Wolves in Italy strongly declined in the past and were confined south of the Alps since the turn of the last century, reduced in the 1970s to approximately 100 individuals surviving in two fragmented subpopulations in the central-southern Apennines. The Italian wolves are presently expanding in the Apennines, and started to recolonize the western Alps in Italy, France and Switzerland about 16 years ago. In this study, we used a population genetic approach to elucidate some aspects of the wolf recolonization process. DNA extracted from 3068 tissue and scat samples collected in the Apennines (the source populations) and in the Alps (the colony), were genotyped at 12 microsatellite loci aiming to assess (i) the strength of the bottleneck and founder effects during the onset of colonization; (ii) the rates of gene flow between source and colony; and (iii) the minimum number of colonizers that are needed to explain the genetic variability observed in the colony. We identified a total of 435 distinct wolf genotypes, which showed that wolves in the Alps: (i) have significantly lower genetic diversity (heterozygosity, allelic richness, number of private alleles) than wolves in the Apennines; (ii) are genetically distinct using pairwise F(ST) values, population assignment test and Bayesian clustering; (iii) are not in genetic equilibrium (significant bottleneck test). Spatial autocorrelations are significant among samples separated up to c. 230 km, roughly correspondent to the apparent gap in permanent wolf presence between the Alps and north Apennines. The estimated number of first-generation migrants indicates that migration has been unidirectional and male-biased, from the Apennines to the Alps, and that wolves in southern Italy did not contribute to the Alpine population. These results suggest that: (i) the Alps were colonized by a few long-range migrating wolves originating in the north Apennine subpopulation; (ii) during the colonization process there has been a moderate bottleneck; and (iii) gene flow between sources and colonies was moderate (corresponding to 1.25-2.50 wolves per generation), despite high potential for dispersal. Bottleneck simulations showed that a total of c. 8-16 effective founders are needed to explain the genetic diversity observed in the Alps. Levels of genetic diversity in the expanding Alpine wolf population, and the permanence of genetic structuring, will depend on the future rates of gene flow among distinct wolf subpopulation fragments.

Assessing conservation strategies requires reliable estimates of abundance. Because detecting all individuals is most often impossible in free-ranging populations, estimation procedures have to account for a <1 detection probability. Capture-recapture methods allow biologists to cope with this issue of detectability. Nevertheless, capture-recapture models for open populations are built on the assumption that all individuals share the same detection probability, although detection heterogeneity among individuals has led to underestimating abundance of closed populations. We developed multievent capture-recapture models for an open population and proposed an associated estimator of population size that both account for individual detection heterogeneity (IDH). We considered a two-class mixture model with weakly and highly detectable individuals to account for IDH. In a noninvasive capture-recapture study of wolves we based on genotypes identified in feces and hairs, we found a large underestimation of population size (27% on average) occurred when IDH was ignored.

Summary1. Under increasing environmental and financial constraints, ecologists are faced with making decisions about dynamic and uncertain biological systems. To do so, stochastic dynamic programming (SDP) is the most relevant tool for determining an optimal sequence of decisions over time.2. Despite an increasing number of applications in ecology, SDP still suffers from a lack of widespread understanding. The required mathematical and programming knowledge as well as the absence of introductory material provide plausible explanations for this. 3. Here, we fill this gap by explaining the main concepts of SDP and providing useful guidelines to implement this technique, including R code. 4. We illustrate each step of SDP required to derive an optimal strategy using a wildlife management problem of the French wolf population. 5. Stochastic dynamic programming is a powerful technique to make decisions in presence of uncertainty about biological stochastic systems changing through time. We hope this review will provide an entry point into the technical literature about SDP and will improve its application in ecology.

In this study, Orr-Loygue rectopexy had a lower long-term recurrence rate. However, this surgical procedure is associated with a higher complication rate. We believe that Delorme's procedure is still a valuable option in selected patients with postoperative minimal morbidity but higher recurrence rate.

17While large carnivores are recovering in Europe, assessing their distributions can help to predict and 18 mitigate conflicts with human activities. Because they are highly mobile, elusive and live at very low 19 density, modeling their distributions presents several challenges due to i) their imperfect detectability, 20 ii) their dynamic ranges over time and iii) their monitoring at large scales consisting mainly of 21 opportunistic data without a formal measure of the sampling effort. Not accounting for these issues can 22 lead to flawed inference about the distribution. 23Here, we focused on the wolf (Canis lupus) that has been recolonizing France since the early 90's. We while accounting for species imperfect detection and time-and space-varying sampling effort using 27 dynamic site-occupancy models. 28Ignoring the effect of sampling effort on species detectability led to underestimating the number of 29 occupied sites by 50% on average. Colonization increased with increasing number of occupied sites at 30 short and long-distances, as well as with increasing forest cover, farmland cover and mean altitude. 31Colonization decreased when high-altitude increased. The growth rate, defined as the number of sites 32 newly occupied in a given year divided by the number of occupied sites in the previous year, decreased 33 over time, from over 100% in 1994 to 5% in 2014. This suggests that wolves are expanding in France 34 but at a rate that is slowing down. Our work shows that opportunistic data can be analyzed with species 35 distribution models that control for imperfect detection, pending a quantification of sampling effort. 36Our approach has the potential for being used by decision-makers to target sites where large carnivores 37 are likely to occur and mitigate conflicts. 39 Key words 40Canis lupus, gray wolf, large carnivores, occupancy models, opportunistic data, sampling effort, species 41 detectability, species distribution models, recolonization 42 43 not peer-reviewed)