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The biogeography of ignorance: gaps in the knowledge of Amazonian amphibian biodiversity

Marcos Penhacek,R. Castro‐Souza,Geiziane Tessarolo,J. A. Diniz-Filho,T. Sobral‐Souza,Sergio Santorelli Junior,D. J. Rodrigues

Published 2025 in Biological Journal of the Linnean Society

ABSTRACT

A large volume of biodiversity information is currently available in scientific repositories. However, its quality needs to be quantified, especially in megadiverse regions, such as the Amazon. In this study, we analyse the quality of Amazonian amphibian-sampling data, based on a robust database with >242 000 occurrence records for 951 species. The data quality was based on three metrics: sampling completeness, taxonomic completeness, and temporal completeness. Subsequently, we built a knowledge ignorance map overlapping all the metrics. We also investigated potential drivers of ignorance represented by river density, protected-area density, species richness, human footprint index, and travel time to the nearest human settlement. Our findings indicated that only 14% of the Amazon is well sampled, with large sampling gaps and low taxonomic accuracy, especially in southern and southeastern Amazonia. Sampling efforts were sporadic and recent, limiting the understanding of the temporal dynamics of Amazonian amphibian biodiversity. Our results also highlighted areas of high ignorance in many spatially disjunct Amazonian regions. Amazonian sites with low river density and localities with low human footprint have few records. Sites with shorter travel times are better known, and therefore have a lower ignorance index. The density of protected areas and species richness are related to anuran ignorance only in small areas in the Amazon. Our findings highlight that the data quality of Amazonian amphibians has gaps, mainly influenced by accessibility and anthropization. These shortcomings require coordinated efforts in research, curation, and data sharing. Thus, this study sheds light on a widespread issue of biodiversity ignorance in the Amazon, serving as a warning about the quality of collected data and potential limitations to its use.

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