1. Bayesian data analysis (BDA) is a powerful tool for making inference from ecological data, but its full potential has yet to be realized. Despite a generally positive trajectory in research surrounding model development and assessment, far too little attention has been given to prior specification.
2. Default priors, a sub‐class of non‐informative prior distributions that are often chosen without critical thought or evaluation, are commonly used in practice. We believe the fear of being too ‘subjective’ has prevented many researchers from using any prior information in their analyses despite the fact that defending prior choice (informative or not) promotes good statistical practice.
3. In this commentary, we provide an overview of how BDA is currently being used in a random sample of articles, discuss implications for inference if current bad practices continue, and highlight sub‐fields where knowledge about the system has improved inference and promoted good statistical practices through the careful and justified use of informative priors.
4. We hope to inspire a renewed discussion about the use of Bayesian priors in Ecology with particular attention paid to specification and justification. We also emphasize that all priors are the result of a subjective choice, and should be discussed in that way.

中文翻译：

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贝叶斯先验在生态学中的使用：好的，坏的和不好的

1. 贝叶斯数据分析（BDA）是从生态数据进行推断的强大工具，但其全部潜力尚未实现。尽管在围绕模型开发和评估的研究中总体上具有积极的发展轨迹，但对现有规范的关注却很少。
2. 缺省优先级是非信息性优先级分布的子类，通常在没有批判性思考或评估的情况下选择缺省优先级。我们相信，担心过于“主观”会阻止许多研究人员在分析中使用任何先验信息，尽管捍卫先验选择（信息性与否）会促进良好的统计实践。
3. 在此评论中，我们概述了随机样本中当前如何使用BDA，讨论了如果当前不良实践继续存在，则对推断的影响，并重点介绍了有关系统的知识改善了推断并促进了良好统计实践的子领域。通过谨慎合理地使用信息先验。
4. 我们希望激发有关贝叶斯先验在生态学中使用的重新讨论，特别注意规范和理由。我们还强调，所有先验都是主观选择的结果，应该以这种方式进行讨论。