The theory of the nested species–area relationship: geometric foundations of biodiversity scaling

The relationship between sampled area and the number of species within that area, the species–area relationship (SAR), is a major biodiversity pattern and one of a few law‐like regularities in ecology. While the SAR for isolated units (islands or continents) is assumed to result from the dynamics of species colonization, speciation and extinction, the SAR for contiguous areas in which smaller plots are nested within larger sample areas can be attributed to spatial patterns in the distribution of individuals. The nested SAR is typically triphasic in logarithmic space, so that it increases steeply at smaller scales, decelerates at intermediate scales and increases steeply again at continental scales. I will review current theory for this pattern, showing that all three phases of the SAR can be derived from simple geometric considerations. The increase of species richness with area in logarithmic space is generally determined by overall species rarity, so that the rarer the species are on average, the higher is the local slope z. Rarity is scale‐dependent: species occupy only a minor proportion of area at broad spatial scales, leading to upward accelerating shape of the SAR at continental scales. Similarly, species are represented by only a few individuals at fine spatial scales, leading to high SAR slope also at small areas. Geometric considerations reveal links of the SAR to other macroecological patterns, namely patterns of β‐diversity, the species–abundance distribution, and the relationship between energy availability (or productivity) and species richness. Knowledge of the regularities concerning nested SARs may be used for standardizing unequal areas, upscaling species richness and estimating species loss due to area loss, but all these applications have their limits, which also follow from the geometric considerations.     

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.     

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.     

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.     

Functional redundancy modifies species–area relationship for freshwater phytoplankton

Although species–area relationship (SAR ) is among the most extensively studied patterns in ecology, studies on aquatic and/or microbial systems are seriously underrepresented in the literature. We tested the algal SAR in lakes, pools and ponds of various sizes (10^?2–10⁸ m²) and similar hydromorphological and trophic characteristics using species‐specific data and functional groups. Besides the expectation that species richness increases monotonously with area, we found a right‐skewed hump‐shaped relationship between the area and phytoplankton species richness. Functional richness however did not show such distortion. Differences between the area dependence of species and functional richness indicate that functional redundancy is responsible for the unusual hump‐backed SAR . We demonstrated that the Small Island Effect, which is a characteristic for macroscopic SAR s can also be observed for the phytoplankton. Our results imply a so‐called large lake effect, which means that in case of large lakes, wind‐induced mixing acts strongly against the habitat diversity and development of phytoplankton patchiness and finally results in lower phytoplankton species richness in the pelagial. High functional redundancy of the groups that prefer small‐scale heterogeneity of the habitats is responsible for the unusual humpback relationship. The results lead us to conclude that although the mechanisms that regulate the richness of both microbial communities and communities of macroscopic organisms are similar, their importance can be different in micro‐ and macroscales.     

Plant communities affect the species–area relationship on Carex sempervirens tussocks

The species–area relationship (SAR) has been extensively studied in a wide range of plant communities, but very few studies have directly addressed how plant communities affect the SAR and what are the underlying mechanisms. Many graminoids form distinct tussocks where many other plant species grow, but no study has investigated whether the SAR holds true for the vegetation on tussocks. In four plant communities on an abandoned subalpine pasture in the Swiss National Park, we made releves on 600 tussocks of Carex sempervirens and measured tussock basal area and other tussock traits. In all four communities, species richness on C. sempervirens tussocks was strongly positively related to tussock basal area (R²0.74), while other tussock traits explained very little (R²<0.04). Slope and intercept of the SAR on C. sempervirens tussocks differed significantly among the four communities. This was because plant communities affected richness in smaller tussocks (basal diameter <10 cm) but not that in large tussocks (basal diameter10 cm). We conclude that the SAR holds true for vegetation on C. sempervirens tussocks and changes with plant communities. Changes in the SAR on C. sempervirens tussocks are very likely because smaller tussocks are less independent of the plant communities than the larger ones, regarding disturbance or nutrients.     

Effects of habitat area, isolation, and landscape diversity on plant species richness of calcareous grasslands

Calcareous grasslands harbour a high biodiversity, but are highly fragmented and endangered in central Europe. We tested the relative importance of habitat area, habitat isolation, and landscape diversity for species richness of vascular plants. Plants were recorded on 31 calcareous grasslands in the vicinity of the city of Göttingen (Germany) and were divided into habitat specialist and generalist species. We expected that habitat specialists were more affected by area and isolation, and habitat generalists more by landscape diversity. In multiple regression analysis, the species richness of habitat specialists (n = 66 species) and habitat generalists (n = 242) increased with habitat area, while habitat isolation or landscape diversity did not have significant effects. Contrary to predictions, habitat specialists were not more affected by reduced habitat area than generalists. This may have been caused by delayed extinction of long-living plant specialists in small grasslands. Additionally, non-specialists may profit more from high habitat heterogeneity in large grasslands compared to habitat specialists. Although habitat isolation and landscape diversity revealed no significant effect on local plant diversity, only an average of 54% of habitat specialists of the total species pool were found within one study site. In conclusion, habitat area was important for plant species conservation, but regional variation between habitats contributed also an important 46% of total species richness.     

Speciation-rate dependence in species–area relationships

The general tendency for species number (S) to increase with sampled area (A) constitutes one of the most robust empirical laws of ecology, quantified by species–area relationships (SAR). In many ecosystems, SAR curves display a power-law dependence, S∝A^z. The exponent z is always less than one but shows significant variation in different ecosystems. We study the multitype voter model as one of the simplest models able to reproduce SAR similar to those observed in real ecosystems in terms of basic ecological processes such as birth, dispersal and speciation. Within the model, the species–area exponent z depends on the dimensionless speciation rate ν, even though the detailed dependence is still matter of controversy. We present extensive numerical simulations in a broad range of speciation rates from ν=10^-3 down to ν=10^-11, where the model reproduces values of the exponent observed in nature. In particular, we show that the inverse of the species–area exponent linearly depends on the logarithm of ν. Further, we compare the model outcomes with field data collected from previous studies, for which we separate the effect of the speciation rate from that of the different species lifespans. We find a good linear relationship between inverse exponents and logarithm of species lifespans. However, the slope sets bounds on the speciation rates that can hardly be justified on evolutionary basis, suggesting that additional effects should be taken into account to consistently interpret the observed exponents.     

Fragmentation,grazing and the species–area relationship

Habitat loss is one of the greatest threats to species persistence. Gauging the scale of this problem requires quantitative methods that can predict the number of extinctions resulting from habitat loss. For the past three decades, the species–area relationship, an empirical relationship between the number of species present in an area and the size of that area, has been this tool. However, it fails to incorporate threats to species aside from habitat loss and the heterogeneous distribution of these threats across habitats. Recent studies have improved species–area predictions by incorporating not only direct effects of area on richness, but also indirect effects of area (through area‐mediated predator abundance), on prey species richness. We extend this work to test the hypotheses that the indirect effects of the multiple threats of grazing and trampling in addition to fragmentation will amplify the effect of area on species richness and that this effect will be greatest in zones closest to the fragment edge. Further, we test for species and population level effects of fragmentation and grazing, including the non‐random pattern of species loss and the decline in population sizes. We test our hypotheses with a field study of land snail richness in fragments with and without the additional threats of grazing and trampling. Our study supports the hypotheses that fragments with multiple threats in addition to habitat loss harbour fewer species than fragments without these threats, and that this effect is non‐uniform across fragments, populations and species.