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

Type Article
Acceptance Date 2021 IN PRESS
Language English
Author(s) Chambault PhilippineORCID1, 2, Hattab Tarek1, 2, Mouquet Pascal3, Bajjouk Touria4, Jean Claire5, Ballorain Katia6, 7, Ciccione Stéphane5, Dalleau Mayeul6, Bourjea JeromeORCID1, 2
Affiliation(s) 1 : MARBEC, Univ. Montpellier, CNRS, Ifremer, IRD Sète, France
2 : IFREMER Inst. Français pour l'Exploitation de la Mer Sète ,France
3 : Saint Leu La Réunion, France
4 : Inst. Français pour l'Exploitation de la Mer (IFRMER), DYNECO/LEBCO Plouzané ,France
5 : Kelonia, l'Observatoire des Tortues Marines, Saint Leu La Réunion,France
6 : Centre d'Etude et de Découverte des Tortues Marines (CEDTM), Saint‐Leu La Réunion, France
7 : Biodiversity French Agency, Mayotte and Glorieuses Marine Nature Parks, Saint‐Leu La Réunion , France
Source Ecography (0906-7590) (Wiley) In Press
DOI 10.1111/ecog.05436
Keyword(s) GPS tracking, green turtles, Indian Ocean, pseudo-absences, Shannon index, spatial modelling
Abstract

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.

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Publisher's official version 12 3 MB Open access
Appendix S1 21 KB Open access
Appendix S2 1 354 KB Open access
Appendix S3 1 333 KB Open access
Appendix S4 8 674 KB Open access
Appendix S5 1 12 KB Open access
Appendix S6 1 5 KB Open access
Appendix S7 1 117 KB Open access
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How to cite 

Chambault Philippine, Hattab Tarek, Mouquet Pascal, Bajjouk Touria, Jean Claire, Ballorain Katia, Ciccione Stéphane, Dalleau Mayeul, Bourjea Jerome A methodological framework to predict the individual and population‐level distributions from tracking data. Ecography IN PRESS. Publisher's official version : https://doi.org/10.1111/ecog.05436 , Open Access version : https://archimer.ifremer.fr/doc/00682/79412/