Restrictions on roaming: Until the past century or so, the movement of wild animals was relatively unrestricted, and their travels contributed substantially to ecological processes. As humans have increasingly altered natural habitats, natural animal movements have been restricted. Tucker et al. examined GPS locations for more than 50 species. In general, animal movements were shorter in areas with high human impact, likely owing to changed behaviors and physical limitations. Besides affecting the species themselves, such changes could have wider effects by limiting the movement of nutrients and altering ecological interactions. Science , this issue p. 466

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

Summary: 1. Ecological count data (e.g. number of individuals or species) are often log‐transformed to satisfy parametric test assumptions.2. Apart from the fact that generalized linear models are better suited in dealing with count data, a log‐transformation of counts has the additional quandary in how to deal with zero observations. With just one zero observation (if this observation represents a sampling unit), the whole data set needs to be fudged by adding a value (usually 1) before transformation.3. Simulating data from a negative binomial distribution, we compared the outcome of fitting models that were transformed in various ways (log, square root) with results from fitting models using quasi‐Poisson and negative binomial models to untransformed count data.4. We found that the transformations performed poorly, except when the dispersion was small and the mean counts were large. The quasi‐Poisson and negative binomial models consistently performed well, with little bias.5. We recommend that count data should not be analysed by log‐transforming it, but instead models based on Poisson and negative binomial distributions should be used.

The selection of variables in regression problems has occupied the minds of many statisticians. Several Bayesian variable selection methods have been developed, and we concentrate on the following methods: Kuo & Mallick, Gibbs Variable Selection (GVS), Stochastic Search Variable Selection (SSVS), adaptive shrinkage with Jeffreys' prior or a Laplacian prior, and reversible jump MCMC. We review these methods, in the context of their different properties. We then implement the methods in BUGS, using both real and simulated data as examples, and investigate how the different methods perform in practice. Our results suggest that SSVS, reversible jump MCMC and adaptive shrinkage methods can all work well, but the choice of which method is better will depend on the priors that are used, and also on how they are implemented.