Simulating the efficacy of wolf–dog hybridization management with individual-based modeling

• Published in: Conservation Biology
• Main Authors: Nina Luisa Santostasi, Sarah Bauduin, Oksana Grente, Olivier Gimenez, Paolo Ciucci
• Other Authors: Santostasi, NINA LUISA, Bauduin, Sarah, Grente, Oksana, Gimenez, Olivier, Ciucci, Paolo
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2024
• Subjects: Projection modeling Population dynamics Individual-based model Hybridization Management Canis lupus; Canis lupus
• Online Access: https://hdl.handle.net/11573/1715980; https://doi.org/10.1111/cobi.14312

Introgressive hybridization between wolves and dogs is a conservation concern due to its potentially deleterious long-term evolutionary consequences. European legislation requires that wolf–dog hybridization be mitigated through effective management. We developed an individual-based model (IBM) to simulate the life cycle of gray wolves that incorporates aspects of wolf sociality that affect hybridization rates (e.g., the dissolution of packs after the death of one/both breeders) with the goal of informing decision-making on management of wolf–dog hybridization. We applied our model by projecting hybridization dynamics in a local wolf population under different mate choice and immigration scenarios and contrasted results of removal of admixed individuals with their sterilization and release. In several scenarios, lack of management led to complete admixture, whereas reactive management interventions effectively reduced admixture in wolf populations. Management effectiveness, however, strongly depended on mate choice and number and admixture level of individuals immigrating into the wolf population. The inclusion of anthropogenic mortality affecting parental and admixed individuals (e.g., poaching) increased the probability of pack dissolution and thus increased the probability of interbreeding with dogs or admixed individuals and boosted hybridization and introgression rates in all simulation scenarios. Recognizing the necessity of additional model refinements (appropriate parameterization, thorough sensitivity analyses, and robust model validation) to generate management recommendations applicable in real-world scenarios, we maintain confidence in our model's potential as a valuable conservation tool that can be applied to diverse situations and species facing similar threats.