Abstract
ABSTRACTSpecies 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 Washington’s wolf recovery goals, along with other metrics related to population status. Population recovery was defined under Washington’s Wolf Conservation and Management Plan as four breeding pairs in each of three recovery regions and three additional breeding pairs anywhere in the state. Most scenarios indicated a high 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 (0.11, 0.18, and 0.27, respectively) of meeting recovery goals across all years (2021-2070). However, while recovery goals were not predicted to be met in those scenarios, all 12 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.
Publisher
Cold Spring Harbor Laboratory