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.     

Does size really matter? Species–area relationships in human settlements

Aim To describe species–area relationships in human settlements and compare them with those from a non‐urban habitat. Location West‐central Mexico. Methods We surveyed breeding birds in 13 human settlements and five shrubland patches. We estimated bird species richness using an abundance‐based coverage estimator with equal sample sizes to eliminate biases related to sampling effort differences. To assess species–area relationships, we performed log–log linear regressions between the size of the studied patches and their estimated bird richness. We also used a logarithmic approach to determine how the species–area relationship asymptoted and made use of the Michaelis–Menten model to identify the size at which the studied patches reached their maximum species richness. We also investigated (1) possible relationships among the estimated bird richness and other variables known to affect urban‐dwelling birds (built cover, plant species richness, tree cover or human population density) and (2) changes in bird community composition related to the size of the studied human settlements. Results Species–area relationships exhibited different patterns among the studied habitats. The log–log regression slope was steeper in human settlements, while the intercept was higher in shrublands. The maximum number of species was more than twofold higher in shrublands. Human settlement patch size was the only variable significantly related to bird richness. Our community composition results show that two main bird groups are related to human settlement size, and that as the size of human settlements increases, bird community similarity in relation to the largest city increases. Main conclusions Human settlements act as ecological islands, with pronounced species–area relationships. Our results suggest that an important threshold for bird species richness and community composition is reached in human settlements > 10.2 km². This threshold is unlikely to be generalizable among bio‐regions, and thus should be quantified and considered when studying, managing and/or planning urban systems.     

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.     

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.     

Assessing biodiversity with species accumulation curves; inventories of small reptiles by pit‐trapping in Western Australia

Abstract We examined 11 non‐linear regression models to determine which of them best fitted curvilinear species accumulation curves based on pit‐trapping data for reptiles in a range of heterogeneous and homogenous sites in mesic, semi‐arid and arid regions of Western Australia. A well‐defined plateau in a species accumulation curve is required for any of the models accurately to estimate species richness. Two different measures of effort (pit‐trapping days and number of individuals caught) were used to determine if the measure of effort influenced the choice of the best model(s). We used species accumulation curves to predict species richness, determined the trapping effort required to catch a nominated percentage (e.g. 95%) of the predicted number of species in an area, and examined the relationship between species accumulation curves with diversity and rarity. Species richness, diversity and the proportion of rare species in a community influenced the shape of species accumulation curves. The Beta‐P model provided the best overall fit (highest r²) for heterogeneous and homogeneous sites. For heterogeneous sites, Hill, Rational, Clench, Exponential and Weibull models were the next best. For homogeneous habitats, Hill, Weibull and Chapman–Richards were the next best models. There was very little difference between Beta‐P and Hill models in fitting the data to accumulation curves, although the Hill model generally over‐estimated species richness. Most models worked equally well for both measures of trapping effort. Because the number of individuals caught was influenced by both pit‐trapping effort and the abundance of individuals, both measures of effort must be considered if species accumulation curves are to be used as a planning tool. Trapping effort to catch a nominated percentage of the total predicted species in homogeneous and heterogeneous habitats varied among sites, but even for only 75% of the predicted number of species it was generally much higher than the typical effort currently being used for terrestrial vertebrate fauna surveys in Australia. It was not possible to provide a general indication of the effort required to predict species richness for a site, or to capture a nominated proportion of species at a site, because species accumulation curves are heavily influenced by the characteristics of particular sites.     

Power-law species–area relationships and self-similar species distributions within finite areas

The species–area relationship (SAR) is often expressed as a power law, which indicates scale invariance. It has been claimed that the scale invariance – or self‐similarity at the community level – is not compatible with the self‐similarity at the level of spatial distribution of individual species, because the power law would only emerge if distributions for all species had identical fractal dimensions (FD). Here we show that even if species differ in their FD, the resulting SAR is approximately linear on a log–log scale because observed spatial distributions are inevitably spatially restricted – a phenomenon we term the ‘finite‐area effect’. Using distribution atlases, we demonstrate that the apparent power law of SARs for central European birds is attributable to this finite‐area effect affecting species that indeed reveal self‐similar distributions. We discuss implications of this mechanism producing the SAR.     

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.     

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.     

Sampling design and the shape of species–area curves on the regional scale

Species–area relationships (SARs) are one of the fundamental patterns in ecology. However, how the way they were constructed influences resulting SAR shapes has gained astonishingly little attention. We use data of the distribution atlas of Polish butterflies to compare SARs constructed from four different designs: adding up species numbers of independent areas (species accumulation curves using contiguous and non-contiguous areas), using a nested design, and comparing species numbers of independent areas of different sizes. It appeared that the way of constructing SARs influences the outcome. We attribute this influence to the pronounced faunal dissimilarities of more distant areas (spatial species turnover). The nested design resulted in significantly higher slopes and lower intercepts of power function SARs than the other designs. SARs from all four sampling designs showed a pronounced downward curvature on small spatial scales. Only the nested design predicted species densities correctly. The implications of these results for the use of SARs in bioconservation are discussed.     

Contrasting alien and native plant species–area relationships: the importance of spatial grain and extent

Aim The proportion of alien plant species in floras is increasingly being used to indicate the threat of invasions to native species and/or the homogenization of biodiversity. However, this indicator is only valuable if it is independent of the spatial extent and grain of observation. This study tested the equivalence of native and alien species–area relationships (SARs) in order to assess the support for scale invariance in the proportion of alien species in floras. Location England, UK. Methods Nested SARs were generated by assessing the richness of native and alien plant species drawn from the New atlas of the British and Irish flora for six areas comprising 100, 400, 900, 1600, 2500 and 3600 km² with each larger area containing all smaller areas. Five replicate sets of nested areas encompassing northern, southern, eastern, western and central regions were chosen. For each set of nested areas, the log‐transformed species richness was regressed on log‐transformed area to fit a power function to the SAR. Results Native and alien plant SARs reveal consistent differences in slope, highlighting that the proportion of alien species is a function of spatial grain. Aliens are more rare than natives and have higher spatial turnover leading to faster accumulation of species as area increases. However, equivalent samples drawn from a larger spatial extent reveal similar alien and native SARs. Main conclusions The significant differential scale dependence in native and alien species richness observed in this study reflects dissimilar influences of regional drivers such as habitat, but potentially also propagule pressure and introduction history, that leads to the relative rarity and high spatial turnover of alien species. Maps of invasion hotspots that identify areas where the proportion of the alien flora is particularly high should therefore be treated with considerable caution since patterns across most grains used for species monitoring will be scale dependent.     

Effects of productivity on species–area curves in herbaceous vegetation: evidence from experimental and observational data

The effects of productivity on the parameters of the species–area curve were investigated in this paper using two data sets on terrestrial plant communities: (1) one including 48 plots in 12 experimental sites on ploughed, formerly cultivated fields in the Siena region, Italy, and (2) one including 40 plots in hay meadows in the Bremen region, Germany. In both regions, species presence of vascular plants was recorded in nested plots ranging in size from 0.004 to 256 m² and 0.001 to 1000 m², respectively. Productivity was estimated as dry standing biomass. In the Siena data set, species richness showed a humped‐back relation to biomass in the plot sizes up to 1 m². For the larger plot sizes, no significant correlations were found. In the Bremen data set, positive relation between species number and biomass was observed at the smallest spatial scale (0.001 m²), whereas the relation disappeared or tended to be negative for the larger plot sizes. In general, the slopes z of the log species–log area curves (SAC) were negatively related to biomass in both data sets, while the intercept c increased with biomass in the Siena data set and was unrelated to biomass in the Bremen data set. The relationship between c and z was negative in the Siena data set and positive in the Bremen data set. The above results differed somewhat depending on which plot sizes were considered for the calculation of the SAC. Literature data confirmed that there are no clear patterns in the inter‐correlations between productivity, small scale and large scale species richness. Sites differing in productivity and in the slopes and intercepts of SAC may thus give rise to different species richness–productivity relationships. There can be one possible relation between species richness and biomass at one spatial scale (e.g. humped‐back) and another type of relation, even opposite, at another spatial scale. This suggests that the properties of species–area curves do not respond in a uniform way to the changes in productivity, but depend on the type of habitat or plant community and its particular properties. The parameter of the SAC can then hardly be used as scale‐independent parameter to investigate the effects of ecological factors, such as productivity, on species richness. The lack of clear patterns in the relations between small scale and large scale species richness implies that the predictions of the species‐pool hypothesis may fail when applied to plot sizes as dealt with in this study.     

Estimating species richness and catch per unit effort from boat electro‐fishing in a lowland river in temperate Australia

Abstract Biodiversity estimates are typically a function of sampling effort and in this regard it is important to develop an understanding of taxon‐specific sampling requirements. Northern hemisphere studies have shown that estimates of riverine fish diversity are related to sampling effort, but such studies are lacking in the southern hemisphere. We used a dataset obtained from boat electro‐fishing the fish community along an essentially continuous 13‐km reach of the Murrumbidgee River, Australia, to investigate sampling effort effects on fish diversity estimates. This represents the first attempt to investigate relationships between sampling effort and the detection of fish species in a large lowland river in Australia. Seven species were recorded. Species‐specific patterns in catch per unit effort were evident and are discussed in terms of solitary and gregarious species, recreational fishing and the monitoring of rare and threatened species. There was a requirement to sample substantial lengths of river to describe total species richness of the fish community in this river reach. To this end, randomly allocated sampling effort and use of species richness estimators produced accurate estimates of species richness without the requirement for excessive levels of effort. Twenty operations were required to estimate species richness at this site, highlighting the need for comparable studies of river fish communities in lowland rivers elsewhere in Australia and the southern hemisphere.     

Limited sampling hampers “big data” estimation of species richness in a tropical biodiversity hotspot

Macro‐scale species richness studies often use museum specimens as their main source of information. However, such datasets are often strongly biased due to variation in sampling effort in space and time. These biases may strongly affect diversity estimates and may, thereby, obstruct solid inference on the underlying diversity drivers, as well as mislead conservation prioritization. In recent years, this has resulted in an increased focus on developing methods to correct for sampling bias. In this study, we use sample‐size‐correcting methods to examine patterns of tropical plant diversity in Ecuador, one of the most species‐rich and climatically heterogeneous biodiversity hotspots. Species richness estimates were calculated based on 205,735 georeferenced specimens of 15,788 species using the Margalef diversity index, the Chao estimator, the second‐order Jackknife and Bootstrapping resampling methods, and Hill numbers and rarefaction. Species richness was heavily correlated with sampling effort, and only rarefaction was able to remove this effect, and we recommend this method for estimation of species richness with “big data” collections.     

What causes geographical variation in the species–area relationships? A test from forests in China

The increase of species richness with sampling area and the decrease with latitude and altitude are two of the most frequently studied patterns in biogeography. However, few studies have simultaneously examined these two patterns to investigate how species–area relationships (SAR) vary with latitude and altitude. In this study, we explore the spatial patterns of SAR in forests in China by investigating numbers of species by life form group (trees, shrubs and herbs) in 32 nested plots from 12 mountains ranging from 18.7°N to 51.9°N in latitude and from 300 to 3150 m in altitude. The slopes of the power law SAR (z‐values) decreased with increasing latitude for all life forms except herbaceous plants, and also decreased with increasing altitude for all life forms but not for shrubs. Latitude and altitude, as well as their interactions, together explained 65.4, 61.8, 48.9 and 45.3% of the variation in z‐values for overall species, trees, shrubs and herbaceous plants, respectively. In addition, actual evapotranspiration affected SAR significantly, but this effect varied significantly among life forms. We concluded that there are significant geographical patterns of SAR for China's forests, which is primarily controlled by energy availability.     

Global pattern and local variation in species–area relationships

Aim We conducted a meta‐analysis of species–area relationships (SARs) by combining several data sets and important covariates such as types of islands, taxonomic groups, latitude and spatial extent, in a hierarchical model framework to study global pattern and local variation in SARs and its consequences for prediction. Location One thousand nine hundred and eighteen islands from 94 SAR studies from around the world. Methods We developed a generalization of the power‐law SAR model, the HSARX model, which allows: (1) the inclusion of multiple focal parameters (intercept, slope, within‐study variance), (2) use of multiple effect modifiers based on a collection of SAR studies, and (3) modelling of the between‐ and within‐study variability. Results The global pattern in the SAR was the average of local SARs and had wide confidence intervals. The global SAR slope was 0.228 with 90% confidence limits of 0.059 and 0.412. The intercept, slope and within‐study variability of local SARs showed great heterogeneity as a result of the interaction of modifying covariates. Confidence intervals for these SAR parameters were narrower when other covariates in addition to area were accounted for, thus increasing the accuracy of the predictions for species richness. The significant effect of latitude and the interaction of latitude, taxa and island type on the SAR slope indicated that the ‘typical’ latitudinal diversity gradient can be reversed in isolated systems. Main conclusions The power‐law relationship underlying the HSARX model provides a good fit for non‐nested SARs across vastly different spatial scales by taking into account other covariates. The HSARX framework allows researchers to explore the complex interactions among SAR parameters and modifying variables, to explicitly study the scale dependence, and to make robust predictions on multiple levels (island, study, global) with associated prediction intervals. From a prediction perspective, it is not the global pattern but the local variation that matters.     

Species accumulation curves and their applications in parasite ecology

Species accumulation curves (SACs) chart the increase in recovery of new species as a function of some measure of sampling effort. Studies of parasite diversity can benefit from the application of SACs, both as empirical tools to guide sampling efforts and predict richness, and because their properties are informative about community patterns and the structure of parasite diversity. SACs can be used to infer interactivity in parasite infracommunities, to partition species richness into contributions from different spatial scales and different levels of the host hierarchy (individuals, populations and communities) or to identify modes of community assembly (niche versus dispersal). A historical tendency to treat individual hosts as statistically equivalent replicates (quadrats) seemingly satisfies the sample-based subgroup of SACs but care is required in this because of the inequality of hosts as sampling units. Knowledge of the true distribution of parasite richness over multiple host-derived and spatial scales is far from complete but SACs can improve the understanding of diversity patterns in parasite assemblages.     

Small mammal species richness and turnover along elevational gradient in Yulong Mountain,Yunnan, Southwest China

Understanding the species diversity patterns along elevational gradients is critical for biodiversity conservation in mountainous regions. We examined the elevational patterns of species richness and turnover, and evaluated the effects of spatial and environmental factors on nonvolant small mammals (hereafter “small mammal”) predicted a priori by alternative hypotheses (mid‐domain effect [MDE], species–area relationship [SAR], energy, environmental stability, and habitat complexity]) proposed to explain the variation of diversity. We designed a standardized sampling scheme to trap small mammals at ten elevational bands across the entire elevational gradient on Yulong Mountain, southwest China. A total of 1,808 small mammals representing 23 species were trapped. We observed the hump‐shaped distribution pattern of the overall species richness along elevational gradient. Insectivores, rodents, large‐ranged species, and endemic species richness showed the general hump‐shaped pattern but peaked at different elevations, whereas the small‐ranged species and endemic species favored the decreasing richness pattern. The MDE and the energy hypothesis were supported, whereas little support was found for the SAR, the environmental stability hypothesis, and the habitat complexity. However, the primary driver(s) for richness patterns differed among the partitioning groups, with NDVI (the normalized difference vegetation index) and MDE being the most important variables for the total richness pattern. Species turnover for all small mammal groups increased with elevation, and it supported a decrease in community similarity with elevational distance. Our results emphasized for increased conservation efforts in the higher elevation regions of the Yulong Mountain.     

Relating species abundance distributions to species-area curves in two Mediterranean-type shrublands

Abstract. Based on both theoretical and empirical studies there is evidence that different species abundance distributions underlie different species‐area relationships. Here I show that Australian and Californian shrubland communities (at the scale from 1 to 1000 m²) exhibit different species‐area relationships and different species abundance patterns. The species‐area relationship in Australian heathlands best fits an exponential model and species abundance (based on both density and cover) follows a narrow log normal distribution. In contrast, the species‐area relationship in Californian shrublands is best fit with the power model and, although species abundance appears to fit a log normal distribution, the distribution is much broader than in Australian heathlands. I hypothesize that the primary driver of these differences is the abundance of small‐stature annual species in California and the lack of annuals in Australian heathlands. Species‐area is best fit by an exponential model in Australian heathlands because the bulk of the species are common and thus the species‐area curves initially rise rapidly between 1 and 100 m². Annuals in Californian shrublands generate very broad species abundance distributions with many uncommon or rare species. The power function is a better model in these communities because richness increases slowly from 1 to 100 m² but more rapidly between 100 and 1000 m² due to the abundance of rare or uncommon species that are more likely to be encountered at coarser spatial scales. The implications of this study are that both the exponential and power function models are legitimate representations of species‐area relationships in different plant communities. Also, structural differences in community organization, arising from different species abundance distributions, may lead to different species‐area curves, and this may be tied to patterns of life form distribution.     

The species–area relationship and confounding variables in a threatened monkey community

This study investigates the species–area relationship (SAR) for forest monkeys in a biodiversity hotspot. The Udzungwa Mountains of Tanzania are well‐suited to investigate the SAR, with seven monkey species in a range of fragment sizes (0.06–526 km²). We test the relationship between species richness and forest fragment size, relative to human and environmental factors. We distinguish resident and transitory species because the latter have an “effective patch size” beyond the area of forest. Forest area was the strongest (log‐linear) predictor of species richness. However, forest area, elevation range and annual moisture index were intercorrelated. Previous knowledge of the relationship between elevation and tree communities suggests that the SAR is largely a result of habitat heterogeneity. Isolation by farmland (matrix habitat) also had a significant negative effect on species richness, probably exacerbated by hunting in small forests. The effect of area and isolation was less for transitory species. The human influence on species' presence/absence was negatively related to the extent of occurrence. Weaker relationships with temperature and precipitation suggest underlying climatic influences, and give some support for the influence of productivity. A reduced area relationship for smaller forests suggests that fragment sizes below 12–40 km² may not be reliable for determining SAR in forest monkeys. Further practical implications are for management to encourage connectivity, and for future SAR research to consider residency, matrix classification and moisture besides precipitation. Am. J. Primatol. 72:325–336, 2010. © 2009 Wiley‐Liss, Inc.     

Pitfalls in Small-Scale Species-Area Sampling and Analysis

Analyses of the dependency of species richness (S) on area (A), the so-called species-area relationships (SARs), are widespread approaches to characterize and compare biodiversity patterns. This article highlights – with a focus on small-scale SARs of plants in continuous ecosystems – how inappropriate sampling methods or theoretical misconceptions can create artifacts and thus may lead to wrong conclusions. Most of these problems have been recognized before but continue to appear regularly in the scientific literature. The following main points are reviewed and discussed: i) Species richness values and SARs depend on the measurement method (any-part vs. grid-point system); ii) Species-richness values depend on the shape of the analyzed plots; iii) Many published SARs are not true SARs but instead represent species sampling curves or their data points consist of richness totals for incontiguous subplots; iv) Nested-plot design is the preferred sampling method for SARs (the claim that this approach would cause pseudoreplication is erroneous); v) SARs should be constructed using mean values of several counts for the smaller areas; vi) SAR functions can be fitted and selected both in the S- and the log S-space but this must be done consistently for all compared function types. It turns out that the finding of non-power function SARs in many studies is due to a lack of awareness of one or several of the named points. Thus, power-function SARs are even more widespread than a recent review would suggest. I therefore propose to use the power law as a universal model for all types of SARs but to test whether the slope z varies with spatial scale. Finally, I urge readers to be aware of the many pitfalls in SAR studies, to fully disclose methodology, and to apply a meaningful and consistent terminology, especially by restricting the terms “species-area relationship” and “species density” to situations in which each data point represents a contiguous area.