The effect of area on local and regional elevational patterns of species richness

Aim To calculate the degree to which differences between local and regional elevational species richness patterns can be accounted for by the effects of regional area. Location Five elevational transects in Costa Rica, Ecuador, La Réunion, Mexico and Tanzania. Methods We sampled ferns in standardized field plots and collated regional species lists based on herbarium and literature data. We then used the Arrhenius function S = cA^z to correct regional species richness (S) for the effect of area (A) using three slightly different approaches, and compared the concordance of local and regional patterns prior to and after accounting for the effect of area on regional richness using linear regression analyses. Results We found a better concordance between local and regional elevational species richness after including the effect of area in the majority of cases. In several cases, local and regional patterns are very similar after accounting for area. In most of the cases, the maximum regional richness shifted to a higher elevation after accounting for area. Different approaches to correct for area resulted in qualitatively similar results. Main conclusions The differences between local and regional elevational richness patterns can at least partly be accounted for by area effects, suggesting that the underlying causes of elevational richness patterns might be the same at both spatial scales. Values used to account for the effect of area differ among the different study locations, showing that there is no generally applicable elevational species–area relationship.     

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.     

The carrying capacity for species richness

The idea that the number of species within an area is limited by a specific capacity of that area to host species is old yet controversial. Here, we show that the concept of carrying capacity for species richness can be as useful as the analogous concept in population biology. Many lines of empirical evidence indicate the existence of limits of species richness, at least at large spatial and phylogenetic scales. However, available evidence does not support the idea of diversity limits based on limited niche space; instead, carrying capacity should be understood as a stable equilibrium of biodiversity dynamics driven by diversity‐dependent processes of extinction, speciation and/or colonization. We argue that such stable equilibria exist even if not all resources are used and if increasing species richness increases the ability of a community to use resources. Evaluating the various theoretical approaches to modelling diversity dynamics, we conclude that a fruitful approach for macroecology and biodiversity science is to develop theory that assumes that the key mechanism leading to stable diversity equilibria is the negative diversity dependence of per‐species extinction rates, driven by the fact that population sizes of species must decrease with an increasing number of species owing to limited energy availability. The recently proposed equilibrium theory of biodiversity dynamics is an example of such a theory, which predicts that equilibrium species richness (i.e., carrying capacity) is determined by the interplay of the total amount of available resources, the ability of communities to use those resources, environmental stability that affects extinction rates, and the factors that affect speciation and colonization rates. We argue that the diversity equilibria resulting from these biodiversity dynamics are first‐order drivers of large‐scale biodiversity patterns, such as the latitudinal diversity gradient.     

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.     

sars: an R package for fitting,evaluating and comparing species–area relationship models

The species–area relationship (SAR) constitutes one of the most general ecological patterns globally. A number of different SAR models have been proposed. Recent work has shown that no single model universally provides the best fit to empirical SAR datasets: multiple models may be of practical and theoretical interest. However, there are no software packages available that a) allow users to fit the full range of published SAR models, or b) provide functions to undertake a range of additional SAR‐related analyses. To address these needs, we have developed the R package ‘sars’ that provides a wide variety of SAR‐related functionality. The package provides functions to: a) fit 20 SAR models using non‐linear and linear regression, b) calculate multi‐model averaged curves using various information criteria, and c) generate confidence intervals using bootstrapping. Plotting functions allow users to depict and scrutinize the fits of individual models and multi‐model averaged curves. The package also provides additional SAR functionality, including functions to fit, plot and evaluate the random placement model using a species–sites abundance matrix, and to fit the general dynamic model of oceanic island biogeography. The ‘sars’ R package will aid future SAR research by providing a comprehensive set of simple to use tools that enable in‐depth exploration of SARs and SAR‐related patterns. The package has been designed to allow other researchers to add new functions and models in the future and thus the package represents a resource for future SAR work that can be built on and expanded by workers in the field.     

The more‐individuals hypothesis revisited: the role of community abundance in species richness regulation and the productivity–diversity relationship

Species richness increases with energy availability, yet there is little consensus as to the exact processes driving this species–energy relationship. The most straightforward explanation is the more‐individuals hypothesis (MIH). It states that higher energy availability promotes a higher total number of individuals in a community, which consequently increases species richness by allowing for a greater number of species with viable populations. Empirical support for the MIH is mixed, partially due to the lack of proper formalisation of the MIH and consequent confusion as to its exact predictions. Here, we review the evidence of the MIH and evaluate the reliability of various predictions that have been tested. There is only limited evidence that spatial variation in species richness is driven by variation in the total number of individuals. There are also problems with measures of energy availability, with scale‐dependence, and with the direction of causality, as the total number of individuals may sometimes itself be driven by the number of species. However, even in such a case the total number of individuals may be involved in diversity regulation. We propose a formal theory that encompasses these processes, clarifying how the different factors affecting diversity dynamics can be disentangled.     

Plot size matters: Toward comparable species richness estimates across plot‐based inventories

To understand the state and trends in biodiversity beyond the scope of monitoring programs, biodiversity indicators must be comparable across inventories. Species richness (SR) is one of the most widely used biodiversity indicators. However, as SR increases with the size of the area sampled, inventories using different plot sizes are hardly comparable. This study aims at producing a methodological framework that enables SR comparisons across plot‐based inventories with differing plot sizes. We used National Forest Inventory (NFI) data from Norway, Slovakia, Spain, and Switzerland to build sample‐based rarefaction curves by randomly incrementally aggregating plots, representing the relationship between SR and sampled area. As aggregated plots can be far apart and subject to different environmental conditions, we estimated the amount of environmental heterogeneity (EH) introduced in the aggregation process. By correcting for this EH, we produced adjusted rarefaction curves mimicking the sampling of environmentally homogeneous forest stands, thus reducing the effect of plot size and enabling reliable SR comparisons between inventories. Models were built using the Conway–Maxell–Poisson distribution to account for the underdispersed SR data. Our method successfully corrected for the EH introduced during the aggregation process in all countries, with better performances in Norway and Switzerland. We further found that SR comparisons across countries based on the country‐specific NFI plot sizes are misleading, and that our approach offers an opportunity to harmonize pan‐European SR monitoring. Our method provides reliable and comparable SR estimates for inventories that use different plot sizes. Our approach can be applied to any plot‐based inventory and count data other than SR, thus allowing a more comprehensive assessment of biodiversity across various scales and ecosystems.     

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.

     

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.     

Lizard predation alters the effect of habitat area on the species richness of insect assemblages on Bahamian isles

Aim Anolis lizard invasions are a serious threat world‐wide, and information about how this invasive predator affects the diversity of prey assemblages is important for many strategic conservation goals. It is hypothesized that these predators reduce the slope of species–area relationships (SARs) of their prey assemblages. The effects of island area and predation by anolis lizards on the species richness of insular insect assemblages were investigated. Location Twenty‐four isles around Staniel Cay, Exuma Cays, Bahamas. Methods Flying insects were sampled using half‐sized Malaise traps for three consecutive days on each island in May 2007. First, the effect of island area on the probability of lizard presence was evaluated. Then, the effects of the presence–absence of predatory lizards on SARs were analysed for the overall insect assemblage and for the assemblages of five dominant insect orders. Results Our results indicated that anolis lizards occurred primarily on larger islands. The species richness of the overall insect assemblage and five dominant insect orders significantly increased with island area. The interaction between island area and predator presence–absence significantly affected the overall insect assemblage and Diptera and Hymenoptera assemblages (but not Coleoptera, Hemiptera and Lepidoptera assemblages). The presence of predators caused decreases in the slope of the SARs. Main conclusions The presence of predatory lizards strongly affects species richness of insular insect assemblages with the island area being a crucial determinant of the species richness. Therefore, the slope of the SAR can serve as a measure of the consequence of invasive predatory species on native insect assemblages.     

Spatio‐temporal scaling of biodiversity and the species–time relationship in a stream fish assemblage

1. The increase of species richness with the area of the habitat sampled, that is the species–area relationship, and its temporal analogue, the species–time relationship (STR), are among the few general laws in ecology with strong conservation implications. However, these two scale‐dependent phenomena have rarely been considered together in biodiversity assessment, especially in freshwater systems. 2. We examined how the spatial scale of sampling influences STRs for a Central‐European stream fish assemblage (second‐order Bernecei stream, Hungary) using field survey data in two simulation‐based experiments. 3. In experiment one, we examined how increasing the number of channel units, such as riffles and pools (13 altogether), and the number of field surveys involved in the analyses (12 sampling occasions during 3 years), influence species richness. Complete nested curves were constructed to quantify how many species one observes in the community on average for a given number of sampling occasions at a given spatial scale. 4. In experiment two, we examined STRs for the Bernecei fish assemblage from a landscape perspective. Here, we evaluated a 10‐year reach level data set (2000–09) for the Bernecei stream and its recipient watercourse (third‐order Kemence stream) to complement results on experiment one and to explore the mechanisms behind the observed patterns in more detail. 5. Experiment one indicated the strong influence of the spatial scale of sampling on the accumulation of species richness, although time clearly had an additional effect. The simulation methodology advocated here helped to estimate the number of species in a diverse combination of spatial and temporal scale and, therefore, to determine how different scale combinations influence sampling sufficiency. 6. Experiment two revealed differences in STRs between the upstream (Bernecei) and downstream (Kemence) sites, with steeper curves for the downstream site. Equations of STR curves were within the range observed in other studies, predominantly from terrestrial systems. Assemblage composition data suggested that extinction–colonisation dynamics of rare, non‐resident (i.e. satellite) species influenced patterns in STRs. 7. Our results highlight that the determination of species richness can benefit from the joint consideration of spatial and temporal scales in biodiversity inventory surveys. Additionally, we reveal how our randomisation‐based methodology may help to quantify the scale dependency of diversity components (α, β, γ) in both space and time, which have critical importance in the applied context.     

Prediction of species richness of breeding birds by landscape-level factors of urban woods in Osaka Prefecture, Japan

We examined some models to predict the species richness of forest birds by the landscape-level factors at urban woods in Osaka Prefecture, Japan. The environmental factors examined were area (A) and elongation (E) of woodlands, the distance to mountain (D), distance to the nearest woods (> 10ha) (D ₁₀), and the proportion of woods (Pw) and field (Pf) within 25 km² outside the parks. The species–area relationship at 28 parks was better fitted by the power function (r²=0.704) and by the logistic function (r²=0.696) than by the exponential function (r²=0.637). A woods that was planted 7 years ago had extremely few species, but there was no significant difference in species richness between woods < 50 years old and those older. We built Principal Component Regression (PCR) models to predict the species richness, because collinearity was detected between D and Pw. PCR, log[S/(43–S)] = –1.820 + 0.224logA – 0.0113 D + 0.133 logPw – 0.0588E (R² = 0.939, n = 27) was estimated. The species–area relationship was caused by difference in the occurrence pattern of bird species. This was attributable to the differences in feeding habit or micro-habitat use. We estimated that birds living between the forest interior and the edge, such as Dendrocopos kizuki, required at least 20 ha of woodland.