A framework for assessing the ability to detect macroscale effects on fish growth

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.