Conservation implications and opportunities of mining activities for terrestrial mammal habitat

Abstract

Mining companies increasingly commit to a net positive impact on biodiversity. However, assessing the industry's progress toward achieving this goal is limited by knowledge of current mining threats to biodiversity and the relevant opportunities available for them to improve conservation outcomes. Here, we investigate the global exposure of terrestrial mammal habitat to mining activities, revealing the 136 species with >30% of their habitat within 10 km of a mining property or exploration site. One third (n = 42) of these species are already threatened with extinction according to the International Union for Conservation of Nature (IUCN), suggesting projected increased demand for minerals may push some species beyond critical thresholds. Moreover, 28% (n = 33) of species are Data Deficient, illustrating tangible ways for industry to fill current knowledge gaps. However, large discrepancies between our results and the species currently listed as threatened by mining in the IUCN Red List, suggest other species may be at risk and that conservation tools and analyses based on these data may underestimate the benefits of averting such threats. We recommend ways to better capture mining threats to species within IUCN Red List assessments and discuss how these changes could improve conservation outcomes in mineral‐rich areas. Mining companies can help provide the knowledge and resources needed to conserve threatened species in mineral‐rich regions.

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CONTRIBUTED PAPER
Conservation implications and opportunities of mining
activities for terrestrial mammal habitat
Laura J. Sonter
1,2
| Thomas J. Lloyd
1,2
| Stephen G. Kearney
1,2
|
Moreno Di Marco
3
| Christopher J. O'Bryan
1,2
| Richard K. Valenta
4
|
James E. M. Watson
1,2
1
School of Earth and Environmental
Sciences, The University of Queensland,
St Lucia, Australia
2
Centre for Biodiversity & Conservation
Science, The University of Queensland, St
Lucia, Australia
3
Department of Biology and
Biotechnologies, Sapienza Università di
Roma, Rome, Italy
4
Sustainable Minerals Institute, The
University of Queensland, St Lucia,
Australia
Correspondence
Laura J. Sonter, Level 2, Building
03, University of Queensland, Brisbane
4072, Australia.
Email: l.sonter@uq.edu.au
Funding information
MUR Rita Levi Montalcini; Sustainable
Minerals Institute's Complex Orebodies
Program; Australian Research Council
Discovery Early Career Researcher Award,
Grant/Award Number: DE170100684
Abstract
Mining companies increasingly commit to a net positive impact on
biodiversity. However, assessing the industry's progress toward achieving this
goal is limited by knowledge of current mining threats to biodiversity and the
relevant opportunities available for them to improve conservation outcomes.
Here, we investigate the global exposure of terrestrial mammal habitat to min-
ing activities, revealing the 136 species with >30% of their habitat within
10 km of a mining property or exploration site. One third (n=42) of these spe-
cies are already threatened with extinction according to the International
Union for Conservation of Nature (IUCN), suggesting projected increased
demand for minerals may push some species beyond critical thresholds. More-
over, 28% (n=33) of species are Data Deficient, illustrating tangible ways for
industry to fill current knowledge gaps. However, large discrepancies between
our results and the species currently listed as threatened by mining in the
IUCN Red List, suggest other species may be at risk and that conservation
tools and analyses based on these data may underestimate the benefits of
averting such threats. We recommend ways to better capture mining threats to
species within IUCN Red List assessments and discuss how these changes
could improve conservation outcomes in mineral-rich areas.
1|INTRODUCTION
Land use change drives habitat loss and degradation, which
has led to global declines in biodiversity (Díaz et al., 2019;
Newbold et al., 2015). Mining activities occupy more than
57 thousand square kilometers of Earth's land surface
(Maus et al., 2020;Werneretal.,2020) and affect land use
and ecosystems far beyond this immediate footprint
(Bebbington et al., 2018;Sonteretal.,2017). Habitat loss
caused by a single mine can negatively affect multiple
species—sometimes threatening extinction to habitat spe-
cialists and those with narrow ranges (Sigwart et al., 2019).
Cumulative impacts of multiple mines and their required
infrastructure can affect habitat for other species and pose
significant threats even to those that were once wide rang-
ing (Johnson et al., 2020). Species with habitat at risk of
mining may be ecologically linked to their underlying geol-
ogy (Erskine et al., 2012; Jaffé et al., 2016), or simply occur
Received: 2 December 2021 Revised: 22 February 2022 Accepted: 16 April 2022
DOI: 10.1111/csp2.12806
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2022 The Authors. Conservation Science and Practice published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.
Conservation Science and Practice. 2022;4:e12806. wileyonlinelibrary.com/journal/csp2 1of11
https://doi.org/10.1111/csp2.12806

in mineral-rich landscapes by chance, but suffer conse-
quences of land clearing to extract, process, and transport
materials (Edwards et al., 2014).
Managing land use impacts on biodiversity is central
to achieving the United Nations Sustainable Develop-
ment Goals (United Nations, 2015) and the Convention
on Biological Diversity's post-2020 mission to prevent
species extinctions and reverse population declines
(UNEP, 2021). Mitigating negative impacts of mining is
also now a policy requirement in many countries (Bull &
Strange, 2018) and achieving a net positive impact on
biodiversity is an increasingly common commitment
made by mining companies seeking project finance
(IFC, 2012) and membership to leading industry bodies
(ICMM, 2020). Designing effective conservation, restora-
tion, and impact mitigation plans requires a comprehen-
sive understanding of mining threats to biodiversity
(Sonter et al., 2018). Past work has examined fossil fuel
extraction risks to biodiverse sites (Harfoot et al., 2018)
and spatial coincidence between mines and biodiversity
conservation priorities (Edwards et al., 2014; Sonter
et al., 2020). Yet, none has examined potential conse-
quences of global mining activities on specific species
habitat—a major gap to understanding mining risks and
opportunities for industry investment in conservation.
The International Union for Conservation of Nature's
(IUCN) Red List of Threatened Species is the globally
accepted standard in characterizing the status of, trends
in, and threats to, species (IUCN, 2020,2021). The Red
List directly informs conservation actions (Bennun
et al., 2018; Mair et al., 2021; Rodrigues et al., 2006) and
has influenced mining industry decisions in many
nations (Bennun et al., 2018). The Red List has catego-
rized extinction risk for more than 142,500 species as of
February 2022 (IUCN, 2021). Of these, 10,511 species are
listed as directly threatened by mining and quarrying
activities (IUCN, 2012), including 457 species of terres-
trial mammals (7% out of 5968 listed extant mammal spe-
cies; IUCN, 2021). Mammals are one of the best studied
taxonomic groups and our focus here, given the direct
threats of mining to them via habitat loss and fragmenta-
tion and indirect consequences via increased bushmeat
hunting and wildlife trade (Edwards et al., 2014).
Given that mining often occurs in remote, data-poor
environments and current assessments may not explicitly
capture indirect or future mining threats, many more
species than those included in Red List assessments may
be at risk. Thus, the objective of this study was to use
recent advances in spatial assessments of the global min-
ing sector to examine coincidence with habitat for terres-
trial mammals. Specifically, we asked: (1) which
countries have mining areas overlapping habitats sup-
porting high mammal species richness, (2) which species
have large proportions of habitat within mining areas,
and (3) how are these species at risk categorized in IUCN
Red List assessments? We explore the implications of our
findings for conservation practice—both within and
beyond the mining sector—and recommend ways to
improve industry engagement in achieving an increas-
ingly ambitious post-2020 biodiversity conservation
agenda.
2|METHODS
Our analysis draws on two spatially explicit global data-
sets. First, we used maps of terrestrial areas potentially
influenced by mining activities (Sonter et al., 2020),
which were created by mapping 10 km buffers at 1 km
2
resolution around 62,381 known mining properties and
mineral exploration sites (SNL, 2018). A 10 km distance
was used as a conservative estimate of the direct and
indirect effects of mining land use. While the direct
(on site) effects vary among mines, a 10 km radius could
occupy a substantial proportion of some large mine sites.
For example, Werner et al. (2020) mapped 295 metal mines
globally and found average mine area to be 12.7 km
2
(2.0 km radius, assuming a circular mine footprint) but
large variation among commodities and countries—ranging
from 0.047 km
2
(0.12 km radius) for a gold mine in the
United States to 213 km
2
(8.2 km radius) for a copper
mine in Indonesia. Indirect effects of mining on habitat are
caused by offsite infrastructure development or induced
land use changes that follow establishment of a mine. These
indirect land use changes occur in many mining regions
globally (Giljum et al. 2022) and have been shown to occur
up to 70 km from large scale metal mines in the Brazilian
Amazon (Sonter et al., 2017).
Our analysis used several subsets of the mining maps
produced by Sonter et al. (2020). We examined mining
areas when only including operational mines and separately
for areas that also captured mineral exploration sites and
closed and abandoned mines. All three mining activities
(exploration, operational and closed/abandoned) potentially
threaten species habitat—even exploration activities can
have direct and indirect land use effects on biodiversity
(Edwards et al., 2014)—but the extent of these impacts will
likely differ among them. We also examined the subset of
mining areas that contained properties that targeted
(as their listed primary commodity) one of the 31 minerals
deemed critical for, among other uses, establishing renew-
able energy infrastructure and energy storage batteries that
are necessary to fuel a green energy transition (World
Bank, 2020). Comparing the threats to mammal habitat
between mines that target energy transition minerals versus
mines that produce other resources, such as fossil fuels,
2of11 SONTER ET AL.

illustrates the potential new threats of a green energy transi-
tion and thus opportunities to address trade-offs between
climate change mitigation and biodiversity conservation.
We compared mining maps to a second spatially
explicit dataset of habitat suitability for 5297 terrestrial
mammal species (Rondinini et al., 2011). A species' Area
of Habitat (AOH) is bounded by its IUCN-identified
potential range and modeled using three environmental
variables (land cover, hydrological features, and eleva-
tion) along with information on species' habitat prefer-
ences (Brooks et al., 2019). These maps have been used
already to assess broader human pressure influences on
mammals (Crooks et al., 2017; Di Marco et al., 2018), and
here we use them to examine the specific threats from
mining. While mining activities could affect all three
environmental variables used to develop AOH models,
none of the variables explicitly considered mining; thus,
we expected to find overlap between mining and AOH in
our analysis (Rondinini et al., 2011). Specifically, we uti-
lized the portion of AOH considered highly suitable
(i.e., primary habitat required for a species' persistence),
as opposed to habitat of medium or low suitability
(Rondinini et al., 2011). To determine the countries with
large proportions of mining areas containing habitat sup-
porting high mammal diversity (Question 1), we inter-
sected mining areas for each country with a species
richness map, created by stacking AOH maps for all 5297
species at 1 km resolution (Rondinini et al., 2011). To
identify mammal species with large proportions of habi-
tat within mining areas (Question 2), we intersected
mining maps with highly suitable habitat for each spe-
cies. We also then identified species with >30% of their
habitat extent within mining areas and, for species listed
on the IUCN Red List, we investigated their threat status
and key threatening process(es) (Question 3).
3|RESULTS
Almost all mining areas (6.67 million km
2
, 99.5% of the
total extent), across 161 countries intersected with habitat
for at least one mammal species (Figure 1; Table S1). We
found the country of Guyana contained mining areas
overlapping with habitat suitable for 196 mammal species
per 1 km
2
—the highest richness value in our data
(Figure 1). Some countries had both large mining areas
and high mean richness within mining areas, such as
Brazil, which ranked 7th for mining area and 11th for
mean richness. Other countries had relatively small min-
ing areas but that ranked high in maximum richness
values (e.g., Suriname ranked 2nd, French Guiana
ranked 5th; Table S1).
We found 4432 species (83% of all species assessed)
had some proportion of their habitat within mining
areas (Table S2). On average, 6.9% of species' habitat
(n=5297) occurred within mining areas, with percent
overlap values reaching up to 100% for two Data Defi-
cient species in Papua New Guinea (Figure 2;Table1).
We also found 3766 species (71% of all species assessed)
had habitat within operating mining areas, an average
FIGURE 1 Mammal habitat richness (number of species with suitable habitat per 1 km
2
) within mining areas (i.e., sites within 10 km
of a pre-operational, operational, or closed mining property). Richness values are shown on histograms, which illustrate the distribution of
values (a) within mining areas and (b) for all terrestrial land area (within and outside mining areas).
SONTER ET AL.3of11

of 2.1% across all species and a maximum of 87% for a
critically endangered rodent in Mexico (Figure S1;
Table 1).
We found 136 species (2.5% of all species assessed) had
>30% of their habitat within mining areas (Figure 3),
including 17 species with >30% habitat within operating
mining areas (Figure S2; Table S2). These species had an
average of 11,588 km
2
of habitat within mining areas
(median =422 km
2
,min=0.36 km
2
,max=185,413 km
2
;
for operating areas mean =159 km
2
,median=50 km
2
,
min =0.63 km
2
,max=645 km
2
). Further, most of the spe-
cies with >30% within mining areas occurred in areas tar-
geting minerals needed for renewable energy production
(Figure 2).
Of the 136 species with >30% of habitat within min-
ing areas, 131 species were listed on the IUCN Red List
(Table S2; that is, 5 species were unidentifiable as they
underwent a change in taxonomy). Of these species, 32%
(n=42) were listed as Threatened with extinction
(i.e., Critically Endangered, Endangered, or Vulnerable),
25% (n=33) were listed as Data Deficient, and 13%
(n=17) as directly threatened by mining or quarrying
(Figure 4). When only considering operational mine, the
17 species with >30% of habitat within mining areas
included 35% (n=6) listed as threatened, 53% (n=9 spe-
cies) as Data Deficient, and 35% (n=6) as directly threat-
ened by mining or quarrying.
4|DISCUSSION
Our analysis revealed considerable overlap between min-
ing areas and mammal habitat. Almost all land within
10 km of a mining property or mineral exploration site
intersected with habitat for at least one mammal species,
and 136 species had >30% of their habitat within these
mapped mining areas. These results reinforce the need to
better understand mining threats to species and act on
opportunities to strengthen conservation actions in regions
under pressure from mineral demand. The IUCN Red List of
Threatened Species is a valuable resource that is used to
inform multi-sectoral decisions affecting biodiversity
(Bennun et al., 2018;Mairetal.,2021; Rodrigues et al.,
2006). However, we found discrepancies emerged between
our results and the IUCN Red List assessments in the identi-
fication and characterization of mining threats to mammals.
Here, we discuss potential reasons for these discrepancies,
the implications they pose for biodiversity conservation
actions and goal setting (both within and beyond the mining
sector), and opportunities to fill current data gaps and move
toward a more systematic treatment of mining threats to
mammals.
4.1 |Discrepancies between data sets
and approaches used to identify mining
threats to mammals
Species listed by IUCN Red List as threatened by mining
(n=361) differed from those revealed by our analysis to
have >30% of habitat within mining areas (n=136;
FIGURE 2 Mammal habitat within mining areas across IUCN
Red List threat categories (CR =Critically Endangered,
EN =Endangered, VU =Vulnerable, DD =Data Deficient,
NT =Near Threatened, LC =Least Concerned). Black dots are
mean values (±SE) and violin plots illustrate distributions.
FIGURE 3 Percent of habitat within mining areas for each of
the 5297 mammal species analyzed here, separated into those
within mining areas targeting the materials needed to deliver
renewable energy (y axis) and mining areas targeting other
materials (x axis), such as fossil fuels. N on figure adds to
137 because 1 species had >30% habitat within both types of
mining regions.
4of11 SONTER ET AL.

Figure 4). Surprisingly, only 17 species were common to
both lists (Table S2). To understand these results and
determine whether they represent systematic issues with
data and methods, we examined Red List assessment
notes for key mammal species.
We found that 72% of tmammals listed by IUCN as
threatened by mining had <10% of their habitat within
mapped mining areas and 30 species had no overlap what-
soever. In some cases, our data may have been unable to
detect the threats identified by Red List assessments. For
example, our mining maps ignore small-scale artisanal
mining, illegal mining activities, and quarries—all known
threats to species, their habitat and biodiversity
(e.g., Clements et al., 2006;Siqueira-Gay&S

anchez, 2021).
TABLE 1 Selected mammal species with habitat (black polygons depict each species highly suitable habitat area) occurring within
mining areas (mining symbols indicate mines or mineral exploration sites)
a. Myoictis wavicus (Tate's Three-striped Dasyure), Papua New
Guinea. Data Deficient; not threatened by mining
(Woolley, 2016).
Species has 100% of highly suitable habitat (28.4 km
2
) within
mining area. Includes exploration for gold, silver and copper
and operational gold mine (Edie Creek).
b. Rattus omichlodes (Arianus's Rat), Indonesia. Data Deficient,
not threatened by mining (Gerrie & Kennerley, 2017).
Species has 70% of highly suitable habitat (2.4 km
2
) within
operating mining area. Habitat is adjacent to large operational
copper mine.
c. Batomys russatus (Russet Batomys), Philippines.
Endangered; threatened by mining (Kennerley, 2017).
Species has 62% of highly suitable habitat (478 km
2
) within
mining area. Habitat covers almost entire island and thus
nearby 14 nickel mines.
d. Amblysomus robustus (Robust Golden Mole), South Africa.
Vulnerable; threatened by mining (Rampartab, 2015).
Species has 45% of highly suitable habitat (1095 km
2
) within
mining areas, including 9 operational mine sites targeting
platinum (to north) and coal (to south).
e. Habromys schmidlyi (Schmidly's Deer Mouse), Mexico.
Critically Endangered; not threatened by mining (
´
Alvarez-
Castañeda et al., 2018).
Species has 86% of highly suitable habitat (50.5 km
2
) within
mining areas, including operational silver mines.
f. Pan paniscus (Bonobo), DRC Endangered; threatened by
mining (Fruth et al., 2016).
Species has 0.02% of highly suitable habitat (369,008 km
2
) within
mining areas. Large habitat requirement; mining listed as future
risk to minority of the population.
Note: Google Earth images often represent a combination of dates. Here, we include the most recent images as of February 2022.
SONTER ET AL.5of11

This indeed explained the lack of overlap for Hipposideros
hypophyllus, a critically endangered bat imperiled by illegal
granite mining nearby its only known roost (Chakravarty
et al., 2016). Threat assessments need to utilize multiple
datasets—although many of these do not yet exist at a
global scale (Joppa et al., 2016)—along with expert and
local knowledge to determine the actions that will most
effectively conserve habitat critical for their survival. This is
particularly important for artisanal mining, given that their
biodiversity losses often go unmitigated and do not always
occur nearby large-scale industrial mining (World
Bank, 2019).
In other cases, the extent of overlap between mining
areas and species habitat measured in our study may be a
poor indicator of mining impact. This may be true for
species where mining plays only a small role in the
cumulative threats to a species. For example, Pan panis-
cus—the Endangered Bonobo from the Democratic
Republic of the Congo—was listed as primarily threat-
ened by poaching, residue from civil warfare, habitat loss
and alteration (from logging and agriculture), and hous-
ing development and disease largely due to human
population growth and migration; mining activities were
listed to potentially add to these other threats in future
(Fruth et al., 2016; Table 1). Further, mining infrastruc-
ture (e.g., roads, waste storage and processing facilities)
that does not cause extensive direct habitat loss, may con-
tribute to landscape-scale habitat fragmentation. This
was particularly true for primate species (Table S2), such
as Aotus miconax (Shanee et al., 2020), with severely frag-
mented habitats. These results illustrate the importance
of understanding local context in identifying threats and
their severity.
We also found 39 Near Threatened and threatened
species (Vulnerable, Endangered, or Critically Endan-
gered) with >30% of their habitat within mapped mining
areas that were not listed by IUCN as threatened by min-
ing. Most (n=36) of these species had a large proportion
of habitat within 10 km of a mineral exploration site, sug-
gesting a lack of consideration of potential current or
future mining threats (Joppa et al., 2016). The other three
species had >30% habitat within 10 km of an operational
mine, including the species with the greatest overlap:
Habromys schmidlyi, a critically endangered rodent with
FIGURE 4 Mammal species with
>30% habitat within mining areas,
color-coded according IUCN Red List
categories (Threatened =Critically
Endangered, Endangered, and
Vulnerable). Asterisks (*) indicates the
17 species that also have >30% habitat
within operational mining areas. Black
boxes around columns indicate the
17 species listed by IUCN Red List as
directly threatened by mining and
quarrying. Species are ordered based on
proportional overlap (height of the
histogram bars) to show that species
with greater overlap also trend to be
threatened with extinction or data
deficient.
6of11 SONTER ET AL.

86% of its habitat within 10 km of a large silver mine in
Mexico (Table 1). H. schmidlyi was listed as directly
threatened by habitat loss due to Biological Resource
Use (logging and wood harvest) (
´
Alvarez-Castañeda
et al., 2018) and, while the motivation of this logging and
wood harvest was not listed, it is possible some of this
land would then be subsiquently utilized for mining.
Similar results were found for 11 of the other 20 species
with >10% habitat within 10 km of an operational mine
site, although the assessment notes for one species (Ato-
pogale cubana; Kennerley et al., 2018) did mention min-
ing as a key threat of habitat loss without explicitly
listing mining as a threat in the classification.
4.2 |Implications of missing threat data
for conservation planning and practice
Many conservation initiatives aiming to assess and man-
age threats to biodiversity draw on information from the
IUCN Red List of Threatened Species. Misclassifying or
failing to detect some threats could thus affect conserva-
tion actions implemented by multiple stakeholders. One
key tool used by industry is the Integrated Biodiversity
Assessment Tool (IBAT; UNEP-WCMC 2020). Mining
companies and decision makers use this tool to identify
biodiversity risks and opportunities within or close to a
project boundary. While data deficiencies on species
populations and trajectories may affect identification of
those species threatened by proposed projects, errors in
threat mapping and classification will also influence deci-
sions around conservation actions by companies. In turn,
poorly designed mitigation efforts may affect the quality
of corporate reports on biodiversity performance—a key
benefit of the IBAT tool for business operations.
The recently published Species Threat Abatement
and Restoration (STAR) metric also relies on IUCN Red
List assessments (Mair et al., 2021). STAR is used by the
international conservation community to inform and
deliver on conservation targets and goals at different
scales and, in 2021, STAR was integrated into the IBAT
to allow organizations, such as mining companies, to
measure their relative contributions toward reducing spe-
cies extinction risks. The STAR metric quantifies contri-
butions that abating threats and restoring habitats in
specific places offers toward reducing extinction risks, for
individual species and across geographies. But for STAR to
be effective, it needs to rely on accurate threat information
and the relative contribution each threat makes toward spe-
cies' risk of extinction. Given that mining threats appear to
sometimes be missed or misclassified, it is not surprising
that recent analyses show the threat of mining is the smal-
lest contributor to aggregated species extinction risk globally
(Mair et al., 2021). We argue that capturing the true scale of
abatement opportunities will require moving toward a sys-
tematic approach to classifying mining threats to species,
and several key remaining data gaps in Red List assess-
ments. This will ensure tools like the STAR metric will be
utilized to their full potential.
4.3 |A more systematic approach to
capture mining threats to species
The IUCN Red List classifies the direct threats to species
(IUCN, 2012), defined as “proximate human activities or
processes that have caused, are causing, or may cause the
destruction, degradation and/or impairment of biodiver-
sity targets”(Salafsky et al., 2008). The scope, severity
and timing of each threat is recorded and used to deter-
mine remaining knowledge gaps and appropriate conser-
vation actions designed to ensure species persistence.
However, only a small proportion of mining threats
emerge through direct processes (Sonter et al., 2018)and,as
described in the previous section, failing to comprehensively
capture the role of mining in threat assessments limits the
rolethesedataplayinconservationaction.Here,wemake
three recommendations to broaden the treatment of mining
threats in Red List assessments, to capture indirect threats
and interactions among threats. Threat assessment for some
species already implement these recommendations; how-
ever, clearer guidance and a more systematic process will
help improve conservation outcomes.
Recommendation 1:Record when current direct
threats are driven by future mining operations. Min-
eral demand and mining operations indirectly drive other
non-mining threats to species through indirect processes.
In some cases, other non-mining proximate causes of
habitat loss may take place in the lead up to a planned
mine development. Vegetation cleared prior to mining,
for example, is sometimes harvested for its resource value
and this may become a more common practice in future
(Annandale et al., 2021). However, it is currently a judg-
ment call as to whether this deforestation and habitat loss
be classified as Threat 3.2 'Mining and Quarrying' or
Threat 5.3 'Biological Resource Use' (IUCN, 2012). We
suggest that both threats be recorded, particularly if their
interactions over time will undermine a species recover-
ability post-mining. This information could either be
recorded as the motivation for Threat 5.3, or by simply
recording both threats with different timings (Threat 5.3
current/ongoing, Threat 3.2 future).
Recommendation 2:Capture interactions among
multiple current threats. The operation of mine sites
will often be linked to other direct threats to biodiversity.
Current guidance is to record the most direct threat
SONTER ET AL.7of11

(IUCN, 2012), where, for example, sediment or toxic
chemical runoff from mining should be classed as Threat
9.2. 'Industrial and Military Effluents', rather than due to
the emergence of the mine itself. Again, as above, we sug-
gest that both threats be recorded, and their interactions
noted. Reviewing the 17 mammal species with >30% hab-
itat within mapped mining areas and listed as threatened
by mining revealed that all but two species (Bathyergus
janetta; Phyllotis osgoodi) had more than one threat
listed, some of which may indeed be linked; however,
none of the assessments explicitly mentioned these
potential links. In addition to links among threats already
identified in IUCN guidance, many others may exist,
such as Threat 7.2. 'Dams & Water Management/Use'
(Northey et al., 2016).
Recommendation 3:Consider future threats
facilitated by current mines. Mining operations and
their associated infrastructure can themselves facilitate
future threats to species (Sonter et al., 2018). Some assess-
ments attempt to capture these indirect threats. For example,
Allochrocebus lhoesti is listed as threatened by mining, given
the effect it may have on opening up formerly remote areas
to exploration, leading to more habitat loss, bushmeat trade
and poaching in the future (Ukizintambara et al., 2019). The
current lack of knowledge about when, where and how min-
ing operates as a driver of future land use change may limit
operationalizing this recommendation; however, previous
research does suggest that mining can facilitate or amplify
Threat 1. 'Residential and Commercial Development' (Owen
&Kemp,2017), Threat 2. 'Agriculture and Aquaculture' (Pij-
pers, 2014), and Threat 5.1. 'Hunting' (Edwards et al., 2014).
Thus, we urge threat assessment teams to keep these threats
in mind for those species currently listed as threatened by
mining and quarrying.
4.4 |Opportunities to overcome
remaining data deficiencies
Thirty-three mammal species with habitat exposed to
mining activities were listed as Data Deficient (Figures 2
and 4) and some may be at imminent risk of extinction
(Bland et al., 2017). Filling these gaps is important for
conservation action; without new information, these spe-
cies may be ignored in environmental impact assess-
ments and thus unknowingly vanish if mining pushes
them beyond critical thresholds. This is particularly
important for mining threats, given that this threat has
the least number of datasets out of any IUCN threat cate-
gory that are available at an appropriate spatial resolu-
tion (Joppa et al., 2016). Generating new spatial data on
mining will help fill these gaps; however, other opportu-
nities to engage industry and society exist too.
Our analysis can inform where to target data collec-
tion efforts and our results suggest large knowledge gains
could be made by a relatively small effort. Collecting data
on only 33 species would overcome all known data defi-
ciencies for IUCN listed mammals with >30% habitat
within mining areas (Table S2). Given that many of these
are dominated by small-bodied mammals, which receive
less attention in research and action (Kennerley et al.,
2021), filling these gaps could have a significant impact
on conservation action. Mining companies are well
placed to finance scientific efforts to fill these gaps, given
they already operate in these regions and are typically
required to conduct surveys as part of environmental
licensing conditions. It is not unprecedented for compa-
nies to provide data to the Red List (Bennun et al., 2018),
nor is it unusual for industry to lead the discovery of new
species (e.g., Lehr et al., 2021). The costs to fill these gaps
are increasingly well understood (Stewart et al., 2021)
and thus could be budgeted for by companies and in
mine site feasibility assessments. To enhance these
opportunities, though, companies need better access to
what information is missing in sites they are operating
in (or plan to operate in) and, in instances where new
information reveals additional threats, governments
must provide clarity on reporting requirements. Such
efforts could be integrated within existing tools, such
as IBAT, to enable companies to identify opportunities
(UNEP-WCMC 2020).
Other uncertainties exist in the data we used to iden-
tify mammal habitat within mining areas. Mining maps
from Sonter et al. (2020) were constructed from the SNL
Metals & Mining database (SNL 2020). Despite being con-
sidered one of the most comprehensive datasets available,
it is not perfect. As already mentioned, this dataset does
not capture artisanal and illegal mining or quarrying
activities. SNL also underestimates mineral extracted
from China for most commodities and, while African
countries are well reported, gold extraction from DR
Congo is only 60% that reported elsewhere (Maus
et al., 2020). These two countries contain habitat for 1098
mammal species (China =607 species; DRC =491) and,
although they tend to have lower-than-average data
deficiencies (9% in both China and DRC, compared to
average of 14% across all countries), incomplete data
underestimates the number of species threatened and
opportunities to address uncertainties and improve con-
servation outcomes.
Mapping global species distribution is also associated
with issues of bias and uncertainty. Here we considered
the geographic distribution of mammals in the IUCN
Red List, which are often considered to overestimate spe-
cies distributions. We limit this issue by using habitat
suitability models, specifically the AOH method, which
8of11 SONTER ET AL.

represents only the suitable portion of each species
ranges and exclude areas less likely to be occupied
(Brooks et al., 2019; Rondinini et al., 2011). It is also pos-
sible that IUCN maps exclude some area from species
ranges that are occupied, but this can only be corrected
for a limited number of species with accurate and repre-
sentative point locality data (Boitani et al., 2011). How-
ever have no reason to believe uncertainty around
species mapping depends on the presence of mining
areas, hence any uncertainty in the underlying biodiver-
sity data should not introduce biases in our results.
4.5 |Mining and the broader global
conservation agenda
Achieving global conservation goals (UNEP, 2021) will
require nations to seriously consider mining threats to
biodiversity in national plans and policies. Some coun-
tries have mining areas tightly correlated with important
habitat for threatened species and some mammals are
particularly exposed to mining activities. Improving
methods to detect, assess, and characterize mining
threats to species will become even more important in
future, particularly as mineral demand grows to support
a green energy transition (Sovacool et al., 2020). Indeed,
we found evidence that mammal species tend to have
larger proportions of habitat within mining areas that tar-
get materials needed for an energy transition (Figure 3).
Despite these ongoing and emerging threats to biodiver-
sity, mining is still permitted in many sites considered vital
for species conservation (Sonter et al., 2020)andthereislit-
tle evidence to suggest current management approaches
achieve no net biodiversity loss (zu Ermgassen et al., 2019),
let alone the net gain targets evident in the UN Convention
on Biological Diversity (CBD) Global Biodiversity Frame-
work (UNEP, 2021). Several efforts have already made sig-
nificant progress on engaging industry to improve practice
in this space, including an international expert workshop
and report on “Biodiversity Mainstreaming in the Sectors of
Energy and Mining, Manufacturing and Processing and
Infrastructure”(CBD, 2018a) and the related decision
adopted by Parties at COP14 to establish an Informal Advi-
sory Group on Mainstreaming of Biodiversity (CBD, 2018b).
Despite this, the draft post-2020 Global Biodiversity Frame-
work provides little guidance on how to make decisions
when minerals vital for sustainable development will nega-
tively affect species and, as revealed by our analysis, further
progress is needed to improve the characterization of min-
ing threats within IUCN Red List assessments and related
tools, such as IBAT and STAR, which utilize this
valuable data.
AUTHOR CONTRIBUTIONS
Laura J. Sonter conceived the idea, Laura J. Sonter and
Thomas J. Lloyd analyzed the data, all authors inter-
preted results and wrote the manuscript.
ACKNOWLEDGMENTS
Laura J. Sonter acknowledges Australian Research Council
Discovery Early Career Researcher Award (DE170100684)
and the Sustainable Minerals Institute's Complex Orebodies
program. Moreno Di Marco acknowledges support from the
MUR Rita Levi Montalcini program.
DATA AVAILABILITY STATEMENT
This study uses secondary datasets, which can be sourced
from their primary source, as referenced.
ORCID
Laura J. Sonter https://orcid.org/0000-0002-6590-3986
Thomas J. Lloyd https://orcid.org/0000-0001-9940-8767
Stephen G. Kearney https://orcid.org/0000-0002-0026-
970X
Moreno Di Marco https://orcid.org/0000-0002-8902-
4193
Christopher J. O'Bryan https://orcid.org/0000-0002-
6472-6957
Richard K. Valenta https://orcid.org/0000-0003-3861-
3948
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SUPPORTING INFORMATION
Additional supporting information can be found online
in the Supporting Information section at the end of this
article.
How to cite this article: Sonter, L. J., Lloyd, T. J.,
Kearney, S. G., Di Marco, M., O'Bryan, C. J.,
Valenta, R. K., & Watson, J. E. M. (2022).
Conservation implications and opportunities of
mining activities for terrestrial mammal habitat.
Conservation Science and Practice,4(12), e12806.
https://doi.org/10.1111/csp2.12806
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