Informing management with monitoring data: the value of Bayesian forecasting

Ecosphere

Ryan J. Monello¹  Therese L. Johnson²  Alison C. Ketz³  N. Thompson Hobbs³ 

[1] National Park Service Inventory and Monitoring Program Pacific Island Network P.O. Box 52 Hawai'i Volcanoes National Park Hawaii 96718 USA;National Park Service Rocky Mountain National Park 1000 West Highway 36 Estes Park Colorado 80517 USA;Natural Resource Ecology Lab Department of Ecosystem Science and Sustainability, Graduate Degree Program in Ecology Colorado State University Fort Collins Colorado 80523 USA;

关键词: Bayes;    Bayesian statistics;    Cervus elaphus nelsoni;    Colorado;    demographic model;    elk;   

DOI  :  10.1002/ecs2.1587

来源: DOAJ

【 摘 要 】

Abstract Inventory and Monitoring Programs in the National Park Service (NPS) provide information needed to support wise planning, management, and decision making. Mathematical and statistical models play a critical role in this process by integrating data from multiple sources in a way that is honest about uncertainty. We show the utility of Bayesian hierarchical models for supporting decisions on managing natural resources of national parks. These models can assimilate monitoring data to provide true forecasts, resulting in probabilistic predictions of future states of park ecosystems accompanied by rigorous estimates of uncertainty. We discuss a novel approach for communicating these forecasts to decision makers who need to evaluate the probability that NPS goals will be met given different management actions, including the null model of no action. We illustrate how this approach has been used successfully to inform decisions on the elk (Cervus elaphus nelsoni) population management in Rocky Mountain National Park based on 47 yr of monitoring data. Forecasts from a discrete time, stage‐structured population model assimilated with annual census and sex and age classifications are being used annually to help park managers decide on actions needed to meet goals for elk and vegetation. In particular, park managers were able to determine the probability that the elk population would fall within a desired population range, which led to both population reduction actions and no action depending on the year of interest. Moreover, this approach allowed multiple survey methodologies from the last 47 years to be incorporated into a single model with associated estimates of uncertainty. Models like this one are especially useful for adaptive management where continuous improvement in models and data results in long‐term improvement in the wisdom of policy and management.

【 授权许可】

Unknown