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Multi-objective optimisation of species distribution models for river management.

Sacha Gobeyn1, Peter L M Goethals1

  • 1Ghent University, Department of Animal Sciences and Aquatic Ecology, Coupure Links 653, B-9000, Ghent, Belgium.

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|July 27, 2019
PubMed
Summary

Multi-objective optimization (MOO) enhances prevalence-adjusted species distribution models (SDMs) for freshwater management. This approach improves decision-making by balancing environmental impacts and costs, leading to more objective environmental standards.

Keywords:
Environmental standard limitsMulti-objective optimisationNon-dominated sorting genetic algorithm II (NSGA-II)Prevalence-adjusted model trainingRiver decision managementSpecies distribution models

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Area of Science:

  • Environmental Science
  • Ecology
  • Computational Biology

Background:

  • River managers face challenges balancing environmental impacts and implementation costs in decision-making.
  • Species distribution models (SDMs) are used for diagnostic analysis but can over- or underestimate species presence.
  • Prevalence-adjusted SDM training aims to mitigate estimation biases, with multi-objective optimization (MOO) being a key approach.

Purpose of the Study:

  • To introduce and evaluate the practice of multi-objective optimization (MOO)-based prevalence-adjusted SDM training for freshwater decision management.
  • To compare the effectiveness of Pareto-based MOO (NSGA-II) against traditional single-objective optimization methods for SDM training.

Main Methods:

  • Numerical experiment comparing non-dominated sorting genetic algorithm II (NSGA-II) with single-objective optimization.
  • Training SDMs for 11 pollution-sensitive freshwater macroinvertebrate species using the Limnodata set (Netherlands, 30 years, 20,000 locations).
  • Evaluating the ability to identify a wide range of solutions in the Pareto space and model reliability for diagnostic analysis.

Main Results:

  • NSGA-II increased the ability to identify a broad distribution of solutions in the Pareto space by two to four times compared to single-objective optimization.
  • Average runtime increased by only four percent for a single run when using NSGA-II.
  • NSGA-II proved effective in identifying reliable SDMs suitable for diagnostic analysis.

Conclusions:

  • MOO-based prevalence-adjusted SDM training is a valuable tool for freshwater decision-making, facilitating collaboration between model developers and managers.
  • This approach enables setting environmental standard limits on a more objective basis.
  • The methodology shows potential for application in broader environmental decision-making contexts.