Converse, S. J. | Search bioRxiv

bioRxiv2023

Forecasting dynamics of a recolonizing wolf population under different management strategies

Species recovery can be influenced by a wide variety of factors, such that predicting the spatiotemporal dynamics of recovering species can be exceedingly difficult. These predictions, however, are valuable for decision makers tasked with managing species and determining their legal status. We applied a novel spatially explicit projection model to estimate population viability of gray wolves (Canis lupus) from 2021-2070 in Washington State, USA, where wolves have been naturally recolonizing since the establishment of the first resident pack in 2008. Using this model, we predicted the effects of 12 scenarios of interest relating to management actions (e.g., lethal removals, translocation, harvest) and system uncertainties (e.g., immigration from out of state, disease) on the probability of meeting Washingtons wolf recovery goals, along with other metrics related to population status. Population recovery was defined under Washingtons Wolf Conservation and Management Plan as four breeding pairs in each of three recovery regions and three additional breeding pairs anywhere in the state. The baseline, translocation, and 50% immigration scenarios indicated a high (>60%) probability of wolf recovery in Washington over the next 50 years, but scenarios related to harvest mortality (removal of 5% of the population every six months), increased lethal removals (removal of 30% of the population every four years), and cessation of immigration from out of state resulted in low probabilities (0.07, 0.12, and 0.12, respectively) of meeting recovery goals across all years (2021-2070). All but one management scenarios exhibited a geometric mean of population growth that was [≥]1, indicating long-term population stability or growth, depending on the scenario. Our results suggest that wolves will continue to recolonize Washington and that recovery goals will be met so long as harvest and lethal removals are not at unsustainable levels and adjacent populations support immigration into Washington.

ecology↗

bioRxiv2023

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

Recolonizing 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 States 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.

ecology↗