Canadian Journal of Fisheries and Aquatic Sciences, Volume 78, Issue 2, Page 165-172, February 2021.
Various abiotic and biotic factors affect fish and their habitats at macroscales. For example, changes in global temperatures will likely alter demographic rates, including growth. However, to date, there is no statistical framework for assessing the ability to detect macroscale effects on fish growth under different sampling scenarios. We provide a generalized framework for calculating the frequentist and Bayesian power of detecting macroscale effects on fish growth. We illustrate this framework for a range of sampling scenarios that varied in the number of fish sampled per lake, the number of lakes sampled, and the magnitude of the temperature effect on growth for two case study species. However, the framework can be adapted to investigate other species, sampling scenarios, and environmental drivers. The ability to detect macroscale effects was more affected by the number of lakes sampled rather than the number of fish sampled from each lake. Confidently detecting macroscale effects likely requires sampling hundreds of lakes. This was true for both case study species, despite different life histories and extents of spatial variability in growth.

中文翻译：

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评估检测宏观尺度对鱼类生长影响的能力的框架

《加拿大渔业和水生科学杂志》，第78卷，第2期，第165-172页，2021年2月。
各种非生物和生物因素在宏观上影响鱼类及其栖息地。例如，全球温度的变化可能会改变人口增长率，包括增长。但是，迄今为止，尚没有用于评估检测不同采样方案下对鱼类生长的宏观影响的能力的统计框架。我们提供了一个通用框架，用于计算检测鱼类的宏观影响的频繁性和贝叶斯能力。我们为一系列抽样方案说明了该框架，该方案在两个案例研究物种中，每个湖泊的鱼类数量，湖泊的数量以及温度对生长的影响的大小各不相同。但是，该框架可以适用于调查其他物种，采样方案和环境驱动因素。检测宏观尺度影响的能力受采样湖泊数量的影响更大，而不是受每个湖泊采样鱼的数量的影响。自信地检测到宏观尺度的影响可能需要对数百个湖泊进行采样。尽管生活史和生长空间变异程度不同，但对两个案例研究物种都是如此。