Using big data and a translational ecology approach to inform full annual cycle conservation of migratory birds in the Appalachian Mountains

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Forest management, including restoration and protection, for the conservation of bird species has generally focused on improving habitat quality for breeding birds. Thus, these efforts have been singularly focused on breeding habitats, despite the fact that many species also migrate through and rely on critical stopover habitats along the way. Neotropical migratory birds continue to decline dramatically and implementation of a more comprehensive approach to conservation has been limited by a lack of scientific understanding of the habitat needs of birds during migration.

Further, when the science is available, its usefulness to inform management is often limited by a lack of input or buy-in from the land managers and owners who will ultimately need to apply recommendations on the ground. Appalachian Mountain landscapes face numerous threats including the loss of natural fire and grazing that historically helped maintain dynamic and resilient forest landscapes.

Tasked with restoring and sustaining native birds and their habitats across the Appalachian region, the Appalachian Mountains Joint Venture partnership has addressed this issue by facilitating science-based conservation. This project will address these research and implementation challenges through the use of newly available big data sources to map stopover habitat use and quantify forest structure across broad spatial extents with emerging machine learning tools.

Project partners will employ a translational ecology approach that centers on collaboration between science producers and science users with frequent engagement, clear communication, a well-developed participatory process, and a decision-making framework to address complicated environmental issues. This work will enhance habitat for bird species throughout the full annual cycle in the Virginia Highlands Focal Landscape, and the research and process will ultimately transform our ability to sustain healthy bird populations throughout the Appalachian region.

The project will bring together the state-of-the-art technologies of active remote sensing, meteorological surveillance radar, and interpretable machine learning to transform understanding of the habitat needs of birds during migration. It will characterize the aspects of forest and landscape structure that contribute to stopover habitat use during migration, compare stopover habitat use to habitat use by breeding birds, and evaluate the spatial scales at which these different features have their strongest association.

This research will answer one of the most pressing problems in bird conservation science: How and where can forest management enhance habitat suitability for bird species using the same landscapes during multiple phases of the annual cycle? This research will take a translational ecology approach, addressing these questions in the context of goals and needs of science users to effectively bridge the research-implementation gap through a collaborative process that uses advances in big data and data science to inform regional conservation planning, site-level forest management implementation and ongoing monitoring to evaluate the success of the plan.

This project is jointly funded by the Divisions of Environmental Biology and Integrative Organismal Systems through the Partnership to Advance Conservation Science and Practice Program.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.