Mapping risks and landscapes: conservation insights for Italian small ruminant populations

Local livestock breeds are valuable genetic resources that support ecosystem services, cultural identity, and the sustainability of animal husbandry. However, these populations face increasing threats from intensive farming, land abandonment, and environmental risks. This study aimed to assess Italian small ruminant populations’ exposure to environmental threats and distribution across different landscapes. Therefore, we integrated geolocation data of 3,712 active registered farms of 41 goat and 47 sheep breeds with publicly available data on environmental hazards, including seismic activity, landslides, floods, and projected climate change, alongside landscape characterization maps of Italy. Additionally, we evaluated extinction risks based on effective population size (Ne) and calculated biodiversity indices at the provincial level to describe small ruminant biodiversity in Italy. Our findings indicate that over a quarter of Italian small ruminant populations are at short-term risk of extinction, while nearly half face long-term threats. The two most pressing environmental concerns include seismic risk, particularly for breeds in southern Italy, and climate change projections indicating shifts to warmer and drier conditions, especially for Alpine breeds traditionally adapted to colder environments. Landscape characterization revealed that sheep farming is predominantly associated with agricultural landscapes, whereas goat farms are more frequently linked to woodlands and mountainous areas. Each breed exhibits specific environmental adaptations, underscoring their role in sustainable land management. Biodiversity analyses highlighted significant regional disparities, with high diversity in provinces such as Cuneo and Frosinone, whereas others exhibit limited breed representation. Understanding the spatial distribution of local breeds, their exposure to environmental risks, and their interactions with landscapes is essential for developing targeted conservation strategies. Integrating ecological, genetic, and spatial data allows for effective prioritization of conservation efforts to safeguard genetic diversity, support rural communities, and promote sustainable livestock farming.