Mapping and explaining wolf recolonization in France using dynamic occupancy models and opportunistic data

• Published in: Ecography
• Main Authors: Louvrier, Julie, Duchamp, Christophe, Lauret, Valentin, Marboutin, Eric, Cubaynes, Sarah, Choquet, Rémi, Miquel, Christian, Gimenez, Olivier
• Other Authors: GDR Ecologie Statistique, Univ. of Montpellier and ONCFS for grants she received to conduct her work
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2017
• Subjects: Canis lupus
• Online Access: http://dx.doi.org/10.1111/ecog.02874; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fecog.02874; https://onlinelibrary.wiley.com/doi/pdf/10.1111/ecog.02874

While large carnivores are recovering in Europe, assessing their distributions can help to predict and mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low density, modeling their distributions presents several challenges due to 1) their imperfect detectability, 2) their dynamic ranges over time and 3) their monitoring at large scales consisting mainly of opportunistic data without a formal measure of the sampling effort. Here, we focused on wolves Canis lupus that have been recolonizing France since the early 1990s. We evaluated the sampling effort a posteriori as the number of observers present per year in a cell based on their location and professional activities. We then assessed wolf range dynamics from 1994 to 2016, while accounting for species imperfect detection and time‐ and space‐varying sampling effort using dynamic site‐occupancy models. Ignoring the effect of sampling effort on species detectability led to underestimating the number of occupied sites by more than 50% on average. Colonization appeared to be negatively influenced by the proportion of a site with an altitude higher than 2500 m and positively influenced by the number of observed occupied sites at short and long‐distances, forest cover, farmland cover and mean altitude. The expansion rate, defined as the number of occupied sites in a given year divided by the number of occupied sites in the previous year, decreased over the first years of the study, then remained stable from 2000 to 2016. Our work shows that opportunistic data can be analyzed with species distribution models that control for imperfect detection, pending a quantification of sampling effort. Our approach has the potential for being used by decision‐makers to target sites where large carnivores are likely to occur and mitigate conflicts.