Sierra Nevada web tools
A clearing house of maps, static/dynamic tools, and related publications.
Supported by the Sierra Nevada Acoustic Monitoring Program, an array of nearly 2,000 autonomous recording units that span the entire Sierra Nevada forest ecosystem. The project generates 1,000,000 hours of audio annually, which is analyzed with the BirdNET algorithm, yielding data about >70 species of birds, mammals, and amphibians.
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Research Tools
Bird Population Projections: Fire, Restoration, and Climate
Changing fire regimes are projected to influence the distribution of many species in the Sierra Nevada, and many forest management practices are designed to influence fire regimes. Thus, it can be difficult to project future distributions of sensitive species. We combined newly updated fire projections with population models of six birds that serve as “indicator species” of different forest conditions, and generated maps of where different fire and management scenarios might result in changes to species habitat suitability.
Interactive Biodiversity + Management Simulation Maps
Use this interactive webtool to simulate how forest management activities, such as prescribed fires, may affect bird populations within a user-defined area. The tool is powered by bird population models that were built using passive acoustic survey data from 2021 and a state-of-the-art forest structure map. The forest structure data is updated annually, and the estimated baseline bird occupancy rates change accordingly.
Select Publications
- Arresting the spread of invasive species in continental systems. (2022). Describes the successful removal of invasive Barred Owls from the entire Sierra Nevada.
- Forest restoration limits megafires and supports species conservation under climate change. (2021). A synthesis of wildland fire models and a California Spotted Owl population model that served as a model for the ‘Bird Population Projections’ project.
- BirdNET: a deep learning solution for avian diversity monitoring. (2021). A technical description of the machine learning tool used to generate avian population data in this project.
- Detecting small changes in populations at landscape scales: A bioacoustic site-occupancy framework. (2019). Simulation-based power analyses that laid the groundwork for the monitoring program’s long-term design.
Contributors
Dr. Connor Wood, K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology
Dr. Zach Peery, Dept. of Forest and Wildlife Ecology, University of Wisconsin Madison
Dr. Alina Cansler, University of Montana
Dr. Gavin Jones, Rocky Mountain Research Station, USDA Forest Service
Dr. Van Kane, University of Washington
Support
- USDA Forest Service Region 5
- NASA
- CAL-FIRE
- Cornell Lab of Ornithology
Connor M. Wood 