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Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration
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 Carl...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5016564/ https://www.ncbi.nlm.nih.gov/pubmed/27613291 http://dx.doi.org/10.1007/s10661-016-5526-6 |
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author | Conner, Mary M. Saunders, W. Carl Bouwes, Nicolaas Jordan, Chris |
author_facet | Conner, Mary M. Saunders, W. Carl Bouwes, Nicolaas Jordan, Chris |
author_sort | Conner, Mary M. |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10661-016-5526-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5016564 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-50165642016-09-19 Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration Conner, Mary M. Saunders, W. Carl Bouwes, Nicolaas Jordan, Chris Environ Monit Assess Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10661-016-5526-6) contains supplementary material, which is available to authorized users. Springer International Publishing 2016-09-08 2016 /pmc/articles/PMC5016564/ /pubmed/27613291 http://dx.doi.org/10.1007/s10661-016-5526-6 Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Conner, Mary M. Saunders, W. Carl Bouwes, Nicolaas Jordan, Chris Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration |
title | Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration |
title_full | Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration |
title_fullStr | Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration |
title_full_unstemmed | Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration |
title_short | Evaluating impacts using a BACI design, ratios, and a Bayesian approach with a focus on restoration |
title_sort | evaluating impacts using a baci design, ratios, and a bayesian approach with a focus on restoration |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5016564/ https://www.ncbi.nlm.nih.gov/pubmed/27613291 http://dx.doi.org/10.1007/s10661-016-5526-6 |
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