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Presence of indicator plant species as a predictor of wetland vegetation integrity

Plant Ecology

By:
, ,
DOI: 10.1007/s11258-013-0168-z

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Abstract

We fit regression and classification tree models to vegetation data collected from Ohio (USA) wetlands to determine (1) which species best predict Ohio vegetation index of biotic integrity (OVIBI) score and (2) which species best predict high-quality wetlands (OVIBI score >75). The simplest regression tree model predicted OVIBI score based on the occurrence of three plant species: skunk-cabbage (Symplocarpus foetidus), cinnamon fern (Osmunda cinnamomea), and swamp rose (Rosa palustris). The lowest OVIBI scores were best predicted by the absence of the selected plant species rather than by the presence of other species. The simplest classification tree model predicted high-quality wetlands based on the occurrence of two plant species: skunk-cabbage and marsh-fern (Thelypteris palustris). The overall misclassification rate from this tree was 13 %. Again, low-quality wetlands were better predicted than high-quality wetlands by the absence of selected species rather than the presence of other species using the classification tree model. Our results suggest that a species’ wetland status classification and coefficient of conservatism are of little use in predicting wetland quality. A simple, statistically derived species checklist such as the one created in this study could be used by field biologists to quickly and efficiently identify wetland sites likely to be regulated as high-quality, and requiring more intensive field assessments. Alternatively, it can be used for advanced determinations of low-quality wetlands. Agencies can save considerable money by screening wetlands for the presence/absence of such “indicator” species before issuing permits.

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Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Presence of indicator plant species as a predictor of wetland vegetation integrity
Series title:
Plant Ecology
DOI:
10.1007/s11258-013-0168-z
Volume
214
Issue:
2
Year Published:
2013
Language:
English
Publisher:
Springer
Contributing office(s):
Great Lakes Science Center
Description:
12 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Plant Ecology
First page:
291
Last page:
302
Number of Pages:
12
Country:
United States
State:
Ohio