Ecological niche models improve home range estimations

Home ranges in animals can be estimated by different methods like minimum convex polygons, characteristic hulls or kernels while correlative ecological niche models (ENMs) are commonly employed for forecasting species' ranges. However, ENMs can also model the distribution of individuals if environmental very high spatial resolution data are available. Indeed, remote sensing (RS) can provide images with pixel sizes of few centimetres. Here, we modelled the distribution of individual lizards (Podarcis bocagei) combining aerial-like photographs recorded with a compact camera and a matrix of temperature/humidity data-loggers to obtain several environmental layers with very high spatial resolution. We recorded lizards' positions in a 20 x 20 m mesocosm with a high precision GPS device (similar to 10 cm of error), multiple times per day throughout the whole period of daily activity. We built an orthophoto map (pixels of 20 cm(2)) from camera pictures, a digital surface model, and a land-cover supervised classification map. We recreated climate-like variables by combining data-logger data. For each individual, we calculated the distance to males and females, excluding the focal lizard. We computed individual realized niche models with Bioclim, GAM, GLM, Maxent and random forest. Models attained a very high evaluation score in most cases. The most contributing variables were related to microclimate (isothermality, minimum temperature and humidity) and distance to conspecifics. Our very high spatial resolution models provided robust information on how space is used by each lizard. Correlative models can identify the most suitable areas inside the home range, similar to core areas estimated from kernel algorithms, but allowed better statistical inference. Overall, RS tools generated high-quality environmental data, and when combined with ENMs, improved the robustness of the predictions on spatial patterns of small terrestrial animals.

Automated summary

This study utilized aerial-like photographs and temperature/humidity data-loggers to model the distribution of individual lizards with high spatial resolution. The evaluation scores of the models were generally high, with key variables such as microclimate and distance playing important roles.