The Value of Remotely-Sensed Data in Terrestrial Habitat Corridor Design for Large Migratory Species

The authors develop an integer-programming modeling approach that leverages innovative new data products to propose a cost effective, landscape-scale conservation planning approach and apply it to the Cody elk herd range within the Greater Yellowstone Ecosystem.

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Date

Oct. 20, 2022

Authors

Kailin Kroetz, Bryan Leonard, Laura Gigliotti, and Arthur Middleton

Publication

Working Paper

Reading time

1 minute

Abstract

Cost-effective conservation program design to support seasonal migratory species is urgently needed, but to-date has received little attention by economists. Conserving migratory corridors is a complicated design problem because of the large spatial scales over which migratory species can travel and the weakest-link characteristic of the problem. If one section or area of a potential migratory corridor is unable to support species movement, the migration through that route will not be successful. We develop and apply an integer-programming modeling approach that leverages innovative new data products to propose a cost-effective, landscape-scale conservation planning approach. We apply our approach to the Cody elk herd range within the Greater Yellowstone Ecosystem (GYE), leveraging satellite data on crop type and density over time and GPS collar data on elk migrations. We provide empirical evidence that using new satellite data products can avoid unconnected corridors and increase the cost effectiveness of corridor construction. In the Cody context, we estimate that achieving the conservation outcome associated with using satellite data on both costs and benefits would cost close to twice as much when using satellite benefit data but only limited cost data and about three times as much when using satellite cost data but only limited benefit data. Empirical work across additional herds is needed to provide additional insights into characteristics of contexts under which we expect gains from satellite and/or GPS collar data.

Authors

Bryan Leonard.jpg

Bryan Leonard

Arizona State University

Laura Gigliotti.jpg

Laura Gigliotti

University of California, Berkeley

Arthur Middleton.jpg

Arthur Middleton

University of California, Berkeley

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