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.

Using passive acoustic monitoring to estimate northern spotted owl landscape use and pair occupancy

“…With the transition to passive acoustic monitoring, future analyses will need to adapt to a modeling framework based on spotted owl occupancy and a stronger linkage to USDA forest inventory data (Lesmeister & Jenkins, 2022). Therefore, understanding the relationships between landscape use, pair occupancy, and abundance for spotted owls will be critical to detect population changes accurately and rapidly.…”

“…The combination of bioacoustics, neural networks, and occupancy models is a promising approach for monitoring threatened species (Campos‐Cerqueira & Aide, 2016) and forest raptors in particular (Duchac et al, 2020, 2021; Maegawa et al, 2021; Shonfield et al, 2018). A new framework using habitat modeling and detection/nondetection data of unmarked owls from bioacoustics is the basis for the second phase of spotted owl monitoring (Davis et al, 2022; Glenn et al, 2017; Lesmeister & Jenkins, 2022; Lint et al, 1999).…”

Audio tagging of avian dawn chorus recordings in California, Oregon and Washington

“…The first phase focused on estimating vital rates and demographic performance using mark-resight data of spotted owls from historical territories in eight study areas ( Franklin et al 2021 ). The second phase used passive acoustic monitoring to collect data to estimate spotted owl occupancy and habitat associations across its range ( Lesmeister et al 2021 , Lesmeister and Jenkins 2022 ). The transition to the second monitoring phase marks a potential watershed moment for conserving and managing forested lands in the Pacific Northwest.…”