A niche for null models in adaptive resource management

David N. Koons, Thomas V. Riecke, G. Scott Boomer, Benjamin S. Sedinger, James S. Sedinger, Perry J. Williams, Todd W. Arnold

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

As global systems rapidly change, our collective ability to predict future ecological dynamics will become increasingly important for successful natural resource management. By merging stakeholder objectives with system uncertainty, and by adapting actions to changing systems and knowledge, adaptive resource management (ARM) provides a rigorous platform for making sound decisions in a changing world. Critically, however, applications of ARM could be improved by employing benchmarks (i.e., points of reference) for determining when learning is occurring through the cycle of monitoring, modeling, and decision-making steps in ARM. Many applications of ARM use multiple model-based hypotheses to identify and reduce systematic uncertainty over time, but generally lack benchmarks for gauging discovery of scientific evidence and learning. This creates the danger of thinking that directional changes in model weights or rankings are indicative of evidence for hypotheses, when possibly all competing models are inadequate. There is thus a somewhat obvious, but yet to be filled niche for including benchmarks for learning in ARM. We contend that carefully designed “ecological null models,” which are structured to produce an expected ecological pattern in the absence of a hypothesized mechanism, can serve as suitable benchmarks. Using a classic case study of mallard harvest management that is often used to demonstrate the successes of ARM for learning about ecological mechanisms, we show that simple ecological null models, such as population persistence (Nt+1 = Nt), provide more robust near-term forecasts of population abundance than the currently used mechanistic models. More broadly, ecological null models can be used as benchmarks for learning in ARM that trigger the need for discarding model parameterizations and developing new ones when prevailing models underperform the ecological null model. Identifying mechanistic models that surpass these benchmarks will improve learning through ARM and help decision-makers keep pace with a rapidly changing world.

Original languageEnglish (US)
Article numbere8541
JournalEcology and Evolution
Volume12
Issue number1
DOIs
StatePublished - Jan 2022

Bibliographical note

Funding Information:
We thank Delta Waterfowl and the California Department of Water Resources for funding that spurred this collaboration. DNK is supported by the James C. Kennedy Endowment to CSU for Wetland and Waterfowl Conservation. BSS is supported by the Kennedy–Grohne Endowment to UWSP for Waterfowl and Wetlands Conservation. TVR was supported by Delta Waterfowl and the Ducks Unlimited Canada Bonnycastle Fellowship in Wetlands and Waterfowl Biology.

Publisher Copyright:
© 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

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