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

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 mechanisticstatistical 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.

Diffusion modeling reveals effects of multiple release sites and human activity on a recolonizing apex predator

“…Application of a diffusion model similar to the one we implemented revealed the intrinsic growth rate of wolves colonizing parts of France varied between about 0.3 and 0.7, depending on the amount of forest cover [ 71 ]. However, modeling intrinsic growth—the theoretical maximum rate of increase of the population—as a function of covariates, as [ 71 ] did, implicitly assumes that those covariates have a density-independent effect on population growth.…”

“…Application of a diffusion model similar to the one we implemented revealed the intrinsic growth rate of wolves colonizing parts of France varied between about 0.3 and 0.7, depending on the amount of forest cover [ 71 ]. However, modeling intrinsic growth—the theoretical maximum rate of increase of the population—as a function of covariates, as [ 71 ] did, implicitly assumes that those covariates have a density-independent effect on population growth. In contrast, we chose to model the density dependence parameter K ( s ) as a function of spatial covariates because we hypothesized those covariates would affect how density moderates population growth (e.g., through reduced prey availability at higher population densities), rather than be density-independent.…”

Population genetic structure of wolves in the northwestern Dinaric‐Balkan region

“…Nowadays, European wolves are divided into several populations (Kaczensky et al, 2013), with habitat loss, human-wildlife conflicts, hybridization with domestic dogs (C. l. familiaris), and other processes affecting the observed structure (Loxterman, 2011;Sinclair et al, 2001;Walker et al, 2002;Woodroffe & Frank, 2005). However, various changes, including the implementation of numerous management conservation programs in recent decades (Chapron et al, 2014), have allowed wolves to recolonize substantial parts of their former ranges and facilitated reconnection of previously separated populations (e.g., Louvrier et al, 2020;Nowak et al, 2016;Schley et al, 2021).…”

The Impact of Climate Change on Potential Invasion Risk of Oryctes monoceros Worldwide

“…Environmental niche modeling uses correlation and mechanistic or process-based models to estimate species' habitat requirements. The mechanistic species distribution model (e.g., CLIMEX) considers how environmental conditions constrain the physiological characteristics of species at a given location (Louvrier et al, 2020). In contrast, correlative models establish mathematical correlations between observable species distributions (presence or absence) and environmental variables [e.g., Maximum Entropy, Boosted Regression Trees (BRT), and Random Forest (RF)] (Phillips et al, 2006;Yu et al, 2020).…”