The role of habitat diversity in generating the small-island effect

The small-island effect (SIE) has become a widespread pattern in island biogeography and biodiversity research. However, in most previous studies only area is used for the detection of the SIE, while other causal factors such as habitat diversity is rarely considered. Therefore, the role of habitat diversity in generating SIEs is poorly known. Here, we compiled 86 global datasets that included the variables of habitat diversity, area and species richness to systematically investigate the prevalence and underlying factors determining the role of habitat diversity in generating SIEs. For each dataset, we used both path analysis and breakpoint regressions to identify the existence of an SIE. We collected a number of system characteristics and employed logistic regression models and an information–theoretic approach to determine which combination of variables was important in determining the role of habitat diversity in generating SIEs. Among the 61 datasets with adequate fits, habitat diversity was found to influence the detection of SIEs in 32 cases (52.5%) when using path analysis. By contrast, SIEs were detected in 26 of 61 cases (42.6%) using breakpoint regressions. Model selection and model-averaged parameter estimates showed that Number of sites, Habitat range and Species range were three key variables that determined the role of habitat diversity in generating SIEs. However, Area range, Taxon group and Site type received considerably less support. Our study demonstrates that the effect of habitat diversity on generating SIEs is quite prevalent. The inclusion of habitat diversity is important because it provides a causal factor for the detection of SIEs. We conclude that for a better understanding of the causes of SIEs, habitat diversity should be included in future studies.     

Species diversity and endemism of five major Malesian islands: diversity–area relationships

Aim A comparison of biodiversity patterns within Malesia in relation to surface area. Location Analysis of the patterns in species richness and endemism of vascular plants in the five major Malesian islands, i.e. Java, Sulawesi, Sumatra, Borneo and New Guinea. Methods Available data on species richness and species ranges in correlation with the surface area of the respective islands were examined in this work. Estimations of total species numbers for these islands are presented based on extrapolation of all available published Flora Malesiana information and recent checklists, all in all comprising 12,000 different species. The regression analysis of overall species richness and endemism were studied for all species together as well as for different plant families to compare the fit with the Arrhenius species–area model. Results The five islands form a series of independent areas of increasing size suited for an analysis of the species–area relationships at the regional scale. All species taken together and those of families with even distribution throughout Malesia show significant species–area relationships. Non‐significant relationships were detected in families with western or eastern‐centred Malesian distribution patterns. Relationships between number of endemic species and surface area are significant for all species and for the majority of the families with significant species–area relationships. Main conclusions Species–area relationships of families appear to be dependent on species number. Families with high numbers of species usually have a significant species–area relationship whereas small families have not. For the families that display an eastern or western Malesian centred pattern, a historical biogeographical explanation should be invoked. Island surface area appears to be a predictor for island percent endemism in Malesian vascular plants. None of the islands appears to be a hotspot of endemism nor of species diversity, as no significant departure from the Arrhenius model was noted for any of them.     

The power of time: spatiotemporal scaling of species diversity

The species–area relationship (SAR) provides the foundation for much of theoretical ecology and conservation practice. However, by ignoring time the SAR offers an incomplete model for biodiversity dynamics. We used long‐term data from permanent plots in Kansas grasslands, USA, to show that the increase in the number of species found with increasing periods of observation takes the same power‐law form as the SAR. A statistical model including time, area, and their interaction explains 98% of variation in mean species number and demonstrates that while the effect of time depends on area, and vice versa, time has strong effects on species number even at relatively broad spatial scales. Our results suggest equivalence of underlying processes in space and time and raise questions about the diversity estimates currently used by basic researchers and conservation practitioners.     

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.     

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.

     

Global patterns of diversity among the specialist birds of riverine landscapes

1. Despite the growing view that biodiversity provides a unifying theme in river ecology, global perspectives on richness in riverine landscapes are limited. As a result, there is little theory or quantitative data on features that might have influenced global patterns in riverine richness, nor are there clear indications of which riverine landscapes are important to conservation at the global scale. As conspicuous elements of the vertebrate fauna of riverine landscapes, we mapped the global distributions of all of the world's specialist riverine birds and assessed their richness in relation to latitude, altitude, primary productivity and geomorphological complexity (surface configuration). 2. Specialist riverine birds, typical of high‐energy riverine landscapes and dependent wholly or partly on production from river ecosystems, occur in 16 families. They are represented by an estimated 60 species divided equally between the passerines and non‐passerines. Major radiation has occurred among different families on different continents, indicating that birds have evolved several times into the niches provided by riverine landscapes. 3. Continental richness varies from four species in Europe to 28 in Asia, with richness on the latter continent disproportionately larger than would be expected from a random distribution with respect to land area. Richness is greatest in mountainous regions at latitudes of 20–40°N in the riverine landscapes of the Himalayan mountains, where 13 species overlap in range. 4. Family, genus and species richness in specialist riverine birds all increase significantly with productivity and surface configuration (i.e. relief). However, family richness was the best single predictor of the numbers of species or genera. In keeping with the effect of surface configuration, river‐bird richness peaks globally at 1300–1400 m altitude, and most species occur typically on small, fast rivers where they feed predominantly on invertebrates. Increased lengths of such streams in areas of high relief and rainfall might have been responsible for species–area effects. 5. We propose the hypothesis that the diversity in channel forms and habitats in riverine landscapes, in addition to high temperature and primary productivity, have been prerequisites to the development of global patterns in the richness of specialist riverine organisms. We advocate tests of this hypothesis in other taxonomic groups. We draw attention, however, to the challenges of categorically defining riverine organisms in such tests because (i) rivers grade into many other habitat types across several different ecotones and (ii) `terrestrialisation' processes in riverine landscapes means that they offer habitat for organisms whose evolutionary origins are not exclusively riverine.     

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.     

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.     

Separating the effects of number of individuals sampled and competition on species diversity: an experimental and analytic approach

1 Species richness typically increases with the number of individuals sampled, although many ecological processes that influence species richness are also well known to depend on density of individuals. We separated the effects of density on species richness that are due to sampling, from those due to density-dependent ecological processes such as competition or predation, by manipulating the density of an entire community.
2 A seed bank from a community of desert annual plants that occur on semi-stabilized sand dunes in Israel was collected from the field and sown in an experimental garden at a range of densities from 1/16 to eight times the natural density. The species pool observed in the lowest density plots was used as the null community, which was repeatedly sampled to calculate the species richness (and other diversity indices) in higher density plots that would be expected from sampling considerations alone. The significance of deviations of observed diversity from this expected diversity was then evaluated.
3 Both observed and expected number of species increased substantially with the experimental increase in density. However, observed species richness, the Shannon–Wiener diversity index and Simpson's diversity index were often significantly lower than that expected based on sampling considerations. The magnitude of the deviation from expected increased significantly with increasing density for richness and the Shannon–Wiener index. This provides some of the first direct experimental evidence from diverse natural assemblages that increasing competition among all the individuals in a community can lead to competitive exclusion.     

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.     

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.     

The effects of archipelago spatial structure on island diversity and endemism: predictions from a spatially‐structured neutral model

Islands are particularly suited to testing hypotheses about the ecological and evolutionary mechanisms underpinning community assembly. Yet the complex spatial arrangements of real island systems have received little attention from both empirical studies and theoretical models. Here, we investigate the extent to which the spatial structure of archipelagos affects species diversity and endemism. We start by proposing a new spatially structured neutral model that explicitly considers archipelago structure, and then investigate its predictions under a diversity of scenarios. Our results suggest that considering the spatial structure of archipelagos is crucial to understanding their diversity and endemism, with structured island systems acting both as “museums” and “cradles” of biodiversity. These dynamics of diversification may change the traditionally expected pattern of decrease in species richness with distance from the mainland, even potentially leading to increasing patterns for taxa with high speciation rates in archipelagos off species‐poor continental areas. Our results also predict that, within spatially structured archipelagos, metapopulation dynamics and evolutionary processes can generate higher diversity on islands more centrally placed than at the periphery. We derive from our results a set of theoretical predictions, potentially testable with empirical data.     

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.     

Patterns in species richness and endemism of European freshwater fish

Aim  To analyse the patterns in species richness and endemism of the native European riverine fish fauna, in the light of the Messinian salinity crisis and the Last Glacial Maximum (LGM).
Location  European continent.
Methods  After gathering native fish faunistic lists of 406 hydrographical networks, we defined large biogeographical regions with homogenous fish fauna, based on a hierarchical cluster analysis. Then we analysed and compared the patterns in species richness and endemism among these regions, as well as species–area relationships.
Results  Among the 233 native species present in the data set, the Cyprinidae family was strongly dominant (> 50% of the total number of species). Seven biogeographical regions were defined: Western Peri-Mediterranea, Central Peri-Mediterranea, Eastern Peri-Mediterranea, Ponto-Caspian Europe, Northern Europe, Central Europe and Western Europe. The highest regional species richness was observed for Central Peri-Mediterranea and Ponto-Caspian Europe. The highest endemic richness was found in Central Peri-Mediterranea. Species–area relationships were characterized by high slope values for Peri-Mediterranean Europe and low values for Central and Western Europe.
Main conclusions  The results were in agreement with the 'Lago Mare' hypothesis explaining the specificity of Peri-Mediterranean fish fauna, as well as with the history of recolonization of Central and Western Europe from Ponto-Caspian Europe following the LGM. The results also agreed with the mechanisms of speciation and extinction influencing fish diversity in hydrographical networks. We advise the use of the seven biogeographical regions for further studies, and suggest considering Peri-Mediterranean Europe and Ponto-Caspian Europe as 'biodiversity hotspots' for European riverine fish.     

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.