Success and failure of ecological management is highly variable in an experimental test.

Academic Article


  • When managing natural systems, the importance of recognizing the role of uncertainty has been formalized as the precautionary approach. However, it is difficult to determine the role of stochasticity in the success or failure of management because there is almost always no replication; typically, only a single observation exists for a particular site or management strategy. Yet, assessing the role of stochasticity is important for providing a strong foundation for the precautionary approach, and learning from past outcomes is critical for implementing adaptive management of species or ecosystems. In addition, adaptive management relies on being able to implement a variety of strategies in order to learn-an often difficult task in natural systems. Here, we show that there is large, stochastically driven variability in success for management treatments to control an invasive species, particularly for moderate, and more feasible, management strategies. This is exactly where the precautionary approach should be important. Even when combining management strategies, we show that moderate effort in management either fails or is highly variable in its success. This variability allows some management treatments to, on average, meet their target, even when failure is probable. Our study is an important quantitative replicated experimental test of the precautionary approach and can serve as a way to understand the variability in management outcomes in natural systems which have the potential to be more variable than our tightly controlled system.
  • Authors

  • White, Easton
  • Cox, Kyle
  • Melbourne, Brett A
  • Hastings, Alan
  • Status

    Publication Date

  • November 12, 2019
  • Keywords

  • Animals
  • Conservation of Natural Resources
  • Introduced Species
  • Models, Biological
  • Tribolium
  • Uncertainty
  • adaptive management
  • ecological management
  • invasive species
  • stochasticity
  • Digital Object Identifier (doi)

    Start Page

  • 23169
  • End Page

  • 23173
  • Volume

  • 116
  • Issue

  • 46