TY - JOUR
T1 - Using landscape limnology to classify freshwater ecosystems for multi-ecosystem management and conservation
AU - Soranno, Patricia A.
AU - Spence Cheruvelil, Kendra
AU - Webster, Katherine E.
AU - Bremigan, Mary T.
AU - Wagner, Tyler
AU - Stow, Craig A.
N1 - Funding Information:
A portion of this research was supported by a grant from the US Environmental Protection Agency Office of Wetlands, Oceans, and Watershed National Lakes Assessment Planning Project to P. A. S., K. E. W., K. S. C., and M. T. B. We thank the many agency professionals and university researchers who provided access to lake databases; in particular, John Downing (Iowa); Timothy Asplund (Wisconsin); Michael Vanni, William Renwick, and Jeff DeShon (Ohio); Jody Connor (New Hampshire); and Linda Bacon, Peter Vaux, and Kathleen Bell (Maine). We thank Michigan State University’s Remote Sensing and Geographic Information Science Research and Outreach Services for quantification of the landscape data. Thanks to Emi Fergus, Eric Torng, and three anonymous reviewers for helpful comments on an earlier draft. This is contribution 1546 for the Great Lakes Environmental Research Laboratory. Finally, we thank Wayne Wurtsbaugh for coining the term “landscape limnology.” Use of trade names does not imply endorsement by the federal government.
PY - 2010/6
Y1 - 2010/6
N2 - Governmental entities are responsible for managing and conserving large numbers of lake, river, and wetland ecosystems that can be addressed only rarely on a case-by-case basis. We present a system for predictive classification modeling, grounded in the theoretical foundation of landscape limnology, that creates a tractable number of ecosystem classes to which management actions may be tailored. We demonstrate our system by applying two types of predictive classification modeling approaches to develop nutrient criteria for eutrophication management in 1998 north temperate lakes. Our predictive classification system promotes the effective management of multiple ecosystems across broad geographic scales by explicitly connecting management and conservation goals to the classification modeling approach, considering multiple spatial scales as drivers of ecosystem dynamics, and acknowledging the hierarchical structure of freshwater ecosystems. Such a system is critical for adaptive management of complex mosaics of freshwater ecosystems and for balancing competing needs for ecosystem services in a changing world.
AB - Governmental entities are responsible for managing and conserving large numbers of lake, river, and wetland ecosystems that can be addressed only rarely on a case-by-case basis. We present a system for predictive classification modeling, grounded in the theoretical foundation of landscape limnology, that creates a tractable number of ecosystem classes to which management actions may be tailored. We demonstrate our system by applying two types of predictive classification modeling approaches to develop nutrient criteria for eutrophication management in 1998 north temperate lakes. Our predictive classification system promotes the effective management of multiple ecosystems across broad geographic scales by explicitly connecting management and conservation goals to the classification modeling approach, considering multiple spatial scales as drivers of ecosystem dynamics, and acknowledging the hierarchical structure of freshwater ecosystems. Such a system is critical for adaptive management of complex mosaics of freshwater ecosystems and for balancing competing needs for ecosystem services in a changing world.
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U2 - 10.1525/bio.2010.60.6.8
DO - 10.1525/bio.2010.60.6.8
M3 - Article
AN - SCOPUS:77953519401
SN - 0006-3568
VL - 60
SP - 440
EP - 454
JO - BioScience
JF - BioScience
IS - 6
ER -