After centuries of population decline and range contraction, gray wolves (Canis lupus) are now expanding in Europe. Understanding wolf social structu

We reviewed 20 studies on wolf diet in Italy, to relate the changes in diet composition to the increase of wild ungulate population in Italy. Researches covered the period from 1976 to 2004 and the whole range of wolves from southern Apennines to western Alps. We used the frequency of occurrence of seven food categories and of the wild ungulate species occurring in the diet. Estimates of wild ungulate populations were obtained from the literature and we extrapolated their trend in the period considered. Differences among geographic areas (south-central Apennines, northern Apennines, and western Alps) were tested by nonparametric multivariate analysis of variance, while the trends of the wild ungulate and livestock use and of diet breadth were analysed by regression and curve-fit analyses. We used the same method to support the relationships between the use and availability of wild ungulates. Wolves preyed on wild herbivores more in the northern Apennines and in the western Alps than in the southern Apennines; the contrary was the case for livestock. Among wild ungulate species, wild boar, roe deer and red deer were the main prey of the wolf. The occurrence of wild ungulates in the wolf diet increased from 1976 to 2004 together with a decrease of livestock; the increase was mainly due to roe deer, red deer and chamois. The results of scat analysis in the province of Genoa showed an increase of the occurrence of wild ungulates from 1987 to 2005, in particular roe deer and fallow deer. Wolves in Italy seem to select wild ungulates over domestic ones where the former are available with rich and diversified guilds and abundant populations.

While biological distributions are not static and change/evolve through space and time, nonstationarity of climatic and land‐use conditions is frequently neglected in species distribution models. Even recent techniques accounting for spatiotemporal variation of species occurrence basically consider the environmental predictors as static; specifically, in most studies using species distribution models, predictor values are averaged over a 50‐ or 30‐year time period. This could lead to a strong bias due to monthly/annual variation between the climatic conditions in which species' locations were recorded and those used to develop species distribution models or even a complete mismatch if locations have been recorded more recently. Moreover, the impact of land‐use change has only recently begun to be fully explored in species distribution models, but again without considering year‐specific values. Excluding dynamic climate and land‐use predictors could provide misleading estimation of species distribution. In recent years, however, open‐access spatially explicit databases that provide high‐resolution monthly and annual variation in climate (for the period 1901–2016) and land‐use (for the period 1992–2015) conditions at a global scale have become available. Combining species locations collected in a given month of a given year with the relative climatic and land‐use predictors derived from these datasets would thus lead to the development of true dynamic species distribution models (D‐SDMs), improving predictive accuracy and avoiding mismatch between species locations and predictor variables. Thus, we strongly encourage modelers to develop D‐SDMs using month‐ and year‐specific climatic data as well as year‐specific land‐use data that match the period in which species data were collected.