Comparative methods as a statistical fix: the dangers of ignoring an evolutionary model.
ABSTRACT Abstract Comparative methods are widely used in ecology and evolution. The most frequently used comparative methods are based on an explicit evolutionary model. However, recent approaches have been popularized that are without an evolutionary basis or an underlying null model. Here we highlight the limitations of such techniques in comparative analyses by using simulations to compare two commonly used comparative methods with and without evolutionary basis, respectively: generalized least squares (GLS) and phylogenetic eigenvector regression (PVR). We find that GLS methods are more efficient at estimating model parameters and produce lower variance in parameter estimates, lower phylogenetic signal in residuals, and lower Type I error rates than PVR methods. These results can very likely be generalized to eigenvector methods that control for space and both space and phylogeny. We highlight that GLS methods can be adapted in numerous ways and that the variance structure used in these models can be flexibly optimized to each data set.
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ABSTRACT: We examined whether plant-soil feedback and plant-field abundance were phylogenetically conserved. For 57 co-occurring native and exotic plant species from an old field in Canada, we collected a data set on the effects of three soil biota treatments on plant growth: net whole-soil feedback (combined effects of mutualists and antagonists), feedback with arbuscular mycorrhizal fungi (AMF) collected from soils of conspecific plants, and feedback with Glomus etunicatum, a dominant mycorrhizal fungus. We found phylogenetic signal in both net whole-soil feedback and feedback with AMF of conspecifics; conservatism was especially strong among native plants but absent among exotics. The abundance of plants in the field was also conserved, a pattern underlain by shared plant responses to soil biota. We conclude that soil biota influence the abundance of close plant relatives in nature.Ecology Letters 10/2014; DOI:10.1111/ele.12378 · 13.04 Impact Factor
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ABSTRACT: There is little appreciation of the level of extinction risk faced by one-sixth of the over 65,000 species assessed by the International Union for Conservation of Nature. Determining the status of these data-deficient (DD) species is essential to developing an accurate picture of global biodiversity and identifying potentially threatened DD species. To address this knowledge gap, we used predictive models incorporating species' life history, geography, and threat information to predict the conservation status of DD terrestrial mammals. We constructed the models with 7 machine learning (ML) tools trained on species of known status. The resultant models showed very high species classification accuracy (up to 92%) and ability to correctly identify centers of threatened species richness. Applying the best model to DD species, we predicted 313 of 493 DD species (64%) to be at risk of extinction, which increases the estimated proportion of threatened terrestrial mammals from 22% to 27%. Regions predicted to contain large numbers of threatened DD species are already conservation priorities, but species in these areas show considerably higher levels of risk than previously recognized. We conclude that unless directly targeted for monitoring, species classified as DD are likely to go extinct without notice. Taking into account information on DD species may therefore help alleviate data gaps in biodiversity indicators and conserve poorly known biodiversity. Predección del Estado de Conservación de Especies con Deficiencia de Datos.Conservation Biology 08/2014; DOI:10.1111/cobi.12372 · 4.36 Impact Factor
04/2014; 23(1):21-26. DOI:10.7818/ECOS.2014.23-1.04