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Modeled hydrologic metrics show links between hydrology and the functional composition of stream assemblages
Author(s) -
Patrick Christopher J.,
Yuan Lester L.
Publication year - 2017
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1002/eap.1554
Subject(s) - trophic level , environmental science , ecology , invertebrate , biological dispersal , streams , watershed , base flow , index of biological integrity , beta diversity , hydrology (agriculture) , habitat , geography , biology , drainage basin , computer science , population , geology , computer network , demography , cartography , geotechnical engineering , machine learning , sociology
Flow alteration is widespread in streams, but current understanding of the effects of differences in flow characteristics on stream biological communities is incomplete. We tested hypotheses about the effect of variation in hydrology on stream communities by using generalized additive models to relate watershed information to the values of different flow metrics at gauged sites. Flow models accounted for 54–80% of the spatial variation in flow metric values among gauged sites. We then used these models to predict flow metrics in 842 ungauged stream sites in the mid‐Atlantic United States that were sampled for fish, macroinvertebrates, and environmental covariates. Fish and macroinvertebrate assemblages were characterized in terms of a suite of metrics that quantified aspects of community composition, diversity, and functional traits that were expected to be associated with differences in flow characteristics. We related modeled flow metrics to biological metrics in a series of stressor–response models. Our analyses identified both drying and base flow instability as explaining 30–50% of the observed variability in fish and invertebrate community composition. Variations in community composition were related to variations in the prevalence of dispersal traits in invertebrates and trophic guilds in fish. The results demonstrate that we can use statistical models to predict hydrologic conditions at bioassessment sites, which, in turn, we can use to estimate relationships between flow conditions and biological characteristics. This analysis provides an approach to quantify the effects of spatial variation in flow metrics using readily available biomonitoring data.