Environmental DNA (eDNA) metabarcoding is transforming biodiversity monitoring in aquatic environments where the method has repeatedly shown comparable or better performance than conventional approaches to fish monitoring. This method has been developed and deployed, primarily using shoreline sampling during the winter months, across 101 lakes in Great Britain alone, covering a wide spectrum of lake types and ecological quality. Previous analyses on a subset of these lakes indicated that 20 water samples per lake are sufficient to reliably estimate fish species richness, but it is unclear how reduced eDNA sampling effort affects richness, or other biodiversity estimates and metrics. As the number of samples strongly influences the cost of monitoring programmes, it is essential that sampling effort is optimised for a specific monitoring objective. The aim of this project was to explore the effect of reduced eDNA sampling effort on biodiversity metrics (namely species richness and community composition) using algorithmic and statistical resampling techniques. The results showed that reliable estimation of lake fish species richness could in fact usually be achieved with a much lower number of samples. For example, in almost 90% of lakes, 95% of complete fish richness could be detected with only 10 water samples, regardless of lake area. Similarly other measures of alpha and beta-diversity were not greatly affected by a reduction in sample size from 20 to 10 samples. We also found that there is no significant difference in detected species richness between shoreline and offshore sampling transects, allowing for simplified field logistics. This could potentially allow the effective sampling of a larger number of lakes within a given monitoring budget. However, rare species were more often missed with fewer samples, with potential implications for monitoring of invasive or endangered species. These results should inform the design of eDNA sampling strategies, so that these can be optimised to achieve specific monitoring goals.