Integrating new technologies to broaden the scope of northern spotted owl monitoring and linkage with USDA forest inventory data

Abstract

Wildlife monitoring programs designed to inform forest management and conservation decisions in the face of climate change benefit from long-term datasets with consistent methodology. Nevertheless, many monitoring programs may seek to transition to alternative methods because emerging technologies can improve trend tracking and expand the number of target populations, increase spatial scale, and reduce long-term costs. Integrated models strengthen the capacity to adapt long-term monitoring programs to next generation methods. Here we present a case study of northern spotted owl (Strix occidentalis caurina) population monitoring that is under transition. The first monitoring phase focused on territory occupancy and mark-resighting individual owls. Owing to rapidly declining populations and increasing costs, traditional methods are less viable for long-term monitoring. A non-invasive approach, passive acoustic monitoring, is effective for detecting spotted owl presence, estimating occupancy rates, distinguishing sex, detecting trends in populations, and monitoring many additional species. A key component to support transition to passive acoustic monitoring was the development of machine learning models to automate species detections that enable rapid and effective data processing and analysis workflows. Coupling passive acoustic monitoring networks with Forest Inventory and Analysis (FIA) and gradient nearest neighbor (GNN) datasets provide powerful tools for predicting forest change impacts on wildlife populations and identify winners and losers in dynamic landscapes. The second monitoring phase will leverage new technologies, expand the scope of inference, link forest inventory and remote sensing datasets, and transition the program to broad biodiversity monitoring that assists managers as they face myriad challenges in dynamic landscapes.