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A Comparison of Fish-based Classification Schemes for Reference Streams and Rivers in Nebraska
Journal of Environmental Quality
|January 21, 2015
Summary
Cluster analyses using presence/absence data best classify lotic ecosystems, distinguishing natural variations from human impacts. This approach identifies key environmental factors influencing fish assemblages for effective ecosystem management.
Area of Science:
- Ecology
- Ichthyology
- Environmental Science
Background:
- Accurate assessment of lotic ecosystems requires differentiating natural conditions from anthropogenic disturbances.
- Fish species assemblages are key indicators of ecological health in rivers and streams.
Purpose of the Study:
- To evaluate the strength and ecological interpretability of different classification schemes for Nebraska's rivers and streams based on fish species.
- To identify environmental predictors of fish assemblage structure to aid in distinguishing natural variability from human influences.
Main Methods:
- Utilized multiple response permutation procedures to assess classification strength of ecoregions, watersheds, and hydrologic-landscape regions.
- Employed nonmetric multidimensional scaling (NMDS) ordinations and ANOVAs to test ecological interpretability.
- Used nonparametric ANOVA to identify environmental predictors of fish assemblage structure.
Main Results:
- Hydrologic-landscape regions showed high classification strength, but cluster analysis groups exhibited superior ecological interpretability.
- Presence/absence fish data yielded stronger classification and interpretability than abundance data.
- Temperature, stream size, total phosphorus, and fine substrate percentage significantly correlated with fish assemblage structure.
Conclusions:
- Cluster analysis using presence/absence fish data is the most effective classification scheme for lotic ecosystems in this region.
- Identified key environmental variables crucial for determining natural similarities in biotic assemblages.
- Provides a valuable framework for separating natural ecological variability from anthropogenic impacts in riverine systems.
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