Taxonomic and regional uncertainty in species-area relationships and the identification of richness hotspots.

Laboratoire Ecosystèmes Lagunaires, Unité Mixte de Recherche 5119, Centre National de la Recherche Scientifique-IFREMER-UM2, Université Montpellier 2, cc 093, Place Eugène Bataillon, 34095 Montpellier Cedex 5, France.
Proceedings of the National Academy of Sciences (Impact Factor: 9.81). 11/2008; 105(40):15458-63. DOI: 10.1073/pnas.0803610105
Source: PubMed

ABSTRACT Species-area relationships (SARs) are fundamental to the study of key and high-profile issues in conservation biology and are particularly widely used in establishing the broad patterns of biodiversity that underpin approaches to determining priority areas for biological conservation. Classically, the SAR has been argued in general to conform to a power-law relationship, and this form has been widely assumed in most applications in the field of conservation biology. Here, using nonlinear regressions within an information theoretical model selection framework, we included uncertainty regarding both model selection and parameter estimation in SAR modeling and conducted a global-scale analysis of the form of SARs for vascular plants and major vertebrate groups across 792 terrestrial ecoregions representing almost 97% of Earth's inhabited land. The results revealed a high level of uncertainty in model selection across biomes and taxa, and that the power-law model is clearly the most appropriate in only a minority of cases. Incorporating this uncertainty into a hotspots analysis using multimodel SARs led to the identification of a dramatically different set of global richness hotspots than when the power-law SAR was assumed. Our findings suggest that the results of analyses that assume a power-law model may be at severe odds with real ecological patterns, raising significant concerns for conservation priority-setting schemes and biogeographical studies.

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    ABSTRACT: AimAlthough the increase in species richness with increasing area is considered one of the few laws in ecology, the role of environmental and taxon-specific features in shaping species–area relationships (SARs) remains controversial. Using 421 land-plant floras covering continents, continental islands and oceanic islands, we investigate whether variations in SAR parameters can be interpreted in terms of differences among lineages in speciation mode and dispersal capacities (TAXON), or of geological history and geographical isolation between continents and islands (GEO).LocationGlobal.Methods Linear mixed-effects models describing variation in SARs, depending on the factors GEO and TAXON and controlling for differences between realms (REALM) and biomes (BIOME).ResultsThe best random-effect structure included both random slopes and random intercepts for GEO, TAXON, REALM and BIOME. This accounted for 77% of the total variation in species richness, substantially more than the 27% statistically explained by the model with fixed effects only (i.e. the simple SAR). The slopes of the SARs were higher for oceanic islands than for continental islands and continents, and higher in spermatophytes than in pteridophytes and bryophytes. The intercepts largely exhibited the reverse trend. TAXON was included in best-fit models restricted to oceanic and continental islands, but not continents. Analysing each plant lineage separately, the intercept of GEO was only included in the random structure of spermatophytes.Main conclusionsSAR parameters varied considerably depending on geological history and taxon-specific traits. Such differences in SARs among land plants challenge the neutral theory that the accumulation of species richness on islands is controlled exclusively by extrinsic factors. Taxon-specific differences in SARs were, however, confounded by interactions with geological history and geographical isolation. This highlights the importance of applying integrative frameworks that take both environmental context and taxonomic idiosyncrasies into account in SAR analyses.
    Global Ecology and Biogeography 11/2014; in press. DOI:10.1111/geb.12230 · 7.24 Impact Factor
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    Dataset: geb2011
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    Forests 11/2014; 5:2882-2904. DOI:10.3390/f5112882 · 1.14 Impact Factor

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