Cooperative Fish and Wildlife Research Units Program: Washington
Education, Research and Technical Assistance for Managing Our Natural Resources

Petracca LS, B Gardner, BT Maletzke, and SJ Converse. 2023. Merging integrated population models and individual-based models to project population dynamics of a recolonizing species. Biological Conservation 289:110340.


Recolonizing species exhibit unique population dynamics, namely dispersal to and colonization of new areas, that have important implications for their management. One challenge is how to simultaneously model demographic and movement processes so that recolonizing species can be accurately projected over both time and space. Integrated population models (IPMs) have proven useful for making inference about population dynamics by integrating multiple data streams related to population states and demographic rates. However, traditional IPMs are not capable of representing complex dispersal and colonization processes, and the data requirements for building spatially explicit IPMs to do so are often prohibitive. Contrastingly, individual-based models (IBMs) have been developed to describe dispersal and colonization processes but do not traditionally integrate an estimation component, which is a major strength of IPMs. We introduce a framework for spatially explicit projection modeling that answers the challenge of how to project an expanding population using IPM-based parameter estimation while harnessing the individual-based movement modeling made possible by an IBM. Our model has two main components: [1] a Bayesian IPM-driven age- and state-structured population model that governs the population state process and estimation of demographic rates, and [2] an IBM-driven spatial model describing the dispersal of individuals and colonization of new territories. We applied this model to estimate current and project future dynamics of gray wolves (Canis lupus) in Washington State, USA. We used data from 74 telemetered wolves and yearly pup and pack size counts to parameterize the model, and then projected statewide dynamics over the next 50 years. Mean population growth was 1.29 (95% CRI 1.26-1.33) during initial recolonization from 2009 - 2020 and decreased to 1.03 (95% Prediction Interval 1.00-1.05) in the projection period (2021-2070). Our results suggest that gray wolves have a very high (>99%) probability of colonizing the last of Washington State’s three specified recovery regions by 2030, depending on alternative assumptions about how dispersing wolves select new territories. The spatially explicit modelling framework developed here can be used to project the dynamics of any species for which spatial spread is an important driver of population dynamics.