Characterizing long‐term population conditions of the elusive red tree vole with dynamic individual‐based modeling

Abstract

AbstractOld growth forests are declining globally, threatening dependent wildlife. Many arboreal old‐growth obligates, such as the threatened red tree vole, are difficult to monitor for changes in habitat occupancy, and abundance. Yet, conservation planning relies on this information to prevent population declines. We integrated a range of species, habitat, and landscape change information to develop a dynamic habitat‐population model. The spatial individual‐based model simulated dynamic patterns of occupancy that responded to annual habitat maps, describing 36 years of observed change. We simulated population dynamics and local movement to characterize changes in occupancy and abundance, and the capacity of remaining habitat to support red tree voles. Red tree vole redistribution patterns strongly corresponded to wildfire footprints and timber extraction locations. Population strongholds are likely to exist in clumped pockets of old‐growth forest that were unaffected by wildfire and in protected old forest reserves. However, the exact number and locations of local clusters remain uncertain. Simulated population losses occurred at different paces in different places, underscoring the need for recurring evaluation of population changes with field occupancy surveys and modeled evaluations that can anticipate potential connectivity and extirpation thresholds. This modeling approach was effective at leveraging existing information for a data‐light species to assess how historical changes to the quantity, quality, and configuration of habitat likely influenced the potential landscape capacity, species abundance, and distribution. Dynamic individual‐based modeling can benefit conservation planning for red tree vole and other reclusive forest species by providing biologically nuanced assessments of abundance and distribution. Such models can also project the long‐term benefits and impacts of spatially explicit land management plans.