Evolutionary patterns and physicochemical properties explain macroinvertebrate sensitivity to heavy metals.

Ecological risk assessment depends strongly on species sensitivity data. Typically, sensitivity data are based on laboratory toxicity bioassays, which for practical constraints cannot be exhaustively performed for all species and chemicals available. Bilinear models integrating phylogenetic information of species and physicochemical properties of compounds allow to predict species sensitivity to chemicals.

Palaeohistological Evidence for Ancestral High Metabolic Rate in Archosaurs.

Metabolic heat production in archosaurs has played an important role in their evolutionary radiation during the Mesozoic, and their ancestral metabolic condition has long been a matter of debate in systematics and palaeontology. The study of fossil bone histology provides crucial information on bone growth rate, which has been used to indirectly investigate the evolution of thermometabolism in archosaurs. However, no quantitative estimation of metabolic rate has ever been performed on fossils using bone histological features.

Using phylogenetic information and chemical properties to predict species tolerances to pesticides.

Direct estimation of species' tolerance to pesticides and other toxic organic substances is a combinatorial problem, because of the large number of species-substance pairs. We propose a statistical modelling approach to predict tolerances associated with untested species-substance pairs, by using models fitted to tested pairs. This approach is based on the phylogeny of species and physico-chemical descriptors of pesticides, with both kinds of information combined in a bilinear model.

Multiscale codependence analysis: an integrated approach to analyze relationships across scales.

The spatial and temporal organization of ecological processes and features and the scales at which they occur are central topics to landscape ecology and metapopulation dynamics, and increasingly regarded as a cornerstone paradigm for understanding ecological processes. Hence, there is need for computational approaches which allow the identification of the proper spatial or temporal scales of ecological processes and the explicit integration of that information in models. For that purpose, we propose a new method (multiscale codependence analysis, MCA) to test the statistical significance of the correlations between two variables at particular spatial or temporal scales.