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Creating simple predictive models in ecology, conservation and environmental policy based on Bayesian belief

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This study introduces BBNet, a user-friendly R package for building predictive models with simple spreadsheet tools. BBNet simplifies complex modeling for management and conservation insights.

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Area of Science:

  • Environmental science
  • Ecological modeling
  • Computational biology

Background:

  • Predictive models offer valuable insights for management, conservation, and policy-making but are often complex.
  • Existing modeling tools can require significant mathematical and programming expertise, limiting accessibility.

Purpose of the Study:

  • Introduce BBNet, a novel R package designed for accessible predictive modeling.
  • Enable users with limited programming backgrounds to construct, analyze, and visualize complex models.
  • Facilitate ordinal predictions and sensitivity testing for system dynamics.

Main Methods:

  • Utilizes modified Bayesian belief networks (BBNs) with a focus on straightforward interaction concepts.
  • Models are constructed using basic spreadsheet tools and loaded into the R package.
  • Parameterization incorporates data, literature, expert opinion, and surveys, using simple ordinal scales for interactions.

Main Results:

  • BBNet provides a simplified approach to building and interpreting predictive models.
  • Models can be analyzed, visualized, and sensitivity tested to understand information flow and predict outcomes.
  • Ordinal comparison of model outcomes allows for ranking scenarios from 'best' to 'worse'.

Conclusions:

  • BBNet democratizes predictive modeling, making it accessible beyond specialized programming expertise.
  • The tool supports informed decision-making in environmental and ecological management, with potential applications in diverse fields.
  • Modified BBNs offer a flexible framework for ordinal predictions and system analysis.