Comparing Hydrogeomorphic Approaches to Lake Classification

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)...

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Bibliographic Details
Published in:	Environmental management (New York) 2011-11, Vol.48 (5), p.957-974
Main Authors:	Martin, Sherry L., Soranno, Patricia A., Bremigan, Mary T., Cheruvelil, Kendra S.
Format:	Article
Language:	eng
Subjects:	Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Biogeochemistry Carts Chemistry Chlorophyll Chlorophyll - metabolism Chlorophylls Clarity Classification Comparative studies Earth and Environmental Science Ecological and Environmental Phenomena Ecology Ecosystems Environment Environmental Management Environmental Monitoring Eutrophication Forestry Management Geomorphology Hydrogen-Ion Concentration Hydrology Lakes Lakes - analysis Mathematical models Models, Statistical Models, Theoretical Nature Conservation Nitrogen - analysis Phosphorus - analysis Principal components analysis Regions Variables Waste Water Technology Water chemistry Water color Water Management Water Pollution Control Water Quality Water Supply - analysis Water Supply - standards
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Summary:	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 (ω i 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).
ISSN:	0364-152X 1432-1009