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

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
i) their imperfect detectability, ii) their dynamic ranges over time and
iii) 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
90’s. 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 2500m and positively influenced by the
number of observed occupied sites at short and longdistances , 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 decisionmakers to
target sites where large carnivores are likely to occur and mitigate
conflicts.