Predicting Species Numbers Using Species–Area and Endemics–Area Relations

Current ecological theory predicts an allometric relation between the number of species with restricted range size (endemics) and area (the endemics–area relation EAR), a pattern similar to the common species–area relation (SAR). Using SARs and EARs we can estimate species loss after habitat loss. A comparison of the predictive power of both approaches (using a patch occupancy model and data from European butterflies) revealed that the EAR approach is less reliable than the SAR. Contrary to current theory it appeared that EARs are relations in their own right that describe spatial distributions of endemic species. They do not simply follow from the underlying SAR. The implications of these results for the applicability of SARs and EARs in biodiversity forecasting are discussed.     

How diversification rates and diversity limits combine to create large-scale species-area relationships

Species-area relationships (SARs) have mostly been treated from an ecological perspective, focusing on immigration, local extinction and resource-based limits to species coexistence. However, a full understanding across large regions is impossible without also considering speciation and global extinction. Rates of both speciation and extinction are known to be strongly affected by area and thus should contribute to spatial patterns of diversity. Here, we explore how variation in diversification rates and ecologically mediated diversity limits among regions of different sizes can result in the formation of SARs. We explain how this area-related variation in diversification can be caused by either the direct effects of area or the effects of factors that are highly correlated with area, such as habitat diversity and population size. We also review environmental, clade-specific and historical factors that affect diversification and diversity limits but are not highly correlated with region area, and thus are likely to cause scatter in observed SARs. We present new analyses using data on the distributions, ages and traits of mammalian species to illustrate these mechanisms; in doing so we provide an integrated perspective on the evolutionary processes shaping SARs.     

Drivers of moth species richness on tropical altitudinal gradients: a cross-regional comparison

Aims We examine the role of species–area relationships (SARs), climatic parameters and phylogeny in shaping the altitudinal species richness patterns of moths. With respect to SARs, we investigate whether habitat heterogeneity is a probable mechanism for mediating area effects. We investigate the consistency of patterns by comparing several discrete regions. Location Nine mountainous regions in tropical Asia and the Malay Archipelago. Methods Presence‐only records for 292 species of the Lepidopteran family Sphingidae were used to measure interpolated species richness in 200‐m altitudinal bands. Species richness was correlated with area measures, which were calculated from both two‐dimensional map projections and three‐dimensional digital elevation models (DEMs). We used data simulations of homogeneous communities to test for effects of sample (i.e. habitat) heterogeneity as a mechanism causing SARs. Species richness patterns were compared among regions and between the two major sphingid clades, and were related to regional climatic characteristics. Results The area of altitudinal bands was a strong (statistical) explanation of species richness, particularly if area was calculated from three‐dimensional DEMs, but SARs often over‐predict species richness in lowland areas. There was no evidence for habitat heterogeneity as a mechanism of altitudinal SARs (tested for Borneo only). Species richness patterns varied considerably between the nine regions, which may, as an alternative to SARs, be explained by climatic differences such as (temperature) seasonality. Phylogenetic clades differed in species richness patterns exhibited. Main conclusion SARs provide strong empirical explanations for (regional) altitudinal patterns of species richness, but lack of evidence for the most likely mechanism cautions against a priori ‘corrections’ of species richness data for area. Furthermore, SARs are often not a sufficient explanation for the drop in species richness towards lowlands. Climate, or other collinear variables, may offer alternative explanations for altitudinal SARs. More research is needed to understand the mechanisms for SARs in an altitudinal context in order to evaluate their importance in the face of parameter collinearity.     

Spatial and temporal variation in species-area relationships in the Fynbos biological hotspot

Species-area relations (SARs) are among the few recognized general patterns of ecology, are empirical relations giving the number of species found within an area of a given size and were initially formulated for island environments. The use of SARs has been extended to mainland environments, and to give baseline estimates of extinction rates attending habitat loss. Using current species distributions based on atlas data, we examined the spatial variation of rates of species accumulation and species-area curves for Proteaceae species for all one-minute by one-minute areas within the Cape Floristic Region, South Africa. We compared SARs for current distributions to those generated from modeled future Protea distributions following climate change. Within one biome and for two different scales, there exists a very large spatial variation in turnover rates for current Proteaceae distributions, and we show that these rates will not remain constant as climate warming progresses. As climate changes in coming years, some areas will gain species due to migration, as other areas lose species, and still other areas maintain current rates of species accumulation/turnover. Both current and future distributions show highly variable rates of species accumulation across the landscape. This means that an average species-area relationship will hide a very large interval of variation among SARs, for both current and future Proteaceae distributions. The use of species-area relations to estimate species extinctions following loss of current habitat, or loss of future climatically-suitable area is likely to result in erroneous predictions.     

Nestedness and niche‐based species loss in moorland plant communities

Communities in isolated habitat patches surrounded by inhospitable matrices often form a nested subset pattern. However, the underlying causal mechanisms and conservation implications of nestedness in regional communities remain controversial. The nested ranks of species in a nested species‐by‐site matrix may reflect a gradient of species vulnerability to extinction or of colonization ability. However, nestedness analysis has rarely been used to explore determinants of species rank; consequently, little is known of underpinning mechanisms. In this study, we examined nestedness in moorland plant communities widely interspersed within the subalpine zone of northern Japan. Moorland sites differed in area (1000–160 000 m²) and were naturally isolated from one another to various extents within an inhospitable forest matrix. We also determined whether site characteristics (physical and morphometric measures) and species characteristics (niche position and breadth, based on species’ traits) are related to nestedness. Moorland plant communities in the study area were significantly nested. The pH and moorland kernel density (proxy for spatial clustering of moorlands around the focal site) were the most important predictors of moorland site nested rank in a nestedness matrix. Niche breadths of species (measured as variation in leaf mass area and height) predicted species’ nested ranks. Selective environmental tolerances imposed by environmental harshness and selective extinction caused by declines in site carrying capacities probably account for the nested subset pattern in moorland plant communities. The nested rank of species in the nestedness matrix can therefore be translated into the potential order of species loss explainable by species niche breadths (based on variation in functional traits). Complementary understanding of the determinants of site ranking and species ranking in the nestedness matrix provides powerful insight into ecological processes underlying nestedness and into the ways by which communities are assembled or disassembled by such processes.     

Species–area relationships and biodiversity loss in fragmented landscapes

To estimate species loss from habitat destruction, ecologists typically use species–area relationships, but this approach neglects the spatial pattern of habitat fragmentation. Here, we provide new, easily applied, analytical methods that place upper and lower bounds on immediate species loss at any spatial scale and for any spatial pattern of habitat loss. Our formulas are expressed in terms of what we name the ‘Preston function’, which describes triphasic species–area relationships for contiguous regions. We apply our method to case studies of deforestation and tropical tree species loss at three different scales: a 50 ha forest plot in Panama, the tropical city‐state of Singapore and the Brazilian Amazon. Our results show that immediate species loss is somewhat insensitive to fragmentation pattern at small scales but highly sensitive at larger scales: predicted species loss in the Amazon varies by a factor of 16 across different spatial structures of habitat loss.     

Random placement models predict species–area relationships in duck communities despite species aggregation

Species–area relationships are the product of many ecological processes and their interactions. Explanations for the species–area relationship (SAR) have focused on separating putative niche‐based mechanisms that correlate with area from sampling effects caused by patches with more individuals containing more species than patches with fewer individuals. We tested the hypothesis that SARs in breeding waterfowl communities are caused by sampling effects (i.e. random placement from the regional species pool). First, we described observed SARs and patterns of species associations for fourteen species of ducks on ponds in prairie Canada. Second, we used null models, which randomly allocated ducks to ponds, to test if observed SARs and patterns of species associations differed from those expected by chance. Consistent with the sampling effects hypothesis, observed SARs were accurately predicted by null models in three different years and for diving and dabbling duck guilds. This is the first demonstration that null models can predict SARs in waterbirds or any other aquatic organisms. Observed patterns of species association, however, were not well predicted by null models as in all years there was less observed segregation among species (i.e. more aggregation) than under the random expectation, suggesting that intraspecific competition could play a role in structuring duck communities. Taken together, our results indicate that when emergent properties of ecological communities such as the SAR appear to be caused by random processes, analyses of species associations can be critical in revealing the importance of niche‐based processes (e.g. competition) in structuring ecological communities.     

More individuals drive the species energy–area relationship in an experimental zooplankton community

The shape of the species–area relationship (SAR) often varies with the amount of available energy; SARs from high‐energy habitats typically have higher intercepts and steeper slopes than SARs from low‐energy habitats. Such patterns are often assumed to result from a shift in the mechanisms of coexistence between high and low energy habitats. However, a plausible but unexplored alternative mechanism emerges from proportional sampling, if there are simply more individuals in larger or more productive habitats, without the need to invoke differing coexistence mechanisms. Here, we examined proportional versus disproportional responses of a diverse assemblage of freshwater zooplankton to manipulations of experimental pond size and energy inputs. We found that higher energy treatments had higher species richness in large, but not small, ponds, leading to a steeper SAR with higher energy input. The total abundances of individuals also increased with energy in large, but not small ponds. By using a sample‐independent rarefaction technique (probability of interspecific encounter), we found that SAR patterns resulted from changes in the total, but not relative, abundance of individuals, and thus proportional, rather than disproportional, responses of species. Overall, our results emphasize the need to consider how both the total and relative abundances of species respond to ecological drivers such as energy and area before inferring the underlying mechanisms that lead to biodiversity patterns. Further, our results may implicate a proportionally smaller influence of energy on patterns of biodiversity when habitats are destroyed.     

What ecological factors shape species-area curves in neutral models?

Understanding factors that shape biodiversity and species coexistence across scales is of utmost importance in ecology, both theoretically and for conservation policies. Species-area relationships (SARs), measuring how the number of observed species increases upon enlarging the sampled area, constitute a convenient tool for quantifying the spatial structure of biodiversity. While general features of species-area curves are quite universal across ecosystems, some quantitative aspects can change significantly. Several attempts have been made to link these variations to ecological forces. Within the framework of spatially explicit neutral models, here we scrutinize the effect of varying the local population size (i.e. the number of individuals per site) and the level of habitat saturation (allowing for empty sites). We conclude that species-area curves become shallower when the local population size increases, while habitat saturation, unless strongly violated, plays a marginal role. Our findings provide a plausible explanation of why SARs for microorganisms are flatter than those for larger organisms.     

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.     

Variation between individuals fosters regional species coexistence

Although individual‐level variation (IV) is ubiquitous in nature, it is not clear how it influences species coexistence. Theory predicts that IV will hinder coexistence but empirical studies have shown that it can facilitate, inhibit, or have a neutral effect. We use a theoretical model to explore the consequences of IV on local and regional species coexistence in the context of spatial environmental structure. Our results show that individual variation can have a positive effect on species coexistence and that this effect will critically depend on the spatial structure of such variation. IV facilitates coexistence when a negative, concave‐up relationship between individuals’ competitive response and population growth rates propagates to a disproportionate advantage for the inferior competitor, provided that each species specialises in a habitat. While greater variation in the preferred habitat generally fosters coexistence, the opposite is true for non‐preferred habitats. Our results reconcile theory with empirical findings.     

Linking species diversity to the functioning of ecosystems: on the importance of environmental context

There is currently much interest in understanding how loss of biodiversity might alter ecological processes vital to the functioning of ecosystems. Unfortunately, ecologists have reached little consensus regarding the importance of species diversity to ecosystem functioning because empirical studies have not demonstrated any consistent relationship between the number of species in a system and the rates of ecological processes. We present the results of a simple model that suggests there may be no single, generalizable relationship between species diversity and the productivity of an ecosystem because the relative contributions of species to productivity change with environmental context. The model determined productivity for landscapes varying in species diversity (the number of species in the colonist pool), spatial heterogeneity (the number of habitat types composing the landscape), and disturbance regimes (+/? a non‐selective mortality). Linear regressions were used to relate species diversity and productivity for each of the environmental contexts. Disturbance changed the form of the diversity/productivity relationship by reducing the slope (i.e. the change in productivity per species added to the colonist pool), but spatial heterogeneity increased or decreased this slope depending on the particular habitat types composing the landscape. The cause of the diversity/productivity relationship also changed with environmental context. The amount of variation in productivity explained by species diversity always increased with spatial heterogeneity, while the amount of variation explained by species composition (i.e. the particular species composing the colonist pool) tended to increase with disturbance. These results lead us to conclude that the form and cause of the relationship between species diversity and productivity may be highly dynamic‐changing over both time and space. Because the trends resulted from well‐known mechanisms by which environmental variation alters the absolute and relative abundances of taxa, we suspect this conclusion may be applicable to many different systems.     

Resource-based habitat definition, niche overlap and conservation of two sympatric glacial relict butterflies

The precise knowledge of ecological resources and conditions required by species threatened by rapidly changing environmental conditions is of prime importance for conservation biology. Transferability of this knowledge between species with similar ecological requirements is often assumed, but rarely tested. This is especially the case for glacial relict populations confined to climate‐habitat traps from where they cannot move to rejoin areas with suitable environmental conditions. Using two glacial relict butterflies as model organisms, we first quantitatively define larval and adult resource‐based habitat use of each species. Secondly, we test the transferability of ecological profiles (both habitat and ecological niche) between these two species that share both the same biotope and the same host plant. Our results show that both species have markedly different ecological requirements relating to differences in life history and behavioural traits (i.e. egg‐laying strategies and mate‐locating behaviour). Although the two species share many ecological features, they use different functional habitats within our study site. The high degree of interspecific niche overlap should indicate a high interspecific competition. However, we argue that their co‐existence can be explained by the non‐limiting abundance of some resources (e.g. host plants), by the partial separation in time of adult flight periods and by the territorial behaviour of one of the species. We discuss the following general messages: (1) functional habitat of a (threatened) species should be defined in a spatial context corresponding to individual station keeping, and (2) quick diagnosis based on similar ecological requirements may be misleading for the design of reliable conservation and restoration strategies. Detailed mechanistic and quantitative ecological understanding of resource‐use and environmental tolerances across an organism's life cycle is essential for effective conservation in changing environments, like for glacial relict species.     

Effects of species' ecology on the accuracy of distribution models

In the face of accelerating biodiversity loss and limited data, species distribution models – which statistically capture and predict species’ occurrences based on environmental correlates – are increasingly used to inform conservation strategies. Additionally, distribution models and their fit provide insights on the broad‐scale environmental niche of species. To investigate whether the performance of such models varies with species’ ecological characteristics, we examined distribution models for 1329 bird species in southern and eastern Africa. The models were constructed at two spatial resolutions with both logistic and autologistic regression. Satellite‐derived environmental indices served as predictors, and model accuracy was assessed with three metrics: sensitivity, specificity and the area under the curve (AUC) of receiver operating characteristics plots. We then determined the relationship between each measure of accuracy and ten ecological species characteristics using generalised linear models. Among the ecological traits tested, species’ range size, migratory status, affinity for wetlands and endemism proved most influential on the performance of distribution models. The number of habitat types frequented (habitat tolerance), trophic rank, body mass, preferred habitat structure and association with sub‐resolution habitats also showed some effect. In contrast, conservation status made no significant impact. These findings did not differ from one spatial resolution to the next. Our analyses thus provide conservation scientists and resource managers with a rule of thumb that helps distinguish, on the basis of ecological traits, between species whose occurrence is reliably or less reliably predicted by distribution models. Reasonably accurate distribution models should, however, be attainable for most species, because the influence ecological traits bore on model performance was only limited. These results suggest that none of the ecological traits tested provides an obvious correlate for environmental niche breadth or intra‐specific niche differentiation.     

Species richness and phylogenetic diversity of native and non‐native species respond differently to area and environmental factors

Aim

To test whether native and non‐native species have similar diversity–area relationships (species–area relationships [SARs] and phylogenetic diversity–area relationships [PDARs]) and whether they respond similarly to environmental variables.

Location

United States.

Methods

Using lists of native and non‐native species as well as environmental variables for >250 US national parks, we compared SARs and PDARs of native and non‐native species to test whether they respond similarly to environmental conditions. We then used multiple regressions involving climate, land cover and anthropogenic variables to further explore underlying predictors of diversity for plants and birds in US national parks.

Results

Native and non‐native species had different slopes for SARs and PDARs, with significantly higher slopes for native species. Corroborating this pattern, multiple regressions showed that native and non‐native diversity of plants and birds responded differently to a greater number of environmental variables than expected by chance. For native species richness, park area and longitude were the most important variables while the number of park visitors, temperature and the percentage of natural area were among the most important ones for non‐native species richness. Interestingly, the most important predictor of native and non‐native plant phylogenetic diversity, temperature, had positive effects on non‐native plants but negative effects on natives.

Main conclusions

SARs, PDARs and multiple regressions all suggest that native and non‐native plants and birds responded differently to environmental factors that influence their diversity. The agreement between diversity–area relationships and multiple regressions with environmental variables suggests that SARs and PDARs can be both used as quick proxies of overall responses of species to environmental conditions. However, more importantly, our results suggest that global change will have different effects on native and non‐native species, making it inappropriate to apply the large body of knowledge on native species to understand patterns of community assembly of non‐native species.

     

Habitat selection and coexistence of invasive cockroach species (Dictyoptera) in sugar-cane fields on Réunion island

Selection of habitat has a profound influence on interactions among species and the assembly of ecological communities. We investigated habitat preferences to understand how different cockroach species coexist in sugar-cane fields on Réunion island.Cockroach populations belonging to a guild of seven species were surveyed during one annual cycle in eight sugar-cane fields that differed by several environmental factors, in order to investigate ecological features of cockroach species and their patterns of coexistence.Structure variations of the cockroach communities were analyzed at the field scale, at the sample unit scale, and according to variations of environmental conditions related to the annual sugar-cane growth cycle. A canonical correspondence analysis (CCA) was used to elucidate relationships between species diversity, population abundance and environmental characteristics.The examination of partitioning at different spatial and temporal scales evidenced that each species occupied a particular type of habitat. The main factors influencing spatial habitat selection were at the sample unit scale: presence of ants, edge effect, soil moisture and granulometry, at the field scale: irrigation, annual rainfall, altitude and age of the field. Although a pair of species shared the same type of habitat, annual population peaks of each species did not coincide in time. This suggests that resource partitioning is based both on ecological factors and interspecific competition.Factors enhancing cockroach coexistence and factors favoring population outbursts are discussed as well as specific invasive capacities of these cockroaches and the role of the cockroach community in the sugar-cane trophic web.     

Predicting biodiversity loss in island and countryside ecosystems through the lens of taxonomic and functional biogeography

We investigate how variation in patch area and forest cover quantified for three different spatial scales (buffer size of 500, 1500 and 3000 m radius) affects species richness and functional diversity of bat assemblages in two ecosystems differing in fragment–matrix contrast: a landbridge island system in Panama and a countryside ecosystem in the Brazilian Amazon. Bats were sampled on 11 islands and the adjacent mainland in Panama, and in eight forest fragments and nearby continuous forest in Brazil. Species–area relationships (SAR) were assessed based on Chao1 species richness estimates, and functional diversity–area relationships (FAR) were quantified using Chao1 functional diversity estimates measured as the total branch length of a trait dendrogram. FARs were calculated using three trait sets: considering five species functional traits (FAR_ALL), and trait subsets reflecting ‘diet breadth’ (FAR_DIET) and ‘dispersal ability’ (FAR_DISPERSAL). We found that in both study systems, FAR_ALL was less sensitive to habitat loss than SAR, in the sense that an equal reduction in habitat loss led to a disproportionately smaller loss of functional diversity compared to species richness. However, the inhospitable and static aquatic matrix in the island ecosystem resulted in more pronounced species loss with increasing loss of habitat compared to the countryside ecosystem. Moreover, while we found a significant FAR_DISPERSAL for the island ecosystem in relation to forest cover within 500 m landscape buffers, FAR_DIET and FAR_DISPERSAL were not significant for the countryside ecosystem. Our findings highlight that species richness and functional diversity in island and countryside ecosystems scale fundamentally differently with habitat loss, and suggest that key bat ecological functions, such as pollination, seed dispersal and arthropod suppression, may be maintained in fragments despite a reduction in species richness. Our study reinforces the importance of increasing habitat availability for decreasing the chances of losing species richness in smaller fragments.     

Beyond species area curves: application of a scale‐free measure for spatial variability of species richness

We report a novel pattern in species richness, complementary to the well‐known species–area relationship. We show that, as sample area increases, the variation in relative richness decreases among otherwise comparable spatial units. This pattern holds for southern African birds, French birds, Cape Proteaceae and the trees of Barro Colorado Island. We propose a scale‐free method for quantifying this pattern by measuring the multifractal intensity of species richness, which is the multi‐scale tendency of adjacent patches with the same area to differ in richness. By this measure, spatial variability is strongest for Cape Proteaceae and weakest for Barro Colorado Island trees. Our results have implications for area‐dependent estimates of species‐richness, for example in reserve planning and in simulation‐based studies. They imply that such estimates are most accurate for large areas, and will be subject to substantial uncertainty when the multifractal intensity is high and the area is small. For comparative purposes, multifractal intensity may be used as a supplement or as an alternative to mean richness, as well as for other ecological densities, such as biomass distribution and local abundance.     

Effects of sampling protocol on the shapes of species richness curves

Aim Scheiner (Journal of Biogeography, 2009, 36 , 2005–2008) criticized several issues regarding the typology and analysis of species richness curves that were brought forward by Dengler (Journal of Biogeography, 2009, 36 , 728–744). In order to test these two sets of views in greater detail, we used a simulation model of ecological communities to demonstrate the effects of different sampling schemes on the shapes of species richness curves and their extrapolation capability. Methods We simulated five random communities with 100 species on a 64 × 64 grid using random fields. Then we sampled species–area relationships (SARs, contiguous plots) as well as species–sampling relationships (SSRs, non‐contiguous plots) from these communities, both for the full extent and the central quarter of the grid. Finally, we fitted different functions (power, quadratic power, logarithmic, Michaelis–Menten, Lomolino) to the obtained data and assessed their goodness‐of‐fit (Akaike weights) and their extrapolation capability (deviation of the predicted value from the true value). Results We found that power functions gave the best fit for SARs, while for SSRs saturation functions performed better. Curves constructed from data of 32² grid cells gave reasonable extrapolations for 64² grid cells for SARs, irrespective of whether samples were gathered from the full extent or the centre only. By contrast, SSRs worked well for extrapolation only in the latter case. Main conclusions SARs and SSRs have fundamentally different curve shapes. Both sampling strategies can be used for extrapolation of species richness to a target area, but only SARs allow for extrapolation to a larger area than that sampled. These results confirm a fundamental difference between SARs and area‐based SSRs and thus support their typological differentiation.