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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Software for prioritizing conservation actions based on probabilistic information.

Matthew Watts1,2,3, Carissa J Klein2,3,4, Vivitskaia J D Tulloch2,4,5

  • 1University of New England, Armidale, New South Wales, Australia.

Conservation Biology : the Journal of the Society for Conservation Biology
|December 11, 2020
PubMed
Summary
This summary is machine-generated.

Marxan with Probability enhances protected area design by incorporating uncertainty in feature distribution and threats. This tool helps maximize feature representation and minimize losses in conservation planning.

Keywords:
MarxanMarxan 软件apoyo a decidirbiodiversidadbiodiversitycambio climáticoclimate changedecision supportmodelado de la distribución de especiesoptimizaciónoptimizationpriorización de la conservación espacialprobabilidadprobabilityprotected areasreconocimiento simuladosimulated annealingspatial conservation prioritizationspecies distribution modelingáreas protegidas优化保护区决策支持概率模拟退火法气候变化物种分布建模生物多样性空间优先保护

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Area of Science:

  • Conservation Science
  • Environmental Management
  • Spatial Planning

Background:

  • Marxan is a widely used tool for designing protected areas.
  • The original Marxan software does not account for risks and uncertainties impacting protected areas.
  • Uncertainty includes species/habitat location, condition, and future threats like climate change.

Purpose of the Study:

  • To describe and examine Marxan with Probability, a modified Marxan version.
  • To introduce a tool that explicitly considers four types of uncertainty in conservation planning.
  • To illustrate how uncertainty can inform protected area design through case studies.

Main Methods:

  • Marxan with Probability explicitly models four types of uncertainty.
  • Uncertainty types include feature presence, future loss, future existence, and feature degradation.
  • Five studies were summarized to demonstrate the application of uncertainty in protected area design.

Main Results:

  • Marxan with Probability allows users to maximize feature representation despite distribution uncertainty.
  • The tool helps minimize the loss or degradation of species and habitats when considering threatening processes.
  • Incorporating uncertainty leads to more robust and effective protected area system designs.

Conclusions:

  • Marxan with Probability offers significant advancements for systematic conservation planning.
  • The software provides new avenues for research and practical application by conservation agencies.
  • This tool improves the resilience and effectiveness of protected areas in the face of environmental changes.