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

TL;DR

This paper introduces a mechanistic-statistical model incorporating ecological diffusion to predict wolf colonization in South-Eastern France, accounting for landscape fragmentation, human influence, and imperfect detection, improving forecasting accuracy.

Contribution

It develops a novel spatio-temporal model combining PDEs with ecological processes, explicitly integrating dispersal and growth in a fragmented landscape, advancing species distribution modeling.

Findings

Wolf growth rate linked to forest cover proportion.

Diffusion influenced by human density.

Detection probability increased with survey effort.

Abstract

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 modelling 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.