Predicting present and future habitats using LiDAR to integrate research and monitoring with landscape analyses

Managed forests contribute to both economic and non-timber values, but the ecological role of managed, including planted, forests to biodiversity objectives at the landscape scale needs to be better understood. In this project in collaboration with J.D. Irving, Limited, we: 1) used airborne LiDAR and field data to identify terrestrial habitats; 2) monitored selected taxa by 18 stand type/seral stage habitat types in intensively and extensively managed forests and reserves; 3) assessed effects of management intensity on water quality and aquatic habitat; and 4) projected forest and wildlife habitat under planned management and natural disturbance scenarios. Taxa studied included songbirds, bryophytes and beetle species associated with mature-overmature forests, and several listed ground vegetation species. LiDAR-based enhanced forest inventory provided forest structure variables that improved bird habitat models and spatial predictions of bird habitat, metrics explaining bryophyte composition and richness, and variability in beetle abundance and richness. There was no evidence of negative landscape-level effects of increasing management intensity on bird communities in mature forest stands, suggesting that managed spruce-fir-tolerant hardwood landscapes provide habitat for bird species that need old forest. Richness, diversity, and composition of bryophyte guilds in reference stands in Mount Carleton Provincial Park unmanaged reserve did not differ from stands in the intensively managed District. The landscape focus and stratification into stand type/seral stages were important to understand habitat requirements. Catchments with greater forest management did not show any consistent signs of biological impairment from smaller to larger scales, and all sites had good or very good biological water quality based on the aquatic insect communities. This study helped to evaluate forest management effects on habitat areas, detected with airborne LiDAR data, that need to be addressed to enhance decision making processes.