Merging integrated population models and individual-based models to project population dynamics of recolonizing species

Abstract

ABSTRACTRecolonizing species exhibit unique population dynamics, namely dispersal to and colonization of new areas, that have important implications for management. A resulting challenge is how to simultaneously model demographic and movement processes so that recolonizing species can be accurately projected over 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, 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 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 sites. 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 counts to parameterize the model, and then projected statewide dynamics over 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 (IQR 1.00-1.05) in the projection period (2021-2070). Our results suggest that gray wolves have a >99% probability of colonizing the last of Washington State’s three specified recovery regions by 2030, regardless of alternative assumptions about how dispersing wolves select new territories. The spatially explicit modeling framework developed here can be used to project the dynamics of any species for which spatial spread is an important driver of population dynamics.