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Global land cover maps are key inputs into the biodiversity metrics used by the private sector to align their performance with conservation goals and targets. These maps utilize classification systems depicting combinations of ‘natural’ (vegetation, water bodies) and ‘anthropogenic’ (agriculture and built-up land) cover types, but often miss intensive pressures on biodiversity, such as mining. Here, we reveal that more than half (56–77%) the global land area disturbed by mining is classified by land cover maps as ‘natural’, suggesting metrics based on these maps likely overestimate the current state of biodiversity and underestimate opportunities to improve it. The proportion of mining land classified as natural varies by continent (e.g. 46% in Europe; 69% in Australia), further biasing initial screening efforts to identify where to mitigate negative impacts of mining. Improving the spatial and temporal resolution of land cover maps and better integrating cumulative impact mapping into biodiversity metrics, rather than relying on land cover maps which are not designed to capture land use pressures, is necessary. Current biodiversity metrics that utilise global land cover maps must be supplemented and validated with local data on ecosystem extent and condition, as well as species abundance and extinction risk, through targeted field studies, particularly in regions with large mining sectors and significant biodiversity value.
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1 The Biodiversity Consultancy, Cambridge, UK; Centre for Biodiversity and Conservation Science, University of Queensland, Brisbane, Australia (ROR: https://ror.org/00rqy9422) (GRID: grid.1003.2) (ISNI: 0000 0000 9320 7537); School of the Environment, University of Queensland, Brisbane, Australia (ROR: https://ror.org/00rqy9422) (GRID: grid.1003.2) (ISNI: 0000 0000 9320 7537)
2 Centre for Biodiversity and Conservation Science, University of Queensland, Brisbane, Australia (ROR: https://ror.org/00rqy9422) (GRID: grid.1003.2) (ISNI: 0000 0000 9320 7537); School of the Environment, University of Queensland, Brisbane, Australia (ROR: https://ror.org/00rqy9422) (GRID: grid.1003.2) (ISNI: 0000 0000 9320 7537)
3 University of Cambridge, Cambridge, UK (ROR: https://ror.org/013meh722) (GRID: grid.5335.0) (ISNI: 0000 0001 2188 5934)
4 The Biodiversity Consultancy, Cambridge, UK
5 Institute for Ecological Economics, Vienna University of Economics and Business, Vienna, Austria (ROR: https://ror.org/03yn8s215) (GRID: grid.15788.33) (ISNI: 0000 0001 1177 4763); Advancing Systems Analysis, Novel Data Ecosystems for Sustainability Group, International Institute for Applied Systems Analysis, Laxenburg, Austria (ROR: https://ror.org/02wfhk785) (GRID: grid.75276.31) (ISNI: 0000 0001 1955 9478)