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not-yet-known not-yet-known not-yet-known unknown Biases in Amphibian Sampling in the Amazon: Using Infrastructure and Accessibility Data to Identify Sampling Gaps
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  • Marcos Penhacek,
  • Rodrigo Castro-Souza1,
  • Geiziane Tessarolo,
  • Jose Alexandre Felizola Diniz-Filho,
  • Thadeu Sobral,
  • Domingos Rodrigues
Marcos Penhacek
Universidade Federal de Mato Grosso

Corresponding Author:[email protected]

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Rodrigo Castro-Souza1
Universidade Federal de Mato Grosso
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Geiziane Tessarolo
Universidade Federal de Goiás – UFG
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Jose Alexandre Felizola Diniz-Filho
UFG
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Thadeu Sobral
Universidade Federal de Mato Grosso
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Domingos Rodrigues
Universidade Federal de Mato Grosso
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Abstract

Biogeographic knowledge of Amazonian amphibians presents significant challenges in spatial and temporal coverage, as well as in the taxonomic refinement of their diversity. Despite recent advances, the spatial distribution of sampling and detailed taxonomic knowledge remain limited, potentially causing biases in our understanding of their diversity and distribution. In this study, we conducted a large-scale analysis using an extensive database with 951 species and 213,072 georeferenced occurrence records, distributed across 24,319 sampling points in the Amazon. This analysis aimed to elucidate potential drivers of sampling biases for Amazonian amphibians in the presence of infrastructure factors (cities, hydroelectric dams, and transmission lines) and accessibility (navigable rivers and roads). Among accessibility factors, we found that rivers were the main facilitators in amphibian sampling. On the other hand, roads did not exert a strong influence as expected, due to the late and limited development of land transportation in the region, which has historically been dominated by river transportation. Among the infrastructure factors, both cities and hydroelectric plants had a moderate influence on sampling. The reason for this is that most cities in the Amazon region were established a few decades ago and have limited infrastructure, especially considering the presence of consolidated research centers. Hydroelectric plants have generated extensive databases due to environmental legislation requirements for their installation, but restricted access to information from these reports limited their use in this study. We conclude that Amazonian amphibian sampling exhibits significant geographic bias, attributable to the uneven distribution of research efforts caused by logistical challenges, including accessibility and infrastructure limitations. Overcoming these obstacles requires coordinated efforts between researchers and decision-makers, as well as investment in research infrastructure and data dissemination initiatives, not only for amphibians, but for all biodiversity in the face of increasing deforestation and climate change.