Broaden your horizon: The use of remotely sensed data for modeling populations of forest species at landscape scales

Abstract

Landscape-scale predictions of species abundance or density are of fundamental importance to conservation and management of ecosystems. Yet, developing these models remains challenging, as they require linking broad-scale population data with habitat characteristics that influence species abundance. Advances in remote sensing technology have resulted in increased availability of spatially continuous, high-resolution data that relate to ecologically important habitat characteristics. In forested systems, Light Detection and Ranging (LiDAR) and Digital Arial Photogrammetry (DAP) are of particular interest owing to their ability to estimate vegetative structure that drives variability in abundance or density of some forest-dependent species. We used an extensive dataset on the density of a keystone boreal forest species, the snowshoe hare (Lepus americanus) in northcentral Washington, USA, to examine which LiDAR- and DAP-derived habitat variables most strongly influence snowshoe hare density, and projected these relationships across the landscape to derive a hare density surface for our 53 km2 study area. We found snowshoe hare density is most influenced by habitat variables related to tree height (a proxy for stand age), horizontal cover, and vertical cover, and our model had high predictive performance on a spatially-independent validation dataset. Hare densities increased as horizontal cover and canopy cover increased, with our highest hare densities occurring in areas with >9% horizontal cover (% of LiDAR returns in 1–4 m height stratum), >65% canopy cover and tree height (a proxy for stand age) of ~5–10 m. To demonstrate the management implications of this work, we show that our landscape-scale model of predicted hare density helps understand habitat use by threatened Canada lynx (Lynx canadensis), a primary predator of hare. Our results show how coupling population data with remotely sensed forest structure metrics allows for continuous, large-scale population estimates. Such integration provides an important management tool for examining spatiotemporal changes in populations as boreal ecosystems come under increasing stress from climate and land use change.