Regional fish community indicators of landscape disturbance to catchments of the conterminous United States

• Published in: Ecological indicators Vol. 26; pp. 163 - 173
• Main Authors: Esselman, Peter C., Infante, Dana M., Wang, Lizhu, Cooper, Arthur R., Wieferich, Daniel, Tsang, Yin-Phan, Thornbrugh, Darren J., Taylor, William W.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.03.2013; Elsevier
• Subjects: Agnatha. Pisces; Animal and plant ecology; Animal, plant and microbial ecology; aquatic ecosystems; Biological and medical sciences; Boosted regression; data collection; ecoregions; fish; Fish communities; Fresh water ecosystems; Fundamental and applied biological sciences. Psychology; landscapes; Large-scale multimetric index; regression analysis; River assessment; rivers; screening; Synecology; Vertebrates: general zoology, morphology, phylogeny, systematics, cytogenetics, geographical distribution; watersheds; United States; North America; America; Boosted regression; Fish communities; Large-scale multimetric index; River assessment; Landscape; Regression; Perturbation; Rivers; Regional; Indicator; Freshwater environment; Vertebrata; Animal community; Pisces; Watershed; Stream; Large scale
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2012.10.028

Biological assessments of river conditions are increasingly conducted at regional and continental scales that match the extent of large-scale river management efforts. Multimetric indices composed of biological community indicators are commonly used to assess ecological condition and indices have recently been applied in large regions. Methods for large-scale multimetric index creation emphasize repeatability, comparability across regions, and objective selection of candidate metrics. Here we used an extensive fish dataset to create a large pool of fish community metrics which were screened to create multimetric indices (MMIs) in eight ecoregions covering the conterminous U.S. Candidate metrics were tested for metric range, corrected for natural gradients using boosted regression trees, and then tested for repeatability and sensitivity to landscape disturbance. Temporally stable and repeatable metrics were then evaluated for redundancy and used to compose MMIs for each region. Our MMIs were significantly correlated to independently developed MMIs, accurately reproducing prior index values with moderate to high precision and little bias. Our study demonstrates the utility of boosted regression tree models for correcting metric values for natural abiotic gradients and shows that the order of screening tests has a potentially important influence on metric selection. The resultant regional indices and component metrics provide a basis for assessing condition and testing hypotheses about landscape influences on aquatic ecosystems at a national scale in the US.