Predicting subsurface sonar observations with satellite-derived ocean surface data in the California Current Ecosystem.

Vessel-based sonar systems that focus on the water column provide valuable information on the distribution of underwater marine organisms, but such data are expensive to collect and limited in their spatiotemporal coverage. Satellite data, however, are widely available across large regions and provi...

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Autores principales:	Kellie R Gadeken, Maxwell B Joseph, Joseph McGlinchy, Kristopher B Karnauskas, Carrie C Wall
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/335eda191b354328ad3ba0bd9fc886fe
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spelling	oai:doaj.org-article:335eda191b354328ad3ba0bd9fc886fe2021-12-02T20:14:58ZPredicting subsurface sonar observations with satellite-derived ocean surface data in the California Current Ecosystem.1932-620310.1371/journal.pone.0248297https://doaj.org/article/335eda191b354328ad3ba0bd9fc886fe2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0248297https://doaj.org/toc/1932-6203Vessel-based sonar systems that focus on the water column provide valuable information on the distribution of underwater marine organisms, but such data are expensive to collect and limited in their spatiotemporal coverage. Satellite data, however, are widely available across large regions and provide information on surface ocean conditions. If satellite data can be linked to subsurface sonar measurements, it may be possible to predict marine life over broader spatial regions with higher frequency using satellite observations. Here, we use random forest models to evaluate the potential for predicting a sonar-derived proxy for subsurface biomass as a function of satellite imagery in the California Current Ecosystem. We find that satellite data may be useful for prediction under some circumstances, but across a range of sonar frequencies and depths, overall model performance was low. Performance in spatial interpolation tasks exceeded performance in spatial and temporal extrapolation, suggesting that this approach is not yet reliable for forecasting or spatial extrapolation. We conclude with some potential limitations and extensions of this work.Kellie R GadekenMaxwell B JosephJoseph McGlinchyKristopher B KarnauskasCarrie C WallPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0248297 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kellie R Gadeken Maxwell B Joseph Joseph McGlinchy Kristopher B Karnauskas Carrie C Wall Predicting subsurface sonar observations with satellite-derived ocean surface data in the California Current Ecosystem.
description	Vessel-based sonar systems that focus on the water column provide valuable information on the distribution of underwater marine organisms, but such data are expensive to collect and limited in their spatiotemporal coverage. Satellite data, however, are widely available across large regions and provide information on surface ocean conditions. If satellite data can be linked to subsurface sonar measurements, it may be possible to predict marine life over broader spatial regions with higher frequency using satellite observations. Here, we use random forest models to evaluate the potential for predicting a sonar-derived proxy for subsurface biomass as a function of satellite imagery in the California Current Ecosystem. We find that satellite data may be useful for prediction under some circumstances, but across a range of sonar frequencies and depths, overall model performance was low. Performance in spatial interpolation tasks exceeded performance in spatial and temporal extrapolation, suggesting that this approach is not yet reliable for forecasting or spatial extrapolation. We conclude with some potential limitations and extensions of this work.
format	article
author	Kellie R Gadeken Maxwell B Joseph Joseph McGlinchy Kristopher B Karnauskas Carrie C Wall
author_facet	Kellie R Gadeken Maxwell B Joseph Joseph McGlinchy Kristopher B Karnauskas Carrie C Wall
author_sort	Kellie R Gadeken
title	Predicting subsurface sonar observations with satellite-derived ocean surface data in the California Current Ecosystem.
title_short	Predicting subsurface sonar observations with satellite-derived ocean surface data in the California Current Ecosystem.
title_full	Predicting subsurface sonar observations with satellite-derived ocean surface data in the California Current Ecosystem.
title_fullStr	Predicting subsurface sonar observations with satellite-derived ocean surface data in the California Current Ecosystem.
title_full_unstemmed	Predicting subsurface sonar observations with satellite-derived ocean surface data in the California Current Ecosystem.
title_sort	predicting subsurface sonar observations with satellite-derived ocean surface data in the california current ecosystem.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/335eda191b354328ad3ba0bd9fc886fe
work_keys_str_mv	AT kelliergadeken predictingsubsurfacesonarobservationswithsatellitederivedoceansurfacedatainthecaliforniacurrentecosystem AT maxwellbjoseph predictingsubsurfacesonarobservationswithsatellitederivedoceansurfacedatainthecaliforniacurrentecosystem AT josephmcglinchy predictingsubsurfacesonarobservationswithsatellitederivedoceansurfacedatainthecaliforniacurrentecosystem AT kristopherbkarnauskas predictingsubsurfacesonarobservationswithsatellitederivedoceansurfacedatainthecaliforniacurrentecosystem AT carriecwall predictingsubsurfacesonarobservationswithsatellitederivedoceansurfacedatainthecaliforniacurrentecosystem
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Predicting subsurface sonar observations with satellite-derived ocean surface data in the California Current Ecosystem.

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