Comprehensive marine substrate classification applied to Canada's Pacific shelf.
| Autor: | Gregr EJ; SciTech Environmental Consulting, Vancouver, British Columbia, Canada.; Institute for Resources, Environment, and Sustainability, University of British Columbia, Vancouver, British Columbia, Canada., Haggarty DR; Fisheries and Oceans Canada, Pacific Biological Station, Nanaimo, British Columbia, Canada.; Department of Biology, University of Victoria, Victoria, British Columbia, Canada., Davies SC; Fisheries and Oceans Canada, Pacific Biological Station, Nanaimo, British Columbia, Canada., Fields C; Fisheries and Oceans Canada, Institute of Ocean Sciences, Sidney, British Columbia, Canada., Lessard J; Fisheries and Oceans Canada, Pacific Biological Station, Nanaimo, British Columbia, Canada. |
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| Jazyk: | angličtina |
| Zdroj: | PloS one [PLoS One] 2021 Oct 29; Vol. 16 (10), pp. e0259156. Date of Electronic Publication: 2021 Oct 29 (Print Publication: 2021). |
| DOI: | 10.1371/journal.pone.0259156 |
| Abstrakt: | 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. Competing Interests: This funding does not alter our adherence to PLOS ONE policies on sharing data and materials. The specific roles of these authors are articulated in the ‘author contributions’ section. |
| Databáze: | MEDLINE |
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