Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration.
Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration.
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DOI：
10.1007/s10661-016-5526-6
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发表时间：
2015-10
影响因子：
3
通讯作者：
Jordan, Chris
中科院分区：
文献类型：
--
作者：
Conner, Mary M.;Saunders, W. Carl;Bouwes, Nicolaas;Jordan, Chris
事前-事后控制-影响设计(BACI)是一种在处理地点不能随机选择的情况下评估自然和人为因素对生态变量扰动的有效方法。使用贝叶斯马尔科夫链蒙特卡罗抽样方法，可以直接估计效应大小的概率，例如恢复后密度增加≥20%。虽然BACI和贝叶斯方法被广泛用于现场试验的自然和人为影响评估，但将MCMC抽样的分层贝叶斯建模应用于BACI设计的情况较少。在这里，我们将这些方法结合在一起，并用一个易于解释的比率来扩展结果的典型表示，这为主要研究问题提供了答案--“管理行动或自然干扰有多大影响？”作为这种方法的一个例子，我们评估了一个修复项目对幼鱼存活和密度的影响，该项目实施了类似于河狸大坝的修复项目。结果表明，在大坝安装后，≥增加30%的存活率和密度的概率很高，分别为0.88和0.99，而≥增加50%的概率是可变的，分别为0.17和0.82。这种方法展示了贝叶斯方法的有用扩展，该扩展可以很容易地推广到其他研究设计，从简单的(例如，单因素方差分析、配对t检验)到更复杂的区组设计(例如，交叉、分裂图)。这种方法对于估计恢复影响或其他管理行动的概率很有价值。本文的在线版本(doi：10.1007/s10661-0165526-6)包含补充材料，授权用户可以使用。
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. The online version of this article (doi:10.1007/s10661-016-5526-6) contains supplementary material, which is available to authorized users.
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影响因子：
1.9
作者：
Barker, RJ
通讯作者：
Barker, RJ
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Colee, James
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