Optimizing Large-Scale Biodiversity Sampling Effort: Toward an Unbalanced Survey Design

Acquiring marine biodiversity data is difficult, costly, and time-consuming, making it challenging to understand the distribution abundance of life in ocean. Historically, approaches sampling over large geographic scales have advocated for equivalent effort across multiple sites minimize comparative bias. When cannot be equalized, techniques such as rarefaction been applied biases by reverting diversity estimates numbers samples or individuals. This often results oversampling wasted resources inaccurately characterized communities due undersampling. How, then, can we better determine an optimal survey design characterizing species richness community composition a range conditions capacities without compromising taxonomic resolution statistical power? Researchers Marine Biodiversity Observation Network Pole Americas (MBON Pole) are surveying rocky shore macroinvertebrates algal spanning ~107° latitude 10 biogeographic ecoregions address this question. Here, apply existing form fixed-coverage subsampling complementary multivariate analysis necessary network sites. We show that varied between ~20% 400% at half studied areas, while some locations were undersampled up 50%. Multivariate error also revealed most localities oversampled several-fold benthic composition. From analysis, advocate unbalanced approach support field programs collection high-quality data, where preliminary information used set minimum required generate robust values on site-to-site basis. As part recommendation, provide tools open-source R software aid researchers implementing optimization strategies expanding footprint frequency regional programs.