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Determination of Total Lipid and Lipid Classes in Marine Samples
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Comprehensive marine substrate classification applied to Canada's Pacific shelf.

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  • 1SciTech Environmental Consulting, Vancouver, British Columbia, Canada.

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This study introduces a new random forest classification method for mapping coastal substrate, offering a faster alternative to traditional techniques. The approach provides detailed marine substrate maps crucial for resource management and biodiversity conservation.

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Area of Science:

  • Marine ecology
  • Geospatial analysis
  • Habitat characterization

Background:

  • Accurate marine bottom type maps are vital for managing marine resources, biodiversity, and establishing marine protected areas.
  • Existing methods for mapping bottom types are often time-consuming, grain-size focused, and neglect shallow water environments.

Purpose of the Study:

  • To develop a timely and efficient alternative for mapping coastal substrate using random forest classification.
  • To generate high-resolution marine substrate maps for Canada's Pacific shelf, achieving unprecedented resolution and extent.
  • To assess the influence of model resolution, depth, and regional variations on prediction accuracy.

Main Methods:

  • Utilized a random forest classification model with nearly 200,000 bottom type observations.
  • Correlated observations with environmental variables (depth, derivatives, energy) to predict marine substrate at 100 m and 20 m resolutions.
  • Employed independent datasets for model validation and analyzed regional, depth, and resolution effects on performance.

Main Results:

  • Models showed good fit to training data (TSS 0.56–0.64), with prevalence weighting improving performance.
  • Independent data evaluation revealed lower predictive power (TSS 0.10–0.36), indicating challenges with heterogeneity.
  • Model performance varied significantly with depth and resolution, highlighting spatial non-stationarity.

Conclusions:

  • Random forest classification offers a powerful, efficient method for mapping coastal substrate at high resolution and broad extents.
  • Regional analyses and independent data evaluation are crucial for understanding model performance, limitations, and sampling biases.
  • The findings underscore the complexity of accurately representing marine substrate heterogeneity and the importance of considering spatial variations.