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Tytuł:
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A satellite-based mobile warning system to reduce interactions with an endangered species.
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Autorzy:
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Breece MW; College of Earth, Ocean, and Environment, University of Delaware, Lewes, Delaware, 19958, USA.
Oliver MJ; College of Earth, Ocean, and Environment, University of Delaware, Lewes, Delaware, 19958, USA.
Fox DA; College of Agriculture and Natural Resources, Delaware State University, Dover, Delaware, 19901, USA.
Hale EA; Division of Fish and Wildlife, Delaware Natural Resource and Environmental Control, Dover, Delaware, 19901, USA.; Delaware Sea Grant, University of Delaware, Lewes, Delaware, 19958, USA.
Haulsee DE; College of Earth, Ocean, and Environment, University of Delaware, Lewes, Delaware, 19958, USA.; Hopkins Marine Station, Stanford University, Pacific Grove, California, 93950, USA.
Shatley M; College of Earth, Ocean, and Environment, University of Delaware, Lewes, Delaware, 19958, USA.
Bograd SJ; Environmental Research Division, NOAA Southwest Fisheries Science Center, Monterey, California, 93940, USA.; Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, California, 95064, USA.
Hazen EL; Environmental Research Division, NOAA Southwest Fisheries Science Center, Monterey, California, 93940, USA.; Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, California, 95064, USA.
Welch H; Environmental Research Division, NOAA Southwest Fisheries Science Center, Monterey, California, 93940, USA.; Institute of Marine Sciences, University of California Santa Cruz, Santa Cruz, California, 95064, USA.
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Źródło:
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Ecological applications : a publication of the Ecological Society of America [Ecol Appl] 2021 Sep; Vol. 31 (6), pp. e02358. Date of Electronic Publication: 2021 May 30.
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Typ publikacji:
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Journal Article; Research Support, U.S. Gov't, Non-P.H.S.
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Język:
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English
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Imprint Name(s):
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Publication: Washington, D.C. : Ecological Society of America
Original Publication: Tempe, AZ : The Society, 1991-
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MeSH Terms:
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Ecosystem*
Endangered Species*
Satellite Imagery*
Animals ; Bays ; Delaware ; Fisheries ; Fishes ; Rivers ; Telemetry
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References:
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Ecol Appl. 2017 Mar;27(2):378-388. (PMID: 28221708)
Ecol Appl. 2017 Oct;27(7):2048-2060. (PMID: 28646611)
Ecol Appl. 2021 Sep;31(6):e02358. (PMID: 33870598)
Proc Natl Acad Sci U S A. 2019 Mar 19;116(12):5582-5587. (PMID: 30804188)
Sci Adv. 2018 May 30;4(5):eaar3001. (PMID: 29854945)
Harmful Algae. 2016 Nov;59:1-18. (PMID: 28073500)
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Contributed Indexing:
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Keywords: dynamic management; ecosystem forecasting; interactive mobile/web application
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Molecular Sequence:
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Dryad 10.5061/dryad.jsxksn08b
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Entry Date(s):
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Date Created: 20210419 Date Completed: 20211011 Latest Revision: 20220531
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Update Code:
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20240104
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PubMed Central ID:
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PMC8459280
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DOI:
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10.1002/eap.2358
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PMID:
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33870598
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Earth-observing satellites are a major research tool for spatially explicit ecosystem nowcasting and forecasting. However, there are practical challenges when integrating satellite data into usable real-time products for stakeholders. The need of forecast immediacy and accuracy means that forecast systems must account for missing data and data latency while delivering a timely, accurate, and actionable product to stakeholders. This is especially true for species that have legal protection. Acipenser oxyrinchus oxyrinchus (Atlantic sturgeon) were listed under the United States Endangered Species Act in 2012, which triggered immediate management action to foster population recovery and increase conservation measures. Building upon an existing research occurrence model, we developed an Atlantic sturgeon forecast system in the Delaware Bay, USA. To overcome missing satellite data due to clouds and produce a 3-d forecast of ocean conditions, we implemented data interpolating empirical orthogonal functions (DINEOF) on daily observed satellite data. We applied the Atlantic sturgeon research model to the DINEOF output and found that it correctly predicted Atlantic sturgeon telemetry occurrences over 90% of the time within a 3-d forecast. A similar framework has been utilized to forecast harmful algal blooms, but to our knowledge, this is the first time a species distribution model has been applied to DINEOF gap-filled data to produce a forecast product for fishes. To implement this product into an applied management setting, we worked with state and federal organizations to develop real-time and forecasted risk maps in the Delaware River Estuary for both state-level managers and commercial fishers. An automated system creates and distributes these risk maps to subscribers' mobile devices, highlighting areas that should be avoided to reduce interactions. Additionally, an interactive web interface allows users to plot historic, current, future, and climatological risk maps as well as the underlying model output of Atlantic sturgeon occurrence. The mobile system and web tool provide both stakeholders and managers real-time access to estimated occurrences of Atlantic sturgeon, enabling conservation planning and informing fisher behavior to reduce interactions with this endangered species while minimizing impacts to fisheries and other projects.
(© 2021The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of Ecological Society of America.)
