Search

Abstract: Many indices have been proposed to measure functional diversity and its four distinct dimensions: functional richness, evenness, divergence and redundancy. Identifying indices that reliably measure the functional diversity dimension(s) of interest requires careful testing of how each index responds to species' traits and abundance distributions. In the absence of a convenient simulation tool, tests with artificial data have to date explored only a limited number of scenarios or have altered trait and abundance distributions only indirectly based on principles of evolution and community assembly. We provide simul.comms, an R function that allows users to test the efficacy of functional diversity indices by easily creating artificial species communities with user‐specified abundance and trait distributions for continuous, ordinal and categorical traits. To illustrate the function's utility, we examine the performance of R, a recently published abundance‐sensitive index for functional redundancy. We use two approaches to designing simulation tests for this example analysis. The first uses simul.comms to create six separate sets of artificial communities to qualitatively assess how R responds to predictable changes in functional redundancy. The second uses simul.comms to independently alter seven community composition parameters, whose influence on R is then examined quantitatively via effect sizes in linear regression. Our analyses indicate that R broadly mirrors expected changes in functional redundancy and predictably responds to changes in community composition parameters. R appears, however, to primarily reflect trait distributions, responding minimally to variance in abundance and counter‐intuitively to abundance range. Further refinement of tools to measure functional redundancy may therefore be desirable. The R tool we provide should assist with refining functional diversity measures, a critical step towards improving our ability to understand and mitigate the impacts of biodiversity loss on ecosystem functioning. Because simul.comms simply produces two linked matrices, a species‐by‐traits matrix and a site‐by‐species abundance matrix, it may be equally valuable in exploring questions and analytical approaches in other areas of community ecology. [ABSTRACT FROM AUTHOR]

Global knowledge about the spatial distribution of species is orders of magnitude coarser in resolution than other geographically-structured environmental datasets such as topography or land cover. Yet such knowledge is crucial in deciphering ecological and evolutionary processes and in managing global change. In this review, we propose a conceptual and cyber-infrastructure framework for refining species distributional knowledge that is novel in its ability to mobilize and integrate diverse types of data such that their collective strengths overcome individual weaknesses. The ultimate aim is a public, online, quality-vetted ‘Map of Life’ that for every species integrates and visualizes available distributional knowledge, while also facilitating user feedback and dynamic biodiversity analyses. First milestones toward such an infrastructure have now been implemented. [Copyright &y& Elsevier]

Species distributional or trait data based on range map (extent-of-occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species’ distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing spatial autocorrelation in the errors. However, we found that for presence/absence data the results and conclusions were very variable between the different methods. This is likely due to the low information content of binary maps. Also, in contrast with previous studies, we found that autocovariate methods consistently underestimated the effects of environmental controls of species distributions. Given their widespread use, in particular for the modelling of species presence/absence data (e.g. climate envelope models), we argue that this warrants further study and caution in their use. To aid other ecologists in making use of the methods described, code to implement them in freely available software is provided in an electronic appendix. [ABSTRACT FROM AUTHOR]

In the face of accelerating biodiversity loss and limited data, species distribution models – which statistically capture and predict species’ occurrences based on environmental correlates – are increasingly used to inform conservation strategies. Additionally, distribution models and their fit provide insights on the broad-scale environmental niche of species. To investigate whether the performance of such models varies with species’ ecological characteristics, we examined distribution models for 1329 bird species in southern and eastern Africa. The models were constructed at two spatial resolutions with both logistic and autologistic regression. Satellite-derived environmental indices served as predictors, and model accuracy was assessed with three metrics: sensitivity, specificity and the area under the curve (AUC) of receiver operating characteristics plots. We then determined the relationship between each measure of accuracy and ten ecological species characteristics using generalised linear models. Among the ecological traits tested, species’ range size, migratory status, affinity for wetlands and endemism proved most influential on the performance of distribution models. The number of habitat types frequented (habitat tolerance), trophic rank, body mass, preferred habitat structure and association with sub-resolution habitats also showed some effect. In contrast, conservation status made no significant impact. These findings did not differ from one spatial resolution to the next. Our analyses thus provide conservation scientists and resource managers with a rule of thumb that helps distinguish, on the basis of ecological traits, between species whose occurrence is reliably or less reliably predicted by distribution models. Reasonably accurate distribution models should, however, be attainable for most species, because the influence ecological traits bore on model performance was only limited. These results suggest that none of the ecological traits tested provides an obvious correlate for environmental niche breadth or intra-specific niche differentiation. [ABSTRACT FROM AUTHOR]

Abstract: For effective conservation planning, resource managers around the world require information on species distributions. Available data, however, are often too coarse in spatial resolution to be of practical use. We describe four modelling techniques that harness the information contained in coarse-scale occurrence records to predict species’ distributions at the finer resolutions relevant to conservation officers in the field. The techniques are primarily statistical in nature, but can incorporate expert knowledge on species’ habitat preferences. We highlight the conceptual strengths and weaknesses of each, and perform a preliminary test of their comparative performance, using empirical data on the distribution of nine bird species in Uganda and satellite-derived environmental indices. Two techniques, the point sampling approach and iterative approach, yield encouraging results. Because the dominance of environmental factors controlling species’ distributions shifts with spatial scale, however, cross-scale predictions remain a formidable challenge. Further testing and refinement of the techniques we present here are undoubtedly necessary before they can be applied with any confidence. [Copyright &y& Elsevier]