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Comparing Hydrogeomorphic Approaches to Lake Classification

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Abstract

A classification system is often used to reduce the number of different ecosystem types that governmental agencies are charged with monitoring and managing. We compare the ability of several different hydrogeomorphic (HGM)—based classifications to group lakes for water chemistry/clarity. We ask: (1) Which approach to lake classification is most successful at classifying lakes for similar water chemistry/clarity? (2) Which HGM features are most strongly related to the lake classes? and, (3) Can a single classification successfully classify lakes for all of the water chemistry/clarity variables examined? We use univariate and multivariate classification and regression tree (CART and MvCART) analysis of HGM features to classify alkalinity, water color, Secchi, total nitrogen, total phosphorus, and chlorophyll a from 151 minimally disturbed lakes in Michigan USA. We developed two MvCART models overall and two CART models for each water chemistry/clarity variable, in each case comparing: local HGM characteristics alone and local HGM characteristics combined with regionalizations and landscape position. The combined CART models had the highest strength of evidence (ωi range 0.92–1.00) and maximized within class homogeneity (ICC range 36–66%) for all water chemistry/clarity variables except water color and chlorophyll a. Because the most successful single classification was on average 20% less successful in classifying other water chemistry/clarity variables, we found that no single classification captures variability for all lake responses tested. Therefore, we suggest that the most successful classification (1) is specific to individual response variables, and (2) incorporates information from multiple spatial scales (regionalization and local HGM variables).

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Acknowledgments

This research was financially supported in part through a grant to P.A.S. and M.T.B. from the Michigan Department of Natural Resources Fisheries Division. We thank Michigan State University Remote Sensing and GIS Research and Outreach Services for catchment delineation and quantification of the climate variables; and thanks to Dan Hayes, Brian Maurer, and Ty Wagner for statistical guidance. Thanks to the following personnel for their contributions to the development of the landscape databases: Michael Belligan for the landscape position metric delineation, Jim Breck for the lake polygon coverage, Howard Wandell and Jim Breck for the Storet water quality database, Gary Weissman for guidance on the geology database, and Stephen Bowman, Tyler Rosa, Remy Brim, Cassie Meier, Dave Meyers and Sarah Wills for compiling and quality control of some of the databases.

We appreciate the comments and suggestions of Jim Breck, Kevin Wehrly, Dan Hayes, Brian Maurer, Jan Stevenson, and David Hyndman on earlier drafts of this manuscript.

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Authors and Affiliations

  1. Department of Geological Sciences, Michigan State University, 206 Natural Sciences, East Lansing, MI, 48824, USA

    Sherry L. Martin

  2. Department of Fisheries and Wildlife, Michigan State University, 13 Natural Resources, East Lansing, MI, 48824, USA

    Patricia A. Soranno, Mary T. Bremigan & Kendra S. Cheruvelil

  3. Lyman Briggs College, Michigan State University, 35 East Holmes Hall, East Lansing, MI, 48825, USA

    Kendra S. Cheruvelil

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  1. Sherry L. Martin
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  2. Patricia A. Soranno
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  4. Kendra S. Cheruvelil
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Correspondence to Sherry L. Martin.

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Martin, S.L., Soranno, P.A., Bremigan, M.T. et al. Comparing Hydrogeomorphic Approaches to Lake Classification. Environmental Management 48, 957–974 (2011). https://doi.org/10.1007/s00267-011-9740-2

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