Causes and effects of sampling bias on marine Western Atlantic biodiversity knowledge

Abstract

AbstractAimKnowledge gaps and sampling bias can lead to underestimations of species richness and distortions in the known distribution of species. The goal of this study is to identify potential gaps and biases in marine organisms sampling at the Western Atlantic Ocean, determine their causes and assess its effect on biodiversity metrics. We tested the potential interference of this bias with the representation of environmental conditions, potentially affecting biodiversity model predictions.LocationWestern Atlantic Ocean.MethodsThis study compiled data of marine species in online and institutional databases. The analysis of sampling effort and bias was conducted by mapping the density of records. A spatial autoregressive model (SAR) was employed to investigate the influence of accessibility as a determinant factor of the sampling bias. We tested whether the effect of the sampling bias could result from environmental bias in the samples, contrasting the environmental variables of the study area with those present in the biodiversity records. We examined the correlation between sampling effort in species richness and endemism.ResultsThe USA has the highest number of records and density of records. There was a low correlation between the vertebrates, invertebrates and algae sample density patterns. Accessibility was identified as one of the main causes of sampling bias. The analysis of environmental bias indicated that the records do not represent all conditions present in the environment. Sampling density showed a strong relationship with endemism and a weaker relationship with species richness.Main ConclusionsWe have identified a strong sampling bias related to ease of access that equally affects vertebrates, invertebrates and algae, resulting in a skewed sampling of the environmental conditions where species occur. Sampling patterns differ among the groups. The intensity of sampling effort significantly impacts measures of richness and endemism, potentially undermining the accurate recognition of real biological diversity patterns.