Exploring the impact of lesser-known social dynamics on wolf populations through an individual-based approach

Abstract

Highlights-We developed an individual-based model (IBM) simulating wolf population dynamics.-The IBM incorporates up-to-date knowledge of pack structure and dynamics.-The IBM is flexible and modular and can be adapted to any wolf populations.-The IBM is written in R language to make its use by ecologists easy. AbstractThe presence of wolf populations in human-dominated landscapes is challenging worldwide because of conflicts with human activities. Modeling is an important tool to predict wolf dynamics and expansion and help in decision making concerning management and conservation.Here we present an individual-based model (IBM) to project wolf population dynamics. IBMs are bottom-up models that simulate the fate of individuals interacting with each other, with population-level properties emerging from the individual-level simulations. IBMs are particularly adapted to represent social species such as the wolf that exhibits complex individual interactions.Our IBM predicts wolf demography including fine-scale individual behavior and pack dynamics processes based on up-to-date scientific literature. The model extends previous attempts to represent wolf population dynamics, as we included important biological processes that were not previously considered such as pack dissolution, asymmetric male and female breeder replacement by dispersers or subordinates, inbreeding avoidance, establishment of dispersing individuals by budding, adoption of young dispersing wolves, long distance dispersal, and density-dependent mortality. We demonstrate two important aspects of our IBM (i.e., modularity and flexibility) by running different series of the processes representing the wolf life cycle. The simulations point out the importance of data records on these biological components when managers are willing to promote wolf population conservation and management strategies. This exercise also shows that the model can flexibly include or exclude different processes therefore being applicable to wolf populations experiencing different ecological and demographic conditions. The model is coded in R to facilitate its understanding, appropriation and adaptation by ecologists. Overall, our model allows testing different scenarios of wolf dynamics, disturbances and alternative management strategies to project wolf populations, and therefore inform decision making to improve wolf management and conservation.