A methodological framework to predict the individual and population‐level distributions from tracking data

• Published in: Ecography (Copenhagen) 2021-05, Vol.44 (5), p.766-777
• Main Authors: Chambault, Philippine, Hattab, Tarek, Mouquet, Pascal, Bajjouk, Touria, Jean, Claire, Ballorain, Katia, Ciccione, Stéphane, Dalleau, Mayeul, Bourjea, Jérôme
• Format: Article
• Language: English
• Subjects: Analysis; Animal behavior; Biodiversity and Ecology; Climatic changes; Dispersal; Environmental Sciences; Generalized linear models; Geographical distribution; Global Changes; Global positioning systems; GPS; GPS tracking; green turtles; Indian Ocean; Lidar; Niche (Ecology); Population; pseudo-absences; Shannon index; Social factors; Social interactions; Spatial data; spatial modelling; Species; Statistical models; Tagging; Tracking; Turtles
• ISSN: 0906-7590; 1600-0587
• DOI: 10.1111/ecog.05436

Despite the large number of species distribution modelling (SDM) applications driven by tracking data, individual information is most of the time neglected and traditional SDM approaches commonly focus on predicting the potential distribution at the species or population‐level. By running classical SDMs (population approach) with mixed models including a random factor to account for the variability attributable to individual (individual approach), we propose an innovative five‐steps framework to predict the potential and individual‐level distributions of mobile species using GPS data collected from green turtles. Pseudo‐absences were randomly generated following an environmentally‐stratified procedure. A negative exponential dispersal kernel was incorporated into the individual model to account for spatial fidelity, while five environmental variables derived from high‐resolution Lidar and hyperspectral data were used as predictors of the species distribution in generalized linear models. Both approaches showed a strong predictive power (mean: AUC > 0.93, CBI > 0.88) and goodness‐of‐fit (0.6 < adjusted R2 < 0.9), but differed geographically with favorable habitats restricted around the tagging locations for the individual approach whereas favorable habitats from the population approach were more widespread. Our innovative way to combine predictions from both approaches into a single map provides a unique scientific baseline to support conservation planning and management of many taxa. Our framework is easy to implement and brings new opportunities to exploit existing tracking dataset, while addressing key ecological questions such as inter‐individual plasticity and social interactions.