EARLY HEARING DETECTION AND INTERVENTION VIRTUAL CONFERENCE
MARCH 2-5, 2021
(Virtually the same conference, without elevators, airplane tickets, or hotel room keys)
5/20/2019 | 2:00 PM - 2:15 PM | PREDICTING HYDROLOGIC DISTURBANCE OF STREAMS USING SPECIES OCCURRENCE DATA | 254 B
PREDICTING HYDROLOGIC DISTURBANCE OF STREAMS USING SPECIES OCCURRENCE DATA
Rapid landscape change coupled with a growing human demand for surface and groundwater have altered natural flow regimes of many rivers and streams on a global scale. Using a machine learning approach and long-term, georeferenced species occurrence data compiled by the USGS Aquatic Gap and state agencies, we modeled and mapped spatial patterns of hydrologic disturbance for streams in Arkansas, Missouri, and eastern Oklahoma. Fish presence/absence data had a similar overall model prediction accuracy of 77% (95% CI: 0.74, 0.80) as flow variables 76% (CI: 0.73, 0.80). Including topographic variables in the fish model increased the RF prediction accuracy to 90% (CI: 0.88, 0.92) compared to 86% (CI: 0.84, 0.89) for flow metrics. Correlation analysis of HDI by flow regime showed groundwater stable streams had the lowest disturbance frequency, with over 50% of stream reaches with low HDI located in forested land cover. HDI was highest for big rivers and intermittent runoff streams and streams associated with agricultural land use. Our results show that long-term georeferenced biological data can provide a valuable resource for predictive modeling of hydrologic disturbance for ungaged rivers and streams.
- Fish
- Disturbance
- Biodiversity
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Presenters/Authors
J. Tyler Fox
(), Arkansas Cooperative Fish and Wildlife Research Unit, University of Arkansas, jtfox@uark.edu;
ASHA DISCLOSURE:
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Daniel Magoulick
(), Arkansas Cooperative Fish and Wildlife Research Unit, University of Arkansas, danmag@uark.edu;
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