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

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

Species richness, environmental heterogeneity and area: a case study based on land snails in Skyros archipelago (Aegean Sea, Greece)

Aim To test the performance of the choros model in an archipelago using two measures of environmental heterogeneity. The choros model is a simple, easy‐to‐use mathematical relationship which approaches species richness as a combined function of area and environmental heterogeneity. Location The archipelago of Skyros in the central Aegean Sea (Greece). Methods We surveyed land snails on 12 islands of the archipelago. We informed the choros model with habitat data based on natural history information from the land snail species assemblage. We contrast this with habitat information taken from traditional vegetation classification to study the behaviour of choros with different measures of environmental heterogeneity. R² values and Akaike's information criterion (AIC) were used to compare the choros model and the Arrhenius species–area model. Path analysis was used to evaluate the variance in species richness explained by area and habitat diversity. Results Forty‐two land snail species were recorded, living in 33 different habitat types. The choros model with habitat types had more explanatory power than the classic species–area model and the choros model using vegetation types. This was true for all islands of the archipelago, as well as for the small islands alone. Combined effects of area and habitat diversity primarily explain species richness in the archipelago, but there is a decline when only small islands are considered. The effects of area are very low both for all the islands of the archipelago, and for the small islands alone. The variance explained by habitat diversity is low for the island group as a whole, but significantly increases for the small islands. Main conclusions The choros model is effective in describing species‐richness patterns of land snails in the Skyros Archipelago, incorporating ecologically relevant information on habitat occupancy and area. The choros model is more effective in explaining richness patterns on small islands. When using traditional vegetation types, the choros model performs worse than the classic species–area relationship, indicating that use of proxies for habitat diversity may be problematic. The slopes for choros and Arrhenius models both assert that, for land snails, the Skyros Archipelago is a portion of a larger biogeographical province. The choros model, informed by ecologically relevant habitat measures, in conjunction with path analysis points to the importance of habitat diversity in island 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.     

How does landscape context contribute to effects of habitat fragmentation on diversity and population density of butterflies?

Aim Studies on habitat fragmentation of insect communities mostly ignore the impact of the surrounding landscape matrix and treat all species equally. In our study, on habitat fragmentation and the importance of landscape context, we expected that habitat specialists are more affected by area and isolation, and habitat generalists more by landscape context. Location and methods The study was conducted in the vicinity of the city of Göttingen in Germany in the year 2000. We analysed butterfly communities by transect counts on thirty‐two calcareous grasslands differing in size (0.03–5.14 ha), isolation index (2100–86,000/edge‐to‐edge distance 55–1894 m), and landscape diversity (Shannon–Wiener: 0.09–1.56), which is correlated to percentage grassland in the landscape. Results A total of 15,185 butterfly specimens belonging to fifty‐four species are recorded. In multiple regression analysis, the number of habitat specialist (n = 20) and habitat generalist (n = 34) butterfly species increased with habitat area, but z‐values (slopes) of the species–area relationships for specialists (z = 0.399) were significantly steeper compared with generalists (z = 0.096). Generalists, but not specialists, showed a marginally significant increase with landscape diversity. Effects of landscape diversity were scale‐dependent and significant only at the smallest scale (landscape context within a 250 m radius around the habitat). Habitat isolation was not related to specialist and generalist species numbers. In multiple regression analysis the density of specialists increased significantly with habitat area, whereas generalist density increased only marginally. Habitat isolation and landscape diversity did not show any effects. Main conclusions Habitat area was the most important predictor of butterfly community structure and influenced habitat specialists more than habitat generalists. In contrast to our expectations, habitat isolation had no effect as most butterflies could cope with the degree of isolation in our study region. Landscape diversity appeared to be important for generalist butterflies only.     

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).     

Dispersal and establishment: spatial patterns and species–area relationships

According to the equilibrium theory of island biogeography, high colonization ability of species is associated with low exponents (z) of the species–area relationship (SAR) and weak spatial patterns in species number and dissimilarity. However, the relationship between z and the strength of these spatial patterns has not been investigated systematically. We used a multispecies metapopulation model to investigate these relationships in an archipelago of islands. We conclude that this relationship can only be predicted if either the dispersal ability or the power of establishment of species is known. With species richness limited by establishment, we generated high z‐values associated with weak spatial patterns in species number and dissimilarity. If species richness was constrained by the dispersal ability of species, we observed low to medium z‐values but strong spatial patterns. If the dispersal ability and the abilities of species to establish were both high, z‐values and spatial pattern tend to be low and species numbers were limited by the size of the regional species pool.     

Species‐area relationships of lemurs in a fragmented landscape in Madagascar

We used species‐area relationships (SARs) to investigate the effects of habitat loss on lemur biogeography. We measured species richness via visual surveys on line transects within 42 fragments of dry deciduous forest at the Ambanjabe field site in Ankarafantsika National Park, Madagascar. We measured human disturbance and habitat characteristics within 38 of the 42 fragments. We measured the distance between each fragment and the nearest settlement, continuous forest, and nearest neighboring fragment. We fit 10 candidate SAR models to the data using nonlinear least squares regression and compared them using Akaike's Information Criterion (AIC). To determine how habitat characteristics, as well as area, influenced species richness, we ran a hierarchical partitioning procedure to select which variables to include in generalized additive models (GAMs) and compared them using AIC. Contrary to expectations, we found that lemurs form convex SARs, without a “small island effect”, and with the power model being the most likely SAR model. Although we found that four variables (area, survey effort, and total human disturbance, and mean tree height) independently contributed greater than 10% of the variation in lemur species richness, only area was included in the most likely model. We suggest that the power model was the most likely SAR model and our inability to detect a “small island effect” are the result of Microcebus spp. being edge tolerant and capable of dispersing through matrix, scale issues in the study design, and low γ‐diversity in the landscape. However, more study is needed to determine what role human disturbance plays in influencing species richness in lemurs.     

Habitat fragmentation and species diversity in competitive communities

Habitat loss is one of the key drivers of the ongoing decline of biodiversity. However, ecologists still argue about how fragmentation of habitat (independent of habitat loss) affects species richness. The recently proposed habitat amount hypothesis posits that species richness only depends on the total amount of habitat in a local landscape. In contrast, empirical studies report contrasting patterns: some find positive and others negative effects of fragmentation per se on species richness. To explain this apparent disparity, we devise a stochastic, spatially explicit model of competitive species communities in heterogeneous habitats. The model shows that habitat loss and fragmentation have complex effects on species diversity in competitive communities. When the total amount of habitat is large, fragmentation per se tends to increase species diversity, but if the total amount of habitat is small, the situation is reversed: fragmentation per se decreases species diversity.     

Species–area models to assess biodiversity change in multi-habitat landscapes: The importance of species habitat affinity

Species–area relationships (SARs) are a common tool to assess the impacts of habitat loss on species diversity. Species–area models that include habitat effects may better describe biodiversity patterns; also the shape of the SAR may be best described by other models than the classical power model. We compared the fit of 24 SAR models, i.e. eight basic models using three approaches: (i) single-habitat models, (ii) multi-habitat models which account for the effect of the habitat composition on total species diversity (= choros models) and (iii) multi-habitat models which also account for the differential use of habitats by different species groups (= countryside models). We use plant diversity data from a multi-habitat landscape in NW Portugal. Countryside models had the best fit both when predicting species–area patterns of species groups and of total species richness. Overall, choros models had a better fit than single-habitat models. We also tested the application of multi-habitat models to land-use change scenarios. Estimates of species richness using the choros model only depended on the number of habitats in the landscape. In contrast, for the countryside model, estimates of species richness varied continuously with the relative proportion of the different habitat types in the landscape, and projections suggest that land-use change impacts may be moderated by a species’ ability to use multiple habitats in the landscape. We argue that the countryside SAR is a better model to assess the impacts of land-use changes than the single-habitat SAR or the choros model, as species often face habitat change instead of real habitat loss, and species response to change is contingent on their differential use of habitats in the landscape.     

Effects of patch size and basal area on avian taxonomic and functional diversity in pine forests: Implication for the influence of habitat quality on the species–area relationship

Relationships between avian diversity and habitat area are assumed to be positive; however, often little attention has given to how these relationships can be influenced by the habitat structure or quality. In addition, other components of biodiversity, such as functional diversity, are often overlooked in assessing habitat patch value. In the Sandhills Ecoregion of Georgia, USA, we investigated the relationship between avian species richness and functional diversity, forest basal area, and patch size in pine forests using basal area as a surrogate for overstory structure which in turn impacts vegetation structure and determines habitat quality within a patch. We conducted bird surveys in planted mature pine stands, during breeding season of 2011. We used three classes of stand basal area (BA): OS, overstocked (BA ≥ 23 m²/ha); FS, fully/densely stocked (13.8 m²/ha ≤ BA < 23 m²/ha); and MS, moderately stocked (2.3 m²/ha ≤ BA < 13.8 m²/ha). MS patches showed more structural diversity due to higher herbaceous vegetation cover than other two pine stocking classes of patches. Total species richness and functional richness increased with the size of MS patches, whereas functional divergence decreased with the size of OS patches (p < 0.05). Functional richness tended to be lower than expected as the size of OS patches increased. Greater richness of pine–grassland species was also found at MS patches. Percent cover of MS patches within a landscape influenced positively the richness of pine–grassland species (p < 0.05). Our results suggest that (a) avian species–habitat area relationship can be affected by habitat quality (structural diversity) and varies depending on diversity indices considered, and (b) it is important to maintain moderate or low levels of pine basal area and to preserve large‐sized patches of the level of basal area to enhance both taxonomic and functional diversity in managed pine forests.     

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.

     

Species–area relationships of red-listed species in old boreal forests: a large-scale data analysis

Aim Species–area relationships are often applied, but not generally approved, to guide practical conservation planning. The specific species group analysed may affect their applicability. We asked if species–area curves constructed from extensive databases of various sectors of natural resource administration can provide insights into large‐scale conservation of boreal forest biodiversity if the analyses are restricted only to red‐listed species. Location Finland, northern Europe. Methods Our data included 12,645 records of 219 red‐listed Coleoptera and Fungi from the whole of Finland. The forest data also covered the entire country, 202,761 km². The units of species–area analyses were 224 municipalities where the red‐listed forest species have been observed. We performed a hierarchical partitioning analysis to reveal the relative importance of different potential explanatory variables. Based on the results, for all red‐listed species, species associated with coniferous trees and for Fungi, the area of economically over‐aged forests explained the best the variation in data. For species associated with deciduous trees and Coleoptera, the forest area explained better variation in data than the area of old forests. In the subsequent log–log species–area regression analyses, we used the best variables as the explanatory variable for each species group. Results There was a strong relationship between the number of all red‐listed species and the area of old forests remaining, with a z‐value of 0.45. The area explained better the number of species associated with conifer trees and Fungi than the number of species associated with deciduous trees and Coleoptera. Main conclusions The high z‐values of species–area curves indicate that the remaining old‐growth patches constitute a real archipelago for the conifer‐associated red‐listed species, since lower values had been expected if the surrounding habitat matrix were a suitable habitat for the species analysed.     

Island biogeography and the species richness of introduced mammals on New Zealand offshore islands

Aim To investigate and establish the significance of various island biogeographic relationships (geographical, ecological and anthropological) with the species richness of introduced mammals on offshore islands. Location The 297 offshore islands of the New Zealand archipelago (latitude: 34–47°S; longitude: 166–179°E). Methods Data on New Zealand offshore islands and the introduced mammals on them were collated from published surveys and maps. The species richness of small and large introduced mammals were calculated for islands with complete censuses and regressed on island characteristics using a Poisson distributed error generalized linear model. To estimate the ‘z‐value’ for introduced mammals on New Zealand islands, least‐squares regression was used [log₁₀ S vs. log₁₀ A]. Results High collinearity was found between the area, habitat diversity and elevation of islands. The island characteristics related to the species richness of introduced mammals differed predictably between large and small mammals. The species richness of introduced large mammals was mostly related to human activities on islands, whereas species richness of introduced small mammals was mostly related to island biogeographical parameters. The ‘z‐value’ for total species richness is found to be expectedly low for introduced mammals. Main conclusions Distance appears to have become ecologically trivial as a filter for introduced mammal presence on New Zealand offshore islands. There is strong evidence of a ‘small island’ effect on New Zealand offshore islands. The species richness of both small and large introduced mammals on these islands appears to be most predominantly related to human use, although there is some evidence of natural dispersal for smaller species. The ecological complexity of some islands appears to make them less invasible to introduced mammals. Some human activities have an interactive effect on species richness. A small number of islands have outlying species richness values above what the models predict, suggesting that the presence of some species may be related to events not accounted for in the models.     

Plants on small islands revisited: the effects of spatial scale and habitat quality on the species–area relationship

Understanding how species diversity is related to sampling area and spatial scale is central to ecology and biogeography. Small islands and small sampling units support fewer species than larger ones. However, the factors influencing species richness may not be consistent across scales. Richness at local scales is primarily affected by small‐scale environmental factors, stochasticity and the richness at the island scale. Richness at whole‐island scale, however, is usually strongly related to island area, isolation and habitat diversity. Despite these contrasting drivers at local and island scales, island species–area relationships (SARs) are often constructed based on richness sampled at the local scale. Whether local scale samples adequately predict richness at the island scale and how local scale samples influence the island SAR remains poorly understood. We investigated the effects of different sampling scales on the SAR of trees on 60 small islands in the Raja Ampat archipelago (Indonesia) using standardised transects and a hierarchically nested sampling design. We compared species richness at different grain sizes ranging from single (sub)transects to whole islands and tested whether the shape of the SAR changed with sampling scale. We then determined the importance of island area, isolation, shape and habitat quality at each scale on species richness. We found strong support for scale dependency of the SAR. The SAR changed from exponential shape at local sampling scales to sigmoidal shape at the island scale indicating variation of species richness independent of area for small islands and hence the presence of a small‐island effect. Island area was the most important variable explaining species richness at all scales, but habitat quality was also important at local scales. We conclude that the SAR and drivers of species richness are influenced by sampling scale, and that the sampling design for assessing the island SARs therefore requires careful consideration.     

Local–regional boundary shifts in oribatid mite (Acari: Oribatida) communities: species–area relationships in arboreal habitat islands of a coastal temperate rain forest, Vancouver Island, Canada

Aim This study investigates the species–area relationship (SAR) for oribatid mite communities of isolated suspended soil habitats, and compares the shape and slope of the SAR with a nested data set collected over three spatial scales (core, patch and tree level). We investigate whether scale dependence is exhibited in the nested sampling design, use multivariate regression models to elucidate factors affecting richness and abundance patterns, and ask whether the community composition of oribatid mites changes in suspended soil patches of different sizes. Location Walbran Valley, Vancouver Island, Canada. Methods A total of 216 core samples were collected from 72 small, medium and large isolated suspended soil habitats in six western redcedar trees in June 2005. The relationship between oribatid species richness and habitat volume was modelled for suspended soil habitat isolates (type 3) and a nested sampling design (type 1) over multiple spatial scales. Nonlinear estimation parameterized linear, power and Weibull function regression models for both SAR designs, and these were assessed for best fit using R² and Akaike's information criteria (ΔAIC) values. Factors affecting oribatid mite species richness and standardized abundance (number per g dry weight) were analysed by anova and linear regression models. Results Sixty‐seven species of oribatid mites were identified from 9064 adult specimens. Surface area and moisture content of suspended soils contributed to the variation in species richness, while overall oribatid mite abundance was explained by moisture and depth. A power‐law function best described the isolate SAR (S = 3.97 × A^0.12, R² = 0.247, F_1,70 = 22.450, P < 0.001), although linear and Weibull functions were also valid models. Oribatid mite species richness in nested samples closely fitted a power‐law model (S = 1.96 × A^0.39, R² = 0.854, F_1,18 = 2693.6, P < 0.001). The nested SAR constructed over spatial scales of core, patch and tree levels proved to be scale‐independent. Main conclusions Unique microhabitats provided by well developed suspended soil accumulations are a habitat template responsible for the diversity of canopy oribatid mites. Species–area relationships of isolate vs. nested species richness data differed in the rate of accumulation of species with increased area. We suggest that colonization history, stability of suspended soil environments, and structural habitat complexity at local and regional scales are major determinants of arboreal oribatid mite species richness.     

From plots to islands: species diversity at different scales

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

How,and how much,natural cover loss increases species richness

Aim The species–area relationship has been applied in the conservation context to predict monotonic species richness declines as natural area is converted to human‐dominated land covers. However, some conversion of natural cover could introduce new habitat types and allow new open habitat species to occur. Moreover, decelerating richness–area relationships suggest that, as natural area is converted to human‐dominated covers, more species will be added to the rare habitat than are lost from the common one. Area effects and increased habitat diversity could each lead to a peaked relationship between species richness and the relative amount of natural area. The purpose of this study is to quantify the effect on avian species richness of conversion of natural area to human‐dominated land cover. Location Ontario, Canada. Methods We evaluated the responses of total avian richness, forest bird richness and open habitat bird richness to remaining natural area within 993 quadrats, each of 100 km². We quantified the amount of natural land cover and land‐cover heterogeneity using remote sensing data. We used structural equation modelling (SEM) to disentangle the relationships among avian richness, natural area and land‐cover heterogeneity. Results Spatial variation in avian richness was a peaked function of remaining natural area, such that losses of up to 44% of the natural area increased avian richness. This partly reflects increased variety of land cover; however, SEM suggests that much of the increase in richness is due to pure area effects. Richness of forest species declined by two species over this range of natural cover loss while open habitat bird richness increased by approximately 20 species. The effect of natural area on species richness is consistent with the sum of species–area curves for natural habitat species and human‐dominated habitat species. Main conclusions At least in northern temperate forests, almost half of the natural land cover can be converted to human‐dominated forms before avian richness declines. Conversion of < 50% of regional natural area to human‐dominated land cover can benefit open‐area species richness with relatively few losses of forest obligate species. However, with > 50% natural area conversion, species begin to drop out of regional assemblages.     

How does habitat diversity affect the species–area relationship?

Aim   To examine the way in which 'area' and 'habitat diversity' interact in shaping species richness and to find a simple and valid way to express this interaction.
Location   The Natura 2000 network of terrestrial protected areas in Greece, covering approximately 16% of the national territory.
Methods   We used the Natura 2000 framework, which provides a classification scheme for natural habitat types, to quantify habitat heterogeneity. We analysed data for the plant species composition in 16,143 quadrats in which 5044 species and subspecies of higher plants were recorded. We built a simple mathematical model that incorporates the effect of habitat diversity on the species–area relationship (SAR).
Results   Our analysis showed that habitat diversity was correlated with area. However, keeping habitat diversity constant, species richness was related to area; while keeping area constant, species richness was related to habitat diversity. Comparing the SAR of the 237 sites we found that the slope of the species–area curve was related to habitat diversity.
Main conclusions   Discussion of the causes of the SAR has often focused on the primacy of area per se versus habitat heterogeneity, even though the two mechanisms are not mutually exclusive and should be considered jointly. We find that increasing habitat diversity affects the SAR in different ways, but the dominant effect is to increase the slope of the SAR. While a full model fit typically includes a variety of terms involving both area and habitat richness, we find that the effect of habitat diversity can be reduced to a linear perturbation of the slope of the species accumulation curve.     

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.     

千岛湖陆桥岛屿鸟类集团对栖息地片段化的敏感性及其季节变化

为探究千岛湖陆桥岛屿不同鸟类集团对栖息地片段化敏感性的差异和季节变化,于2009年4月—2012年1月鸟类繁殖季(4、5、6月)和冬季(11、12、1月)对千岛湖41个陆桥岛屿鸟类集团进行了研究。结果表明,冬季杂食鸟对片段化敏感性高于食虫鸟,繁殖季时二者无显著差异,繁殖季和冬季时下层鸟对片段化敏感性均高于林冠鸟,冬季留鸟对片段化敏感性高于候鸟,繁殖季则无显著差异。杂食鸟和留鸟对片段化敏感性存在季节差异,而食虫鸟、林冠鸟、下层鸟和候鸟对片段化敏感性均无季节差异。不同鸟类集团对栖息地片段化敏感性的差异和季节变化规律,有助于人们在栖息地管理和保护区设计时采取更有针对性的鸟类保护措施。