Global land cover maps are used as inputs to the indicators and metrics informing major risks and opportunities for biodiversity conservation, including those recommended by the Monitoring Framework for the Kunming-Montreal Global Biodiversity Framework40 and the Taskforce on Nature-Related Financial Disclosures41. However, our results reveal a major source of bias in assuming that land cover can be used as a proxy for mining land use pressures on biodiversity. We found that more than half of global mining land use is currently classified as a natural land cover class, suggesting the biodiversity within these regions is under pressure26. Here, we explain how and why these results translate to biases in biodiversity metrics, describe the decisions they are likely to influence, and list key improvements needed to map global mining land use pressures to inform biodiversity mitigation and conservation action in mineral-rich regions. While primary field data must inform and validate any on-ground conservation actions, comprehensive field data is scarce for much of the planet42 and practical solutions are needed now to ensure the robust use of global land cover datasets, which were never intended to be used as proxies for land use pressures to biodiversity.
Anthropogenic and natural land cover within mining polygons
At least 120,000 km2 of the terrestrial land surface area is classified as used for mining by the two major mining land use products43, much of which occurs in biodiverse areas of conservation significance24,44,45. To date, global land cover maps have understandably not included an explicit cover class that would capture mining activities, given that mining is a land use, not a land cover type. Given the diversity of land cover types that constitute “mining” – e.g. mining pits, waste rock dumps, processing and supporting infrastructure, and mined land rehabilitation and closure sites46 – it is unsurprising to detect a range of anthropogenic cover classes within these polygons (Fig. 1; Fig S1). These included urban and built-up or bare land, while these broad classes do correspond to prior knowledge of land cover possibilities within mining sites, there are instances in which these classes may not accurately capture mining land use pressures to biodiversity. Such might be the case for bare land (i.e. the dominant individual class found within mining polygons for ESA), which in a mining context, can also cause large changes in topography (i.e. deep mine voids; mountaintop removal) that are structurally and functionally distinct from relatively flat bare land used as haul-truck roads47.
Similarly, natural land cover classes found within mining polygons spanned many vegetation classes depicted by land cover maps (Fig. 1; Fig S1). Some mining polygons did contain natural or ‘natural looking’ vegetation (Fig. 3) and this explanation may be true if sites include rehabilitation of mined land or patches of remnant vegetation. The biodiversity of these sites, however, likely remains under pressure due to mining operations, including dust, noise, tailings and the risks of contamination or mine waste spills26,48. Our evaluation of natural land cover within mining polygons suggested that most natural land within mining polygons was caused by an omission in global land cover classifications or an inability to distinguish mined areas and infrastructure from natural cover classes, particularly systems dominated by shrublands, grasslands and mixed and mosaic vegetation classes. This is likely due to their similar spectral characteristics to mined land and contribute to the relatively lower overall accuracy of these classes (49% user accuracy for grasslands; <40% for mixed and mosaic vegetation classes; ESA).
Implications for using biodiversity metrics for conservation action
Land cover classes that contain vegetation is likely of better ecological condition than anthropogenic classes and, as a result, classifying mining as natural systematically overestimates the biodiversity that land contains20,34. Similarly, metrics that overestimate biodiversity also underestimate opportunities to improve it, either through conservation or mitigation actions. While the global significance of these errors on total estimates of biodiversity may be relatively small, since mining occupies less than 1% of terrestrial land17, they could have pronounced effects on some biodiversity features that co-occur with mineral resources26, such as those ecosystems classified as natural but significantly degraded by mining in Brazil’s endangered Rupestrian grasslands or Madagascar’s littoral forests49,50 (Fig S5). Further, given that mining has been linked to land with disproportionately high levels of species richness, endemism, or conservation significance25,44,51, global overestimates in biodiversity condition caused by omitting mining from land cover products may be larger than indicated by the extent of natural land within mining land area alone.
Errors in biodiversity metrics yield different risks depending on how and by whom they are used. For example, a global mining company may use metrics to screen their portfolio, identify assets occurring in locations with significant biodiversity value and thus risks to it, and prioritize target setting, impact mitigation, and conservation action at these high risk sites. Our results suggest that biodiversity is likely overestimated at many of these sites, potentially leading to a waste of resources in following up with local studies where biodiversity risks are low. Some regions had larger proportions of mining land classified as natural (Fig. 2A), which may further incorrectly bias prioritization towards assets in countries with either more mining land classified as natural (e.g. Asia; Fig. 2A) or larger proportions classified as natural (e.g. Australia and Oceania; Fig. 2A). A more problematic scenario may occur when metrics are used to assess average global mining impacts to biodiversity for use by companies with minerals in their supply chains. This is done through the application of life cycle assessment tools and is necessary when the location of assets causing impacts are unknown52. Not only are there potentially impacts in the underlying data used for averaging, but the values are highly dependent on which sites they are taken from. Much more transparency is needed around both mineral supply chains and the methods used to calculate average biodiversity risks.
Opportunities to address the resultant bias in biodiversity metrics
Several options exist to overcome, or at least better understand, the bias that exists in using land cover products to infer mining land use pressures. For producers and users of existing biodiversity metrics, we recommend understanding the limitations of their land cover products when mining is of relevance to the decision context, selecting those most capable of detecting it as an anthropogenic class. For example, we found ESA more often classified mining polygons as anthropogenic (Fig. 1), on all continents except South America, where UMD and IGBP were better when using Tang and Werner polygons (Fig. 2A). Relying on land cover products with higher spatial resolution might also help. However, biases often caused by spatial mismatches – the second highest category (Fig. 3) – may be an underestimate given the decision rules used in global land cover products that only detect land cover changes at a 1 km resolution (ESA, 2020). This was particularly true in Asia, South America, and Africa, possibly related to artisanal small-scale mining being a prevalent driver of mining and such polygons were smaller (global geometric mean: 0.12 km230).
Steps can also be taken to compute new global datasets that combine mining land use with global land cover products, and to recalculate biodiversity metrics where relevant. Land cover products could make use of mining polygons as training data to integrate a mining class into global classification schemes. This could help address many mining areas not currently included by manual mapping efforts made to date43, including many non-metal commodities, such as sand and construction materials25. Opportunities also exist to model land cover footprints using information on historic mine land use and production data46. However, an easy first step would involve combining existing land cover and mining products, carefully choosing the mining polygons best suited to the decision context. For example, Tang & Werner polygons would more accurately capture direct mining land use pressures on biodiversity, which may be valuable in calibrating pressure-state for computing biodiversity metrics. Whereas Maus et al. polygons provide a more conservative picture of where pressures may exist and thus be more suitable for screening, as was originally proposed as per the Science Based Targets Network method for mapping Natural Lands23.
Another way to improve derived biodiversity metrics is to utilize cumulative impact mapping methodologies, which combine satellite derived land cover data with curated ‘bottom-up’ products of anthropogenic influence, including human population density, built infrastructure and roads53. These products have been created to overcome issues that land cover classifications ignore many forms of industrial influences that are hard to derive from satellite images54. Beyond mapping the amount of human industrial influence on the planet55, these methodologies are increasingly used to highlight biodiversity risk56,57 but are rarely used in global biodiversity metrics (but see58). A future research priority would be test the utility of these cumulative impact mapping products in contemporary biodiversity metrics and to see if they are better at capturing mining (when compared to land cover products). A second research priority is to establish ways to ensure mining data is imbedded within cumulative impact assessments (a current limitation to many global industrial influence maps, e.g.59), including ways to score the varying pressures mining have on landscapes60.
Future research and data needs
Our research highlights the need of more targeted ecological field studies, particularly in regions with large mining sectors and significant biodiversity value but where there is desperate shortage of ecological data. Doing this upfront should be seen as a strategic investment by governments and industry in areas with significant mineral resource potential. Ensuring local data collection and information disclosures to a global repository would build knowledge and capacity to address mining pressures to biodiversity. This could include much needed improvements of mining into existing platforms, such as the IUCN’s Red List of Threatened Species61. However, this knowledge must also capture other mining pressures, that are not captured by land cover products or may not fall within the responsibility of mining companies. This includes mining as an indirect driver of land use pressures on biodiversity, for example due to regional infrastructure requirements60,62 and non-land based pressures on biodiversity, such as water withdrawals and pollution18. This will require land cover maps to be integrated with other geographical information pre- and post- screening for biodiversity risks and conservation opportunities.
While this research is being generated, we believe companies and other decision making bodies (including national governments and the finance sector) assessing impacts of mining on biodiversity, or opportunities to improve it, within direct operations or supply chains, should be aware that the state of nature – for biodiversity, as indicated in this study, but potentially also for other environmental factors modelled using land cover data, such as carbon storage and water quality – provided by global metrics is likely overestimated, and additional effort is required for validation.
