Global assessment of marine plastic exposure risk for oceanic birds

Abstract

Plastic pollution is distributed patchily around the world’s oceans. Likewise, marine organisms that are vulnerable to plastic ingestion or entanglement have uneven distributions. Understanding where wildlife encounters plastic is crucial for targeting research and mitigation. Oceanic seabirds, particularly petrels, frequently ingest plastic, are highly threatened, and cover vast distances during foraging and migration. However, the spatial overlap between petrels and plastics is poorly understood. Here we combine marine plastic density estimates with individual movement data for 7137 birds of 77 petrel species to estimate relative exposure risk. We identify high exposure risk areas in the Mediterranean and Black seas, and the northeast Pacific, northwest Pacific, South Atlantic and southwest Indian oceans. Plastic exposure risk varies greatly among species and populations, and between breeding and non-breeding seasons. Exposure risk is disproportionately high for Threatened species. Outside the Mediterranean and Black seas, exposure risk is highest in the high seas and Exclusive Economic Zones (EEZs) of the USA, Japan, and the UK. Birds generally had higher plastic exposure risk outside the EEZ of the country where they breed. We identify conservation and research priorities, and highlight that international collaboration is key to addressing the impacts of marine plastic on wide-ranging species.

Full text

Article https://doi.org/10.1038/s41467-023-38900-z
Global assessment of marine plastic
exposure risk for oceanic birds
A list of authors and their affiliations appears at the end of the paper
Plastic pollution is distributed patchily around the world’s oceans. Likewise,
marine organisms that are vulnerable to plastic ingestion or entanglement
have uneven distributions. Understandingwherewildlifeencounters plastic is
crucial for targeting research and mitigation. Oceanic seabirds, particularly
petrels, frequently ingest plastic, are highly threatened, and cover vast dis-
tances during foraging and migration. However, the spatial overlap between
petrels and plastics is poorly understood. Here we combine marine plastic
density estimates with individual movement data for 7137 birds of 77 petrel
species to estimate relative exposure risk. We identify high exposure risk areas
in the Mediterranean and Black seas, and the northeast Pacific, northwest
Pacific, South Atlantic and southwest Indian oceans. Plastic exposure risk
varies greatly among species and populations, and between breeding and non-
breeding seasons. Exposure risk is disproportionately high for Threatened
species. Outside the Mediterranean and Black seas, exposure risk is highest in
the high seas and Exclusive Economic Zones (EEZs) of the USA, Japan, and the
UK. Birds generally had higher plastic exposure risk outside the EEZ of the
country where they breed. We identify conservation and research priorities,
and highlight that international collaboration is key to addressing the impacts
of marine plastic on wide-ranging species.
Plastic pollution harms marine life worldwide1, alongside other threats
including fishing, climate change and invasive species2.Reportsof
entanglement and ingestion impacts are mounting1,3,4,butthereare
large gaps in our understanding, including about factors affecting
plastic encounter, ingestion rates, mortality and population-level
impacts4,5. Marine plastic is unevenly distributed6, accumulating in
patches within ocean gyres and coastal regions7,8, and often drifting
thousands of kilometres in ocean currents8,9. Likewise, marine life is
patchily distributed10, and many species cross oceans and political
boundaries11,12. With plastic production and waste generation con-
tinuing to increase13, identifying at-risk species and populations is
crucial for targeting conservation action and research14–16 because the
vulnerability of populations relates to exposure to a hazard, sensitivity
to damage that impacts survival or reproduction, and the resilience of
the population17.
Many seabird species are sensitive to plastic pollution; they fre-
quently ingest plastic1, which can have lethal and sublethal impacts
caused by chemical contamination18 and physical damage or
blockages19. Numerous factors affect the amount of plastic accumu-
lated by different species including foraging behaviour, at-sea dis-
tribution and gut morphology20–22. Among seabirds, albatrosses and
petrels can contain particularly high loads of plastic ingested directly
or within their prey1,20. Many species rarely regurgitate indigestible
items, except when feeding their chicks23. Petrels are particularly
sensitive because they retain plastic for long periods due to their gut
morphology22, and small species (e.g., storm-petrels and gadflypet-
rels) can suffer greater physical damage or higher metabolic costs
from ingesting plastic relative to larger species5. Petrels are a diverse
group of 123 wide-ranging species that inhabit all the world’s oceans,
making them good sentinels for ocean health2. Many populations are
Received: 12 December 2022
Accepted: 19 May 2023
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e-mail: bethany.louise.clark@gmail.com;ana.carneiro@birdlife.org;ejp69@cam.ac.uk
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unlikely to be resilient to hazards because over half (64) are listed as
globally Threatened or Near Threatened by the International Union for
the Conservation of Nature (IUCN), including 16 Endangered and 12
Critically Endangered species2. Moreover, we know little about the
status of many of their populations or if they are impacted by plastic2.
Assessing risk to petrel populations from plastic pollution
requires a robust understanding of vulnerability to ingestion, for which
exposure at sea is a key component14. Seabirds risk encountering
plastic when they forage near sources associated with dense human
populations24,fisheries25 and shipping lanes26, or in mid-ocean gyres
where floating debris accumulates27–29. Exposure risk can be char-
acterised by estimating contact between organisms and hazards, or
their co-occurrence, and a key goal in ecological risk assessment is to
consider variation in the amount of time spent by animals in different
parts of their range30,31. Plastic exposure risk has not been previously
quantified using methods that account for the time spent in areas of
different densities of plasticpollution, but lightweighttracking devices
have recently provided unprecedented detail about the movements of
petrels of all sizes32, including the time spent in different foraging areas
and across the annual cycle33.
Here, we estimate relative marine plastic exposure risk for 77
petrel species at a global scale by calculating the spatio-temporal
overlap between modelled floating plastic density and the space-use of
tracked birds14. To inform conservation action and futureresearch, we
compare exposure risk across populations, seasons (breeding and
non-breeding), Exclusive Economic Zones (EEZ) and areas beyond
national jurisdiction (the high seas), and found substantial variation.
We identified areas of high risk of exposure to plastic debris in the
Mediterranean and Black seas, the northeast Pacific, the northwest
Pacific, the South Atlantic and the southwestIndian Ocean. Our results
also reveal that Threatened species have greater exposure risk.
Because marine debris and seabirds cross multiple political bound-
aries, our results emphasise that efforts to reduce the amount of
plastic waste in the ocean should not only focus on areas of high
exposure risk. Improved international cooperation and collaboration
are needed to address this global threat.
Results and discussion
Plastic exposure risk for petrels
We analysed 1,736,880 tracked locations for 7137 adults of 77 petrel
species (64% of species within Oceanitidae, Hydrobatidae and
Procellariidae, excluding the two Macronectes species), from 148
populations in 27 countries and Antarctica, between 1995 and 2020
(mean = 2012). For each population, we calculated monthly 95% utili-
sation distributions (UDs) that estimate time spent by tracked petrels
in 10 km grid cells (i.e., smoothed density of 12-hourly tracked loca-
tions; Fig. 1a), and combined monthly UDs into seasons (breeding or
non-breeding). If data were available from multiple populations of a
species, we created species UDs weighted by approximate population
size. We calculated a geometric mean of global marineplastic densities
estimated by three published models6,9,34 formicro- and macro-plastics
(~0.333 mm–40 cm) combined for 2014 in 1 × 1° cells (Fig. 1b). We
aggregated petrel UDs into 1 × 1° grid cells and created an all-species
map by summing species UDs, weighting those tracked only in the
breeding season and so not including the non-breeding part of the
annual cycle at 0.5 (Fig. 1c). We divided the plastic and petrel grids by
their respective cumulative sums so that the values of each global grid
summed to one. We then multiplied each petrel UD by the plastic
density to map spatial overlap as an indicator of estimated exposure
risk14 (e.g., Figure 1d). Summing the values across cells provided an
exposure risk score, which we multiplied by 106to provide an easy-to-
use scale; this gave us monthly population-level scores ranging from
0.0007 to 1091.
We ranked species by plastic exposure risk score (Fig. 2a), ran-
ging from 0.003 to 549 (mean = 28.0; median = 4.9, interquartile
range = 1.8–14.5). Of particular concern are the 19 species scoring over
15.3 (the score any species would receive if plastic was evenly
distributed worldwide), indicating they mostly use areas with above-
average plastic density. These species include the Critically Endan-
gered Balearic shearwater Puffinus mauretanicus and Newell’s
shearwater Puffinus newelli; the Endangered Hawaiian petrel Ptero-
droma sandwichensis; and the Vulnerable yelkouan shearwater Puffi-
nus yelkouan, Cook’spetrelPterodroma cookii and spectacled petrel
Procellaria conspicillata (Fig. 2a). The proportion of total exposure risk
within each IUCN Red List category differs from the proportion of
tracked species within each category, with a greater percentage of the
exposure risk shared among Threatened species, particularly Critically
Endangered species (Fig. 2b). The 20 highest-scoring species had
greatest plastic exposure risk in five areas, both in coastal regions
(Mediterranean/Black Sea, northwest Pacific) and ocean gyres (north-
east and northwest Pacific, South Atlantic, southwest Indian oceans;
Figs. 1d, 2a). Plastic exposure risk was low in upwelling zones (Hum-
boldt and Canary currents) and polar regions (Fig. 1d). For some spe-
cies, scores differed greatlyamong populations (Fig. 2a). For example,
European storm-petrels Hydrobates pelagicus breeding in the Medi-
terranean had much higher scores (306–534) than elsewhere (1.0–1.4;
Supplementary Fig. 1). There was no long-term trend in exposure risk
scores for populations tracked in the same months for more than three
years (Supplementary Fig. 2). By using tracking data to estimate the
relative density of regularised bird locations, instead of using only
estimated presence or absence, we explicitly consider spatio-temporal
variation in seabird distributions, thus providing more detail on global
plastic exposure risk for a subset of species than an analysis based on
range maps, which inferred different geographic hotspots of plastic
exposure risk14.
Breeding and non-breeding season exposure risk
We calculated breeding and non-breeding plastic exposure risk scores
for 107 populations of 60 species. The mean difference between sea-
sons was 34.0, with little difference for most populations (median=
3.6), but substantial differences for some (maximum = 521.8; Fig. 3a).
For example, Scopoli’s shearwaters Calonectris diomedea breed on
Malta in the Mediterranean and migrate to the eastern Atlantic Ocean
where they had a much lower plastic exposure risk score (30.0) than
during the breeding season (496.2). In contrast, yelkouan shearwaters
also breed on Malta (517.5), but had a higher score during non-
breeding (937.7) when they disperse within the Mediterranean and
migrate to the Black Sea (Fig. 3a–c). Seasonal contrasts also varied
among populations of the same species. For example, scores for
Cook’s petrels during non-breeding were much higher for birds
breeding in northern New Zealand that migrate to the northeast Pacific
(159.3), than those breeding in southern New Zealand that migrate to
the Humboldt Current (0.8; Fig. 3a, d, e).
Exposure risk and ingestion
Plastic exposure risk, as indicated by our scores, is necessary but not
sufficient for ingestion to occur and there are not yet enough suitable
samples to quantify this process for most species. The amount of
ingested plastic detected in seabirds is affected by foraging style, body
size, tendency to regurgitate, gut morphology, prey type, age and
breeding stage20,22,23,28. Few ingestion studies have used standardised
protocols to sample different populations of the same species4.Fur-
thermore, ingestion data are influenced by whether samples came
from pellets26 or regurgitates18, or necropsies of birds that were found
dead at a colony29 or on beaches35, recovered after attraction to light
pollution36,bycaughtinfisheries37,ortakenforresearch
28 or human
consumption4. Nonetheless, studies that compared ingestion for dif-
ferent populations of the same species using the same methods con-
trol for these factors, and so can be compared to our exposure risk
scores. For example, flesh-footed shearwaters Ardenna carneipes
Article https://doi.org/10.1038/s41467-023-38900-z
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a) Number of petrel
species’ tracked
ranges
Study
colonies
0
20
10
15
5
100
0
1
d) Plastic
exposure risk for
tracked petrels
a
bc
d
Mediterranean
& Black Sea
NE Pacific
E Asia/NW
Pacific
S Atlantic
SW
Indian
Ocean
0
0.085
c)
Density of
petrel tracking
locations
24227000
0
2422700
b)
Estimated
n plastic
pieces per km2
0.5
Fig. 1 | Mappingpetrels and plastics. a Species richness based on presence within
95% utilisation distributions isopleth contours from tracking data for 77 petrel
species. Red diamonds indicate the colonies from which tracking data were
obtained. bPlastic density at the ocean surface, showing the square root of the
number of plastic pieces (~0.333mm–0.4 m) estimated per km2in each 1 × 1° grid
cell. For visualisation only, the values are capped at 10% due to extreme values.
cSummed 95% utilisation distributions for all species, with species weighted
equally if year-round tracks were available or by 0.5if tracks were only available for
the breeding season. If we had data from multiple populations for a species,
densities were weighted by approximate population size. dExposure risk to plastic
was calculated by multiplying the density value in each cell for plastics (scaled to
sum to 1) by the value for petrels (scaled to sum to 1). For visualisation only, the
values are capped at 1% due to extreme values, and all other values are shown on a
linear scale. Blackellipses relateto the areas identified from the 20 specieswith the
highest exposure risk scores (Fig. 2a). n= number. White = no data. Robinson Pro-
jection. Land polygons from Natural Earth. Source data for colony locations are
provided as a Source Data file.
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sampled in eastern parts of their breeding range contained sig-
nificantly more plastic20, consistent with our higher scores during the
non-breeding season for populations migrating to the northwest
Pacific (New Zealand = 44.9; Lord Howe = 47.1) compared with those
migrating to the eastern Indian Ocean (Western Australia = 13.6).
Additionally, the EcologicalQuality Objective for part of the North Sea
target of <10% of northern fulmars Fulmarus glacialis containing ≥0.1 g
of plastic was exceeded more in the North Sea than Arctic Canada38,
mirroring our exposure risk scores for those tracked from the UK (1.4)
and Canada (0.25). There are clear examples of high ingested plastic
loads in high exposure risk areas in the Mediterranean37,northeast
Pacific39 and southwestIndian Ocean36. However, plastic loads are both
low and high in areas with low exposure risk40, indicating that birds
may still be at risk while foraging in marine areas with low estimated
plastic densities. Plastic has been ingested even by the species with the
lowest exposure risk score of 0.003 (4% of 27 sampled snow petrels
Pagodroma nivea, which forage around Antarctica, contained
plastic40), indicating that the ubiquitous availability of plastic is con-
cerning across all oceans worldwide, not only in areas where plastic
aggregates.
Jurisdictions and policy
Plastic exposure risk for tracked petrels occurred mostly in the Med-
iterranean and Black Seas (Fig. 4a, b), where breeding European storm-
petrels and Scopoli’s, yelkouan and Balearic shearwaters are at risk,
with high plastic loads recorded37,41. Elsewhere, the high seas are used
by 75 of our 77 tracked species, and accounted for 25% of global
plastics exposure risk, mainly within oceanic gyres. The US EEZ
accounted for a high proportion of the exposure risk, noticeably
northeast of Hawai’i, followed by the EEZs of Japan, and the UK, mainly
around the Overseas Territories of Tristan da Cunha and Bermuda
(Fig. 4a, b). The New Zealand EEZ ranked highly despite low plastic
levels due to the exceptionally high petrel occurrence and diversity.
Moderate plastic exposure risk scores(0.15–1.00% of total)occurred in
the EEZs of France, Australia, Brazil,Portugal, Mauritius, China, Russia,
Argentina, Madagascar, Bahamas, and Mexico (Fig. 4a).
Our results indicate that mitigating plastic pollution in the
breeding country’s EEZ alone would not adequately protect most
species throughout the annual cycle. We identified links between the
countries within which each tracked petrel population breeds
(including overseas territories) and the jurisdictions where those
populations were exposed to plastic (Fig. 4c). Exposure risk primarily
occurred outside the breeding country’s EEZ (theoretical EEZ in the
Mediterranean because actual EEZs are not clearly defined), except for
7 of the 29 highest-scoring populations (e.g., wedge-tailed shearwaters
Ardenna pacifica in the USA, and streaked shearwaters Calonectris
leucomelas in Japan). Of the 29 highest-scoring populations, 25 were
exposed to plastic in multiple EEZs. For example, streaked shearwaters
breeding in South Korea were exposed in China, Malaysia, the Phi-
lippines, South Korea, Indonesia and Vietnam (Fig. 4c). Exposure risk
Kermadec petrel (1,0.5) LC
White-necked petrel (1,0.5) VU
Manx shearwater (3,1) LC
Black-winged petrel (2,1) LC
Monteiro’s storm-petrel (1,0.5) VU
Fluttering shearwater (1,1) LC
Great-winged petrel (1,0.5) LC
Cape Verde shearwater (1,1) NT
Great shearwater (2,1) LC
Band-rumped storm-petrel (3,1) LC
Sooty shearwater (3,1) NT
MacGillivray’s prion (1,1) EN
Desertas petrel (1,1) VU
Cory’s shearwater (4,1) LC
Black-capped petrel (1,1) EN
Mottled petrel (1,1) NT
Bulwer’s petrel (4,1) LC
Bermuda petrel (1,1) EN
Mascarene petrel (1,1) CR
Buller’s shearwater (1,1) VU
Soft-plumaged petrel (2,1) LC
Atlantic petrel (1,1) EN
Wedge-tailed shearwater (7,1) LC
Barau’s petrel (1,1) EN
Broad-billed prion (2,1) LC
Murphy’s petrel (1,1) LC
Flesh-footed shearwater (3,1) NT
Trindade petrel (2,1) VU
Providence petrel (1,1) VU
Streaked shearwater (4,1) NT
European storm-petrel (5,1) LC
Spectacled petrel (1,1) VU
Cook’s petrel (2,1) VU
Newell’s shearwater (1,0.5) CR
Hawaiian petrel (1,0.5) EN
Scopoli’s shearwater (8,1) LC
Balearic shearwater (1,0.5) CR
Yelkouan shearwater (2,1) VU
0 200 400 600
Plastic exposure risk scores for tracked petrels
a b
% of tracked
petrel species
per category
% of exposure
risk for all tracked
petrel species
per category
IUCN Red
List threat
category
100%
75%
25%
50%
0%
100
Species score
Population score
Uniform plastic
distribution score
Mediterranean & Black Sea
Northeast Pacific
South Atlantic
East Asia/Northwest Pacific
Southwest Indian Ocean
Location of highest
exposure risk
Snow petrel (1,1) LC
Southern fulmar (2,1) LC
Wilson’s storm-petrel (1,0.5) LC
Antarctic petrel (2,1) LC
Peruvian diving-petrel (1,0.5) EN
Blue petrel (2,1) LC
South Georgia diving-petrel (1,1) LC
Whenua Hou diving-petrel (1,1) CR
Cape Verde storm-petrel (1,0.5) LC
Galapagos petrel (1,0.5) CR
Cape petrel (2,1) LC
Slender-billed prion (2,1) LC
Northern fulmar (8,1) LC
White-headed petrel (1,1) LC
Common diving-petrel (3,1) LC
Cape Verde petrel (1,1) NT
Tahiti petrel (1,0.5) NT
Grey petrel (4,1) NT
Pycroft’s petrel (1,1) VU
Pink-footed shearwater (1,1) VU
Tropical shearwater (1,1) LC
Westland petrel (1,1) EN
Leach’s storm-petrel (3,1) VU
Audubon’s shearwater (3,1) LC
Chatham petrel (1,1) VU
White-winged petrel (2,1) VU
Little shearwater (2,1) LC
Hutton’s shearwater (1,0.5) EN
Fairy prion (2,1) LC
Antarctic prion (2,1) LC
Short-tailed shearwater (1,1) LC
Black-vented shearwater (1,0.5) NT
Grey-faced petrel (1,0.5) LC
Magenta petrel (1,1) CR
Black petrel (1,1) VU
Zino’s petrel (1,1) EN
White-faced storm-petrel (2,0.5) LC
White-chinned petrel (5,1) VU
Fork-tailed storm-petrel (1,1) LC
0
Fig. 2 | Plastic exposure riskscores for77 petrel species. a Species are ranked by
exposure risk from the top-left to the bottom-right. Colours represent the location
that contributed most to the score for the five areas of highest exposure risk. Where
there are multiple populations per species (grey diamonds), the mean of all popu-
lations (black circles) is weighted by the population size. The vertical dashed line
indicates the theoretical exposure risk score if plastic was uniformly distributed
across all cells (15.3). Values in parentheses are the number of populations, followed
by 1 if the species was tracked in breeding and non-breeding seasons or by 0.5 if only
tracked in one season. Two-letter codes indicate the IUCN Red List assessment threat
category (Least Concern (LC; n= 36), Near Threatened (NT; 9), Vulnerable (VU; 16),
Endangered (EN; 10), Critically Endangered (CR; 6)). bThepercentageoftracked
petrel species within each IUCN threat category and the percentage of total exposure
risk attributed to species in each category. Source data are provided as a Source
Data file.
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was greatest in the high seas for 15 of the 29 highest-scoring popula-
tions, particularly those breeding in the USA, New Zealand, UK, Brazil,
Australia, France, and Mauritius (Fig. 4b). For each petrel population,
we provide the percentage of exposure risk occurring in each EEZ and
the high seas to facilitate targeting mitigation and policy efforts
towards key areas (Supplementary Data 1).
Marine vertebrates and plastic debris are globally distributed and
highly mobile, and cross political boundaries within and beyond
national jurisdictions11. Therefore, mitigating plastic pollution from
marine and terrestrial sources will require efforts targeted across
multiple jurisdictions and the high seas42. International cooperation,
collaboration, resource mobilisation and information exchange are
key to addressing marine plastic pollution43 by limiting still-increasing
plastic waste production13, improving waste management, and clean-
ing up existing plastic. The International Convention for the Preven-
tion of Pollution from Ships (MARPOL) Annex V prohibiting plastic
waste discharge from vessels entered into force 31st December 198844,
but plastics from marine sources still affect seabirds26 and account for
at least 22% of ocean plastics45.Ghostfishing gear is a priority because
it presents deadly entanglement risk25 and food web contamination
after degradation at sea. Pollution from vessels could be reduced with
more resources and incentives for monitoring and managing waste,
and enforcing MARPOL and local regulations, particularly among
developing countries46. A coordinated approach for plastic waste
management could be achieved, for instance, through a global-scale
treaty on plastics43, which could operate in synergy with MARPOL and
other relevant bodies and frameworks, such as the Convention on
Biological Diversity, Convention on the Conservation of Migratory
Species, Agreement on the Conservation of Albatrosses and Petrels,
Regional Seas Conventions and Action Plans.
Research priorities
Greater use of standard methods for future ingestion studies would
facilitate comparison and help identify the drivers of plastic
ingestion4,47. The relationship between exposure risk, ingestion and
impact could be examined by concurrently sampling ingested plastic
and tracking movements41,48, and measuring physiological impacts.
Interspecific differences could be clarified by systematically compar-
ing plastic loads in species that have similar geographic ranges and
exposure risk scores. Crucially, it is unclear for which species or
populations plastic ingestion reduces survival or productivity and how
much exposure they can tolerate; so, studies of population-level
impacts and how to separate these from known causes of population
declines will be vital2,5. Four species with high plastic exposure risk
a
Soft-plumaged petrel, Gough
Murphy’s petrel, Henderson Island
Broad-billed prion, Tristan da Cunha
Streaked shearwater, Iwate
Buller’s shearwater, Aorangi Island
Streaked shearwater, Mikura
Flesh-footed shearwater, New Zealand
Providence petrel, Lord Howe Island
Flesh-footed shearwater, Lord Howe Island
Black-winged petrel, Chatham Islands
European storm-petrel, Malta
Streaked shearwater, Korea
Yelkouan shearwater, Iles Hyeres
Cook’s petrel, Te Hauturu-o-Toi/Little Barrier
Scopoli’s shearwater, La Maddalena
Scopoli’s shearwater, Chafarinas
Yelkouan shearwater, Malta
Scopoli’s shearwater, Balearic Archipelago
Scopoli’s shearwater, Malta
Scopoli’s shearwater, Zembra
0 250 500 750
Non-
breeding
season
plastic
exposure
risk for
tracked
petrels
Non-
breeding
season
25%
utilisation
distribution
outline
Low
High
Cook’s Petrel
Te Hauturu-o-Toi/Little Barrier Island
Score = 159.3
Cook’s Petrel
Whenua Hou/Codfish Island
Score = 0.8
Season-specific plastic exposure
risk scores for tracked petrels
d
e
bc
Scopoli’s shearwater
Malta
Score = 30.0
Yelkouan shearwater
Malta
Score = 937.7
Study
colonies
Location
Breeding season
Non-breeding season
Med. & Black Sea
NE Pacific
S Atlantic
E Asia/NW Pacific
Fig. 3 | Season-specific plastic exposure risk scores. a Scores during breeding
(grey circles) and non-breeding seasons (black circles) for the 20 populations with
the greatest differences between seasons (grey lines). bNon-breeding season
plastic exposure risk for Scopoli’s shearwaters (non-breeding score = 30.0, breed-
ing season score = 496.24) and cyelkouan shearwaters (non-breeding = 937.7,
breeding =517.5) for tracked from Malta, and for Cook’s petrels breeding either at
dTe Hauturu-o-Toi/Little Barrier Island (non-breeding = 159.3, breeding = 5.5) or
eWhenua Hou/Codfish Island (non-breeding= 0.8, breeding = 2.1). Black lines
indicate the outline of the most used area in the non-breeding season (top 25% of
the utilisation distribution). Land polygons from Natural Earth. Source data are
provided as a Source Data file.
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High Seas Mauritius* France
High Seas
High Seas New Zealand*
High Seas Brazil UK*
High Seas France* MDG
*ailartsuAanihCnapaJ High Seas
High Seas
High Seas *dnalaeZweNnapaJ
High Seas UK*
High Seas Japan Australia*
High Seas
Japan* High Seas
High Seas USA
High Seas UK* Brazil
manteiVaisenodnI*aeroKSsenippilihPaisyalaM
anihC
USA* High Seas
High Seas
High Seas USA*
High Seas USA*
aireglA*niapS
*atlaMaisinuTylatIaybiL
aireglAniapS*ylatI France
atlaMaybiLylatI*aisinuT
ylatIairegl
AyekruTniapS
Spain* Algeria
*atlaMaybiLaisinuTylatI
atlaMaybiL*ylatIaisinuT
aireglA*niapS Spain*/Morocco
*atlaMaisinuTeceerGylatIayb
iLyekruT
Trindade petrel, Mauritius
Soft-plumaged petrel, UK
Buller’s shearwater, New Zealand
Atlantic petrel, UK
Barau’s petrel, France
Flesh-footed shearwater, Australia
Murphy’s petrel, UK
Flesh-footed shearwater, New Zealand
Broad-billed prion, UK
Providence petrel, Australia
Trindade petrel, Brazil
Streaked shearwater, Japan
Black-winged petrel, New Zealand
Spectacled petrel, UK
Streaked shearwater, South Korea
Wedge-tailed shearwater, USA
Cook’s petrel, New Zealand
Newell’s shearwater, USA
Hawaiian petrel, USA
Scopoli’s shearwater, Spain
Scopoli’s shearwater, Malta
European storm-petrel, Italy
Scopoli’s shearwater, Tunisia
Yelkouan shearwater, France
Balearic shearwater, Spain
European storm-petrel, Malta
Scopoli’s shearwater, Italy
European storm-petrel, Spain
Yelkouan shearwater, Malta
%001%57%05%52%0
Percentage of plastic exposure risk for tracked petrel populations in EEZs and the high seas
a) Plastic
exposure
risk for all
tracked
petrels
Exclusive
Economic Zones
(EEZs)
Percentage of plastic exposure risk for all tracked petrels in EEZs and the high seas
High Seas Spain Tunisia Italy Libya Turkey Malta
Other
50% 75%
0% 25% 100%
3361seicepsdekcart574153
b
a
c
0
100
1
Algeria
Greece
USA
Japan
UK
New Zealand
3
27
6
41
31
Fill colour as in Fig. 4b
Fig. 4 | Plastic exposure risk for petrels in different jurisdictions. a Map of
plasticexposure riskfor 77 petrel speciesin the Exclusive Economic Zones (EEZs) of
each country (including overseas territories) and the high seas (Areas Beyond
National Jurisdiction). In the Mediterranean, theoretical EEZs are used. For visua-
lisation only, the score is capped at 1% due to extreme values in the Mediterranean
and Black Seas. bThe percentage of plastic exposure risk score attributed to the
high seas and each EEZ/theoretical EEZ accounting for >1% of total exposure risk,
labelled with the number of tracked species using each area (valuesare provided in
Supplementary Table 1). cFor the29 petrel populations by country with the highest
exposure risk scores (ranked from high to low), bars show the proportion of the
exposure risk score in each jurisdiction that accounts for over 5% of the total
exposure risk, with unlabelled bars containing all others. Bars are coloured
according to b. Overlapping territorial claims are shown as claim 1/claim 2.
MDG = Madagascar. Asterisks(*) indicate that the EEZ matches the breeding coun-
try. Land polygons from Natural Earth. Source data are provided as a Source
Data file.
Article https://doi.org/10.1038/s41467-023-38900-z
Nature Communications | (2023) 14:3665 6
Content courtesy of Springer Nature, terms of use apply. Rights reserved

scores but no ingestion data in a recent review1are key research
priorities: Hawaiian petrel and streaked shearwater within the main
high-exposure risk areas, and Bermuda petrel Pterodroma cahow and
Desertas petrel Pterodroma deserta elsewhere. Comparable ingestion
data from different tracked populations of the same species with
contrasting migration patterns (e.g., Cook’s petrel; Fig. 3d, e) would be
particularly valuable.
Our tracking data covered almost all of the world’s oceans and
all ocean regions within the ranges of 70% of analysed species,
broadly matching seabird biodiversity in general10, but also reflecting
known spatial biases in research effort, notably towards the Atlantic
Ocean and latitudes south of 40°S32 (see Supplementary Table 2 for
spatial coverage gaps). Our study included tracking data for all four
petrel species that breed in the Mediterranean, but we identified
14 species that occur in other high-exposure risk areas, making them
priorities for tracking studies (Supplementary Table 3). Additionally,
both petrel tracking and ingestion data are sparse in coastal waters
around east and southeast Asia, where high plastic densities occur,
and the South Pacific and North Atlantic gyres, where moderate
plastic densities occur10,32 (Fig. 1). We identified priority species for
future research in each of these regions (Supplementary Table 4).
Sample sizes varied substantially among species, from 3 to 960
individuals (median = 35, mean = 93), so additional tracking for some
species could be beneficial (Supplementary Data 2). Furthermore,
tracking immature birds or adults when deferring breeding could
reveal differences in exposure risk33. Our method could also be
applied to global-scale, multi-species tracking datasets12 for other
marine megafauna, such as turtles and marine mammals, for which
plastic pollution is also a threat1.
Collecting more data on plastic density, identifying sources, and
developing density models to provide better spatial coverage at a
higher resolution would aid targeted mitigation strategies, and enable
a better understanding of the effects of spatial scale on plastic expo-
sure risk. The models that produced the plastic density estimates used
in our analysis involved interpolating over wide areas, whereas
observed plastic densities tend to be more patchy49.Therewerelim-
ited plastic data, particularly for southeast Asia6, where a recent survey
recorded high plastic levels50. The South Pacific has a high petrel
species richness, but few samples were used to inform the plastic
density models6. The plastic density model estimates covered most of
the Arctic and Antarctic oceans, but had more missing values near the
polesthaninotherregions(Fig.1b), although the Southern Ocean is
not thought to contain much plastic6. However, plastic accumulates
around Svalbard in the Arctic51, which although only important for
northern fulmars among petrels, could affect other taxa. Marine spe-
cies also feed at different depths and so it would be valuable to
examine how plastic varies vertically52. Repeated plastic sampling
across longer timescales would improve temporal matching between
plastic and seabird data and allow investigations into long-term
changes in plastic exposure risk53.Weprovideexampleversionsofthe
code used to produce our results to facilitate future research on dif-
ferent tracking or plastics datasets54.
Methods
In brief, we collated tracking data for petrels and computed gridded
utilisation distributions (UDs) at a monthly scale. We then combined
gridded distributions of marine plastic density and multiplied them by
the petrel UDs to map estimated exposure risk. For each map, we
summed the plastic exposure risk values in all cells to provide a score
representing relative estimated exposure risk. We combined maps and
scores to investigate variation in exposure risk between breeding and
non-breeding seasons, among populations and species, and across
Exclusive Economic Zones (EEZs) and the high seas. Steps for pro-
cessing and analysing the data are described in detail below and
represented graphically in Supplementary Fig. 3. All data handling was
carried out in R55 and R scripts are provided, along with example data
and templates54.
Petrel tracking data collation and processing
We collated tracking data that were collected using Global Positioning
System (GPS) loggers, Platform Terminal Transmitters (PTTs) and
Global Location Sensor (GLS) loggers deployed on adult petrels
(Table S1; Oceanitidae, Hydrobatidae and Procellariidae). We searched
for published and unpublished tracking data for all petrel species
between March and August 2020, excluding the two giant petrel spe-
cies Macronectes giganteus and M. halli because our analyses focused
on marine areas and they regularly feed on land56. Weobtained data for
77 species (64% of the 121 target species) from the Seabird Tracking
Database (www.seabirdtracking.org), ZoaTrack (www.zoatrack.org)57,
Movebank (www.movebank.org)58, and individual researchers (repre-
sented by authors of this study or detailed in the Supplementary
Acknowledgements). We collated 1,736,880 tracked locations for 7137
individuals tracked from 27 countries and Antarctica. Datasets varied
in terms of number of colonies per species, and numbers of indivi-
duals, years, and months tracked per population (Supplementary
Data 2) and species (Supplementary Data 3).
We standardised tracking datasets to contain the following fields
in the same format: latitude, longitude, datetime, species, colony
name, colony latitude, colony longitude and device type. For GLS, we
removed locations around the equinoxes (March equinox: −21,
+7 days; September equinox: −7, +21 days) as they are unreliable59,
unless latitudes were estimated using additional information such as
sea surface temperature prior to our analysis. For GPS and PTT data,
we filtered locations for unrealistic speeds (>90 km/h), and visually
checked maps and removed locations that were clear outliers. We
removed locations within 5 km of the colony for GPS data or within
15 km of the colony for PTT data, but not for GLS locations due to large
location error for these devices. We linearly interpolated and resam-
pled GPS and PTT datasets to the sampling frequency for GLS of two
locations per day.
We grouped data for each species into 148 breeding populations
determined according to jurisdiction, the distance between colonies,
and overlap in at-sea distributions based on the tracking data, i.e., if
distributions overlapped substantially (at a 1 × 1° scale) and colonies
are in close geographical proximity and in the same country, we con-
sidered colonies to belong to the same population.
Density of tracked petrel locations
For each population, we pooled all locations for all individuals across
all years by month, and then removed months with fewer than five
locations. For each month, we reprojected tracked locations onto a
Lambert azimuthal equal area projection centred around the geo-
metric mean of all locations. We estimated kernel densities of tracked
locations to compute a 95% UD, a common home-range metric, which,
because the sampling frequency was standardised, represented the
estimated time spent by all tracked petrels in that population within
that month. We used the adehabitatHR R package60, using a cell size of
10 km2and a smoothing factor of 200 km (based on the magnitude of
error in estimating locations from GLS33). We trimmed all cells that fell
over land (Natural Earth land 1:10 m polygons version 5.1.1 downloaded
from www.naturalearthdata.com/) because these species do not for-
age in terrestrial environments and it is extremely rare for them to
travel over land, so any locations are most likely due to device error33.
We then reprojected the resulting rasters back to a latitude and
longitude projection (WGS84).
Of the 148 tracked populations, 108 (61 species) were tracked
both in the breeding and non-breeding seasons. Forthese populations,
we collated published information on the timing of breeding at a
monthly scale (Supplementary Data 4) for each species or, where
possible, each population. We also labelled months as breeding or non-
Article https://doi.org/10.1038/s41467-023-38900-z
Nature Communications | (2023) 14:3665 7
Content courtesy of Springer Nature, terms of use apply. Rights reserved

breeding based on the tracking data. Locations were not always
available for all months, with March and September often excluded
from GLS datasets due to the uncertainty in light-based geolocation
around equinoxes. We first calculated the distance between each
location at sea and the breeding colony. For each population, we
calculated the mean distance from the colony for each month, and a
mean of those monthly means. If the mean for a month was greater
than the population-specific mean across all months or if no indivi-
duals travelled within 200 km (chosen due to the approximate
200 km error common when using GLS devices) of the colony, this
month was classified as non-breeding. To ensure there was only one
breeding and one non-breeding season, if the classification of one
month differed from the previous and following months, it was re-
classified. We used published values except in cases when a month
was labelled as breeding, but the tracking data showed that the
subset of tracked birds did not attend the colony during that month,
in which case, we used the label identified by the distance-to-colony
method. Breeding and non-breeding months, therefore, do not
necessarily represent the general phenology of the species, but
instead reflect the behaviour (distance from the colony) of the
majority of tracked individuals in that month. A sensitivity analysis
showed that plastic exposure risk scores calculated using published
breeding schedules were highly correlated with those estimated
using the tracking data, Kendall’s tau = 0.98 (z= 13.879, p< 0.001) for
the breeding season, and tau = 0.97 (z= 10.810, p< 0.001) for the
non-breeding season.
Plastic density distribution
We used estimated global marine plastic density (count per km2)in
1 × 1° grid cells, from publicly available outputs from three published
Lagrangian particle tracking models (Maximenko34,Lebreton
9,andvan
Sebille6). The model estimates combined floating micro and macro-
plastics from ~0.333 mm to 40 cm, with different size classes having
similar estimated distributions7. Although petrels can ingest plastic
flexible plastic pieces 40–60 cm long, they generally consume smaller
pieces61. The three models estimated plastic density using records
from ~12,000 surface trawls. They provided particularly good spatial
coverage in the northeast Pacific, northwest Atlantic and Australian
waters, but particularly poor coverage at the poles, the waters around
Southeast Asia, the northwest Indian Ocean, and the South Pacific6.
The models simulate the movement of plastic particles through mul-
tiple years and then create a static probability grid for a single time
point (2014) based on where particles spent most time up until 2014
(equivalent to a utilisation distribution). We do not expect interannual
variation in plastic distribution to be substantial in comparison to the
spatial scale of between-season seabird movement because plastics
travel passively, take decades to break down, and have been released
throughout the study period. Each model uses the trawl data along
with weather conditions,ocean circulation models, and plastic sources
and sinks to inform the movement of plastic particles and predict the
number of particles in each sampled and unsampled 1 × 1° grid cell.
The Maximenko model assumes particles can wash ashore and ori-
ginate from a uniform input across the ocean surface34, the van
Sebille model assumes no sinks for plastic and plastics originate at
the coast6, and the Lebreton model assumes no sinks for plastic and
plastics are sourced from river mouths9. None of the models incor-
porate sinking through the water column52, ingestion by marine
organisms1, or fragmentation processes. For each ocean basin and
model, a prediction value was compared to observed plastic counts,
providing regression coefficients used to scale the model plastics
distribution and predict plastic concentrations within all cells6. Each
model represents observed ocean plastic concentrations well6, with
observations generally falling within 1–2 orders of magnitude around
the model estimate. Further details on the methods used to model
plastic density, including on how regression coefficients were used
and validated, are provided in Maximenko et al.34, Lebreton et al.9,
and van Sebille et al.6. Despite the variation in sampling effort, the
model outputs generally agree with subsequent surveys in the
Mediterranean62, southeast Pacific63 and southeast Asia50.
We took the geometric mean (as opposed to the arithmetic mean)
of the Maximenko34, Lebreton9,andvanSebille
6modelsto avoid bias in
our plastic density layer toward the highest estimate from any indivi-
dual model because the models have log scale variability between their
estimates. Additionally, because the ocean is in constant flux, con-
centrations at any given location are constantly changing53, assuming a
lognormal distribution of concentrations through time, the geometric
mean willbe a better estimate of the central tendency and closer to the
median concentration than the arithmetic mean64.Themodeloutputs
varied inspatial coverage in coastal and polar regions (Supplementary
Fig. 4), and when one of the models did not have an estimate within a
cell, we used the geometric mean of the other models, or the estimate
from the only available model. If there was no estimate from any
model, this was marked as NA, which occurred mostly in the Arctic and
the Antarctic, and in some coastal areas where the marine area was less
than the 1 × 1° grid size. The model outputs were centred around 180°E.
Values in cells at 0–1°W were incorrectly estimated so these were
imputed from the mean values in the three adjacent cells eastand west
(177–180°E and 1–4°W).
Plastic exposure risk scores
We aggregated the monthly 10 × 10 km petrel 95% UDs for each
population33 onto the same 1 × 1° global grid of the plastic density data.
All petrel UDs and the plastic density grid were divided by the
respective cumulative sum for each grid so that the values of each
entire raster grid summed to one. We estimated exposure risk as the
mathematical product of the petrel and the plastic values in each grid
cell14. This gives equal weight to the number of plastic pieces in each
cell and the density estimate for bird trackinglocations in each cell. We
assume that estimated density of bird tracking locations at equal time
intervals is strongly related to the time spent at risk of exposure to
plastic debris, because areas where seabirds spend more time are very
likely to be where foraging is concentrated65, as a result of area-
restricted searching behaviour66–68. We then summed all cell values
and multiplied all scores by 1,000,000 to reduce the number of dec-
imal places to produce a single score for that month (ranging from
0.0007 to 1091). For comparison, we calculated a theoretical score of
15.3, which represents what the exposure risk score would be for any
species if all global grid cells contained the mean plastic density (i.e.,
assuming that plastic was evenly distributed across the world’s
oceans). We combined monthly grids to produce grids for each
population, breeding or non-breeding season (if data were available
for non-breeding months) and species. Scores for each population are
the mean of all tracked months, and scores for each season are the
mean of all months in that season (Supplementary Data 5). We used the
mean to allow comparison between species with different numbers of
tracked months. Maps for most populations are in Supplementary
Fig. 5. For the 33 species for which we had multiple tracked popula-
tions, we searched for published population estimates (Supplementary
Data 6). We calculated species-level scores as the mean of scores for
each population weighted by the population size and multiplied by 0.5
if the population was only tracked during the breeding season (Sup-
plementary Data 7).
We tested how robust our results were in relation to population
size estimates, sampling frequency and tracking year. Population
estimates for some species have large uncertainty, so we tested the
correlation between species-level scores calculated with and without
weighting by population size using Kendall’s tau because scores are
not normally distributed. They were highly correlated (tau = 0.83;
T= 483, p< 0.001), so our results are unlikely to be affected by
uncertainty in population size estimates.
Article https://doi.org/10.1038/s41467-023-38900-z
Nature Communications | (2023) 14:3665 8
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To investigate possible effects of sampling frequency, we repro-
cessed the tracking data without subsampling all datasets to 12-hourly
intervals. We identified 44 populations for which all data were derived
from GPS or PTT devices. For each track, we calculated the median
interval between successive locations and recorded the maximum
median for each population, and if this was less than 6 h, we reg-
ularised tracking locations at that frequency (intervals ranging from
1 min to 5 h, median = 1 h, mean = 82 min). We performed kernel den-
sity estimation with the higher-frequency datasets using a smaller
50 km smoothing factor33 for the remaining 39 populations and used
them to calculate exposure risk scores for each population. The scores
estimated using the higher and lower resolution data were highly
correlated (tau = 0.90, T=703,p< 0.001), so we conclude that 12-hour
sampling intervals and 200 km smoothing parameter are sufficient for
a study of this scale.
Birds were tracked between 1995 and 2020 with a mean tracking
year of 2012. Among the 148 populations, 139 (94%) were tracked
within 5 years of 2014 (2009–2019), the year for which plastic density
was estimated. Given petrels are long-lived and generally faithful to
breeding sites69 and foraging areas during both breeding and non-
breeding seasons70–73, we assumed that distributions were unlikely to
vary substantially across the study period. Data on long-term trends in
plastic ingestion by seabirds have not shown substantial increases
during the study period27,74,75. A subset of 13 populations had been
tracked with geolocators for the same set of months across more than
three years (Supplementary Fig. 2). For these, we calculated an expo-
sure risk score for each year and then tested the effect of population
and year using a generalised linear model with a Gamma distribution
(due to positive continuous right-skewed response variable). We
checked model fit by simulating residuals using the DHARMa R
package76.
We recorded the most recent IUCN Red List assessment threat
category77, where 36 species were Least Concern (LC), 9 Near Threa-
tened (NT), 16 Vulnerable (VU), 10 Endangered (EN) and 6 Critically
Endangered (CR). Red List status categories from the year each species
was first tracked remained the same for 71 of the 77 species, and we
used the most recent assessment for the 6 species for which changes
have occurred. Three were genuine changes relating to altered threats
or conservation action (Westland petrel Procellaria westlandica from
VU in 2016 to EN in 2017; Chatham petrel from CR in 2008 to EN in
2009 to VU in 2015; yelkouan shearwater from LC in 2004 to NT in
2008 to VU in 2012), while three were not genuine changes because
they related to improved evidence for assessment (flesh-footed
shearwater from LC in 2012to NT in 2016; streaked shearwater from LC
in 2012 to NT in 2015; spectacled petrel from CR in 2005 to VU in
2007)77,78. We calculated the proportion of the total of all exposure risk
scores attributed to species in each threat category.
Spatial patterns in plastic exposure risk
We used the ranked species scores to identify global-scale high -
exposure risk areas by recording the region in which each species had
the highest scores. We created an all-species map by summing results
for each species, with those tracked in both breeding and non-
breeding seasons given a weight of 1, while the 16 species that were
tracked only in the breeding season were given a weight of 0.5 to avoid
undue bias towards breeding colonies. We also divided the all-species
distribution grid by the cumulative sum so that all values sum to one
and multiplied this by the plastic density grid to produce an exposure
risk map. We then overlapped this all-species map with EEZs and the
high seas, obtained as an open-source polygon layer79. Because
national jurisdictions in the Mediterranean are not yet clearly defined
or are subject to dispute, we used theoretical EEZs, which are defined
as 200 nautical miles from the coastline or the median point between
two coastlines unless treaties and agreements have been submitted to
the UN80. We calculated the proportion of the global risk of exposure
to plastic for all petrels in each EEZ/theoretical EEZ and in the highseas.
For joint regimes and overlapping claims, the score was divided evenly
between the involved sovereigns. To record the links between the
breeding country and the jurisdictions of plastic exposure risk, we
calculated the proportion of plastic exposure risk for each population
by country in each EEZ/theoretical EEZ and in the high seas11 (Sup-
plementary Data 1).
Spatial coverage and research priorities
To assess spatial coverage and identify research priorities for tracked
species, we compared the distribution of the tracking data for each
species with the estimated range maps77. We assessed whether major
populations (>1% of the global population or 200pairs) of each tracked
petrel species were missing from any of 10 major ocean areas (NW/NE/
SW/SE Atlantic, NW/NE/SW/SE Pacific, Indian or Southern Oceans)
according to the SeaVoX Salt and Fresh Water Body Gazetteer (https://
www.marineregions.org/). Our tracking data covered all ocean regions
within the published estimated ranges of 54 of the 77 species con-
sidered (70%). Our data compilation also revealed the main gaps in
coverage for the remaining 23 species (Supplementary Table 2).
To identify research priorities for high exposure risk areas iden-
tified in this study, we used range maps to identify species or popu-
lations for which tracking data were not included in this study, but
range maps indicated they may overlap (Supplementary Table 3). We
recorded ingestion frequency of occurrence as the percentage of
individuals found to contain plastic and the number examined as
reported in Kühn & van Franeker1. We also carried out this process for
areas for which plastic density is high and range maps showed that
petrel species may use these areas, but no tracking data were available
for our study (Supplementary Table 4).
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
The plastic exposure risk data generated in this study and the plastic
density data used in this study are provided in the Supplementary
Data files, available at https://github.com/BirdLifeInternational/petrels-
plastics and have been deposited in the Zenodo database at https://doi.
org/10.5281/zenodo.7852143. The seabird tracking data are available
under restricted access because the data were collected for other pur-
poses that vary between datasets and revealing the exact locations of
sensitive species may put them at risk. Access can be obtained by
making a request to the owners of each dataset using the mechanisms
provided by each database. Zoatrack (https://zoatrack.org/)datasetIDs:
57, 93, 102–112,159,253,254,762,817.Movebank(https://www.
movebank.org/) dataset IDs: 944960474, 200628745, 241140274. SEA-
TRACK (https://seapop.no/en/seatrack/) for relevant northern fulmar
data.U.S.GeologicalSurveydatarelease:https://doi.org/10.5066/
P9NTEXM6. Seabird Tracking Database (https://www.seabirdtracking.
org/) dataset IDs: 434, 438, 439, 448, 466, 467, 506–511, 517, 518, 554,
555, 561, 571, 607, 609, 610, 627, 628, 634, 635, 637, 639, 658, 659, 662,
663, 667, 668, 670, 672–678, 683, 684, 686, 694–696, 704–706,
708–715, 736, 741, 783–786, 788, 789, 826, 827, 829–831, 836–842, 844,
854, 858–872, 879, 883–886, 888–893, 900, 945, 946, 949, 951–954,
959–963, 966, 967, 970–983, 986–998, 1004, 1028, 1029, 1031–1033,
1055–1061, 1081, 1083, 1084, 1086–1091, 1120, 1121, 1140–1142,
1233–1236, 1238, 1239, 1258, 1259, 1279, 1280, 1282, 1285–1289, 1298,
1314, 1317, 1326, 1343–1347, 1360–1362, 1375, 1386, 1401, 1404, 1409,
1410, 1413–1415, 1422–1425, 1440, 1443, 1449, 1452, 1453, 1460, 1461,
1463, 1481, 1482, 1485–1488, 1494, 1497–1500, 1520–1523, 1541, 1544,
1546, 1549–1551, 1553–1558, 1562–1570, 1574–1577, 1579–1582,
1585–1592, 1594–1600, 1602, 1603, 1606–1608, 1610, 1618, 1619,
1621–1625, 1630, 1665, 1668–1672, 1690, 1711–1717, 1738, 1908–1923,
Article https://doi.org/10.1038/s41467-023-38900-z
Nature Communications | (2023) 14:3665 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved

2036–2038, 2042, 2044–2046–2049, 2051–2056, 2059, 2060,
2063–2066. Source data are provided with this paper.
Code availability
R code used to produce the analysis can be accessed at https://github.
com/BirdLifeInternational/petrels-plastics with the version on the date
of publication archived at https://zenodo.org/record/803386154.
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Acknowledgements
B.L.C., C.H., and A.M. were funded by the Cambridge Conservation
Initiative’s Collaborative Fund sponsored by the Prince Albert II of
Monaco Foundation. E.J.P. was supported by the Natural Environment
Research Council C-CLEAR doctoral training programme (Grant no.
NE/S007164/1). We are grateful to all those who assisted with the col-
lection and curation of tracking data. Further details are provided in the
Supplementary Acknowledgements. Any use of trade, firm, or product
names is for descriptive purposes only and does not imply endorsement
by the U.S. Government.
Author contributions
A.P.B.C., E.J.P., T.A.C., A.M., R.A.P., C.H. and M.P.D. conceived the study.
B.L.C., A.P.B.C., E.J.P., T.A.C., A.M., R.A.P., and M.P.D. designed the
methods. B.L.C., M.-M.R., E.J.P., and T.A.C. processed the petrel tracking
data. W.C. and M.E. processed the plastic density data. B.L.C. analysed
the data and produced the figures and tables. B.L.C., A.P.B.C., E.J.P., M.-
M.R., T.A.C., W.C., A.M., R.A.P., C.H., J.L.L. and M.P.D. drafted the
Article https://doi.org/10.1038/s41467-023-38900-z
Nature Communications | (2023) 14:3665 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved

manuscript. T.A.C., R.A.P., J.G.-S., J.A., Y.V.A.-B., J.A.-S., M.S.A., D.T.A.,
J.M.A., J.P.Y.A., N.J.P.B., C.B., A.M.B., J.Be., E.A.B., D.G.B., M.Be., M.Bi.,
O.K.B., M.Bo., K.A.B.J., J.J.B., K.B., V.B., J.Br., J.V.B., M.deL.B., K.C.B., L.B.,
L.Cal., L.Cam., M.J.C., R.D.C., N.C., A.R.C., P.C., T.C., J.G.C., F.R.C., Y.C.,
C.-Y.C., M.C.-B., R.H.C., J.B.C., V.C., B.C.C., J.D., F.DeP., Z.D., N.D.,
G.Dell’O., K.D., S.D., B.J.Di., H.A.D., J.Du., B.J.Du., L.M.E., A.I.F., A.L.F.,
J.J.F., J.H.F., A.N.D.F., A.F., G.G., D.Ga., C.G., I.S.G.C.G., M.G.F., J.P.G.,
W.J.G., D.Gr., T.G., G.T.H., L.R.H., E.S.H., A.H., M.H., H.H.H., L.M.H.,
H.F.R.H., M.H.-M., M.A.H., P.J.H., S.I., A.J., M.J., P.G.R.J., C.G.J., C.W.J.,
J.E.J., A.K., S.K., Y.Ki., H.K., Y.Ko., P.L.K., L.K., P.L., T.J.L., J.L.L., M.LeC.,
A.L., M.L., J.M., M.M., M.L.M., J.F.M., B.M., S.M., F.Mc.D., L.McF.T., F.M.,
B.J.M., T.M., W.A.M., R.C.M., L.N.-H., V.C.N., D.G.N., M.A.C.N., K.N., S.O.,
D.O., E.O., O.P., V.H.P., D.P., J.M.P., C.P., M.V.P., A.deP., A.T.M.P., P.P.,
P.A.P., I.L.P., B.J.P., T.A.P., C.D.L.P., C.B.P., J.P.-C., P.Q., J.L.Q., A.F.R., H.R.,
I.R., J.A.R., R.R., A.Ra., M.J.R., T.A.R., G.J.Rob., G.J.Roc., D.P.R., R.A.R.,
A.Ro., D.R., K.R., A.Ru., J.C.R., P.G.R., S.Sa., A.S.-A., M.S-S., Y.G.S., K.S.,
W.C.S., S.Sc., S.A.S., N.S., A.S., D.S., I.A.S., M.C.S., A.E.S., C.S., H.S.,
C.A.S., A.T., V.R.V.T., G.A.T., R.J.T., D.R.T., P.M.T., T.L.T., D.V.-S., E.V.,
E.D.W., S.M.W., H.W., H.U.W., T.Y., K.Y., C.B.Z., F.J.Z., and M.P.D. pro-
vided petrel tracking data and contributed to writing the manuscript.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains
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Correspondence and requests for materials should be addressed to
Bethany L. Clark, Ana P. B. Carneiro or Elizabeth J. Pearmain.
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,ElizabethJ.Pearmain
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