Comprehensive marine substrate classification applied to Canada’s Pacific shelf

Maps of bottom type are essential to the management of marine resources and biodiversity because of their foundational role in characterizing species’ habitats. They are also urgently needed as countries work to define marine protected areas. Current approaches are time consuming, focus largely on g...

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Autores principales:	Edward J. Gregr, Dana R. Haggarty, Sarah C. Davies, Cole Fields, Joanne Lessard
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7d13458bb4774a80941bc9aa3ef1f375
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spelling	oai:doaj.org-article:7d13458bb4774a80941bc9aa3ef1f3752021-11-04T06:49:30ZComprehensive marine substrate classification applied to Canada’s Pacific shelf1932-6203https://doaj.org/article/7d13458bb4774a80941bc9aa3ef1f3752021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8555849/?tool=EBIhttps://doaj.org/toc/1932-6203Maps of bottom type are essential to the management of marine resources and biodiversity because of their foundational role in characterizing species’ habitats. They are also urgently needed as countries work to define marine protected areas. Current approaches are time consuming, focus largely on grain size, and tend to overlook shallow waters. Our random forest classification of almost 200,000 observations of bottom type is a timely alternative, providing maps of coastal substrate at a combination of resolution and extents not previously achieved. We correlated the observations with depth, depth-derivatives, and estimates of energy to predict marine substrate at 100 m resolution for Canada’s Pacific shelf, a study area of over 135,000 km2. We built five regional models with the same data at 20 m resolution. In addition to standard tests of model fit, we used three independent data sets to test model predictions. We also tested for regional, depth, and resolution effects. We guided our analysis by asking: 1) does weighting for prevalence improve model predictions? 2) does model resolution influence model performance? And 3) is model performance influenced by depth? All our models fit the build data well with true skill statistic (TSS) scores ranging from 0.56 to 0.64. Weighting models with class prevalence improved fit and the correspondence with known spatial features. Class-based metrics showed differences across both resolutions and spatial regions, indicating non-stationarity across these spatial categories. Predictive power was lower (TSS from 0.10 to 0.36) based on independent data evaluation. Model performance was also a function of depth and resolution, illustrating the challenge of accurately representing heterogeneity. Our work shows the value of regional analyses to assessing model stationarity and how independent data evaluation and the use of error metrics can improve understanding of model performance and sampling bias.Edward J. GregrDana R. HaggartySarah C. DaviesCole FieldsJoanne LessardPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Edward J. Gregr Dana R. Haggarty Sarah C. Davies Cole Fields Joanne Lessard Comprehensive marine substrate classification applied to Canada’s Pacific shelf
description	Maps of bottom type are essential to the management of marine resources and biodiversity because of their foundational role in characterizing species’ habitats. They are also urgently needed as countries work to define marine protected areas. Current approaches are time consuming, focus largely on grain size, and tend to overlook shallow waters. Our random forest classification of almost 200,000 observations of bottom type is a timely alternative, providing maps of coastal substrate at a combination of resolution and extents not previously achieved. We correlated the observations with depth, depth-derivatives, and estimates of energy to predict marine substrate at 100 m resolution for Canada’s Pacific shelf, a study area of over 135,000 km2. We built five regional models with the same data at 20 m resolution. In addition to standard tests of model fit, we used three independent data sets to test model predictions. We also tested for regional, depth, and resolution effects. We guided our analysis by asking: 1) does weighting for prevalence improve model predictions? 2) does model resolution influence model performance? And 3) is model performance influenced by depth? All our models fit the build data well with true skill statistic (TSS) scores ranging from 0.56 to 0.64. Weighting models with class prevalence improved fit and the correspondence with known spatial features. Class-based metrics showed differences across both resolutions and spatial regions, indicating non-stationarity across these spatial categories. Predictive power was lower (TSS from 0.10 to 0.36) based on independent data evaluation. Model performance was also a function of depth and resolution, illustrating the challenge of accurately representing heterogeneity. Our work shows the value of regional analyses to assessing model stationarity and how independent data evaluation and the use of error metrics can improve understanding of model performance and sampling bias.
format	article
author	Edward J. Gregr Dana R. Haggarty Sarah C. Davies Cole Fields Joanne Lessard
author_facet	Edward J. Gregr Dana R. Haggarty Sarah C. Davies Cole Fields Joanne Lessard
author_sort	Edward J. Gregr
title	Comprehensive marine substrate classification applied to Canada’s Pacific shelf
title_short	Comprehensive marine substrate classification applied to Canada’s Pacific shelf
title_full	Comprehensive marine substrate classification applied to Canada’s Pacific shelf
title_fullStr	Comprehensive marine substrate classification applied to Canada’s Pacific shelf
title_full_unstemmed	Comprehensive marine substrate classification applied to Canada’s Pacific shelf
title_sort	comprehensive marine substrate classification applied to canada’s pacific shelf
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7d13458bb4774a80941bc9aa3ef1f375
work_keys_str_mv	AT edwardjgregr comprehensivemarinesubstrateclassificationappliedtocanadaspacificshelf AT danarhaggarty comprehensivemarinesubstrateclassificationappliedtocanadaspacificshelf AT sarahcdavies comprehensivemarinesubstrateclassificationappliedtocanadaspacificshelf AT colefields comprehensivemarinesubstrateclassificationappliedtocanadaspacificshelf AT joannelessard comprehensivemarinesubstrateclassificationappliedtocanadaspacificshelf
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Comprehensive marine substrate classification applied to Canada’s Pacific shelf

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