Using fish community and population indicators to assess the biological condition of streams and rivers of the Chesapeake Bay watershed, USA. (January 2022)

• Record Type: Journal Article
• Authors: Maloney, Kelly O.; Krause, Kevin P.; Cashman, Matthew J.; Daniel, Wesley M.; Gressler, Benjamin P.; Wieferich, Daniel J.; Young, John A.
• Is Part Of: Ecological indicators. Volume 134(2022)
• Publication Date: 2022-01
• Subjects: Freshwater assessment -- Landscape analyses -- Habitat -- Random forest -- Prediction -- Species; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2021.108488
• Languages: English
• ISSNs: 1470-160X

Abstract

Graphical abstract: Highlights: Predictive modeling is an important tool for stream and river management. Community and species were concurrently used to assess fish habitat condition. A novel community level composite index was developed. Reporting of values and uncertainty highlighted areas for management. Predicted condition showed little interannual basin-wide change. Abstract: The development of indicators to assess relative freshwater condition is critical for management and conservation. Predictive modeling can enhance the utility of indicators by providing estimates of condition for unsurveyed locations. Such approaches grant understanding of where "good" and "poor" conditions occur and provide insight into landscape contexts supporting such conditions. However, as assessments are conducted at large extents crossing jurisdictional boundaries, combined datasets are likely not suited for traditional assessment approaches which rely on jurisdictionally-specific reference sites. Here, we used a large dataset compiled from multiple providers to assess the condition of fish habitat for non-tidal streams and rivers in the Chesapeake Bay watershed (CBW), USA. We concurrently used community and species-level analyses to provide a more holistic view of habitat conditions by using random forest models to predict selected metrics and species occurrence with landscape data for inland CBW stream reaches. Community analyses included metrics describing composition, tolerances, habitat preferences, and functional traits of fish communities whereas species-level analyses consisted of distribution models for key sensitive and gamefish species. For community analyses, a final index was calculated as the average of selected metric deciles with higher scores inferring less biologically altered (i.e., better) conditions, providing an alternative to using reference sites. For species analyses, species occurrence was predicted for stream reaches, with presence indicating suitable habitat. Uncertainty was calculated for both approaches using model prediction intervals. Results indicated different numbers of suitable metrics for each region, with most in the Northern Appalachian (15) and least in the Southern Appalachian Piedmont (3). Four species (three sensitive) were suitable for modeling. At the CBW scale, predictions did not vary greatly among deciles for the community or species analyses for 2001, 2006, 2011, and 2016. Most stream reaches did not vary in mean decile rank or in species occurrence between 2001 and 2016; however, the largest community changes occurred in large rivers in the Coastal Plains ecoregion and the largest species occurrence changes occurred in Torrent Suckers in medium-sized rivers. When compared, results from community analyses agreed for one sensitive species (Brook Trout) but not the other three, potentially due to regionally inappropriate tolerance assignment. Comparisons also demonstrated substantial variation among approaches suggesting a lack of redundancy. While each approach traditionally has its targeted audience and respective strengths and weaknesses, concurrent use of these approaches permits direct comparisons and may assuage shortcomings of each approach when considered separately.