fertility, habitat heterogeneity (elevation, number of ecoregions), time since first human settlement. Results We identified two optimal models (delta Akaike information criterion < 2). One (R²= 0.52) included area, with 86% of remaining variation accounted for by random effects for phylogeny. The other (R²= 0.56) included a spatial component, area and a suite of other variables (of which isolation and settlement scale were significant). Of the hypotheses tested (mean growing season, ecological risk, habitat heterogeneity, climate, time since settlement, area–isolation theory), area–isolation performed best, alone explaining 44% of variation in language richness. Main conclusions Language diversity relates strongly to island area, and, after controlling for area, with variables linked to isolation (e.g. distance to continent, time since settlement). The influence of environmental productivity may be scale and context dependent. Although environmental productivity may shape language diversity patterns at a global scale, it plays little role on Pacific islands. Approximately half the variance in language richness remains unexplained. Unlike other taxa, for which area, isolation and environmental conditions explain up to 90% of variation in richness, human diversity patterns appear to also be influenced by other variables (e.g. economic, political and social factors).     

A general dynamic theory of oceanic island biogeography

Aim MacArthur and Wilson’s dynamic equilibrium model of island biogeography provides a powerful framework for understanding the ecological processes acting on insular populations. However, their model is known to be less successful when applied to systems and processes operating on evolutionary and geological timescales. Here, we present a general dynamic model (GDM) of oceanic island biogeography that aims to provide a general explanation of biodiversity patterns through describing the relationships between fundamental biogeographical processes – speciation, immigration, extinction – through time and in relation to island ontogeny. Location Analyses are presented for the Azores, Canaries, Galápagos, Marquesas and Hawaii. Methods We develop a theoretical argument from first principles using a series of graphical models to convey key properties and mechanisms involved in the GDM. Based on the premises (1) that emergent properties of island biotas are a function of rates of immigration, speciation and extinction, (2) that evolutionary dynamics predominate in large, remote islands, and (3) that oceanic islands are relatively short‐lived landmasses showing a characteristic humped trend in carrying capacity (via island area, topographic variation, etc.) over their life span, we derive a series of predictions concerning biotic properties of oceanic islands. We test a subset of these predictions using regression analyses based largely on data sets for native species and single‐island endemics (SIEs) for particular taxa from each archipelago, and using maximum island age estimates from the literature. The empirical analyses test the power of a simple model of diversity derived from the GDM: the log(Area) + Time + Time² model (ATT²), relative to other simpler time and area models, using several diversity metrics. Results The ATT² model provides a more satisfactory explanation than the alternative models evaluated (for example the standard diversity–area models) in that it fits a higher proportion of the data sets tested, although it is not always the most parsimonious solution. Main conclusions The theoretical model developed herein is based on the key dynamic biological processes (migration, speciation, extinction) combined with a simple but general representation of the life cycle of oceanic islands, providing a framework for explaining patterns of biodiversity, endemism and diversification on a range of oceanic archipelagos. The properties and predictions derived from the model are shown to be broadly supported (1) by the empirical analyses presented, and (2) with reference to previous phylogenetic, ecological and geological studies.     

Subsidized Island Biogeography Hypothesis: another new twist on an old theory

We present a new hypothesis for predicting and describing patterns of species diversity on small islands and habitat fragments. We have modified the traditional island biogeography equilibrium theory to incorporate the influence of spatial subsidies from the surrounding matrix, which vary among islands and habitat fragments, on species diversities. The modification indicates three possible directions for the effects of spatial subsidies on diversity, which depend on where the focal community falls on the hypothesized unimodal curve of the productivity–diversity relationship. The idea is novel because no recent syntheses of productivity–diversity–area relationships examine the role of allochthonous resources on recipient communities’ diversity patterns.     

Species richness and structure of ant communities in a dynamic archipelago: effects of island area and age

Aim To assess how ant species richness and structure of ant communities are influenced by island age (disturbance history) in a dynamic archipelago. Location Cabra Corral dam, Salta Province, north‐west Argentina (25°08′ S, 65°20′ W). Methods Ant species richness on remaining fragments (islands) of a flooded forest was determined, as well as island area, isolation and age. Simple linear regressions were performed to assess relationships between ant species richness and those insular variables. Furthermore, a stepwise multiple linear regression analysis was conducted in order to determine the relative influence of each insular variable on ant species richness. Islands were categorized in two age classes (old and young) and co‐occurrence analyses were applied within each class to evaluate changes in community structure because of interspecific competition. Results Simple regression analyses indicated a moderate, positive effect of island area on ant species richness. Weak, marginally non‐significant relationships were found between ant species richness and both island isolation and island age, showing the tendency for there to be a decrease in ant species richness with island isolation and that ant species richness might be higher in old islands. The multiple regression analysis indicated that island isolation and age had no significant effects on the number of ant species, island area being the only independent variable retained in the analysis. On the contrary, whereas a random pattern of species co‐occurrence was found on young islands, ant communities in old islands showed a significantly negative pattern of species co‐occurrence, suggesting that the effect of competition on community structure was stronger on older islands than on younger islands. Main conclusions Island area was the most important variable explaining ant species richness on the islands of Cabra Corral dam. However, both island isolation and island age (or disturbance history) might also contribute to shape the observed community patterns. The present study also shows that island age significantly affects the strength with which interspecific interactions structure ant communities on islands.     

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.     

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.     

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

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

Subjecting the theory of the small‐island effect to Ockham’s razor

Species–area curves from islands and other isolates often differ in shape from sample‐area curves generated from mainlands or sections of isolates (or islands), especially at finer scales. We examine two explanations for this difference: (1) the small‐island effect (SIE), which assumes the species–area curve is composed of two distinctly different curve patterns; and (2) a sigmoid or depressed isolate species–area curve with no break‐points (in arithmetic space). We argue that the application of Ockham’s razor – the principle that the simplest, most economical explanation for a hypothesis should be accepted over less parsimonious alternatives – leads to the conclusion that the latter explanation is preferable. We hold that there is no reason to assume the ecological factors or patterns that affect the shapes of isolate (or island) curves cause two distinctly different patterns. This assumption is not required for the alternative, namely that these factors cause a single (though depressed) isolate species–area curve with no break‐points. We conclude that the theory of the small‐island effect, despite its present standing as an accepted general pattern in nature, should be abandoned.