EARLY HEARING DETECTION AND INTERVENTION VIRTUAL CONFERENCE
MARCH 2-5, 2021
(Virtually the same conference, without elevators, airplane tickets, or hotel room keys)
5/21/2019 | 9:00 AM - 9:15 AM | INFORMING LAND AND FISH MANAGEMENT IN REMOTE AREAS USING ENVIRONMENTAL DNA, LANDSAPE CHARACTERIZATIONS, AND SPECIES DISTRIBUTION MODELING | 250 CF
INFORMING LAND AND FISH MANAGEMENT IN REMOTE AREAS USING ENVIRONMENTAL DNA, LANDSAPE CHARACTERIZATIONS, AND SPECIES DISTRIBUTION MODELING
The Bureau of Land Management (BLM) is tasked with managing oil and gas leasing, exploration and operations within the National Petroleum Reserve on the remote north slope of Alaska. The BLM does not have enough data on the 20 fish species occurring there to make fully informed leasing and management decisions. We used environmental DNA, landscape characterizations from StreamCat, and maximum entropy (MaxEnt) modeling to develop species distribution models for these 20 species. Traditional models only account for spatial variation via static datasets. We assessed whether inclusion of both spatial and temporal variation via dynamic datasets improved model performance. Generally good models were developed, with an average AUC of 0.896 overall species and dataset types, although static datasets outperformed dynamic datasets. The best models had regularization parameters controlling model parsimony between 1.0 (default) and 2.0 (slightly more parsimonious). Temperature, slope and land cover were the most important predictors and tended to have an inverse effect on the probability of fish presence. Approaches like this have great potential for providing critically needed data in rapidly developing but data poor regions like the north slope of Alaska, Africa, and South America.
- Fish
- Management
- Remote Sensing
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Presenters/Authors
Anna Holder
(), Desert Research Institute, aholder@csumb.edu;
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John Olson
(), Dept of Applied Environmental Science, California State University Monterey Bay, CA, USA, joolson@csumb.edu;
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