Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/4520
Title: Evaluating changes in dike field fishes with community information indices
Authors: Oak Ridge National Laboratory.
Environmental and Water Quality Operational Studies (U.S.)
Polovino, Harry N.
Farrell, Michael P.
Pennington, C. H.
Keywords: Community
Fishes
Dikes
Embankments
Mississippi River
Ecosystems
Sampling
Water quality
Ecology
Publisher: Environmental Laboratory (U.S.)
Engineer Research and Development Center (U.S.)
Description: Technical Report
Abstract: Researchers characterizing ecological communities are often faced with the problem of collapsing large species lists into a single numeric expression of community structure. Historically, quantitative species diversity indices have been used to assess spatial and temporal changes in ecological communities. However, other classification estimates (e.g., binary similarity coefficients, and quantitative dissimilarity measures) offer methodologies for delineating community composition that may be more sensitive to sampling error. This study evaluates the relative performance of (1.) binary similarity coefficients, (2.) dissimilarity measures, and (3.) diversity indices, calculated for three sampling methods, in detecting changes in fish communities associated with two dike fields on the Mississippi River. Dike field fish communities were sampled and evaluated for five hydrologic seasons, based on water temperature and flow velocities, and were found to be least similar during high-water low-temperature conditions. During the evaluation, the information derived from electroshocking was the most representative when compared with hoop net and seine information. Of the three families of community information measures, binary similarity coefficients proved to be the most sensitive indicators of change in dike field fish communities. It was further found that measures based on species presence/absence represent a valid alternative method for characterizing change in community structure. This is especially true when species abundance data are highly variable, which is the case in many fisheries assessments.
URI: http://hdl.handle.net/11681/4520
Appears in Collections:Technical Report

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