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Disturbance metrics predict a wetland Vegetation Index of Biotic Integrity

Ecological Indicators

By:
, , , ,
DOI: 10.1016/j.ecolind.2012.06.009

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Abstract

Indices of biological integrity of wetlands based on vascular plants (VIBIs) have been developed in many areas in the USA. Knowledge of the best predictors of VIBIs would enable management agencies to make better decisions regarding mitigation site selection and performance monitoring criteria. We use a novel statistical technique to develop predictive models for an established index of wetland vegetation integrity (Ohio VIBI), using as independent variables 20 indices and metrics of habitat quality, wetland disturbance, and buffer area land use from 149 wetlands in Ohio, USA. For emergent and forest wetlands, predictive models explained 61% and 54% of the variability, respectively, in Ohio VIBI scores. In both cases the most important predictor of Ohio VIBI score was a metric that assessed habitat alteration and development in the wetland. Of secondary importance as a predictor was a metric that assessed microtopography, interspersion, and quality of vegetation communities in the wetland. Metrics and indices assessing disturbance and land use of the buffer area were generally poor predictors of Ohio VIBI scores. Our results suggest that vegetation integrity of emergent and forest wetlands could be most directly enhanced by minimizing substrate and habitat disturbance within the wetland. Such efforts could include reducing or eliminating any practices that disturb the soil profile, such as nutrient enrichment from adjacent farm land, mowing, grazing, or cutting or removing woody plants.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Disturbance metrics predict a wetland Vegetation Index of Biotic Integrity
Series title:
Ecological Indicators
DOI:
10.1016/j.ecolind.2012.06.009
Volume
24
Year Published:
2013
Language:
English
Publisher:
Elsevier
Publisher location:
Amsterdam, Netherlands
Contributing office(s):
Great Lakes Science Center
Description:
7 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
120
Last page:
126