Ordinal ‘discrepancy’ scores indicating the degree of disagreement on determinations between U.S. federal agencies and the National Marine Fisheries Service (NMFS) during section 7 consultations between 2000 and 2017.

Novel data show expert wildlife agencies are important to endangered species protection

Ordinal ‘discrepancy’ scores indicating the degree of disagreement on determinations between U.S. federal agencies and the National Marine Fisheries Service (NMFS) during section 7 consultations between 2000 and 2017.

Novel data show expert wildlife agencies are important to endangered species protection

Abstract

To protect biodiversity, conservation laws should be evaluated and improved using data. We provide a comprehensive assessment of how a key provision of the U.S. Endangered Species Act (ESA) is implemented: consultation to ensure federal actions do not jeopardize the existence of listed species. Data from all 24,893 consultations recorded by the National Marine Fisheries Service (NMFS) from 2000 - 2017 show federal agencies and NMFS frequently agreed (79%) on how federal actions would affect listed species. In cases of disagreement agencies most often (71%) underestimated effects relative to the conclusions of species experts at NMFS. Such instances can have deleterious consequences for imperiled species. In 22 consultations covering 14 species, agencies concluded that an action would not harm species while NMFS determined the action would jeopardize species’ existence. These results affirm the importance of NMFS role in preventing federal actions from jeopardizing listed species. Excluding expert agencies from consultation compromises biodiversity conservation, but we identify approaches that improve consultation efficiency without sacrificing species protections. Find the preprint article here

Publication
Nature Communications
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Michael Evans
Senior Conservation Data Scientist

As a Senior Conservation Data Scientist in the Center for Conservation Innovation at Defenders, Mike leads geoinformatics and data science projects to inform and improve conservation.