Global threats of extractive industries to vertebrate biodiversity

Abstract

Mining is a key driver of land-use change and environmental degradation globally, with the variety of mineral extraction methods used impacting biodiversity across scales. We use IUCN Red List threat assessments of all vertebrates to quantify the current biodiversity threat from mineral extraction, map the global hotspots of threatened biodiversity, and investigate the links between species’ habitat use and life-history traits and threat from mineral extraction. Nearly 8% (4,642) of vertebrates are assessed as threatened by mineral extraction, especially mining and quarrying, with fish at particularly high risk. The hotspots of mineral extraction-induced threat are pantropical, as well as a large proportion of regional diversity threatened in northern South America, West Africa, and the Arctic. Species using freshwater habitats are particularly at risk, while the effects of other ecological traits vary between taxa. As the industry expands, it is vital that mineral resources in vulnerable biodiversity regions are managed in accordance with sustainable development goals.

Full text

Article
Global threats of extractive industries to vertebrate
biodiversity
Highlights
d8% of vertebrates have mineral extraction threats (METs)
dTropics are global hotspots of METs for vertebrates
dEcological traits that correlate with METs differ among taxa
Authors
Ieuan P. Lamb, Michael R. Massam,
Simon C. Mills, Robert G. Bryant,
David P. Edwards
Correspondence
ilamb1@sheffield.ac.uk (I.P.L.),
dpe29@cam.ac.uk (D.P.E.)
In brief
Lamb et al. reveal mineral extraction as a
prominent threat to global vertebrate
biodiversity, identifying hotspots of risk
located pan-tropically and in the Arctic,
as well as species’ ecological traits,
including habitat use, range size, and
slow life history, that correlate with the
likelihood of mineral extraction threats.
Lamb et al., 2024, Current Biology 34, 1–12
August 19, 2024 ª2024 The Authors. Published by Elsevier Inc.
https://doi.org/10.1016/j.cub.2024.06.077 ll

Article
Global threats of extractive industries
to vertebrate biodiversity
Ieuan P. Lamb,
1,4,
*Michael R. Massam,
1
Simon C. Mills,
1
Robert G. Bryant,
2
and David P. Edwards
3,
*
1
Ecology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield S10 2TN, UK
2
School of Geography and Planning, University of Sheffield, Sheffield S10 2TN, UK
3
Department of Plant Sciences and Conservation Research Institute, University of Cambridge, Cambridge CB2 3EA, UK
4
Lead contact
*Correspondence: ilamb1@sheffield.ac.uk (I.P.L.), dpe29@cam.ac.uk (D.P.E.)
https://doi.org/10.1016/j.cub.2024.06.077
SUMMARY
Mining is a key driver of land-use change and environmental degradation globally, with the variety of mineral
extraction methods used impacting biodiversity across scales. We use IUCN Red List threat assessments of
all vertebrates to quantify the current biodiversity threat from mineral extraction, map the global hotspots of
threatened biodiversity, and investigate the links between species’ habitat use and life-history traits and
threat from mineral extraction. Nearly 8% (4,642) of vertebrates are assessed as threatened by mineral
extraction, especially mining and quarrying, with fish at particularly high risk. The hotspots of mineral extrac-
tion-induced threat are pantropical, as well as a large proportion of regional diversity threatened in northern
South America, West Africa, and the Arctic. Species using freshwater habitats are particularly at risk, while
the effects of other ecological traits vary between taxa. As the industry expands, it is vital that mineral re-
sources in vulnerable biodiversity regions are managed in accordance with sustainable development goals.
INTRODUCTION
Mining is rapidly expanding globally to meet growing demand for
metal minerals and construction materials.
1
Mineral and fuel
extraction is one of the most lucrative global industries, with a to-
tal revenue of US$943 billion in 2022 by the largest 40 com-
panies.
2
The rush to provide society with mined commodities
and associated profits means the extractive industry is often
the pioneer of threat to remote and biodiverse environments.
3
Between 2000 and 2018, mine exploration or extraction caused
78% (2,398) of global protected area (PA) downgrading, down-
sizing, or degazettement (PADDD) events,
4
while in sub-Saharan
Africa, the number of mines located <10 km from a PA increased
by 250% between 2000 and 2018.
5
Mineral extraction causes a range of direct and indirect threats
to biodiversity. Direct impacts include habitat loss and degrada-
tion at the extraction sites,
6
whereas indirect threats can impact
environments far from extraction sites. Major infrastructural de-
velopments can increase the access of human populations to
a landscape, catalyzing further habitat degradation and loss, ru-
ral development, and increased hunting and trapping of wild-
life.
3,7
The land area impacted by indirect threats from mineral
extraction could dwarf the area impacted by the direct global
footprint of terrestrial mines. Mines have a relatively small direct
global footprint (101,583 km
2
),
8
yet mining can increase defores-
tation up to 70 km from mining sites in the Amazon,
9
and pollu-
tion from metal mineral mines affects 479,200 km of rivers and
164,000 km
2
of flood plains globally.
10
Thirty-seven percent of
the non-Antarctic terrestrial land mass currently lies within
50 km of a mine (50,000,000 km
2
),
11
500 times the area of land
directly used for mining. Off-site impacts exist across a variety
of industry practices: small-scale artisanal gold mining is the
largest source of mercury pollution globally,
12
while sand mining
changes riverbed structure and water levels across whole river
deltas.
13
In marine environments, oil spills can impact huge
areas: 15,000 km
2
of the Gulf of Mexico in the Deep Horizon
disaster.
14
The proximity of biodiversity impacts to extraction
sites can vary considerably. Meaning, we cannot rely solely on
mine locations as a proxy for threat, and investigation into threat
from the perspective of species is also required.
Given the sheer spatial scale of potentially impacted land, the
variety of impacts, and range in severity of direct and indirect im-
pacts, mineral extraction potentially threatens a significant pro-
portion of global biodiversity, and vulnerability may be linked to
species’ ecological traits. For instance, large-bodied vertebrates
experience severe reductions in abundance in the Amazon and
Congo basins where oil roads facilitate hunting within PAs and
provide access to markets for bushmeat,
15,16
while oil spillages
have caused large-scale mortality of wildlife, particularly sea-
birds.
14,17
At its most extreme, mining threatens species across
their entire range (e.g., Chiku Bent-Toed Gecko Cyrtodactylus
hidupselamanya by a large limestone quarry).
18
It is currently un-
known whether certain taxa or species with particular ecological
traits (e.g., large body size)
19,20
are more vulnerable to threats
from mineral extraction than others.
To avoid biodiversity loss amid the predicted drastic
expansion of the industry, it is vital to understand the extent
that biodiversity is currently at risk. We use three main objectives
to provide the most-complete global assessment of threat to
biodiversity from the extraction of metal minerals, fossil fuels,
and construction materials (hereafter referred to as mineral
extraction): (1) summarize the number of vertebrates listed as
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Figure 1. Percentage of vertebrates with METs stratified by taxonomic groups, IUCN Red List threat status, and type of threat
(A) Upper bar shows the percentage of all vertebrate species with MET in combination with whether they are globally threatened (IUCN Red List categories VU,
EN, and CR), and the lower bar shows the percentage of species affected by each type of MET.
(B) Inner pies show the percentage of species with METs for each vertebrate group; outer rings the percentage of species affected by each type of MET type for
each taxon.
(C) Percentage of species with METs within each IUCN Red List category for each vertebrate group. Threat status: DD, data deficient; LC, least concern; NT, near
threatened; VU, vulnerable; EN, endangered; CR, critically endangered. Upper legend (all panels): yellow indicates the percentage of species with METs that are
(legend continued on next page)
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threatened by mineral extraction by the IUCN across taxonomic
group, IUCN Red List threat category, and different types of min-
eral extraction threat (MET); (2) analyze the relationship between
species’ ecological traits and the likelihood of threat from mineral
extraction, controlling for the expected effect of species’ spatial
distributions; and (3) identify where global hotspots of biodiver-
sity threatened by mineral extraction are currently located. We
address these critical knowledge gaps by focusing on terrestrial,
freshwater, and marine vertebrates (amphibians, birds, fish,
mammals, and reptiles) and using the IUCN Red List’s species
assessments and range maps. Hereafter, ‘‘species with METs’’
describes species with METs in their IUCN assessments, and
‘‘threatened’’ or ‘‘Red List threatened’’ describes species cate-
gorized as vulnerable, endangered, or critically endangered by
the IUCN.
RESULTS AND DISCUSSION
What type of mineral extraction are vertebrates
currently threatened by?
Mineral extraction is recorded as a threat for 4,642 (7.8%) of the
59,803 extant vertebrate species assessed by the IUCN (Fig-
ure 1A). Of these species, 3,775 (81%) have mining and quar-
rying as a threat (category 3.2; mining); 1,000 (22%) have
seepage from mining (cat. 9.2.2) and 584 (12%) have oil spills
(cat. 9.2.1) (combined into the category ‘‘pollution’’; Figure 1);
and 431 (9%) have oil and gas drilling (cat. 3.1; Figures 1A and
1B). Mining and quarrying are the most common threats for all
taxa, with fish commonly having pollution threats. Fish had the
highest number of species with METs with 2,053 (8.1% of
25,247), followed by reptiles 764 (7.6% of 10,164), amphibians
747 (10% of 7,448), birds 558 (5.1% of 11,024), and mammals
520 (8.8% of 5,886; Figure 1B). Mineral extraction is thus a po-
tential risk to biodiversity across all vertebrate groups and from
all IUCN MET types.
Mineral extraction is a driver of extinction risk across IUCN
Red List categories in a broadly consistent pattern between
taxa (Figure 1C). Red List threatened categories (vulnerable, en-
dangered, and critically endangered) have the higher percent-
ages of species with METs than least concern or data deficient
(Figure 1C). This is especially the case with birds, where only
1.4% of least-concerned species but 18.4% of critically endan-
gered species have METs. Species with high extinction risk also
have METs, emphasizing its potential importance as a global
threat to biodiversity. The low percentage of data-deficient spe-
cies with METs likely indicates a lack of current knowledge of
their threats; assessment of these species is critical as many of
them are likely to be globally threatened.
21,22
Although, at the class level, vertebrate groups have similar
numbers of species with METs, this differs at the order level (Fig-
ure S3). Suliriformes (catfish), a large group of mainly freshwater
species,
23
exhibit the highest number of fish species with METs
(490, 18% of 2,689). Worryingly, catfish also have a high
proportion of data-deficient species 25%, suggesting the actual
number of species with METs could be larger.
22
Characi-
formes—a group of tropical freshwater fish
23
—have 324 (22%
of 1,427), whereas only 296 (4% of 6,711) Perciformes and 16
(2% of 876) Anguilliformes (eels) have METs. Sphenisciformes
(penguins) are particularly susceptible with 12 (66% of 18) spe-
cies with METs, mainly from oil spills. For mammals, primates
have disproportionately high numbers of species with METs
(117/552 species; 22%), as do Carnivora (57/297; 19%) and Chi-
roptera (bats; 166/1,332 species; 12%). Rodenta, the largest
mammalian group, have a comparatively low proportion of spe-
cies with METs 65/2,375 (2%). Forty-five vertebrate orders had
no species with METs, the largest being Aulopiforms (lizard
fish—marine ray-finned fish)
24
with 282 species, although 20%
data deficiency suggests a lack of study and full assessment
of the threats they face. This variation in vulnerability to mineral
extraction suggests that threat may be correlated to species
ecological characteristics, their spatial distribution, or potentially
bias within assessments.
Which ecological traits relate to threat?
We find that the likelihood a species has METs varies depending
on a species’ ecological traits. Habitat use, range size, and slow
life history all correlate with the likelihood of having METs for ver-
tebrates, with varying importance for different taxa. Use of ma-
rine, desert, and rocky habitats increases the likelihood of threat
for birds. Mineral extraction is more likely to be a threat for fish
(ray-finned species only; STAR Methods), and amphibians using
freshwater and wetland habitats, whereas amphibians using
savanna habitats are less likely to have METs (Figure 2A;
Table S2). This indicates the extent to which mineral extraction
threatens freshwater ecosystems globally, supporting Olden
et al.
25
that freshwater fish have comparatively high levels of
extinction risk. Mineral extraction can impact watercourses in
numerous ways and a variety of scales, including mercury pollu-
tion from artisanal gold mining,
12
bioaccumulation of selenium
from coal mining,
26
and changing patterns of flow and hydrolog-
ical structures of watercourses and wetlands.
13
Impact mitiga-
tion efforts should consider these as focal habitats, evaluating
restoration possibilities after operations have ceased.
10
They
should also assess the cumulative risks of mineral extraction in
a holistic way; for example, high volumes of oil transport within
ranges of vulnerable marine birds cause increased risk of chronic
oil spill.
27
Range size is an important variable for birds and fish (Figures 2B
and 2E; Table S2) and, to a lesser extent, reptiles and mammals
(Figures 2D and 2C; Table S2), all revealing a negative relationship
between range size and the probability of having METs. Range
size is a driver of extinction risk in vertebrates,
28–31
and the
IUCN Red List category can be determined by global range
size.
32
Species with small range sizes may be more sensitive to
disturbance from mineral extraction because impacts are likely
to occur over a larger proportion of their ranges. This leaves global
also globally threatened and dark blue indicates the percentagebop21ipl of species with METs that are not globally threatened (IUCN threat categories LC and
NT); lower legend (A and B): species with METs (across all IUCN categories) are grouped into three threat types: mining (threat 3.2); oil (threat 3.1); and pollution
(threat 9.2.1 and 9.2.2).
See also Figure S3.
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regions of high endemism vulnerable to increased biodiversity
losses.
3
Few life-history traits are associated with threat from mineral
extraction, and the traits with clear correlations with the likeli-
hood of METs vary across taxa (Figure 2). For birds, threat
from mineral extraction is negatively associated with habitat
breadth, meaning specialists are more likely have METs than
generalists, and body mass—which was also positively corre-
lated with generation length (Pearson correlation coefficient =
0.63)—is positively associated with threat from mineral extrac-
tion (Figure 2B). Mammals with smaller litter sizes are more likely
to have METs (Figure 2C; Table S2; only mammals and birds had
sufficient litter/clutch size data for inclusion in analysis). Litter
size and generation length both limit population growth rate
and, thus, the ability of populations to adapt to and recover
from impacts.
33
The interaction of body size and range size
also has a positive effect for birds: larger-bodied species with
small range sizes are more likely to have MET (Figure 2;
Table S2). This again points to the risks that mineral extraction
poses to species with small ranges and large body size.
19,20,32
Species with ecological traits that we have highlighted may
need special conservation attention to manage the threats and
potential impacts of mineral extraction.
Extinction risk from mineral extraction appears to be strongly
affected by geographical location as well as ecological traits.
The inclusion of a spatial proximity matrix (distance of a species
to all other species) improved all models’ predictive accuracy
(Table S1), indicating that MET tends to be spatially clustered
and a species is more likely to have METs if nearby species
have METs. Therefore, the associations we see between traits
and threat from mineral extraction occur while accounting for
the likelihood that species in similar locations will have similar
traits
34
and similar exposure to mineral extraction impacts.
The IUCN currently uses phylogenetic proximity to infer
threat by currently unconfirmed drivers of risk. For example,
species can by listed as having chytridiomycosis as a threat
because other species within the same genus have said threat
(Plectrohyla acanthodes)
35
or likely collection for food due to
exploitation of closely related species (Conraua alleni).
36
The in-
clusion of spatial terms in future modeling of extinction risk from
mineral extraction also needs to be considered. Additionally,
better trait data (coverage across species and other traits,
e.g., trophic level, breeding traits, and foraging activity) and
mining characteristics would allow more nuanced analysis of
the impacts of specific components of mineral extraction on
biodiversity. Differences in species’ ecological traits can in-
crease extinction risk from many forms of human encroach-
ment. For example, small-ranged birds are especially vulner-
able to historic land-use change,
30
and highly specialized
reptiles are more at risk to climate change.
28
MET is a broad
term that encompasses a great variety of potential impacts to
species that range multiple scales of operation and severities
of disturbance. There are undoubtably more intricate relation-
ships between species traits and specific mineral extraction
Figure 2. Effect size of ecological traits on the likelihood of a species having METs
(A) Amphibians, (B) birds, (C) mammals, (D) reptiles, and (E) fish. Effects are given as point estimates and 95% credible intervals. We interpret effects with a 95%
credible interval overlapping 0 as having no clear directional effect. All trait values are standardized or binomial. Note: the effects of the interactions between
mammal diet variables were omitted from (C) for clarity but can be found in Table S2.
See also Figure S2 and Tables S1,S2,S3, and S4.
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activities that are not captured within our analysis but worthy of
further investigation.
Current global hotspots of biodiversity with METs
Across all vertebrate groups, we uncovered a pantropical con-
centration of threat to species from mineral extraction (Fig-
ure 3A), particularly in the montane tropics, tropical Africa, and
tropical islands. Hotspots of species with METs are the Andes,
coastal West and Central Africa, and Southeast Asia (Figure 3A).
Islands including Sri Lanka, Madagascar, New Caledonia, Davao
and Palawan (Philippines), Papua, Jamaica, and Cuba are also
regions of high levels of threat to biodiversity. Pixels with the
highest threat values from all terrestrial environments occur in
Sri Lanka, New Caledonia, Central Sulawesi (Indonesia), and
the Colombian states of Valle del Cauca and Choco (Figure S4).
Reanalysis to compare hotspots of species with METs to spatial
location of mines
8
—which maps the direct impacts of the mine
footprint but not wider-scale indirect impacts—reveals co-
occurrence of high mine density in these areas (Figure S1). Areas
of high levels of threat from mineral extraction overlap with
many of the world’s most valuable biodiversity hotspots, con-
taining a hyperdiversity of species, high endemism, and unique
habitats.
37,38
This contributes toward and potentially catalyzes
the intense human impacts occurring in these regions of highest
conservation priority.
9
Hotspots of species with METs vary between taxa. The
Atlantic Forest in Brazil is a hotspot for birds (Figure 3E) and
amphibians (Figure 3C). The Congo basin, Central America,
northern South America, Borneo, and Papua are hotspots for
fish. Madagascar and areas of Indochina are hotspots for rep-
tiles and mammals (Figures 3K and 3I), with reptiles also highly
threatened across the Atacama Desert and Chilean Andes (Fig-
ure 3K). Due to current data coverage, we focus on species with
MET without indication of the severity of impact. Reanalysis of
birds, the only taxa with sufficient impact coverage, highlight
similar regions of high impact to those with high threat
(Figures S4N and S4O).
Variation between taxa is likely driven by three main interlinked
effects: (1) spatial variation in mineral extraction methods and
mine characteristics (e.g., open cut mining, underground mining,
alluvial mining, tailing pond, commodities, mine footprint, etc.)
39
and thus threats,
7
combined with (2) variation in species’ re-
sponses to extraction methods and characteristics
40
and/or (3)
variation in hotspots of underlying species diversity and ende-
mism.
19
For example, coal mining causes extensive deforesta-
tion in East Kalimantan, Indonesia,
41
a global hotspot of threat-
ened and endemic mammals,
42,43
artisanal small-scale alluvial
gold mining (ASGM) in Ghana threatens important bird areas
through environmental mercury pollution compounded by high
deforestation pressure for farming,
44–46
while amphibians are
sensitive to the loss of complex habitat structures due to historic
copper mining and smelting areas in Canada.
40
Addressing
conservation issues at a regional level relies on effective impact
assessments that reveal how each species group responds to
different extraction methods and mine characteristics.
All threat values for vertebrates and individual taxonomic
groups were positively correlated with the underlying diversity of
species weighted by range size—their maximum potential threat
values (STAR Methods) (amphibians, rho = 0.569, n= 15,721;
birds, rho = 0.445, n= 15,721; fish, rho = 0.788, n= 18,112; mam-
mals, rho = 0.688, n= 18,684; reptiles, rho = 0.540, n= 16,202; all
vertebrates, rho = 0.853, n= 18,684; pvalues for all tests were
<0.05). Therefore, threat values broadly follow the biogeograph-
ical diversity of small-ranged species (Figure S4M). To account
for this underlying variation in diversity of small-ranged species,
we scaled maps by the maximum potential threat value of each
grid cell to generate a community sensitivity score, where the
scaled value would be 1 if all species within a cell are threatened
by mineral extraction, highlighting areas where a large proportion
of community diversity are threatened by mineral extraction
(Figure 4). Across all vertebrate groups, northern South America,
Chilean Andes, and West Africa remain hotspots (Figure 3B).
Within individual taxa, other hotspots were also similar. For
example, West Africa for amphibians and mammals (Figures 3D
and 3J), Sri Lanka for birds (Figure 3F), the Llanos and Northern
Amazons for fish (Figure 3H), and South-Eastern India for reptiles
(Figure 3L). Focusing conservation efforts within these regions,
where mineral extraction impacts a large proportion of species
in their highly diverse communities, may have a disproportionate
influence on mitigating the effects of mineral extraction on global
biodiversity decline.
There were also substantial differences in hotspots of commu-
nity sensitivity versus hotpots of richness. Across all vertebrate
groups, additional hotspots of community sensitivity highlight
much of the Arctic, plus smaller areas of Western China and
the Sahara Desert (Figure 3B). Here, individual species have
stronger influence on community sensitivity due to low regional
species diversity and endemism
47
(Figure S4M). For example,
in the Arctic, threatened species including gyrfalcon Falco rusti-
colus, long-tailed duck Clangula hyemalis, polar bear Ursus mar-
itimus, and reindeer Rangifer tarandus have more influence on
community sensitivity scores than individual species in regions
with greater diversity of smaller-ranged species, potentially lead-
ing to areas highlighted as hotspots but where threat may not be
occurring across the whole area. Hotspots of community sensi-
tivity, but low species threat, also vary by taxa. For amphibians,
mid-west Canada is a large hotspot of community sensitivity
(Figure 3D); for reptiles, the Gobi and Atacama deserts and the
southern limits of the Eurasian boreal forest (Figure 3L); and for
birds, Alaska. Reanalysis comparing hotspots of community
sensitivity to global mining footprints
8
indicates little direct
impact of mining across these regions (Figure S1B). Further
research is needed to understand if species in these regions
are especially sensitive to threats and/or indirect extraction
Figure 3. Global hotspots of METs
(A and B) All taxa, (C and D) amphibians, (E and F) birds, (G and H) fish, (I and J) mammals, and (K and L) reptiles. Left column (A, C, E, G, I, and K) are hotspotsof
threat value, denoting the number of threatened species weighted by range size. Right column (B, D, F, H, J, and L) are hotspots of community sensitivity,
denoting threat value as a proportion of the total potential threat value (if all species that occur within that cell have METs, the cell value will be 1). Red cells indicate
the top 1% of terrestrial cells, yellow cells indicate the top 5% of cells, and blue cells indicate the presence of a species with METs (threat value >0).
See also Figures S1 and S4.
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Figure 4. Methods flow schematic of data sources and processing stages
(A) Mapping mineral extraction threats and (B) modeling mineral extraction threats via trait analysis. The thickness of the gray lines between IUCN Red List
taxonomic images and trait data sources represent the number of traits used from each data source.
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activities in these regions are particularly extensive in their nega-
tive effects on biodiversity—affecting large proportions of the
community (e.g., oil spills for sea birds).
14,17
Conclusions and management implications
This study identifies that mineral extraction is a threat to a signif-
icant proportion of the world’s vertebrate biodiversity, threat is
linked to ecological traits of species, and the tropics as a global
epicenter for mineral extraction-driven extinction risk across
multiple taxonomic groups, pointing to the need for targeted
conservation action. Conservation concerns arise in particular
from mining activity in areas containing high diversity of small-
ranged species,
48
especially montane regions and islands,
which are also hotspots of extinction risk via other anthropogenic
stressors.
49
Biodiversity risks of mining activity are also high in
close proximity to PAs,
11,50
especially given the risks of off-site
tropical deforestation.
9,41
The spatial and ecological variation in mining-induced threat
across vertebrate biodiversity suggests a nuanced relationship
between threat and the presence of different extraction methods
and characteristics. The impact of mines on biodiversity is
diverse and span a breadth in intensities, varying according to
methods of waste management,
51
active recovery and reclama-
tion efforts (e.g., tree planting and pond creation),
52
infrastruc-
ture,
9
and commodities
53
specific to extraction sites, or simply
the size of operation in relation to species’ available habitat.
For example, birds are highly threatened across the iron quad-
rangle region of Brazil, where endemic mountain top species
have a median 27% of their range within 5 km of mines.
54
Because we do not identify precise locations of extraction-
induced threat, in some areas we will overestimate potential risks
(Figure S1B). Although we do overcome some of this uncertainty
by weighting our threat values by species range size, which gen-
erates more confidence of where small-ranged species (versus a
large-ranged species) are threatened. The categoric threat-
assessment data we use from IUCN is limited in describing the
detail of threats caused by extraction. For example, species
with mining and quarrying (threat 3.2) encompasses those
threatened by ASGM through to large open pit copper mines,
with no more details threat categories available in the IUCN
threat structures.
55
Further analysis investigating how different
extraction methods, mine sizes, extracted commodities, and
longevity impact on biodiversity is vital for informing future
mine developments and conservation planning.
Changing trends in the industry could also influence biodiver-
sity impacts. For example, reduced-impact extraction methods
56
or increased deep-sea nodule mining could reduce the pressure
caused by terrestrial mining and its secondary impacts, but there
will be as-yet unknown threats to marine biodiversity.
57
It is vital
that currently unexploited areas of high vulnerability
3
are not
opened to impacts from secondary threats that follow the infra-
structure developments of mineral projects, including deforesta-
tion
9
and hunting pressure.
58
If unregulated expansion occurs in
high-risk regions, especially the hyperdiverse tropics, then the
extensive threats of mineral extraction will likely increase extinc-
tion pressures to biodiversity. We based our assessments of risk
on the IUCN Red List, which may underestimate threat from min-
eral extraction. This is because the indirect threats from mineral
extraction, such as off-site forest loss and life-cycle impacts
(e.g., failings of tailing storage facilities), are often not captured
within the assessment process since these indirect threats of
mineral extraction require extensive analysis
9,10,41
combined
with ambiguous categorization of threats within the assessment
process—whereby threats from mining infrastructure such as for-
est loss may not result in mining being included in the species
assessment as a threat at all.
55,59
Additionally, species immi-
nently threatened by planned mineral extraction or exploration
are not assessed as threatened,
59
and some data-deficient spe-
cies that lack formal assessment are likely to be threatened due
to their smaller range and population sizes.
22
The resources and power held by the mineral extraction in-
dustry have potential to drive expansion in ecologically impor-
tant areas and impact regions we highlight as vulnerable. Extrac-
tion corporations have access to vast initial capital, meaning
they can build necessary infrastructures and attract migrant
workforces into remote areas, especially for highly profitable
metallic minerals.
60
The power asymmetry between corpora-
tions versus governments and other stakeholders in lower-in-
come countries means they can negotiate unfair deals (some-
times via corruption)
61,62
without proper compensation for
damage to ecosystems and biodiversity,
63,64
including develop-
ment within PAs (i.e., PADDD)
4
and areas of high biodiversity
value.
3
The development of new mines within western nations,
which may have lower biodiversity risks, stricter enforcement,
etc., could subvert many of these issues, but this is often
opposed,
65
thus externalizing development to biodiverse trop-
ical regions. Although tropical mines can still face strong local
resistance, oppression of communities is often greater.
65
For
instance, European demand for lithium could be partially met
by expansion of mining within Europe, reducing pressure to
expand operations in Chile and China,
66
areas that we highlight
as conservation concerns.
The mineral extraction industry faces many challenges, and if
left unchecked, mineral expansion may continue to cause major
direct and indirect threats to biodiversity. Expansion of mineral
extraction is necessary for the drastic transition to renewable en-
ergy sources,
67
but mineral resources are decreasing in grade,
producing more waste for the same quantity of resource.
68
The
industry is also required to reduced fossil fuel use, meet rising
global demands through population growth and development,
and reduce its impact on the environment and biodiversity. Pol-
icy must focus on creating more circular economies, increasing
material recycling and reuse.
69
Where new mineral extraction is
unavoidable, rebalancing power dynamics through supporting
governments in spatial planning, legislation, and enforcement
are important steps.
60,63
Sustainable development licenses to
operate (SDLO) could improve industry transparency and hold
companies accountable to international sustainable develop-
ment goals (SDGs)—including those linked to biodiversity and
habitat protection within hotspots of risk. The collaboration of
corporations, governments, and the conservation community
is imperative if we are going to confront these challenges.
Mining companies are motivated to achieve biodiversity
goals,
70
using environmental impact assessments, mitigation hi-
erarchy, and biodiversity offsetting.
71
Some of these efforts have
been successful at achieving ‘‘no-net loss,’’
71
but social implica-
tions of offsetting are complex. Restricting communities use of
resource can lead to vulnerable people within the communities
ll
OPEN ACCESS
8Current Biology 34, 1–12, August 19, 2024
Please cite this article in press as: Lamb et al., Global threats of extractive industries to vertebrate biodiversity, Current Biology (2024), https://doi.org/
10.1016/j.cub.2024.06.077
Article

suffering the losses of the change, putting SDG 1 (no poverty)
and SDG 17 (partnerships for the goals) at risk.
71
Offsets can
have beneficial impacts on biodiversity but need to be strictly
monitored using stringent and appropriate frameworks as their
premise is trading known losses for uncertain gains, and no-
net loss can also be hard to demonstrate without use of prox-
ies.
71,72
This presents an important opportunity for the research
community and industry to combine efforts in providing detailed
impact assessments of the whole life cycle of extraction opera-
tions, as well as indirect impacts that result from development.
The results we present are a guideline for avoiding development
and further impact within known vulnerable areas, as well as a
base from which further research can fill the potential gaps in
knowledge in terms of what species are threatened that are
missing from IUCN assessments and how might species be fall-
ing through the gaps.
STAR+METHODS
Detailed methods are provided in the online version of this paper and include
the following:
dKEY RESOURCES TABLE
dRESOURCE AVAILABILITY
BLead contact
BMaterials availability
BData and code availability
dEXPERIMENTAL MODEL AND SUBJECT DETAILS
BStudy site information
dMETHOD DETAILS
BAssessment of mineral extraction threat
BSpecies’ global threat status
BCaveats of IUCN and dataset
BTrait data preparation
BTrait data imputation
dQUANTIFICATION AND STATISTICAL ANALYSIS
BTrait analysis
BMapping threat hotspots
SUPPLEMENTAL INFORMATION
Supplemental information can be found online at https://doi.org/10.1016/j.
cub.2024.06.077.
ACKNOWLEDGMENTS
I.P.L. was funded by the Hossein Farmy scholarship. We thank Robert Davies
and Oscar Morton for comments on the manuscript, models, and figures.
AUTHOR CONTRIBUTIONS
Conceptualization, I.P.L., M.R.M., R.G.B., S.C.M., and D.P.E.; data curation
and formal analysis, I.P.L.; writing – initial draft, I.P.L.; writing – review and edit-
ing, I.P.L., M.R.M., R.G.B., and D.P.E.; visualization, I.P.L.; supervision,
M.R.M., R.G.B., and D.P.E.
DECLARATION OF INTERESTS
D.P.E. is on the Scientific Advisory Board of Current Biology.
Received: October 25, 2023
Revised: April 18, 2024
Accepted: June 28, 2024
Published: July 26, 2024
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STAR+METHODS
KEY RESOURCES TABLE
RESOURCE AVAILABILITY
Lead contact
Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Ieuan Lamb (ilamb1@
sheffield.ac.uk).
Materials availability
This study did not generate new unique reagents.
Data and code availability
dAll datasets used can be downloaded from the original sources or requested from the respective authors as listed in the key
resources table.
dAll original code has been deposited in the GitHub repository listed in the key resources table and is publicly available as of the
date of publication.
dAny additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
EXPERIMENTAL MODEL AND SUBJECT DETAILS
Study site information
We focused on global terrestrial land (excluding antarctica). Spatial data processing was done in R, using Mollweide equal-area pro-
jection (ESRI 54009). We used a global grid 110km resolution – including marine areas – then rasterized and clipped all layers to land
areas.
75
REAGENT or RESOURCE SOURCE IDENTIFIER
Software and algorithms
R (version. 4.2.2) The R Foundation https://www.r-project.org/
R Studio version 2023.06.1+524 RStudio https://www.rstudio.com/products/rstudio/
download/
Additional code Author’s own https://github.com/ieuanlamb/Mineral_threat_
to_biodiv
Other
IUCN Red List species assessments IUCN
73
https://www.iucnredlist.org/search
IUCN species range shape files IUCN
74
https://www.iucnredlist.org/resources/
spatial-data-download
Birdspecies range shape files BirdLife International https://datazone.birdlife.org/species/
requestdis
Global terrestrial shapefiles rnaturalearth
75
https://CRAN.R-project.org/package=
rnaturalearth
COMBINE traits Soria et al.
76
https://doi.org/10.1002/ecy.3344
Elton Traits Etard et al.
77
https://doi.org/10.1111/geb.13184
Fishbase Boettiger et al.
78
https://doi.org/10.1111/j.1095-8649.2012.
03464.x
Actinopterygii Phylogeny Rabosky et al.
79
https://doi.org/10.1038/s41586-018-0273-1
Amphibian Phylogeny Jetz and Pyron
80
https://doi.org/10.1038/s41559-018-0515-5
Bird Phylogeny Jetz et al.
81
https://doi.org/10.1038/nature11631
Mammal Phylogeny Upham et al.
82
https://doi.org/10.1371/journal.pbio.3000494
Squamate Reptile Phylogeny Tonini et al.
83
https://doi.org/10.1016/j.biocon.2016.03.039
Mine footprint spatial polygons Maus et al.
8
https://doi.org/10.1594/PANGAEA.942325
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METHOD DETAILS
Assessment of mineral extraction threat
We used IUCN species assessments to determine which species have mineral extraction threats (MET)
73
; species that are catego-
rized in one or more of the IUCN threat categories of oil and gas drilling (category 3.1), mining and quarrying (category 3.2), oil spills
(category. 9.2.1), or seepage from mining (category 9.2.2). The definitions of threat for these categories are:
dOil and gas drilling and exploration: Exploring for, developing, and producing petroleum and other liquid hydrocarbons.
dMining and Quarrying: Exploring for, developing, and producing minerals and rocks.
dIndustrial & Military Effluents: Water-borne pollutants from industrial and military sources including mining, energy production,
and other resource extraction industries that include nutrients, toxic chemicals and/or sediments. Our analysis included sub-
categories: Oil spills and Seepage from Mining only.
Species’ global threat status
Species’ global threat status is also determined by IUCN assessments. Five criteria contribute to a species being categorized as
globally threatened – i.e. vulnerable, endangered, or critically endangered, based upon severity of population size reduction,
geographic range size, small population size and decline, very small population size, and quantitative analysis indicating extinction
probability.
84
IUCN updates the Red List database biannually with assessments for newly described species and reassessment, as-
sessments become out of date after 10 years, and threatened and near threatened species take priority for reassessment, although
reassessment frequency also varies among taxonomic group.
85
Caveats of IUCN and dataset
Our analysis is a current overview of the species assessments conducted by the IUCN, used to guide conservation action and invest-
ment globally.
86
The IUCN likely underestimates future threats and the secondary threats that the mineral extraction industry poses to
biodiversity and could have underlying biases. Sonter et al.
59
found 36 species of mammal with >30% of their habitat within 10 km of
mining sites, many of which were threatened by exploration and potential future mining, yet these were not recognized by IUCN as
being threatened by mining and quarrying. Additionally, off-site deforestation indirectly caused by mining occurs in two-thirds of
countries across the tropics
41
and, within the Brazilian Amazon, there is significantly higher deforestation up to 70 km from extraction
sites.
9
It is unlikely that these effects are captured by IUCN assessments, because in depth data and analyses like this are scarce.
Life-cycle impacts of mines are also highly uncertain, due to stochastic events such as oils spills and tailing storage facility failures
10
and can be difficult to assess as threats to species.
7
Furthermore, we are not able to map data deficient species for which no formal
assessment has been made and species without range data, yet these are more likely to be threatened due to their smaller range and
population sizes.
22
For IUCN to be used in directing global conservation metrics such as STAR (as proposed)
86
it is vital that these
potential underestimations are addressed, and threats are correctly recorded.
A major issue with the way that the IUCN categorizes known threats from mineral extraction is the inherently ambiguity for at the
assessment process, for example, when the presence of mining facilitates logging (potentially through transport infrastructure), min-
ing may not be listed as a threat despite being the underlying reason logging has become possible.
11
The IUCN’s ‘‘Guidance Threat
Classification Scheme December 2022’’ document
55
states the exposition for threat 3.1 Oil and Gas Drilling as: ‘‘Oil and gas pipelines
go into 4.2 Utility & Service Lines. Oil spills that occur at the drill site should be placed here; those that come from oil tankers or
pipelines should go in 4.Transportation & Service Corridors or in 9.2 Industrial & Military Effluents, depending on your perspec-
tive.’’ And threat 3.2 Mining and Quarrying as: ‘‘It is a judgement call whether deforestation caused by strip mining should be in this
category or in 5.3 Logging & Wood Harvesting – it depends on whether the primary motivation for the deforestation is access to the
trees or to the minerals. Sediment or toxic chemical runoff from mining should be placed in 9.2 Industrial & Military Effluents if it is the
major threat from a mining operation.’’ Highlighting the potential for disparity and potential expedition of mineral extraction as athreat
to a species in assessments.
Trait data preparation
The species included in our analysis were limited to those with available geographic range data and phylogenetic trees (35598 spe-
cies). Phylogenies were obtained for Actinopterygii,
79
amphibians,
80
aves,
81
mammals
82
and squamate reptiles.
83
We compiled a master synonym dataset from IUCN synonyms as well as synonyms from trait datasets,
77
amphiaweb,
87
and rfish-
base package (4.1.2.),
78
to match names across the trait databases (see KRT), the phylogenetic tree and IUCN assessment data. We
cross-referenced the IUCN assessed species with trait data and phylogenetic data. We excluded 6553 species across all vertebrate
groups in our trait analysis due to a lack of either species range data, phylogenetic position, or the joining or splitting of species names
used in the phylogenetic tree and IUCN names (species list available in Table S4).
22
Trait data imputation
We imputed 11467 data points for 19 traits across all vertebrate groups using the Rphylopars package,
88
in R. Following guidelines
from Johnson et al.
89
and Gonza
´lez-del-Pliego et al.
22
we imputed traits with >60% coverage and Pagel’s lambda >0.6. The
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imputations were checked for estimation accuracy using leave-one-out (loo) cross validation by comparing imputed traits to their
known values. Imputed traits with a prediction coefficient, P-squared >0.7
90
were accepted. Tables of coverage, lambda,
P-squared values are Table S3. For mammals, traits were obtained from the COMBINE dataset
76
where imputation for traits had
already been conducted (following the same guidelines for pre-imputation coverage >60%).
For birds, three habitat-use traits (desert, shrubland, savanna), nocturnal diel activity, and habitat breadth had >97% coverage but
were not suitable for imputation due to low lambda values. We therefore used two models; one where we removed 121/9143 (1.3%)
species in order to include the five additional traits within the models, and the second we removed the five traits and used all 9143
species (Figure S2;Table S2).
We obtained the most comprehensive trait data for birds (17 and 12 traits for main model and the supplementary model respec-
tively), then mammals (9 traits), Amphibians (7 traits), fish (5 traits), Reptiles (2 traits; see Table S2.).
QUANTIFICATION AND STATISTICAL ANALYSIS
Trait analysis
To assess the effect of ecological traits on whether a species is threatened by mineral extraction we used Bayesian logistic linear
mixed effects models using the brms package
91
in R (version. 4.2.2).
92
We use group level effects of both phylogenetic and spatial
distances (see supplementary methods for the parameter structures of models), as we would expect that species in close spatial and
phylogenetic proximity to also have similar likelihood of threat from mineral extraction, as mineral extraction is mainly a spatially spe-
cific process and related species are more likely to be vulnerable to the same threats.
34
We model each taxonomic group individually,
using the binary response variable: threatened by mineral extraction or not.
Models were fit using weakly informative, non-flat priors: intercept = normal(0,1), Beta = normal(0,0.5), sd = normal(0,1). With four
chains run for 1000 warmup and 1000 post-warm up samples per chain. All models were checked for chain convergence and pos-
terior predictive ability. Model parameters are available in the project code.
Mapping threat hotspots
All mapping and calculations were conducted in R.
92
We mapped global hotspots of threat using the species range shapefile data
from IUCN.
74
We used a global grid with a Mollweide equal-area projection at a 110 km x 110 km resolution (consistent with similar
analytical methods).
93
For each cell, we calculated two threat values. 1) species threat value: the sum of threat certainty values for
each species – where threat certainty is the proportion of a species’ total range that lies within each individual grid cell (Equation 1).
This weighting by species range size accounts for the uncertainty of where a species is actually being threatened by mineral extrac-
tion, as the likelihood that a species’ mining threat status owes to any particular cell is smaller for a large-ranged species than it is for a
small-ranged species. 2) community sensitivity value: species threat values for each cell divided by the total potential threat value for
the cell (Equations 2 and 3). For both threat values, the top first and fifth percentile of cells were used to highlight two levels of global
hotspots of threat.
VT =X
nt
i=1
W
T(Equation 1)
Cell species threat value. Where ntis the total number of species threatened by mineral extraction found within the cell, Wis the
area of the species’ range within the cell, Tis the total area of the species’ range.
VP =X
n
i=1
W
T(Equation 2)
Total possible species threat values. Where nis the total number species found within the cell, Wis the area of the species’ range
within the cell, Tis the total area of the species’ range.
CSi=VTi
VPi
(Equation 3)
Community sensitivity value. Where iis the individual global grid cell, VT is the cell’s species threat value (Equation 1) and VP is the
potential threat value for the cell (Equation 2).
When calculating the area overlap of species ranges with the global grid square, bird range polygons were simplified to a 10 km
resolution using sf::st_simplify() to reduce file sizes and thus computational intensity. Additionally, three species were removed from
the analysis due to errors within the geometries Orcinus orca, Megaptera novaeangliae, Eretmochelys imbricata. The impact of these
removals is expected to be negligible as they have extremely large global ranges and are marine mammals and turtles. The proportion
of terrestrial areas in their ranges is therefore zero or close to zero.
The limitation of this method is that species ranges potentially include areas of unsuitable habitat for the respective species. This
means species could contribute to a regions threat score when the species does not actually occur in that area. We believe our
weighting by range size will somewhat counter for this but accept it cannot fully account for this issue. The trade-off for this reduced
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accuracy is the ability to conduct this analysis on across all vertebrates, as Areas of Habitat (AoH) are not currently available for fish
and reptiles and using AoH is comparatively extremely computationally intensive.
To spatially compare our hotspots of threat to locations of mining activity (Figure S1) we use the most up to date available data of
global mining footprints
8
and recalculated the cell threat values and community sensitivity but only including species with mining
related threats: mining and quarrying (3.2) and seepage from mining (9.2.2). Mining polygons were rasterized to a 110 x 110 km
grid using Mollweide equal area projections.
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