Our aim was to disentangle the effects of different heat sources and the non-thermal properties of the substrate in the microhabitat choices of two lizard species living in savanna habitats of central-western Brazil: the teiid Ameivula aff. ocellifera (N = 43) and the tropidurid Tropidurus oreadicus (N = 23). To this end, a mixed structural resource selection function (mixed-SRSF) approach was used, modelling the probability of finding a lizard on a certain microhabitat based on environmental variables of used and simultaneously available places. First, we controlled for the effects of solar radiation, convection and the physical thermal properties of the substrate on substrate temperature. Then we assessed the effects of solar radiation, convection, conduction and the non-thermal properties of the substrate in the probability of use of a certain microhabitat. Results confirmed that substrate temperature was mediated by: air convection > solar radiation > physical thermal properties of the substrates. Moreover, the mixed-SRSF revealed that direct solar radiation and the non-thermal properties of the substrates were the only drivers of microhabitat selection for both species, with approximately the same strength. Our novel approach allowed splitting of the effect of different mechanisms in the microhabitat selection of lizards, which makes it a powerful tool for assessing the conformation of the interactions between different environmental variables mediating animal behaviour.

Technological advancements in remote sensing and telemetry provide opportunities for assessing the effects of expanding extractive industries on animal populations. Here, we illustrate the applicability of resource selection functions (RSFs) for modelling wildlife habitat selection on industrially-disturbed landscapes. We used grizzly bears (Ursus arctos) from a threatened population in Canada and surface mining as a case study. RSF predictions based on GPS radiocollared bears (nduring mining = 7; npost mining = 9) showed that males and solitary females selected areas primarily outside mineral surface leases (MSLs) during active mining, and conversely inside MSLs after mine closure. However, females with cubs selected areas within compared to outside MSLs irrespective of mining activity. Individual variability was pronounced, although some environmental- and human-related variables were consistent across reproductive classes. For males and solitary females, regional-scale RSFs yielded comparable results to site-specific models, whereas for females with cubs, modelling the two scales produced divergent results. While mine reclamation may afford opportunities for bear persistence, managing public access will likely decrease the risk of human-caused bear mortality. RSFs are powerful tools that merit widespread use in quantitative and visual investigations of wildlife habitat selection on industrially-modified landscapes, using Geographic Information System layers that precisely characterize site-specific conditions.

The widespread use of spatial planning tools in conjunction with increases in the availability of geographic information systems and associated data has led to the rapid growth in the exploration and application of species distribution models. Conservation professionals can choose from a considerable number of modelling techniques, but there has been relatively little evaluation of predictive performance, data requirements, or type of inference of these models. Empirical data for woodland caribou Rangifer tarandus caribou was used to examine four species distribution models, namely a qualitative habitat suitability index and quantitative resource selection function, Mahalanobis distance and ecological niche models. Models for three sets of independent variables were developed and then a temporally independent set of caribou locations evaluated predictive performance. The similarity of species distribution maps among the four modelling approaches was also quantified. All of the quantitative species distribution models were good predictors of the validation data set, but the spatial distribution of mapped habitats differed considerably among models. These results suggest that choice of model and variable set could influence the identification of areas for conservation emphasis. Model choice may be limited by the type of species locations or desired inference. Conservation professionals should choose a model and variable set based on the question, the ecology of the species and the availability of requisite data.