Comparing screening outcomes for private sector nature-related assessments based on national and global biodiversity datasets.

Authors

Takuya Nomura, Luke Kelly, Andrew Skowno, Emily Nicholson

Published in

Conservation biology : the journal of the Society for Conservation Biology. Pages e70368. Aug 25, 2026. Epub Aug 25, 2026.

Abstract

In response to demand for better biodiversity stewardship from the private sector, frameworks such as the Taskforce on Nature-related Financial Disclosures (TNFD) have been developed to help companies assess, manage, and disclose their nature-related impacts, dependencies, risks, and opportunities. A key initial screening step is to identify operations in ecologically sensitive areas. This step is often conducted using global datasets that are readily available and consistent but may be less accurate than national data informed by local expertise and conditions. In this study, we explored the implications of using global versus national biodiversity datasets for identifying ecologically sensitive sites at large scales, using South Africa as a case study. We simulated a screening process by generating sites through stratified random sampling and screened these based on three TNFD criteria for ecologically sensitive sites: biodiversity importance, high ecosystem integrity, and rapid decline in ecosystem integrity. We compared results from multiple global and national datasets, calculating omission errors, where global datasets omit sites identified by national data, and commission errors, where global datasets flag sites as sensitive but national data do not. We found global datasets missed over 39% of areas important for biodiversity, as identified by national datasets. Such omission errors risk ongoing biodiversity loss because sites omitted at early screening stages are unlikely to have resources allocated for conservation or restoration. Our findings suggest a cautious use of global datasets, with an emphasis on encouraging the use of national datasets, particularly when fine-scale, field-validated, and frequently updated data are available.

PMID:
42638583
Bibliographic data and abstract were imported from PubMed on 25 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.