Biodiversity assessments use a variety of data and models. We propose best-practice standards for studies in these assessments.

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

Vultures provide critical ecosystem services, yet populations of many species have collapsed worldwide. We present the first estimates of a 30-year PanAfrican vulture decline, confirming that declines have occurred on a scale broadly comparable with those seen in Asia, where the ecological, economic, and human costs are already documented. Populations of eight species we assessed had declined by an average of 62%; seven had declined at a rate of 80% or more over three generations. Of these, at least six appear to qualify for uplisting to Critically Endangered. Africa's vultures are facing a range of specific threats, the most significant of which are poisoning and trade in traditional medicines, which together accounted for 90% of reported deaths. We recommend that national governments urgently enact and enforce legislation to strictly regulate the sale and use of pesticides and poisons, to eliminate the illegal trade in vulture body parts, as food or medicine, and to minimize mortality caused by power lines and wind turbines.

Predicting how species distributions might shift as global climate changes is fundamental to the successful adaptation of conservation policy. An increasing number of studies have responded to this challenge by using climate envelopes, modeling the association between climate variables and species distributions. However, it is difficult to quantify how well species actually match climate. Here, we use null models to show that species-climate associations found by climate envelope methods are no better than chance for 68 of 100 European bird species. In line with predictions, we demonstrate that the species with distribution limits determined by climate have more northerly ranges. We conclude that scientific studies and climate change adaptation policies based on the indiscriminate use of climate envelope methods irrespective of species sensitivity to climate may be misleading and in need of revision.bioclimatic niche ͉ global change ͉ null models ͉ ornithology ͉ species distribution A s global climates warm, some species distributions are moving upward and poleward (1, 2). Predicting how individual species respond to climate change allows assessment of extinction risk and spatial planning of conservation activity (3, 4). Climate envelopes (or the climatic niche concept) are the current methods of choice for prediction of species distributions under climate change and their use is growing rapidly in many areas of ecology (5-7). However, although climate envelope methods and assumptions have been criticized as ecologically and statistically naïve (8, 9), there exists no quantitative evaluation of the importance of these criticisms.Because it is axiomatic that climate influences species distributions (8), the climate envelope approach of matching distributions to climate is intrinsically appealing. However, the use of such simplistic models is risky on both biological and statistical grounds: there are many reasons why species distributions may not match climate, including biotic interactions (10), adaptive evolution (11), dispersal limitation (12), and historical chance (13). Although debate began before the current explosion in climate envelope studies (8, 9), there remains no quantitative information that would allow assessment of how well, or even if, species distributions match climate. Here, we quantify the match of species distributions to environment by generating synthetic species distributions that retain the spatial structure in the observed distributions but are randomly placed with respect to climate.Ideally, the predictions of climate envelope models would be verified on an entirely independent dataset (14, 15) and some attempts have been made at this, both by prediction of the potential distribution of introduced species in new continents (16) or through backward prediction (hindcasting) of prehistoric distributions reconstructed from the fossil record (17). Unfortunately, truly independent data are generally unavailable, so the usefulness of a climate envelope model is typically measured by how well...

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