Biol. Bull. 207: 173. (October 2004)
© 2004 Marine Biological Laboratory
Spatial Distribution of Land Type in Regression Models of Pollutant Loading
Evan J. Fedorko1,
R. Gil Pontius, Jr.1,
Stephen P. Aldrich2,
Luc Claessens3,
Charles Hopkinson, Jr.4 and
Wilfred M. Wollheim5
1 Clark University, Worcester, Massachusetts
2 Michigan State, East Lansing, Michigan
3 San Diego State, San Diego, California
4 Marine Biological Laboratory, Woods Hole, Massachusetts
5 University of New Hampshire, Durham, New Hampshire
We propose a method to improve landscape-pollution interaction regression models by including a variable that describes the spatial distribution of a land type with respect to the pattern of runoff within a drainage catchment. The proposed indicator is used as an independent variable to enhance the strength, as quantified by R2 values, of regression relationships between empirical observations of in-stream pollutant concentrations and land type, by considering the spatial distribution of key land-type categories within the sample point’s drainage area. We present an indicator that, when used in conjunction with a variable describing the proportion of the land type, adds a new dimension of explanatory power. We demonstrate the usefulness of this indicator by exploring the relationship between nitrate (NO3–) and land type within 40 drainage sub-catchments in the Ipswich River watershed, Massachusetts. Nutrient loads associated with non-point source pollution paths are related to land type within the drainage catchments of sample sites. Past studies have focused on the quantity of a particular land type within a sample point’s drainage catchment. Quantifying the spatial distribution of key land-type categories in terms of location on a runoff surface improves our understanding of the relationship between sampled NO3– concentrations and land type. Regressions that employ the proportion of residential land type within catchments provide a fair fit (R2 = 0.67). However, we find that a regression including a variable that indicates the spatial distribution of residential land improved the overall relationship between in-stream NO3– measurements and associated land type (R2 = 0.712). We test the sensitivity of the results with respect to variations in the index definition in order to determine the conditions under which the spatial indicator variable is worthwhile.
