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.     

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.     

A critique of 'countryside biogeography' as a guide to research in human‐dominated landscapes

Proliferation of redundant terms in ecology and conservation slows progress and creates confusion. ‘Countryside biogeography’ has been promoted as a new framework for conservation in production landscapes, so may offer a replacement for other concepts used by landscape ecologists. We conducted a systematic review to assess whether the 'countryside biogeography' concept provides a distinctive framing for conservation in human‐dominated landscapes relative to existing concepts. We reviewed 147 papers referring to countryside biogeography and 81 papers that did not. These papers were divided into categories representing three levels of use of countryside biogeography concepts (strong, weak, cited only) and two categories that did not use countryside biogeography at all but used similar concepts including fragmentation and matrix. We revealed few distinctions among groups of papers. Countryside biogeography papers made more frequent use of the terms 'ecosystem services', 'intensification' and 'land sparing' compared with non‐countryside biogeography papers. Papers that did not refer to countryside biogeography sampled production areas (e.g. farms) less often, and this related to their focus on habitat specialist species for which patch‐matrix assumptions were reasonable. Countryside biogeography offers a conceptual wrapper rather than a distinctive framework for advancing research in human‐modified landscapes. This and similar wrappers such as ‘conservation biogeography’ and ‘agricultural biogeography’ risk creating confusion among new researchers, and can prevent clear communication about research. To improve communication, we recommend using the suite of well‐established terms already applied to conservation in human‐modified landscapes rather than through an interceding conceptual wrapper.     

The phytogeographical affinities of the Pitcairn Islands – a model for south‐eastern Polynesia?

Aim To identify how the Pitcairn group relates biogeographically to the south‐eastern Polynesian region and if, as a subset of the regions flora, it can then be used as a model for biogeographical analyses. Location The Pitcairn group (25°4′ S, 130°06′ W) comprises four islands: Pitcairn, a relatively young, high volcanic Island; Henderson, an uplifted atoll, the uplift caused by the eruption of Pitcairn; and two atolls, Ducie and Oeno. The remote location, young age and range of island types found in the Pitcairn Island group makes the group ideal for the study of island biogeography and evolution. Methods A detailed literature survey was carried out and several data sets were compiled. Dispersal method, propagule number and range data were collected for each of the 114 species that occurs in the Pitcairn group, and environmental data was also gathered for islands in Polynesia. Analyses were carried out using non‐metric multidimensional scaling and clustering techniques. Results The flora of the Pitcairn Islands is derived from the flora of other island groups in the south‐eastern Polynesian region, notably those of the Austral, Society and Cook Islands. Species with a Pacific‐wide distribution dominate the overall Pitcairn group flora. However, each of the islands show different patterns; Pitcairn is dominated by species with Pacific, Polynesian and endemic distributions, with anemochory as the dominant dispersal method (39.5%); Henderson is also dominated by species with Pacific, Polynesian and endemic distributions, but zoochory is the dominant dispersal method (59.4); Oeno and Ducie are dominated by Pantropic species with hydrochory as the most common dispersal method (52.9% and 100%, respectively). Main conclusions ? Habitat availability is the most significant factor determining the composition and size of the flora. ? South‐east Polynesia is a valid biogeographical unit, and should include the Cook, Austral, Society, Marquesas, Gambier, Tuamotu and Pitcairn Islands with Rapa, but should exclude Easter Island, Tonga and Samoa. ? Regionalization schemes should take island type into consideration. ? The Pitcairn Island group can serve as a useful model for Pacific biogeographical analyses.     

Determinants of species‐area relationships for marsh‐nesting birds

ABSTRACT To clarify the underlying causes of the species‐area relationship in marsh‐nesting birds, I studied eight freshwater tidal marshes of the Connecticut River that differed in area, degree of isolation, mudflat cover, water cover, tidal regime, and extent of individual plant communities. I measured these habitat variables on aerial infrared photos, and surveyed bird populations by mapping the distribution of all birds in marshes under 5 ha in area and establishing 50‐m radius plots in marshes over 5 ha. From surveys, I determined species richness, population densities, and total populations. Analysis revealed a positive relationship between species richness and area, but no correlation between area and habitat heterogeneity. Other habitat variables were poor predictors of species richness. The lack of a relationship between habitat and species richness appeared to be a consequence of most vegetation types present not being sufficiently distinct for birds to differentially associate with them. I also found no relationship between bird population density and area, suggesting that habitat quality in marshes did not improve with increasing size, and species evenness declined with increasing richness because greater richness was associated with the presence of more rare species. Larger marshes had more rare species, species with larger populations, and species with a minimum threshold area for occurrence. Thus, my results are consistent with theoretical predictions that larger populations are less prone to local extinction and, as individuals are added to a community, more rare species are present.     

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.     

Terminology and quantification of environmental heterogeneity in species‐richness research

Spatial environmental heterogeneity (EH) is an important driver of species diversity, and its influence on species richness has been analysed for numerous taxa, in diverse ecological settings, and over a large range of spatial scales. The variety and ambiguity of concepts and terminology, however, have hampered comparisons among studies. Based on a systematic literature survey of 192 studies including 1148 data points, we provide an overview of terms and measures related to EH, and the mechanisms that relate EH to species richness of plants and animals in terrestrial systems. We identify 165 different measures used to quantify EH, referred to by more than 350 measure names. We classify these measures according to their calculation method and subject area, finding that most studies have analysed heterogeneity in land cover, topography, and vegetation, whereas comparatively few studies have focused on climatic or soil EH. Overall, elevation range emerged as the most frequent measure in our dataset. We find that there is no consensus in the literature about terms (such as ‘habitat diversity’ or ‘habitat complexity’), their meanings and associated quantification methods. More than 100 different terms have been used to denote EH, with largely imprecise delimitations. We reveal trends in use of terms and quantification with respect to spatial scales, study taxa, and locations. Finally, we discuss mechanisms involved in EH–richness relationships, differentiating between effects on species coexistence, persistence, and diversification. This review aims at guiding researchers in their selection of heterogeneity measures. At the same time, it shows the need for precise terminology and avoidance of ambiguous synonyms to enhance understanding and foster among‐study comparisons and synthesis.