Web‐based application to guide sampling on salmon farms for more precise estimation of sea louse abundance.

Objective: Efficiently managing sea lice on salmon farms through active surveillance, crucial for lice abundance estimation, is challenging due to the need for effective sampling schemes. To address this, we developed an application that considers infestation levels, farm structure, and management protocols, enhancing the precision of sampling strategies for sea louse abundance estimation. Methods: Simulation‐based methods are valuable for estimating suitable sample sizes in complex studies where standard formulae are inadequate. We introduce FishSampling, an open Web‐based application tailored to determine precise sample sizes for specific scenarios and objectives. Result: The model incorporates factors such as sea lice abundance, farm pen numbers, potential clustering effects among these pens, and the desired confidence level. Simulation outcomes within this application provide practical advice on how to decide on the number of fish and pens to sample, under varying levels of assumed clustering. Conclusion: This approach can be used across the salmon aquaculture sector to improve sampling strategies for sea lice abundance estimation and balance surveillance costs against health objectives. Impact statementThe open‐source application FishSampling enhances sea lice monitoring on salmon farms with a novel simulation‐based approach for sample size determination. It offers precise estimates of sea lice abundance, crucial for regulatory purposes, and aids in the efficient allocation of sampling resources. [ABSTRACT FROM AUTHOR]