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.     

Cichlid species‐area relationships are shaped by adaptive radiations that scale with area

A positive relationship between species richness and island size is thought to emerge from an equilibrium between immigration and extinction rates, but the influence of species diversification on the form of this relationship is poorly understood. Here, we show that within‐lake adaptive radiation strongly modifies the species‐area relationship for African cichlid fishes. The total number of species derived from in situ speciation increases with lake size, resulting in faunas orders of magnitude higher in species richness than faunas assembled by immigration alone. Multivariate models provide evidence for added influence of lake depth on the species‐area relationship. Diversity of clades representing within‐lake radiations show responses to lake area, depth and energy consistent with limitation by these factors, suggesting that ecological factors influence the species richness of radiating clades within these ecosystems. Together, these processes produce lake fish faunas with highly variable composition, but with diversities that are well predicted by environmental variables.     

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.     

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 maximal body mass–area relationship in island mammals

Aim A positive power relationship between maximal body mass and land area has previously been reported of the form M_max ∝ Area^0.5 whilst allometric scaling theory predicts either M_max ∝ Area^1.33 or M_max ∝ Area¹. We provide an analysis of the maximal mass–area relationship for four island systems, to test the hypothesis that community relaxation following isolation converges in each case to a slope of Area^0.5. Location Islands of the Japanese archipelago, the western Mediterranean, the Sea of Cortés and Southeast Asia. Methods We calculated the relationship between island area and the maximal body mass of the largest mammal species on the island using linear regression models with log‐transformed variables, and tested the hypothesis that the slopes were not significantly different from 0.5. Results We found a slope of 0.47 within the Japanese archipelago, 0.42 for western Mediterranean islands, 0.73 for the Sea of Cortés islands and 0.50 for Southeast Asian islands. None of these slopes were significantly different from 0.5. Main conclusions Our results provide further empirical support for previous findings of a general maximal body mass–area relationship of M_max ∝ Area^0.5, but they deviate from theoretical predictions. We hypothesize that this mass–area relationship was the ultimate end point of community relaxation initiated by the isolation of the mammal communities. Maximal body mass on each island today probably reflects the interaction between energetic constraints, home range size and island area.     

On the semiotic dimension of ecological theory: The case of island biogeography

The Macarthur-Wilson equilibrium theory of island biogeography has had a contradictory role in ecology. As a lasting contribution, the theory has created a new way of viewing insular environments as dynamical systems. On the other hand, many of the applications of the theory have reduced to mere unimaginative curve-fitting. I analyze this paradox in semiotic terms: the theory was mainly equated with the simple species-area relationship which became a signifier of interesting island ecology. The theory is, however, better viewed as a theoretical framework that suggests specific hypotheses on the ecology of colonization of insular environments. This paradox is inherent in the use of simplifying analytic models. Analytic models are necessary and fruitful in the work of ecologists, but they ought to be supplemented with a broader, pluralistic appreciation of the role of theories in general.     

Re‐approaching the small island effect

Aim To propose a new approach to the small island effect (SIE) and a simple mathematical procedure for the estimation of its upper limit. The main feature of the SIE is that below an upper size threshold an increase of species number with increase of area in small islands is not observed. Location Species richness patterns from different taxa and insular systems are analysed. Methods Sixteen different data sets from 12 studies are analysed. Path analysis was used for the estimation of the upper limit of the SIE. We studied each data set in order to detect whether there was a certain island size under which the direct effects of area were eliminated. This detection was carried out through the sequential exclusion of islands from the largest to the smallest. For the cases where an SIE was detected, a log‐log plot of species number against area is presented. The relationships between habitat diversity, species number and area are studied within the limits of the SIE. In previous studies only area was used for the detection of the SIE, whereas we also encompass habitat diversity, a parameter with well documented influence on species richness, especially at small scales. Results An SIE was detected in six out of the 16 studied cases. The upper limit of the SIE varies, depending on the characteristics of the taxon and the archipelago under study. In general, the values of the upper limit of the SIE calculated according to the approach undertaken in our study differ from the values calculated in previous studies. Main conclusions Although the classical species–area models have been used to estimate the upper limit of the SIE, we propose that the detection of this phenomenon should be undertaken independently from the species–area relationship, so that the net effects of area are calculated excluding the surrogate action of area on other variables, such as environmental heterogeneity. The SIE appears when and where area ceases to influence species richness directly. There are two distinct SIE patterns: (1) the classical SIE where both the direct and indirect effects of area are eliminated and (2) the cryptic SIE where area affects species richness indirectly. Our approach offers the opportunity of studying the different factors influencing biodiversity on small scales more accurately. The SIE cannot be considered a general pattern with fixed behaviour that can be described by the same model for different island groups and taxa. The SIE should be recognized as a genuine but idiosyncratic phenomenon.