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Spatial correlation of macroinvertebrate assemblages in streams and the implications for bioassessment programs
Michael P Shupryt1, Jered M Studinski2
1Wisconsin Department of Natural Resources, 101 South Webster Street, Madison, WI, 53707, USA. Michael.Shupryt@wisconsin.gov.
Environmental Monitoring and Assessment
|May 4, 2021
Summary
Point samples in stream bioassessment represent a limited stream network distance, typically 1.7-13.5 km. Understanding spatial autocorrelation, influenced by factors like conductivity, improves monitoring efficiency.
Area of Science:
- Ecology
- Environmental Science
- Conservation Biology
Background:
- Stream bioassessment relies on benthic macroinvertebrates to evaluate water quality.
- Spatial autocorrelation and sample independence are critical but poorly understood aspects of bioassessment.
- Macroinvertebrate assemblages change along longitudinal gradients due to environmental factors.
Purpose of the Study:
- To investigate longitudinal patterns of macroinvertebrate assemblages in streams.
- To estimate the distance to independence (DTI) for macroinvertebrate assemblages.
- To identify factors influencing spatial autocorrelation in stream networks.
Main Methods:
- Studied 14 Wisconsin streams, analyzing macroinvertebrate assemblages over tens of kilometers.
- Employed Moran's I and multivariate methods to assess spatial autocorrelation.
- Quantified the distance at which macroinvertebrate assemblages become spatially uncorrelated.
Main Results:
- A direct relationship exists between assemblage dissimilarity and longitudinal distance in most streams.
- Distance to independence (DTI) ranged from 1.7 to 13.5 km.
- Conductivity, watershed size, channel gradient, and riparian slope significantly affected DTI, with conductivity showing the strongest influence.
Conclusions:
- Spatial autocorrelation is a key consideration for effective stream bioassessment design.
- Increased conductivity correlates with increased DTI, indicating more homogenous assemblages in disturbed streams.
- Incorporating spatial correlation can enhance the efficiency and applicability of regulatory bioassessment programs.
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