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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Optimizing management of invasions in an uncertain world using dynamic spatial models.

Kim M Pepin1, Amy J Davis1, Rebecca S Epanchin-Niell2,3

  • 1National Wildlife Research Center, United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, Fort Collins, Colorado, USA.

Ecological Applications : a Publication of the Ecological Society of America
|April 9, 2022
PubMed
Summary

Optimizing invasion control requires integrating ecological theory with management science. Addressing dispersal uncertainty in dynamic spatial models is crucial for effective and robust management strategies against invasive species and pathogens.

Keywords:
alien speciesbioeconomicdecision analysisdiseasedispersalinvasioninvasive speciesmanagementoptimal controlresource allocationspatialuncertainty

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Area of Science:

  • Ecology
  • Invasion Biology
  • Management Science
  • Optimization

Background:

  • Dispersal is a key driver of nonnative species and pathogen invasions.
  • Optimizing invasion management relies on dynamic spatial models incorporating dispersal.
  • Challenges arise from the complexity of time, space, and uncertainty in these models.

Purpose of the Study:

  • To provide a workflow for applying ecological theory to optimize invasion management science.
  • To highlight priorities for optimizing the control of invasions.
  • To address the limited consideration of dispersal uncertainty in current management frameworks.

Main Methods:

  • Synthesizing recent advances from ecology, decision analysis, bioeconomics, natural resource management, and optimization.
  • Developing a workflow for applying ecological theory to management science.
  • Analyzing the impact of dispersal uncertainty and multiple uncertainty types on optimization frameworks.

Main Results:

  • Dispersal uncertainty is critically underrepresented in optimal management frameworks, despite its significant impact on invasion outcomes.
  • Optimization frameworks typically fail to consider multiple types of uncertainty and their interrelationships.
  • Feedbacks magnifying dispersal uncertainty are rarely incorporated into management strategies.

Conclusions:

  • Incorporating dispersal uncertainty is essential for transparent decision-making and robust management strategies.
  • Addressing gaps in optimization using dynamic spatial models will enhance practical application and management consistency.
  • Further research is needed to integrate diverse uncertainty types for improved invasion control.