Optimizing large-scale biodiversity sampling effort: towards an unbalanced survey design

E. MONTES; MOITY N., LONDOÑO-CRUZ E., MULLER-KARGER F.E; BIGATTI G; M. CORDEIRO, N. SIMOES, E. C. MACAYA, N. MOITY, E. LONDOÑO-CRUZ, B. HELMUTH, F. CHOI, E. H. SOTO, P. MILOSLAVICH, F. E. MULLER-KARGER.

OCEANOGRAPHY

OCEANOGRAPHY SOC

Año: 2021

1042-8275

Acquiring marine biodiversity data is difficult, costly, and timeconsuming,making it challenging to understand the distribution and abundance of lifein the ocean. Historically, approaches to biodiversity sampling over large geographicscales have advocated for equivalent effort across multiple sites to minimize comparativebias. When effort cannot be equalized, techniques such as rarefaction have beenapplied to minimize biases by reverting diversity estimates to equivalent numbers ofsamples or individuals. This often results in oversampling and wasted resources orinaccurately characterized communities due to undersampling. How, then, can we betterdetermine an optimal survey design for characterizing species richness and communitycomposition across a range of conditions and capacities without compromisingtaxonomic resolution and statistical power? Researchers in the Marine BiodiversityObservation Network Pole to Pole of the Americas (MBON Pole to Pole) are surveyingrocky shore macroinvertebrates and algal communities spanning ~107° of latitudeand 10 biogeographic ecoregions to address this question. Here, we apply existing techniquesin the form of fixed-coverage subsampling and a complementary multivariateanalysis to determine the optimal effort necessary for characterizing species richnessand community composition across the network sampling sites. We show that oversamplingfor species richness varied between ~20% and 400% at over half of studiedareas, while some locations were undersampled by up to 50%. Multivariate error analysisalso revealed that most of the localities were oversampled by several-fold for benthiccommunity composition. From this analysis, we advocate for an unbalanced samplingapproach to support field programs in the collection of high-quality data, where preliminaryinformation is used to set the minimum required effort to generate robust valuesof diversity and composition on a site-to-site basis. As part of this recommendation,we provide statistical tools in the open-source R statistical software to aid researchers inimplementing optimization strategies and expanding the geographic footprint or samplingfrequency of regional biodiversity survey programs.