, Volume 27, Issue 2, pp 283-295

Watershed landscape indicators of estuarine benthic condition

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Abstract

Do land use and cover characteristics of watersheds associated with small estuaries exhibit a strong enough signal to make landscape metrics useful for identifying degraded bottom communities? We tested this idea with 58 pairs of small estuaries (<260 km2) and watersheds in the U.S. Mid-Atlantic coastal plain (Delaware Bay to Chesapeake Bay). We considered 34 landscape metrics as potential explanatory variables and seven estuarine parameters as response variables. We developed three logistic regression models: one to calculate the probability of degraded benthic environmental quality (BEQ), as defined by chemical parameters, and two for the probability of degraded estuarine bottom communities, one using a benthic index (BI) and a second using the total number of bottom-dwelling species (TNBS, consisting of benthic macroinvertebrates and fishes). We evaluated the discriminatory power of the models with ROC (receiver operating characteristic) curves of sensitivity and specificity. All three models showed excellent discrimination of high and low values. A model using the sum of all human land uses and percent wetlands correctly classified BEQ in 86% of the cases; low benthic index and low total number of bottom species were each associated with degraded BEQ (p<0.01). The BI model used percent riparian urban, riparian wetlands, and agriculture on steep slopes (76% correct classification) and correctly predicted high-low benthic index of an independent data set 79% of the time (p<0.05). The TNBS model used percent wetlands, riparian wetlands, and riparian agriculture (74% correct classification). Watersheds with higher percentages of urban and agricultural land uses were associated with lower benthic environmental quality, benthic index, and biodiversity, whereas those with higher percentages of wetlands were associated with higher numbers. As human development of watersheds increases, statistical prediction rules developed from landscape metrics could be a cost-effective method to identify potentially threatened estuaries.