Incorporating LiDAR metrics into a structure-based habitat model for a canopy-dwelling species

The development of metrics derived from LiDAR to quantify structural attributes of forests has contributed to substantial advances in wildlife-habitat modeling. However, further exploration of the numerous metrics available for quantifying canopy complexity could improve models of forest wildlife habitat while simultaneously increasing understanding of wildlife-habitat relationships. We used the full set of metrics available in the LiDAR-processing software FUSION, including several structural metrics that have not previously been used in published habitat models, to identify those that best quantify structural attributes associated with nest site occupancy by the Northern Spotted Owl (NSO; Strix occidentalis caurina). We identified the best subset of predictor variables for building a parsimonious predictive model using an objective selection process of alternative MaxEnt models. The simple metric maximum canopy height was the single best predictor of NSO occupancy, but three rarely used structural metrics included in our final model provided a novel means of describing the distribution of vegetation throughout the canopy height profile. These metrics critically contributed to the model's ability to distinguish small patches of structurally complex suitable habitat within a matrix of structurally simple intermediate-aged forest. Our results indicate the potential value of rarely used LiDAR metrics readily available for objectively quantifying ecologically important but previously inaccessible habitat attributes for arboreal species.