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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8555849/ https://www.ncbi.nlm.nih.gov/pubmed/34714844 http://dx.doi.org/10.1371/journal.pone.0259156 |
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author | Gregr, Edward J. Haggarty, Dana R. Davies, Sarah C. Fields, Cole Lessard, Joanne |
author_facet | Gregr, Edward J. Haggarty, Dana R. Davies, Sarah C. Fields, Cole Lessard, Joanne |
author_sort | Gregr, Edward J. |
collection | PubMed |
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 km(2). 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 | Online Article Text |
id | pubmed-8555849 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-85558492021-10-30 Comprehensive marine substrate classification applied to Canada’s Pacific shelf Gregr, Edward J. Haggarty, Dana R. Davies, Sarah C. Fields, Cole Lessard, Joanne PLoS One Research Article 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 km(2). 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. Public Library of Science 2021-10-29 /pmc/articles/PMC8555849/ /pubmed/34714844 http://dx.doi.org/10.1371/journal.pone.0259156 Text en © 2021 Gregr et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Gregr, Edward J. Haggarty, Dana R. Davies, Sarah C. Fields, Cole Lessard, Joanne Comprehensive marine substrate classification applied to Canada’s Pacific shelf |
title | 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_short | Comprehensive marine substrate classification applied to Canada’s Pacific shelf |
title_sort | comprehensive marine substrate classification applied to canada’s pacific shelf |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8555849/ https://www.ncbi.nlm.nih.gov/pubmed/34714844 http://dx.doi.org/10.1371/journal.pone.0259156 |
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