Integrating Land Cover Modeling and Adaptive Management to Conserve Endangered Species and Reduce Catastrophic Fire Risk

Authors

David Breininger, NASA Ecological Programs, InoMedic Health ApplicationsFollow
Brean Duncan, NASA Ecological Programs, InoMedic Health ApplicationsFollow
Mitchell Eaton, North Carolina State UniversityFollow
Fred Johnson, Southeast Ecological Science Center, U.S. Geological SurveyFollow
James Nichols, Patuxent Wildlife Research Center, U.S. Geological SurveyFollow

Date of this Version

7-24-2014

Citation

2014 by the authors

Comments

Land 2014, 3, 874-897; doi:10.3390/land3030874

Abstract

Land cover modeling is used to inform land management, but most often via a two-step process, where science informs how management alternatives can influence resources, and then, decision makers can use this information to make decisions. A more efficient process is to directly integrate science and decision-making, where science allows us to learn in order to better accomplish management objectives and is developed to address specific decisions. Co-development of management and science is especially productive when decisions are complicated by multiple objectives and impeded by uncertainty. Multiple objectives can be met by the specification of trade offs, and relevant uncertainty can be addressed through targeted science (i.e., models and monitoring). We describe how to integrate habitat and fuel monitoring with decision-making focused on the dual objectives of managing for endangered species and minimizing catastrophic fire risk. Under certain conditions, both objectives might be achieved by a similar management policy; other conditions require trade offs between objectives. Knowledge about system responses to actions can be informed by developing hypotheses based on ideas about fire behavior and then applying competing management actions to different land units in the same system state. Monitoring and management integration is important to optimize state-specific management decisions and to increase knowledge about system responses. We believe this approach has broad utility and identifies a clear role for land cover modeling programs intended to inform decision-making.