Method to support Total Maximum Daily Load development using hydrologic alteration as a surrogate to address aquatic life impairment in New Jersey streams

Kennen, Jonathan G.; Riskin, Melissa L.; Reilly, Pamela A.; Colarullo, Susan J.

doi:10.3133/sir20135089

Cited by 1 publication

(1 citation statement)

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“…The complexity of these systems produces a wide range of geomorphic features and habitats that support diverse ecological communities (Maddock et al 2013), and a natural streamflow regime serves an important role in maintaining biological diversity and ecological integrity (Dunne and Leopold 1978;Karr 1991;Richter et al 1996;Poff et al 1997). Streamflow characteristics may affect aquatic life directly or indirectly through interconnections with stream habitat, channel substrate, nutrient flux, and connectivity (Richter et al 1997;Bunn and Arthington 2002;Annear et al 2004; Open Access *Correspondence: jrziege@usgs.gov Zimmerman 2010; Kennen et al 2013). Furthermore, streamflow is often considered a "master variable" that affects water chemistry and quality and limits the distribution and abundance of riverine species (Resh et al 1988;Power et al 1995), and regulates the ecological integrity of flowing water systems (Poff et al 1997).…”

Section: Introductionmentioning

confidence: 99%

Quantifying relations between altered hydrology and fish community responses for streams in Minnesota

et al. 2022

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Background Altered hydrology is a stressor on aquatic life, but quantitative relations between specific aspects of streamflow alteration and biological responses have not been developed on a statewide scale in Minnesota. Best subsets regression analysis was used to develop linear regression models that quantify relations among five categories of hydrologic metrics (i.e., duration, frequency, magnitude, rate-of-change, and timing) computed from streamgage records and six categories of biological metrics (i.e., composition, habitat, life history, reproductive, tolerance, trophic) computed from fish-community samples, as well as fish-based indices of biotic integrity (FIBI) scores and FIBI scores normalized to an impairment threshold of the corresponding stream class (FIBI_BCG4). Relations between hydrology and fish community responses were examined using three hydrologic datasets that represented periods of record, long-term changes, and short-term changes to flow regimes in streams of Minnesota. Results Regression models demonstrated significant relations between hydrologic explanatory metrics and fish-based biological response metrics, and the five regression models with the strongest linear relations explained over 70% of the variability in the biological metric using three hydrologic metrics as explanatory variables. Tolerance-based biological metrics demonstrated the strongest linear relations to hydrologic metrics. The most commonly used hydrologic metrics were related to bankfull flows and aspects of flow variability. Conclusions Final regression models represent paired streamgage records and biological samples throughout the State of Minnesota and encompass differences in stream orders, hydrologic landscape units, and watershed sizes. Presented methods can support evaluations of stream fish communities and facilitate targeted efforts to improve the health of fish communities. Methods also can be applied to locations outside of Minnesota with continuous streamgage data and fish-community samples.

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