A mélange of curves – further dialogue about species–area relationships

Scheiner (2003) presented a classification of species–area curves into six types based on the pattern of sampling and how the data are combined to form the curves. Gray et al. (2004) contended that five of those types should be termed ‘species‐accumulation curves’, reserving ‘species–area curve’ for those based on island‐type data. Their proposition contradicts 70 years of usage and confounds curves that are area‐explicit with those that are area‐undefined. In exploring these issues, I highlight additional aspects of species–area and species‐accumulation curves, including the assumption of nesting in Type IV (island) curves, how to convert area‐unspecified curves into area curves, and the effects of the grain of the analysis on the properties of the curve. Further exploration, theoretical development, and dialogue are needed before we will understand all the biology that species–area curves summarize.     

Negative density–area relationships: the importance of the zeros

Aim Estimates of abundances and densities of birds and mammals have often been shown to be scale dependent, in that population sizes over large areas are overestimated if extrapolated from surveys of small plots. Previous tests of the mechanisms suggested to cause this decelerating scaling pattern found evidence of a biased choice of small plots in patches of homogeneous habitat. Here we show that negative density–area relationships can also arise as result of not considering plots where individuals of the species or assemblage of interest are absent in surveys of differing spatial resolution. Location We took a complete census of violets (Viola spp.) in 800 m² of chalk grassland in Wye, Kent, UK, and used human population censuses for Finnish, Swiss and Italian municipalities, English districts, states of the USA and European countries. Methods We used mixed models of logarithmically transformed number of individuals or densities as a function of area. Results The census of violets shows that by increasing the survey resolution and by not considering plots without individuals, the effectively occupied area diminishes and a negative density–area relationship arises. The finding that negative density–area relationships are also common for people is evidence that the non‐random choice of plots in population surveys of varying areas can be responsible for many observed negative density–area relationships. The shallower slope of the people–administrative area relationship for Switzerland and Finland compared with Italy, as well as for England and the USA compared with Europe, confirms that less than proportionate individuals–area relationships can be the consequence of larger plot areas containing a higher proportion of areas without individuals. Main conclusions Densities should be reported together with the effective areas for which they were estimated. It should be clearly conveyed whether or not plots where the surveyed species was absent were included in the density estimation.     

A test of the metapopulation model of the species–area relationship

Abstract Aim The species–area relationship is a ubiquitous pattern. Previous methods describing the relationship have done little to elucidate mechanisms producing the pattern. Hanski & Gyllenberg (Science, 1997, 275 , 397) have shown that a model of metapopulation dynamics yields predictable species–area relationships. We elaborate on the biological interpretation of this mechanistic model and test the prediction that communities of species with a higher risk of extinction caused by environmental stochasticity should have lower species–area slopes than communities experiencing less impact of environmental stochasticity. Methods We develop the mainland–island version of the metapopulation model and show that the slope of the species–area relationship resulting from this model is related to the ratio of population growth rate to variability in population growth of individual species. We fit the metapopulation model to five data sets, and compared the fit with the power function model and Williams's (Ecology, 1995, 76 , 2607) extreme value function model. To test that communities consisting of species with a high risk of extinction should have lower slopes, we used the observation that small‐bodied species of vertebrates are more susceptible to environmental stochasticity than large‐bodied species. The data sets were divided into small and large bodied species and the model fit to both. Results and main conclusions The metapopulation model showed a good fit for all five data sets, and was comparable with the fits of the extreme value function and power function models. The slope of the metapopulation model of the species–area relationship was greater for larger than for smaller‐bodied species for each of five data sets. The slope of the metapopulation model of the species–area relationship has a clear biological interpretation, and allows for interpretation that is rooted in ecology, rather than ad hoc explanation.     

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.     

A field test of the distance sampling method using Golden‐cheeked Warblers

ABSTRACT Criteria for delisting Golden‐cheeked Warblers (Dendroica chrysoparia) include protection of sufficient breeding habitat to ensure the continued existence of 1000 to 3000 singing males in each of eight recovery regions for ≥10 consecutive years. Hence, accurate abundance estimation is an integral component in the recovery of this species. I conducted a field test to determine if the distance sampling method provided unbiased abundance estimates for Golden‐cheeked Warblers and develop recommendations to improve the accuracy of estimates by minimizing the effects of violating this method's assumptions. To determine if observers could satisfy the assumptions that birds are detected at the point with certainty and at their initial locations, I compared point‐transect sampling estimates from 2‐, 3‐, 4‐, and 5‐min time intervals to actual abundance determined by intensive territory monitoring. Point‐transect abundance estimates were 15%, 29%, 43%, and 59% greater than actual abundance (N= 156) for the 2‐, 3‐, 4‐, and 5‐min intervals, respectively. Point‐transect sampling produced unbiased estimates of Golden‐cheeked Warbler abundance when counts were limited to 2 min (N= 154–207). Abundance estimates derived from point‐transect sampling were likely greater than actual abundance because observers did not satisfy the assumption that birds were detected at their initial locations due to the frequent movements and large territory sizes of male Golden‐cheeked Warblers. To minimize the effect of movement on abundance estimates, I recommend limiting counts of singing males to 2‐min per point. Counts for other species in similar habitats with similar behavior and movement patterns also should be limited to 2 min when unbiased estimates are important and conducting field tests of the point‐transect distance sampling method is not possible.     

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.