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Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning.

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A new R package, gbm.auto, simplifies Boosted Regression Trees (BRT) for spatial ecological modeling. This tool enhances data-poor species management by automating complex analyses and improving abundance predictions for conservation efforts.

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Area of Science:

  • Ecology
  • Conservation Biology
  • Statistical Modeling

Background:

  • Spatial approaches are crucial for managing data-limited species.
  • Existing methods for spatial prediction struggle with sparse data or suboptimal variable use.
  • Boosted Regression Trees (BRT) are effective for data-limited species but complex to implement.

Purpose of the Study:

  • To develop an accessible R package simplifying BRT for spatial modeling.
  • To automate the processing and predictive mapping of species abundance data.
  • To enhance the usability of advanced statistical techniques for ecological management.

Main Methods:

  • Developed the "gbm.auto" R package integrating existing and new functions.
  • Automated the processing and spatial modeling of species abundance data using BRT.
  • Incorporated a Decision Support Tool for identifying conservation areas (candidate MPAs).

Main Results:

  • The "gbm.auto" package significantly simplifies BRT spatial modeling.
  • Users can generate abundance maps, visualize their representativeness, and analyze variable influence.
  • The package produces processed model objects and detailed reports.

Conclusions:

  • The "gbm.auto" package bridges advanced statistical methods with conservation practice.
  • It enables improved spatial abundance predictions for better management and decision-making.
  • Applicable to various spatial abundance modeling and area protection scenarios beyond marine fisheries.