A niche for null models in adaptive resource management. Issue 1 (13th January 2022)

• Record Type: Journal Article
• Authors: Koons, David N.; Riecke, Thomas V.; Boomer, G. Scott; Sedinger, Benjamin S.; Sedinger, James S.; Williams, Perry J.; Arnold, Todd W.
• Is Part Of: Ecology and evolution. Volume 12:Issue 1(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-13
• Subjects: adaptive harvest management -- climate change -- forecast -- knowledge -- learning -- persistence -- prediction; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.8541
• Languages: English
• ISSNs: 2045-7758

Abstract

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. Abstract : Ecological null models based on sound logic can be used as benchmarks for learning in ARM that trigger the need for new model parameterizations when prevailing models underperform the ecological null model. Identifying mechanistic models that surpass these benchmarks will improve learning through ARM and help guide decision making in a rapidly changing world.