A mechanistic-statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France

• Published in: Spatial Statistics
• Main Authors: Louvrier, Julie, Papaix, Julien, Duchamp, Christophe, Gimenez, Olivier
• Other Authors: Centre National de la Recherche Scientifique (CNRS), Biostatistique et Processus Spatiaux (BioSP), Institut National de la Recherche Agronomique (INRA), Office National de la Chasse et de la Faune Sauvage (ONCFS)
• Format: Conference Object
• Language: French
• Published: HAL CCSD 2019
• Subjects: Forecasting; Hierarchical Bayesian modeling; Measurement error; Partial differential equations; Spatio-temporal occupancy; Species distribution models; [SDV]Life Sciences [q-bio]; [SDE]Environmental Sciences; [MATH]Mathematics [math]; [INFO]Computer Science [cs]; Canis lupus
• Online Access: https://hal.inrae.fr/hal-02787637; https://hal.inrae.fr/hal-02787637/document; https://hal.inrae.fr/hal-02787637/file/S2211675320300221.pdf; https://doi.org/10.1016/j.spasta.2020.100428

National audience Species distribution models (SDMs) are important statistical tools for ecologists to understand and predict species range. However, standard SDMs do not explicitly incorporate dynamic processes like dispersal. This limitation may lead to bias in inference about species distribution. Here, we adopt the theory of ecological diffusion that has recently been introduced in statistical ecology to incorporate spatio-temporal processes in ecological models. As a case study, we considered the wolf (Canis lupus) that has been recolonizing Eastern France naturally through dispersal from the Apennines since the early 90's. Using partial differential equations for modeling species diffusion and growth in a fragmented landscape, we develop a mechanistic-statistical spatio-temporal model accounting for ecological diffusion, logistic growth and imperfect species detection. We conduct a simulation study and show the ability of our model to i) estimate ecological parameters in various situations with contrasted species detection probability and number of surveyed sites and ii) forecast the distribution into the future. We found that the growth rate of the wolf population in France was explained by the proportion of forest cover, that diffusion was influenced by human density and that species detectability increased with increasing survey effort. Using the parameters estimated from the 2007-2015 period, we then forecasted wolf distribution in 2016 and found good agreement with the actual detections made that year. Our approach may be useful for managing species that interact with human activities to anticipate potential conflicts.