Sierra Nevada Avian Diversity

The combination of a novel machine learning algorithm, BirdNET, with one of the largest passive acoustic monitoring programs will enable similarly novel ecological research. BirdNET was developed by my colleague Dr. Stefan Kahl, and the acoustic monitoring program in California’s Sierra Nevada is an ongoing outcome of my dissertation research. Since 2017, we have been collecting audio across the northern Sierra Nevada; in 2021 we expanded to include over of the entire range, north to south! With approximately 2,000 survey locations spanning 18,000 km2 (see below), we will be able to generate avian biodiversity data with a fine resolution at landscape scales.

How do disturbances like fire, drought-induced tree mortality, and ecosystem restoration treatments affect biodiversity? What environmental features (and at what spatial scales) determine the spatial structuring of biodiversity? What are the population trends of key focal species across their range? These questions are now within reach! Stay tuned…

This project is particularly collaborative. Field operations are run through Dr. Zach Peery’s Lab at the University of Wisconsin – Madison, and we work closely with the U.S. Forest Service Region 5 and the National Park Service to co-develop research questions to maximize the conservation impacts of our work. External funding sources include NASA and CAL FIRE.

Survey coverage in 2021

Representative Publications

  1. Wood, CM, S Kahl, P Chaon, MZ Peery, and H Klinck. 2021. Survey coverage, recording duration, and species composition affect observed species richness in passive acoustic surveys. Methods in Ecology and Evolution. 12:885-896.
  2. Kahl, S, CM Wood, M Eibel, and H Klinck. 2021. BirdNET: a deep learning solution for avian diversity monitoring. Ecological Informatics. 61:101236.
  3. Wood, CM1, VD Popescu1, H Klinck, JJ Keane, RJ Gutiérrez, SC Sawyer, and MZ Peery. Detecting small changes in populations at landscape scales: A bioacoustic site-occupancy framework. Ecological Indicators 98: 492-507. 1contributed equally