The power of baseline data from fixed sampling sites: a simulation-based power analysis of marine harvest refugia

Marine harvest refugia help conserve biodiversity and ecosystems; however, evaluating their effectiveness with adequate statistical power is challenging. Quantifying adequately powered sampling needs can improve data collection methods and study design, precision, and minimize costs. Conventional power equations are not designed for complex ecosystem models and, together with sparse pre-intervention data, have resulted in limited evidence of refugia effectiveness. We conditioned a spatially explicit Gaussian Markov random field model to fish density data from remotely operated vehicles, and compared two sampling schemes using a simulation-based power analysis. We quantified power to detect abundance recovery by comparing Early-Later-Control-Impact and Control-Impact designs with different sampling strategies and effort levels. Results indicate that (1) early samples were highly valuable, especially given initial fish abundance differed substantially from expectations; (2) small sample sizes inflated effect estimates, suggesting that type M (magnitude) errors should be considered; and (3) repeatedly sampling fixed locations—rather than selecting new random sites within or across an expanded sampling domain—improved power up to 35%, reducing the necessary sample size. These findings provide guidance for designing cost-effective monitoring programs that can reliably detect recovery in marine harvest refugia.