Journal
SCIENCE OF THE TOTAL ENVIRONMENT
Volume 650, Issue -, Pages 438-451Publisher
ELSEVIER
DOI: 10.1016/j.scitotenv.2018.08.418
Keywords
Ordination; Macroinvertebrates; Headwaters; Bayesian; Network Modeling
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The ecosystem function of headwaters is important and increasingly well-recognized, but institutional structures to administer their protection and management are lacking or poorly developed. Although the reasons for this mismatch are various, one of practical concern is the potential administrative burden imposed by the sizable number of headwaters. Two essential components of an administrative framework for managing waters is classification by type so that proper expectations can be set, and development of indicators that measure whether those expectations are being met. Ordinations of macroinvertebrate assemblages sampled from 1016 sites in 934 headwater streams draining <13 km(2) across Ohio, USA, revealed a highly distinct subset of sites characterized by a combination of taxa having an affinity for cold water and sensitivity to environmental disturbance. Bayesian Network (BN) modeling revealed that several environmental variables, notably water temperature, percent forest cover, and drainage area predict membership in this subset. More generally across all streams, macroinvertebrate assemblages signaled ecological status along a stressor gradient defined by habitat quality and intensity of land uses. Collectively, these results suggest a hierarchical administrative framework wherein stream habitat quality, as measured by summary habitat index scores, can screen and assign protections to waters generally expected to support assemblages consistent with good ecological status. Forest cover and water temperature can serve as an additional screen to assign higher levels of protection consistentwith higher ecological status. In cases where levels of protection based on screening are questioned or likely to be contentious, assessment of the macroinvertebrate assemblage can demonstrate the appropriate level. (C) 2018 Elsevier B.V. All rights reserved.
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