CAREER: Understanding the conservation and restoration potential of retired agricultural land: An ecoinformatics approach

University Of California-Santa Barbara, Santa Barbara CA

Investigators

Abstract

The conversion of land both to and away from agriculture is a central component of global environmental change, with implications for both people and natural systems worldwide. In developed countries including the US, land area in agricultural production is contracting leaving millions of hectares of former or “retired” agricultural land often nested in a mosaic of productive and semi-natural lands. It remains unclear whether, where, and when retired agricultural lands are beneficial to biodiversity and ecosystem services in these landscapes. Given the ubiquity of agricultural landscapes and the dramatic biodiversity declines therein, such a gap is crucial to fill. Using a combination of data-driven methods, this research will advance understanding of the drivers of revegetation and the potential conservation value of retired agricultural lands, which can help policy-makers and scientists manage these lands for the well-being of human and natural communities. The training component of this CAREER award will include curriculum development and workshops on the application of data-driven techniques to ecological and environmental problems targeted at high school through PhD students. This research will leverage remote sensing, causal inference, and connectivity modeling methods applied to intensively managed agricultural landscapes to 1) elucidate the mechanisms governing revegetation and succession on retired agricultural lands, 2) understand how the spatial distribution of retired lands influences landscape fragmentation and connectivity, and 3) test whether retired land functions as a net source or sink of crop pests. The education component will engage local high school and first year undergraduates in hands-on research and training in spatial ecology and data-driven environmental science, and train professional master’s and PhD students in managing, processing and analyzing large spatial datasets and data products to address tractable but complex agricultural ecology questions. 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.