Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration
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- 作者：Mary M. Conner ; W. Carl Saunders…
- 关键词：Bayesian approach ; BACI ; Hierarchical model ; MCMC ; Oncorhynchus mykiss ; Restoration impact ; Steelhead
- 刊名：Environmental Monitoring and Assessment
- 出版年：2016
- 出版时间：October 2016
- 年：2016
- 卷：188
- 期：10
- 全文大小：837 KB
- 刊物类别：Earth and Environmental Science
- 刊物主题：Environment
Monitoring, Environmental Analysis and Environmental Ecotoxicology
Ecology
Atmospheric Protection, Air Quality Control and Air Pollution
Environmental Management - 出版者：Springer Netherlands
- ISSN：1573-2959
- 卷排序：188
文摘
Before-after-control-impact (BACI) designs are an effective method to evaluate natural and human-induced perturbations on ecological variables when treatment sites cannot be randomly chosen. While effect sizes of interest can be tested with frequentist methods, using Bayesian Markov chain Monte Carlo (MCMC) sampling methods, probabilities of effect sizes, such as a ≥20 % increase in density after restoration, can be directly estimated. Although BACI and Bayesian methods are used widely for assessing natural and human-induced impacts for field experiments, the application of hierarchal Bayesian modeling with MCMC sampling to BACI designs is less common. Here, we combine these approaches and extend the typical presentation of results with an easy to interpret ratio, which provides an answer to the main study question—“How much impact did a management action or natural perturbation have?” As an example of this approach, we evaluate the impact of a restoration project, which implemented beaver dam analogs, on survival and density of juvenile steelhead. Results indicated the probabilities of a ≥30 % increase were high for survival and density after the dams were installed, 0.88 and 0.99, respectively, while probabilities for a higher increase of ≥50 % were variable, 0.17 and 0.82, respectively. This approach demonstrates a useful extension of Bayesian methods that can easily be generalized to other study designs from simple (e.g., single factor ANOVA, paired t test) to more complicated block designs (e.g., crossover, split-plot). This approach is valuable for estimating the probabilities of restoration impacts or other management actions.