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Human activities can globally modify natural ecosystems determining ecological, demographic and range perturbations for several animal species. These changes can jeopardize native gene pools in different ways, leading either to genetic homogenization, or conversely, to the split into genetically divergent demes. In the past decades, most European wild boar (Sus scrofa) populations were heavily managed by humans. Anthropic manipulations have strongly affected also Italian populations through heavy hunting, translocations and reintroductions that might have deeply modified their original gene pools. In this study, exploiting the availability of the well-mapped porcine genome, we applied genomic tools to explore genome-wide variability in Italian wild boar populations, investigate their genetic structure and detect signatures of possible introgression from domestic pigs and non-native wild boar. Genomic data from 134 wild boar sampled in six areas of peninsular Italy and in Sardinia were gathered using the Illumina Porcine SNP60 BeadChip (60k Single Nucleotide Polymorphisms – SNPs) and compared with reference genotypes from European specimens and from domestic pigs (both commercial and Italian local breeds), using multivariate and maximum-likelihood approaches. Pairwise FST values, multivariate analysis and assignment procedures indicated that Italian populations were highly differentiated from all the other analyzed European wild boar populations. Overall, a lower heterozygosity was found in the Italian population than in the other European regions. The most diverging populations in Castelporziano Presidential Estate and Maremma Regional Park can be the result of long-lasting isolation, reduced population size and genetic drift. Conversely, an unexpected similarity was found among Apennine populations, even at high distances. Signatures of introgression from both non-Italian wild boar and domestic breeds were very limited. To summarize, we successfully applied genome-wide procedures to explore, for the first time, the genomic diversity of Italian wild boar, demonstrating that they represent a strongly heterogeneous assemblage of demes with different demographic and manipulation histories. Nonetheless, our results suggest that a native component of genomic variation is predominant over exogenous ones in most populations.

In the early 1800s, the European roe deer (Capreolus capreolus) was probably extirpated from Switzerland, due to overhunting and deforestation. After a federal law was enacted in 1875 to protect lactating females and young, and limiting the hunting season, the roe deer successfully recovered and recolonized Switzerland. In this study, we use mitochondrial DNA and nuclear DNA markers to investigate the recolonization and assess contemporary genetic structure in relation to broad topographic features, in order to understand underlying ecological processes, inform future roe deer management strategies, and explore the opportunity for development of forensic traceability tools. The results concerning the recolonization origin support natural, multidirectional immigration from neighboring countries. We further demonstrate that there is evidence of weak genetic differentiation within Switzerland among topographic regions. Finally, we conclude that the genetic data support the recognition of a single roe deer management unit within Switzerland, within which there is a potential for broad-scale geographic origin assignment using nuclear markers to support law enforcement.

Over the last 40 years the gray wolf (Canis lupus) re-colonized its historical range in Italy increasing human-predator interactions. However, temporal and spatial trends in wolf mortality, including direct and indirect persecution, were never summarized. This study aims to fill this gap by focusing on the situation of Tuscany and Emilia-Romagna regions, believed to host a significant proportion of the Italian wolf population, by: (i) identifying the prevalent causes of wolf mortality, (ii) summarizing their temporal and spatial patterns and (iii) applying spatially-explicit Generalized Linear Models to predict wolf persecution. Between October 2005 and February 2021, 212 wolf carcasses were collected and subjected to necropsy, being involved in collisions with vehicles (n = 104), poisoned (n = 45), wounded with gunshot (n = 24) or blunt objects (n = 4) and being hanged (n = 2). The proportion of illegally killed wolves did not increase through time. Most persecution events occurred between October and February. None of our candidate models outperformed a null model and covariates such as the density of sheep farms, number of predations on livestock, or human density were never associated to the probability of having illegally killed wolves, at the municipal scale. Our findings show that conventional correlates of wolf persecution, combined with a supposedly high proportion of non-retrieved carcasses, fail to predict illegal wolf killings in areas where the species have become ubiquitous. The widespread spatial distribution of illegal killings indicates that persecution probably arises from multiple kinds of conflicts with humans, beyond those with husbandry. Wolf conservation in Italy should thus address cryptic wolf killings with multi-disciplinary approaches, such as shared national protocols, socio-ecological studies, the support of experts’ experience and effective sampling schemes for the detection of carcasses.

Additional file 5: Table S5. List of 194 selected samples representing 115 individual bear genotypes out of 125 (ISPRA BIO-CGE). When available, samples from invasive (blood and tissues) and systematic sampling (hairs) were preferred to non-invasive and opportunistic ones, the more recent to the older samples, hairs to feces. Since samples from 10 genotypes (Italian national reference biobank, ISPRA BIO-CGE) were not available, their genotypes were not updated with CXX20 and REN144A06 loci

Additional file 1: Table S1. List of invasive samples used to verify matches among genotypes and calculate scores’ calibration. List of the 6 selected samples representing 6 genetically different individuals sharing the lowest number of alleles, therefore resuming the observed variability of the whole data set. These samples were analyzed by Lab2 during the Life Arctos Project [52] and by Lab3 during the present study, in order to verify matches among genotypes and calculate scores’ calibration between labs.

Additional file 2: Table S2. Four different STR markers sets compared in this study. Both labs use the Amelogenin gene (AMG) to assess gender. a [52], b [14, 41, 50].

Additional file 4: Table S4. Putative identical genotypes that differ between labs. Genotypes ram0587 and HS374 were identified by Lab2, genotypes Gen 108 and Gen 105 were identified by Lab3. a missing data, b mismatched loci.

Additional file 7: Fig. S1. Relationship between theoretical probability of identity and number of loci assayed using four heterozygosity levels. (a) randomly sampled individuals and (b) sibs. From [37].

Additional file 8: Table S7. Allelic patterns in 2000–2010 (pop1 - pre-arctos) and 2011–2017 (pop2 - arctos & post arctos). Na number of different alleles, Na Freq. ≥5% number of alleles with a frequency ≥ 5%, Ne number of effective alleles, I Shannon Information Index, No. Private Alleles number of private alleles, Ho observed heterozygosity and He expected heterozygosity.

Additional file 6: Table S6. Sampling trend of Apennine brown bear individuals over times. Genotypes were subdivided into two groups based on the years of sampling: 2000–2010 pre-arctos bears, 2011–2017 arctos and post arctos bears. Individuals sampled in both periods were eliminated from the analysis.

Vercillo Francesca, Lucentini Livia, Mucci Nadia, Ragni Bernardino, Randi Ettore & Panara Fausto Dipartimento di Biologia Animale ed Ecologia, Universita di Perugia, I-6123 Perugia, Italy; Dipartimento di Biologia Cellulare e Molecolare, Universita di Perugia, I-6123 Perugia, Italy; INFS, Ozzano dell’Emilia, I-4610 Bologna, Italy (*Author for correspondence, fax: +39-075-5855733; e-mail: lynx@unipg.it)