Analysis
https://doi.org/10.1038/s41893-021-00745-z

Pathways to sustaining tuna-dependent PacificIsland economies during climate change
Johann D. Bell 1,2 ✉, Inna Senina 3, Timothy Adams 2,4, Olivier Aumont5, Beatriz Calmettes3,
Sangaalofa Clark6, Morgane Dessert7,8, Marion Gehlen 9, Thomas Gorgues7, John Hampton 10,
Quentin Hanich2, Harriet Harden-Davies2, Steven R. Hare10, Glen Holmes11, Patrick Lehodey3,
Matthieu Lengaigne 5,12, William Mansfield13, Christophe Menkes 14, Simon Nicol 10,15,
Yoshitaka Ota16, Coral Pasisi17, Graham Pilling10, Chis Reid18, Espen Ronneberg19, Alex Sen Gupta 20,
Katherine L. Seto2,21, Neville Smith10, Sue Taei1,22, Martin Tsamenyi2 and Peter Williams10
Climate-driven redistribution of tuna threatens to disrupt the economies of Pacific Small Island Developing States (SIDS)
and sustainable management of the world’s largest tuna fishery. Here we show that by 2050, under a high greenhousegas emissions scenario (RCP 8.5), the total biomass of three tuna species in the waters of ten Pacific SIDS could decline by anaverage of 13% (range = −5% to −20%) due to a greater proportion of fish occurring in the high seas. The potential implications for Pacific Island economies in 2050 include an average decline in purse-seine catch of 20% (range = −10% to −30%),
an average annual loss in regional tuna-fishing access fees of US$90 million (range = −US$40 million to –US$140 million)
and reductions in government revenue of up to 13% (range = −8% to −17%) for individual Pacific SIDS. Redistributionof tuna under a lower-emissions scenario (RCP 4.5) is projected to reduce the purse-seine catch from the waters ofPacific SIDS by an average of only 3% (range = −12% to +9%), indicating that even greater reductions in greenhouse gas
emissions, in line with the Paris Agreement, would provide a pathway to sustainability for tuna-dependent Pacific Island
economies. An additional pathway involves Pacific SIDS negotiating within the regional fisheries management organization
to maintain the present-day benefits they receive from tuna, regardless of the effects of climate change on the distributionof the fish.

Fishing for tuna plays a vital role in the economic development
and/or food security of most of the 22 Pacific Island countries
and territories1,2. However, in the case of ten of these Pacific
Small Island Developing States (SIDS), the contributions of tuna to
the economy are so substantial that these SIDS are ‘tuna-dependent’(Fig. 1 and Supplementary Note 1). More than 95% of all tuna caught
from the jurisdictions of the 22 Pacific Island countries and territories comes from the combined exclusive economic zones (EEZs)of these 10 SIDS (Supplementary Table 1), and access fees paid byindustrial fishing fleets provide an average of 37% (range = 4−84%)of their government revenue (excluding grants) (Supplementary
Table 2). These extraordinary benefits have been secured mainly
through cooperative management of the purse-seine fishery within
the combined EEZs of nine of the ten Pacific SIDS under the Parties
to the Nauru Agreement (PNA) Vessel Day Scheme (VDS) (Box 1).
Longline and pole-and-line fishing also occur within the watersof tuna-dependent Pacific SIDs but generally make minor contributions to these economies compared with purse-seine fishing(Supplementary Note 1).
The socioeconomic importance of tuna has been recognized by Pacific Islands Leaders in their Regional Roadmap for
Sustainable Pacific Fisheries3, which aims to sustain harvests, add
value to catches, increase employment associated with tuna fishing and processing, and allocate more tuna for local food security.
The Roadmap’s sustainability goal is being achieved; over the past
decade, annual purse-seine catches from the EEZs of the ten Pacific
SIDS have deviated little from the ten-year average of 1.4 million
tonnes (Supplementary Table 3) (coefficient of variation = 9%) andare approximately half as variable as catches from high-seas areas(Supplementary Table 4) (coefficient of variation = 17%). In addition, none of the tuna species caught by purse-seine in the region
are overfished or subject to overfishing4, due largely to cooperative

1
Center for Oceans, Conservation International, Arlington, VA, USA. 2Australian National Centre for Ocean Resources and Security (ANCORS), Universityof Wollongong, Wollongong, New South Wales, Australia. 3Collecte Localisation Satellite, Toulouse, France. 4Gonedau Foundation, Port Ouenghi, New
Caledonia. 5LOCEAN-IPSL, IRD/Sorbonne Universités/CNRS/MNHN, Paris, France. 6Parties to the Nauru Agreement Office, Majuro, Marshall Islands.
7
Univ. Brest, CNRS, IRD, Ifremer, Laboratoire d’Océanographie Physique et Spatiale (LOPS), IUEM, Brest, France. 8Université de Bretagne Occidentale,
Brest, France. 9LSCE-IPSL, CEA/CNRS/ Université Paris-Saclay, Gif-sur-Yvette, France. 10Fisheries, Aquaculture and Marine Ecosystems Division, The
Pacific Community (SPC), Noumea, New Caledonia. 11The Pew Charitable Trusts, Brisbane, Queensland, Australia. 12MARBEC, Université Montpellier,
CNRS, Ifremer, IRD, Sète, France. 13Paekakariki, Wellington, New Zealand. 14ENTROPIE, IRD/Université de la Réunion/Université de Nouvelle-Calédonie/
CNRS, Ifremer, Noumea, New Caledonia. 15Centre for Conservation Ecology and Genomics, Institute for Applied Ecology, University of Canberra, Bruce,
Australian Capital Territory, Australia. 16School of Marine and Environmental Affairs, University of Washington, Seattle, WA, USA. 17Office of the Director
General, The Pacific Community (SPC), Noumea, New Caledonia. 18Pacific Islands Forum Fisheries Agency, Honiara, Solomon Islands. 19Secretariat of the
Pacific Regional Environment Programme, Apia, Samoa. 20Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, University
of NSW, Kensington, New South Wales, Australia. 21Environmental Studies Department, University of California at Santa Cruz, Santa Cruz, CA, USA.
22
Deceased: Sue Taei. ✉e-mail: jbell@conservation.org

900

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Analysis

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20° N

Northern
MarianaIslands

Access fees
= US$7.1 million
9.4% government
revenue

47.6% government
revenueMarshall Islands

Access fees
= US$128.3 million

Access fees
= US$134.3 million

Access fees
= US$29.5 million

4.0% government
revenue

31.2% government
revenuePapua New Guinea

70.6% government
revenue
Nauru

Equator

Kiribati(Gilbert Islands)
Kiribati(Phoenix Islands)

Access fees
= US$25.6 million
SolomonIslands

Tuvalu53.9% government
revenue

Tokelau

9.6% government
revenue

Access fees
= US$13.4 million

VanuatuFiji

84.2% government
revenue

New Caledonia

140° E

Access fees
= US$13.5 million

Kiribati(Line Islands)

10.6% government
revenue

Wallis& Futuna Samoa

Access fees
= US$41.3 million

20° S

Participants in the PNA VDS

47.8% government
revenue

Federated States of Micronesia

Palau

0°

Guam

HawaiiAccess fees
= US$31.0 million

Access fees
= US$68.4 million

American
Samoa

Cook Islands
French Polynesia

Niue

Tonga

160° E180°160° W

140° W

Fig. 1 | Tuna-dependent Pacific SIDS in the western and central Pacific Ocean. Average annual tuna-fishing access fees (US$) for the period 2015−2018
earned by the ten Pacific SIDS, together with the average percentage contributions of access fees to total government revenue (excluding grants). The nine
Pacific SIDS participating in the PNA VDS (Box 1) are also shown. Access fee and government revenue data sourced from ref. 1 and the Pacific Islands Forum
Fisheries Agency.

management by PNA members (Box 1) (Supplementary Notes 3
and 4) under the auspices of the Western and Central PacificFisheries Commission (WCPFC).
Cooperative management of the purse-seine fishery has alsoenabled tuna-dependent economies to adapt to the profound effectsof the El Niño–Southern Oscillation (ENSO) on the extent of thewestern Pacific warm pool (‘warm pool’) and the associated distribution of the abundant skipjack tuna5,6 (Box 1). However, therealization that climate change will alter the tropical Pacific Ocean,and cause further modifications to ENSO7,8 and the warm pool(Supplementary Note 5), has prompted the regional organizations
assisting Pacific SIDS to manage their tuna resources, the PacificCommunity, Pacific Islands Forum Fisheries Agency and WCPFC,
to support modelling of the effects of climate change on tuna
biomass9–12. This modelling, which focused mainly on the EEZsof Pacific SIDS, projected progressive redistribution of tuna
biomass in equatorial waters to the east and, to a more modest
extent, to higher latitudes. Provided tuna biomass remains highwithin the combined EEZs of PNA participants, the provisions of theVDS (Box 1) are expected to limit the implications of climate-driven
tuna redistribution for tuna-dependent economies and the goals of
the Roadmap.
However, many Pacific SIDS are increasingly concerned that
redistribution to the east could lead to decreases in tuna biomass
within their EEZs, and increases in high-seas areas, undermining the socioeconomic benefits they derive from tuna fishing(Supplementary Note 6) and the strong management of tropical Pacific tuna resources. Preliminary analyses justify these concerns13,14. Here, we evaluate the risks to the sustainability of the ten
tuna-dependent Pacific Island economies by simulating changes
to tuna biomass in their EEZs and in high-seas areas in the western and central Pacific Ocean (WCPO) and eastern Pacific Ocean(EPO) under different greenhouse gas (GHG) emissions scenarios

by 2050, using a more-robust modelling approach than in previous studies. Our analysis then cascades the results of the biomass
modelling to assess the potential implications for future purse-seine
catches within the EEZs of the ten Pacific SIDS and for the vital
government revenue flowing to these highly vulnerable states frompurse-seine fishing. Finally, the projected patterns of tuna redistribution are used to identify the most promising pathways forenabling tuna-dependent economies to retain the socioeconomic
benefits they now receive from tuna.

Projected changes in tuna biomass

We used the Spatial Ecosystem and Population Dynamics Model(SEAPODYM)15–17, informed by projected changes to the tropical Pacific Ocean derived from four Earth system models (ESMs),
to simulate the responses of tuna species caught by purse-seine
fishing (skipjack, yellowfin and bigeye tuna) to climate change.
SEAPODYM simulates the spatial dynamics of tuna under theeffects of fishing and key environmental variables (temperature,primary production, oceanic currents and dissolved oxygen) andthe predicted distributions of tuna prey in three layers of the watercolumn between the surface and a depth of ~1,000 m (Methods).
This modelling framework synthesises current knowledge on the
biology, ecology and population dynamics of the key life stages oftuna species (from larvae to mature fish) in their oceanic ecosystemand has been validated against a large set of observations (Methods
and Supplementary Note 7). We assume that the relationships andmechanisms inherent in SEAPODYM will remain valid over the
next few decades.
We modelled the responses of tuna biomass to theIntergovernmental Panel on Climate Change (IPCC) representative concentration pathway (RCP) 8.5, RCP 4.5 and RCP 2.6 emissions scenarios for 2050 in the EEZs of Pacific SIDS, focusing
on the ten tuna-dependent SIDS (Fig. 1), and in high-seas areas

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equivalent to RCP 4.5 and RCP 2.6 in 2050 (Methods). Estimating
the effects of the lower-emissions scenarios in this way was necessary because appropriate ocean forcings, with bias-corrected physical and biogeochemical variables, do not yet exist for RCP 4.5 and
RCP 2.6.
Our modelling of climate-driven redistribution of tuna under
RCP 8.5 by 2050 (Fig. 2 and Supplementary Fig. 2) indicates that
total biomass of the three species (tuna biomass) in the combined
jurisdictions of the 10 Pacific SIDS would decrease by an average of
13% (range = −5% to −20%) (Fig. 3a) and by up to ~30% in 10 of the
12 individual EEZ areas (noting that Kiribati has three separate EEZ
areas) (Supplementary Table 5). Conversely, tuna biomass is projected to increase by an average of 23% (range = 13% to 32%) in thecentral EPO (EPO-C, Fig. 3a), the high-seas area where most tuna
are caught (Supplementary Table 4). Tuna biomass is also projected
to increase by an average of up to ~20% in nine other high-seas
areas (Supplementary Table 6) and by an average of 12% in all
high-seas areas combined (Supplementary Fig. 5). These projectedchanges in tuna biomass by 2050 generally reflect the contrasting
responses of sea surface temperature (SST), primary production
and the prey organisms of tuna to RCP 8.5 in the WCPO and EPO(Supplementary Figs. 3 and 4).
The projected effects of the more moderate, surrogate RCP 4.5
emissions scenario (RCP 4.5) on redistribution of tuna biomass(Fig. 2) are far less pronounced (Fig. 3a). Under this scenario, total
biomass of tuna in the combined EEZs of the ten Pacific SIDS
decreases by an average of only 1% (range = −9% to +8%), anddecreases occur on average in only three EEZ areas (Supplementary
Table 7). By contrast, the projected effects of RCP 4.5 on tuna
biomass in high-seas areas are similar to those for RCP 8.5; thatis, there is an average increase of 18% (range = +9% to +32%) in
EPO-C (Fig. 3a), an average increase of up to 19% in 11 of the otherhigh-seas areas (Supplementary Table 8) and an average increaseof 11% in all high-seas areas combined (Supplementary Fig. 5).
A possible explanation for the substantial increases in tuna biomassin high-seas areas under RCP 4.5 by 2050 is the stronger response
to increased GHG emissions by the food web supporting tuna in the
EPO than in the WCPO (Supplementary Fig. 4).
On average, the biomass of tuna in the combined EEZs of allother Pacific SIDS (which occur mainly in sub-equatorial waters)also increased under RCP 8.5 and RCP 4.5 (Supplementary Tables 9
and 10). However, this increase was less than 20% of the increase in
EPO-C (Supplementary Fig. 5), indicating that most redistributionof tuna biomass within the Pacific Ocean is projected to occur inequatorial areas.
Results for the strong mitigation RCP 2.6 simulations are notpresented because the uncertainty associated with using RCP
8.5 forcings to estimate RCP 2.6 is too high to have reasonableconfidence in the results (Methods).

Box 1 | The PNA VDS

Pacific SIDS that are the PNA (Federated States of Micronesia,
Kiribati, Marshall Islands, Nauru, Palau, Papua New Guinea,
Solomon Islands and Tuvalu) have developed a system for jointly
managing the purse-seine fishery targeting skipjack tuna within
their combined EEZs. It is called the Vessel Day Scheme (VDS)69(Supplementary Note 4). Tokelau also participates in the VDS
under a memorandum of understanding with PNA.
The ‘cap and trade’ VDS sets the total annual purse-seine
fishing effort within the combined EEZs of PNA members at~45,000 fishing days and allocates these days to members on thebasis of individual EEZ areas and their past 8−10 years of fishingeffort history. The VDS provides a trading mechanism among
PNA members70,71, allowing them to respond to the profoundeffects of ENSO on the warm pool and the prime fishing grounds
for skipjack tuna5,6. During La Niña events, the best catches ofskipjack tuna are made in the west of the region (see top panelin the following diagram), whereas during El Niño events fishingis most efficient up to 4,000 km to the east (see bottom panel).
During La Niña events, the VDS enables countries in the west
to buy fishing days from members in the east, enabling fleets
to keep fishing in the west. The reverse occurs during El Niño
events. Therefore, regardless of where the tuna are caught, all
PNA members receive access fees every year. In this way, the VDS
evens out the previously high interannual variability in accessfees received by PNA members and helps stabilize government
revenue for tuna-dependent economies.
La Niña
2011

El Niño
2015

Purse-seine effort

Implications for tuna catches

SST > 28.5 °C

The various provisions of the VDS—transferability of fishingdays among PNA members, ‘pooling’ of days by groups ofmembers, and ‘roaming’ of vessels from PNA member countries
among their collective EEZs70—also provide non-confrontational
adaptations to the progressive redistribution of skipjack tunawithin the combined EEZs of PNA members due to ocean
warming70,71 (Supplementary Note 4). However, the VDS does
not encompass adaptations for the redistribution of tuna fromthe EEZs of PNA members to high-seas areas.
(Supplementary Fig. 1) (Methods). Projections for RCP 4.5 and RCP
2.6 were estimated on the basis of examining changes in the RCP
8.5 simulation at the times when CO2 concentrations reach those902

The projected changes in tuna biomass due to increased GHG
emissions are expected to affect purse-seine catches of tuna fromthe EEZs and high-seas areas (Methods). By 2050 under RCP
8.5, the total purse-seine catch from the combined EEZs of theten tuna-dependent Pacific SIDS is estimated to decrease by anaverage of 20% (range = −30% to −10%), that is, 284,000 tonnes(range = −428,000 to −143,000 tonnes); it is estimated to increaseby an average of 27% (range = +15% to +37%), that is, 125,000
tonnes (range = +69,000 to +169,000 tonnes), in EPO-C (Table 1,
Fig. 3b and Supplementary Tables 11 and 12).
The projected changes in purse-seine catch by 2050 under
RCP 4.5 also follow the patterns in tuna biomass, decreasing by
an average of 3% (range = −12% to +9%), that is, 47,000 tonnes(range = −165,000 to +124,000 tonnes), in the combined EEZs of
the ten Pacific SIDS and increasing in EPO-C by an average of 18%

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2015

Skipjack

Yellowfin

Bigeye

300

200
0°

150
100

20° S

50
40° S

120
100
80
60
40
20

0
150° E

160° W

70
60
50
40
30
20
10
0

0
150° E110° W

160° W

Average biomass distribution (kg km−2)

20° N

140

Average biomass distribution (kg km−2)

250

Average biomass distribution (kg km−2)

40° N

110° W

150° E

160° W110° W

RCP 8.5 2050
40° N

60

100

0

20° S

–50

40° S

0
–20
–40

–100
150° E

160° W

10

0

–10

–20

–60

110° W

150° E

160° W

Mean anomaly (kg km−2)

0°20Mean anomaly (kg km−2)

50

Mean anomaly (kg km−2)

20° N

20
40

110° W

150° E

160° W110° W

RCP 4.5 2050

50

0°

0

20° S

–50

40° S

20
40
20
0
–20
–40

–100
150° E

160° W110° W

10

0

–10

–20

–60
150° E

160° W110° W

Mean anomaly (kg km−2)

20° N

60

Mean anomaly (kg km−2)

100
Mean anomaly (kg km−2)

40° N

150° E

160° W110° W

Fig. 2 | Projected effects of climate change on the distributions of the three tuna species caught by purse-seine fishing in the Pacific Ocean. Averagebiomass distributions (kg km–2) of skipjack, yellowfin and bigeye tuna in the Pacific Ocean basin for 2015 (2011−2020) (top row) and mean anomalies(kg km–2) from the average 2015 biomass distribution of each tuna species projected to occur by 2050 (2044−2053) under two emissions scenarios,
RCP 8.5 (middle row) and RCP 4.5 (bottom row). Shading indicates areas where projections from all four ESMs (Methods) agree in the sign of change,
excluding near-zero changes (white zones).

(range = +7% to +34%), that is, 84,000 tonnes (range = +32,000
to +154,000 tonnes) (Table 1, Fig. 3b and Supplementary
Tables 13 and 14).

Effects on tuna-dependent economies

The estimated changes in purse-seine catch under RCP 8.5 could
reduce total annual fishing access fees earned by the ten Pacific
SIDS by an average of US$90 million (range = –US$40 million to−US$140 million) per year compared with the average annual revenue received between 2015 and 2018 (Table 2 and Supplementary
Table 15). Losses in access fees are estimated to occur in all ten
Pacific SIDS under RCP 8.5, and reduce total government revenue
by up to 13% (range = −8% to −17%) for individual Pacific SIDS, by
2050 (Table 2 and Supplementary Table 15).
Under RCP 4.5, the average change in access fees for all ten Pacific
SIDS represents a loss of US$12 million (range = −US$54 million to+US$48 million) per year (Table 2 and Supplementary Table 16).
Due to the more-limited loss of access fees under RCP 4.5, total
government revenue in 2050 is estimated to decrease by an average of 1% or less in only three of the ten Pacific SIDS (Table 2 andSupplementary Table 16).
The estimates of reduced access fees, and flow-on losses ingovernment revenue, due to climate-driven redistribution of tuna
include a number of assumptions (Methods) but, overall, are probably conservative because they do not account for the control that theten Pacific SIDS exert in the marketplace. At present, these Pacific
SIDS command high access fees because ~90% of the catch fromthe purse-seine fishing grounds within the Pacific Island regionof the WCPO comes from their combined EEZs (Supplementary
Note 1). However, if there is substantial movement of fish from the
EEZs to high-seas areas, the ten Pacific SIDS would be unlikely toobtain the same daily rates for fees. Any such effects are also likely
to occur to some extent under RCP 4.5, which is projected to reducecatches in the combined EEZs of the ten Pacific SIDS by ~50,000
tonnes and increase catches in high-seas areas by more than 100,000
tonnes (Table 1).
Even at conservative levels, the estimated losses in fishingaccess fees are expected to have substantial implications for
economic development. They would coincide with the needfor increased financial resources and flexibility to adapt toclimate change, including sustained government facilitationof community-based initiatives18. The projected reductions intuna biomass and catch are also expected to affect the ability ofmany of the ten Pacific SIDS to harmonize the employment,
value-adding and food security goals of the Roadmap3 and achievesustainable development19,20. With a lower biomass of tuna within
their EEZs, several of the ten Pacific SIDS may need to use agreater proportion of their tuna resources for local consumption2, further limiting the scope for earning access fees and potentially reducing the supply of tuna that supports employment innational canneries.

Implications for fisheries management

The projected climate-driven redistribution of tuna biomass andpurse-seine catches also has potential implications for sustainable
management of the world’s largest tuna fishery. In a scenario where

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Ten Pacific SIDS

EPO-C
RCP 8.5

RCP 4.5

RCP 8.5

1,000+31.5%

1,000

Biomass

500

+8.2%

Tonnes (× 1,000)aTonnes (× 1,000)+23.3%

0
–1.3%
–5.1%
–500
–12.6%–1,000–9.0%

RCP 8.5

0–500

RCP 4.5

RCP 8.5

RCP 4.5500

250

Tonnes (× 1,000)

Tonnes (× 1,000)

+8.8%

EPO-C

500

Catch

+18.3%–1,000–20.3%Ten Pacific SIDSb

500 +13.4%

RCP 4.5+31.8%

+8.8%
0
–3.4%
–10.1%–250–11.9%

250

0+36.9%+27.4%+15.0%+33.8%+18.3%+7.0%–250–20.2%–500–30.4%–500

Fig. 3 | Redistribution of tuna biomass and purse-seine catch from the combined EEZs of the ten tuna-dependent Pacific SIDS to the central eastern
Pacific Ocean. a, Projected changes in total biomass of skipjack, yellowfin and bigeye tuna in the combined EEZs of the ten Pacific SIDS and EPO-C under
the RCP 8.5 and RCP 4.5 emissions scenarios in 2050 relative to the average biomass from these areas in 2009−2018. b, Projected changes in total
purse-seine catch in the EEZs of the ten Pacific SIDS and EPO-C under the RCP 8.5 and RCP 4.5 emissions scenarios in 2050 relative to the average catchfrom these areas in 2009−2018. The dark column in each histogram represents the average change from the four ESMs. The columns on either side of
the average represent the maximum and minimum changes projected by the range of ESMs. See Supplementary Tables 5–8 for further details of changes
in biomass and Supplementary Tables 11–14 for details of changes in purse-seine catch. Projected changes in biomass and catch for all high-seas areas
combined are presented in Supplementary Fig. 5.

a lower proportion of tuna resources is under the jurisdiction of
the PNA VDS (Box 1), the sustainability of tuna catches could beat greater risk because the monitoring, control and surveillance
required to combat illegal, unreported and unregulated fishing,and impose penalties for non-compliance, are more difficult inhigh-seas areas21. This is because responsibility for compliance with
904

fishing regulations on the high seas rests with the states that ‘flag’fishing vessels (often resulting in self-regulation), whereas compliance within EEZs is under the purview of coastal states. With continued GHG emissions, the onus will be on WCPFC to implementtighter controls on fishing for tropical tuna species by all vessels
operating in high-seas areas of the WCPO.

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Table 1 | Average projected changes in purse-seine catch from the EEZs of tuna-dependent Pacific SIDS and high-seas areas
Area

Average catch (t)RCP 8.5 2050

RCP 4.5 2050

Catch (t)Change (t)Change (%)

Catch (t)Change (t)Change (%)

EEZs of Pacific SIDS
Cook Islands

11,08010,640−440−4.0

12,065

+985

+8.9

FSM

178,587

155,407−23,180−13.0

173,773−4,815−2.7

Kiribati

396,048

363,520−32,528−8.2

423,251+27,202+6.9

Gilbert Islands(260,073)(225,177)(−34,896)(−13.4)(278,023)(+17,950)(+6.9)

Phoenix Islandsa(94,696)(92,140)(−2,557)(−2.7)(101,132)(+6,435)(+6.8)

Line Islandsa(41,279)(46,203)(+4,924)(+11.9)(44,096)(+2,817)(+6.8)

Marshall Islands

37,00336,728−275−0.7

37,778

+775

+2.1

Nauru

110,794

86,886−23,908−21.6

117,059+6,266

+5.7

Palau

2,655

2,646

−9

−0.3

2,738

+82

+3.1

Papua New Guinea

461,032

308,404−152,628−33.1

389,654−71,378−15.5

Solomon Islands

116,877

86,399−30,477−26.1

106,740−10,137−8.7

Tokelau

21,392

17,954−3,438−16.1

22,610+1,218+5.7

Tuvalu73,08055,992−17,088−23.4

75,589+2,509+3.4

Total EEZs

1,408,548

1,124,577−283,971−20.2

1,361,257−47,291−3.4

I1

15,330

11,396−3,934−25.7

13,541−1,790−11.7

I2

23,083

16,413−6,670−28.9

20,738−2,345

−10.2

I3

47

60

+13

+27.8

61

+14

+29.8

I4

21,443

21,773+330+1.5

22,727+1,284+6.0

I523,231

28,021+4,790+20.6

26,194+2,963+12.8

I616,211

16,868+657+4.1

17,800+1,589

+9.8

I7

16.7

18

+1.3

+9.0

17

+0.2

+1.3

I8

2.2

3

+0.8

+15.5

3

+0.4

+20.2

I9

33.2

41

+7.8

+24.7

36

+3

+8.9

H4

20,893

17,796−3,097−14.8

23,308+2,415+11.6

H546,51749,502+2,985+6.4

48,360+1,842

+4.0

EPO-N

84,175

100,443+16,268+19.3

98,130+13,955

+16.6

EPO-C

457,664

583,082+125,418+27.4

541,194+83,530

+18.3

EPO-S

3,293

4,339

+1,046

+31.8

3,747

+454

+13.8

Total high seas

711,939849,755+137,816+19.4

815,856+103,917+14.6

aHigh-seas areas

Ten-year (2009−2018) average purse-seine tuna catches in tonnes (t) from the EEZs of ten Pacific SIDS and high-seas areas together with average projected changes to these catches by 2050 in tonnes
and percentage terms under the RCP 8.5 and RCP 4.5 emissions scenarios (see Supplementary Fig. 6 and Supplementary Tables 11–14 for ranges of projected changes in catch). aThe three EEZ areas of
Kiribati, which have been integrated to produce the total for Kiribati. FSM, Federated States of Micronesia; see Supplementary Fig. 1 for locations and definitions of all high-seas areas.

Sustainable management of tropical Pacific tuna resources will
also be challenged by the substantial projected increases in averagetuna biomass in the EPO-C high-seas area, particularly under RCP
8.5 (Fig. 3a and Table 1). This will necessitate closer collaboration
between WCPFC and the regional fisheries management organization for the EPO, the Inter-American Tropical Tuna Commission(IATTC). The shared governance arrangements between WCPFC
and IATTC that are already in place for the overlap in their convention areas (Supplementary Fig. 1) will need to be expanded andstrengthened to avoid the problems that have accompanied management of climate-driven shifts in fish distribution in other jurisdictions22,23 and to address the economic impacts that reductions inaccess fees would have on tuna-dependent Pacific SIDS.
Increased tuna biomass in high-seas areas will also need to beconsidered during the application of the emerging instrument forconservation and sustainable use of marine biological diversity inareas beyond national jurisdiction (BBNJ Agreement) under theUnited Nations Convention on the Law of the Sea24 (Supplementary
Note 8). Although the BBNJ Agreement has been designed to avoid
undermining existing governance regimes in high-seas areas25,26,
questions remain regarding interactions between fisheries management and components of the agreement related to area-based
management, transfer of technology and capacity building27,28.
Accordingly, WCPFC and IATTC will need to develop transparent
systems for information exchange and cooperation with the BBNJ
institutional framework and manage any new impacts between
fisheries and high-seas biodiversity caused by climate change.

Pathways to sustaining tuna-dependent economies

Development of responsible systems for the sustainable management of tropical tuna resources and maintenance of high-seasbiodiversity are not the only challenges as the Pacific Ocean is transformed by GHG emissions. The projected declines in government
revenue for tuna-dependent Pacific Island economies due to tuna

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Table 2 | Projected changes in tuna-fishing access fees and government revenue for the ten tuna-dependent Pacific SIDS
Pacific SIDS

Average 2015−2018
Government
Access fees
revenue (million (million
US$)
US$)

Access fees
as % ofgovernment
revenue

Change by 2050 (RCP 8.5)
Purse-seine
tuna catch(%)aAccessfees(millionUS$)

Government
revenue (%)

Change by 2050 (RCP 4.5)
Purse-seine
tuna catch(%)aAccess fees Government(millionrevenue (%)US$)

Cook Islands

126.1

13.5

10.6

−4.0

−0.5

−0.4

+8.9

+1.2

+1.0

FSM

150.6

68.4

47.6

−13.0

−8.9

−5.9

−2.7

−1.8

−1.2

Kiribati

181.7

128.3

70.6

−8.2

−10.5

−5.8

+6.9

+8.9

+4.9

Marshall Islands

66.1

31.0

47.8

−0.7

−0.2

−0.3

+2.1

+0.7

+1.0

Nauru

98.6

29.5

31.1

−21.6

−6.4

−6.5

+5.7

+1.7

+1.7

Palau

75.2

7.1

9.4

−0.3

−0.02

−0.03

+3.1

+0.2

+0.3

PNG

3,360.8

134.3

4.0

−33.1

−44.4

−1.3

−15.5

−20.8

−0.6

Solomon Islands

429.0

41.3

9.6

−26.1

−10.8

−2.5

−8.7

−3.6

−0.8

Tokelau

16.0

13.4

84.2

−16.1

−2.1

−13.4

+5.7

+0.8

+4.8

Tuvalu

47.4

25.6

53.9

−23.4

−6.0

−12.6

+3.4

+0.9

+1.9

Total

−89.9

492.4

−12.0

Average government revenue (excluding grants), tuna-fishing access fees and the percentage of government revenue derived from access fees for ten tuna-dependent Pacific SIDS between 2015 and 2018,
together with estimated changes in purse-seine tuna catch, access fees and government revenue, by 2050 under the RCP 8.5 and RCP 4.5 emissions scenarios. See Supplementary Tables 15 and 16 forranges of estimated percentage changes in access fees and government revenue by 2050, and details of the calculations summarized here. PNG, Papua New Guinea. aProjected change in average total
purse-seine catch due to climate-driven redistribution of total tuna biomass (Supplementary Tables 17 and 18).

redistribution also highlight the need to identify pathways to sustain these economies and secure climate justice29 for these Pacific
SIDS (Supplementary Note 9).
The United Nations Convention on the Law of the Sea is silent
on climate change. However, the most important pathway to sustaining Pacific Island economies is common to all climate justiceaction—reduction of GHG emissions to limit global temperaturerise to 1.5 °C by the end of the century30. Although the effects of
RCP 2.6 (which limits warming to ~2 °C by 2100, approximating
the aspirations of the Paris Agreement) could not be assessed effectively during this study, the marked difference between projected
tuna catches across the tropical Pacific Ocean under RCP 8.5 and
RCP 4.5 (Table 1 and Fig. 3) indicates that further reductions to
GHG emissions to achieve the goals of the Paris Agreement should
substantially reduce the potential impacts of climate change fortuna-dependent economies. This conclusion is supported by modelling for other fisheries31.
However, another pathway based on a regional approach isalso essential for sustaining the economies of tuna-dependent
Pacific SIDS in case there is inadequate progress in attaining thegoals of the Paris Agreement. This pathway involves negotiation,
through the Convention on the Conservation and Management ofHighly Migratory Fish Stocks in the Western and Central Pacific
Ocean (WCPF Convention)32, to enable Pacific SIDS to retain thesocioeconomic benefits they now receive from tuna, regardless ofclimate-driven redistribution of the fish (Supplementary Note 10).
The WCPF Convention is the appropriate vehicle for these negotiations because it is based on the important principles of international cooperation and long-term sustainability established underthe 1995 United Nations Fish Stocks Agreement33. The conservationand management measures within the WCPFC provide the framework for negotiations to ensure that catches are consistent with theobjective of long-term conservation of tuna resources, conservation and management measures consider the special requirementsof developing States and do not place a disproportionate burden of
conservation action on such States, and the respective dependence
on fish resources by members of the WCPF Convention, including Pacific SIDS, are taken into account. Such negotiations should906

recognize the injustice of climate-driven tuna redistribution for
Pacific SIDS, be guided by WCPFC Resolution 2019-01 on Climate
Change (Supplementary Note 6) and consider conferring more
responsibility for management of tuna in high-seas areas of the
WCPO to Pacific SIDS.
On the basis of its existing conservation and management measures, WCPFC agreed in 2017 to begin a process for adopting hard
limits for the high-seas purse-seine fishery for tropical tuna and allocating access rights34 (Supplementary Note 11). This process will becompatible with the existing limits that apply to EEZs and provide
a mechanism for WCPFC to ensure that the benefits of rights that
presently accrue to Pacific SIDS are locked in for the long term35.
Allocation of long-term rights would substantially mitigate the projected effects of tuna biomass declines within the EEZs of the ten
tuna-dependent Pacific SIDS and maintain the revenues they derivefrom industrial fishing. A member of WCPFC (South Korea) has
already suggested exploring the possibility of making PNA vesseldays (Box 1) transferable to high-seas areas, under present-day conditions when fish are more abundant there, in ways that do not jeopardize the sovereign rights or aspirations of PNA members36. Suchan arrangement could lay the foundation for enabling distant-water
fishing fleets to continue paying revenue to PNA members foraccess to their EEZs but use the vessel days to fish on the high seas
when catch rates are better there. It is a prime example of a possible
equitable solution within the ‘negotiation’ pathway.
In the event that negotiations within the WCPFC are unsuccessful, Pacific SIDS could use a conciliation commission, based on
the dispute settlement mechanism under the WCPF Convention
and international law, to identify a lasting solution that incorporates an appropriate level of flexibility and stability. Other possible mechanisms available to the tuna-dependent Pacific SIDS
include the United Nations Framework Convention on Climate
Change Warsaw International Mechanism for Loss and Damage(Supplementary Note 12) and the Climate Security Mechanism of
the United Nations (Supplementary Note 13).
Although the rationale for the negotiations outlined here is basedon the projected redistribution of tuna, we recognize that there arealso systemic and procedural issues associated with the prevailing

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circumstances37–39. These issues should also be examined to
identify the full dimensions of justice and equity for tuna-dependent
Pacific SIDS.

Discussion

This analysis demonstrates that sustainable development oftuna-dependent economies in the Pacific Island region is likely to beat substantial risk from continued high GHG emissions. Althoughconsiderable uncertainty remains, our modelling provides sufficient
information to indicate that it is not a question of ‘if ’ tuna biomass
will shift from the combined EEZs of the ten Pacific SIDS but ‘when,how quickly and to what extent’. It is important that this risk be recognized by the United Nations Framework Convention on Climate
Change and included in the rationale for limiting global warming inline with the Paris Agreement.
The process to identify a mechanism to eliminate or substantially reduce this risk for tuna-dependent Pacific SIDS, based on theprinciples of cooperation and long-term sustainability through the
WCPF Convention, should also begin immediately. Ultimately,
the necessary international negotiations will be facilitated by reducing uncertainty in the timing and extent of tuna redistribution andthe associated impacts on catch, access fees and government revenue.
Reducing the remaining uncertainty in redistribution of tuna
biomass will depend on improving tuna modelling to increase thespatial resolution (for example, up to 0.5°), incorporating ocean
forcings for all emissions scenarios considered in the Coupled
Model Intercomparison Project Phase 6 (CMIP6), integrating additional and enhanced biogeochemical models into the simulation
ensemble for the impacts of ocean warming and acidification on
the food webs that support tuna, and assessing the effects of theInterdecadal Pacific Oscillation40 on the onset of accelerated ocean
warming. Preliminary genetic research, showing that some tropical tuna species are composed of multiple, self-replenishing populations (‘stocks’)41–44, indicates that efforts to reduce uncertainty inredistribution of tuna biomass will also be strengthened by identifying the stock structure of each species. This would enable theresponse of each stock to climate change to be modelled separately
and then aggregated to produce a more accurate understanding oftuna redistribution from EEZs to high-seas areas.
Once improved estimates of biomass redistribution are available,and policy alternatives have been developed to limit the vulnerability of Pacific SIDS, bio-economic modelling will be needed to help
reduce uncertainty in future purse-seine catches and the associatedcontributions to tuna-dependent economies. Such modelling should
explore the effects of different fleet-dynamics and market-force scenarios on catch rates and government revenue derived from accessfees. It should also examine the extent to which various proposed
policies and regional fisheries management arrangements empower
the ten Pacific SIDS to maintain control over their historical levelsof purse-seine catch.
These investments will not only enable tuna-dependent Pacific
SIDS to negotiate more effectively for the international agreements
and management actions required to sustain their economies, they
will also provide WCPFC and IATTC with the information needed
to identify shared stocks, improve stock assessments and harmonize
their conservation and management measures—information that isessential for minimizing the impact of climate change on the sustainability of tuna resources45.

Methods

Ocean forcings. The Nucleus for European Modelling of the Ocean (NEMO)
ocean framework46, which includes an online coupling with the biogeochemicalcomponent PISCES in a 2° latitude × 2° longitude configuration47,48, was used tosimulate the historical oceanic environment (hindcast simulation). This historicalsimulation was forced by the Drakkar Forcing Sets 5.2 (DFS5.2)49 on the basisof a corrected set of the European Centre for Medium-Range Weather Forecasts(ECMWF) Reanalysis - Interim (ERA-Interim) over the period 1979−2011.

Salinity, temperature and biogeochemical tracer concentrations (nitrate, phosphate,iron, silicate, alkalinity, dissolved oxygen and dissolved organic and inorganiccarbon) were initialized from the World Ocean Atlas climatology (WOA09)50 andprevious model climatology for iron and dissolved organic carbon51. To minimize
any substantial numerical drift in the simulations related to a non-equilibrated
initial state, we applied a spin-up of the ocean model and biogeochemical modelfor 66 years, cycling twice over the DFS5.2 forcing sets48.
Overall, the model simulates basin-scale, historical SST and salinity
distribution, together with seasonal and interannual (ENSO) variability withgood fidelity52. Classical biases are associated with the coarse (2°) resolution, forexample, the latitudinal position of the Kuroshio Current. In the tropical Pacific,
there is a cold bias of −1 °C in the central equatorial zone (between 170° W and100° W) and a warm bias of +1 °C in the eastern part of the basin (east of 90° W).
Despite some local discrepancy between simulation outputs and satellite-derived
chlorophyll concentration around islands and near the American coasts, simulated
mean chlorophyll in the equatorial Pacific Ocean is close to observed values51,52.
For future ocean projections, we first selected several ESMs from theCMIP5 intercomparison project53 on the basis of the ability of the models to
produce accurate ENSO variability in the Pacific54. The four ESMs selected were
IPSL-CM5A55, MIROC56, GFDL-ESM2G57 and MPI-MR58. We then extracted
atmospheric fields from these models for the period 2011−2100 under RCP 8.5 tosimulate ‘business-as-usual’ climate anomalies to build forcing sets for the NEMO–PISCES ocean model.
All ESMs display large biases in their representation of Pacific climate,including the important South Pacific Convergence Zone59,60. These atmosphericbiases propagated uncertainties associated with future atmospheres into thecoupled, dynamical-biogeochemical oceanic framework. For example, they resultin prominent distortions in the extension and position of the warm pool61 andcan be expected to affect modelling of the open ocean ecosystem up to the higher
trophic levels12.
To mitigate the mean state model biases in the selected ESMs, we used a‘pseudo-warming’ anomaly approach to force the ocean model. To do this, we
extracted monthly anomalies (relative to 2010) of surface atmospheric temperature,zonal and meridional wind speeds, radiative heat fluxes, relative humidity andprecipitation from the ESM models over the 2010–2100 period and applied a31-year-wide Hanning filter to remove variability on timescales less than 15 years.
Each ESM-filtered timeseries was superimposed onto the repeating 30-year
historical forcing (that is, repeated three times to span the twenty-first century)
to provide the forcing for the NEMO–PISCES projections. This procedureenabled us to retain a realistic climatology and high-frequency variability fromobservations subject to long-term trends due to climate change based on the ESMs
(Supplementary Fig. 7).
For consistency, the control simulation of NEMO–PISCES was forced using
the same three, repeated, 30-year historical periods to correct any long-term drift
generated internally without climate change forcing.
It is important to note that use of all ESM acronyms (for example, IPSL) in thefollowing text refers to NEMO–PISCES or SEAPODYM simulations derived fromthe ESM anomaly forcing, and not to the ESM models themselves.
The four NEMO–PISCES simulations of future ocean conditions produced
contrasting results in terms of dynamics and biogeochemistry (Supplementary
Fig. 8). In particular, there was strong warming in the IPSL and MIROC
simulations and weaker warming for GFDL and especially MPI. Spatial patterns inocean warming produced by the NEMO–PISCES simulations differed mostly inintensity rather than spatial structure.
Using NEMO–PISCES outputs to produce SEAPODYM forcings. The outputsof NEMO–PISCES were used to provide environmental forcing variables for
SEAPODYM, the model used to project the responses of the key life stages ofskipjack, yellowfin and bigeye tuna to climate change (Supplementary Note
7). The following physical and biochemical forcing variables were used in
SEAPODYM applications: three-dimensional (3D) temperature, dissolved oxygen(O2) concentration, zonal/meridional currents and primary production, and
2D euphotic depth. Before running SEAPODYM, these forcing variables wereinterpolated to a regular 2° Arakawa A grid and placed in the centre of the gridcells. Primary production was then vertically integrated throughout the watercolumn, whereas the other 3D variables were integrated within three pelagic
layers, defined according to the euphotic depth to provide the mean 2D fields
for each variable per layer. Selected environmental variables from the historicalocean reanalysis and from four climate-driven ocean outputs are shown in
Supplementary Fig. 3.
These integrated variables were then used to force the SEAPODYM-LMTL(lower and mid-trophic level) sub-model. SEAPODYM-LMTL relies on primary
production, temperature and ocean currents to simulate the biomass of six
functional groups of micronekton—mid-trophic-level prey organisms of tunas(Supplementary Fig. 4)—residing or migrating through three pelagic layers within
the upper 1,000 m of the water column (the epipelagic layer and the upper andlower mesopelagic layers), with depths linked to the depth of euphotic layer Z as
1.5Z, 4.5Z and 10Z (with 10Z limited to 1,000 m). The definition of these pelagic
layers is derived from the diurnal vertical distributions of micronekton species62.

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Optimal parameterization of SEAPODYM during historical period. The
parameterization of SEAPODYM for each tuna species is highly sensitive to ocean
forcing; that is, in its average state it is free from systematic biases, and it representsinterannual variability and ENSO correctly. This sensitivity enables the model toreproduce observed variability within large, geo-referenced datasets of tuna catches
and length distributions reflecting changes in fish abundance12. The environmental
forcings in this study were obtained from the historical NEMO–PISCES reference
simulations using a realistic atmospheric reanalysis based on a consistent setof atmospheric observations. Historical fishing datasets used to achieve model
optimal parameterizations were compiled from the combination of data provided
by the Pacific Community for the WCPO and by IATTC for the EPO. The model
spatial resolution was 2° × 2°, and the resolution for time and age dimensions
was one month. The skipjack tuna reference model was obtained by integrating
all available geo-referenced data—catch, length-frequency of catch and tagging
release–recapture data—into a likelihood function and obtaining the solution
using the maximum likelihood estimation (MLE) approach (Supplementary Note
7). The initial habitat and movement parameters for bigeye and yellowfin tuna
were also estimated by integrating tagging data into the model; however, the final
parameterizations of the reference models for these two species were based mainly
on fisheries data. The methodology and optimal reference solutions obtained forskipjack, yellowfin and bigeye tuna, and model validations with statistical metrics,
are described in other publications documenting the use of SEAPODYM13,63–65.
The structures of the populations of the three tuna species in December 2010
(the last time-step of the reanalysis) were used to set the initial conditions for the
projections starting in 2011. A second historical simulation was run to remove theeffects of fishing mortality (Supplementary Figs. 9 and 10) to establish the initialconditions for the unfished tuna populations (Supplementary Fig. 10). In theselatter simulations, the stocks increase and reach an equilibrium state in a timethat is defined by the lifespan of the species and the estimated stock–recruitment
relationship. We assume that at the end of the 30-year reanalysis (December 2010),
stocks of all three tropical tuna species are at their virgin (unfished) state andinfluenced by environmental variability and demographic processes only.
Projections of climate change impacts on tuna. Previous studies on the impact ofclimate change on tropical tuna species in the Pacific Ocean produced projections
based on the full-field NEMO–PISCES output from a single ESM (IPSL) underthe IPCC business-as-usual scenario6,10,12,66,67. These projections were subject tobiases, resulting in poor coherence between historical and projected environmental
forcings and abrupt changes and biases when switching from a historical reanalysis
to a projected time series12. To reduce this problem, we used an approach basedon the four, bias-corrected, projected climates from NEMO–PISCES outputs(Supplementary Methods).
Simulations of the SEAPODYM tuna model were run with parameters fromthe reference MLE models for the three tuna species, with forcings from the four
NEMO–PISCES and mid-trophic simulations, under the RCP 8.5 scenario toproject tuna population dynamics until mid-century. We estimated the virginbiomass of each species in the decade 2011−2020 and computed the relativechange in biomass by 2050 (2044−2053) as follows:)2020 (
1 ∑ B (t + Δt)
δ B (2050) =(1)−1
N t=2011
B(t)where Δt is the time interval corresponding to 33 years and N is the number ofmonthly time steps in the selected time period (120 months between 2011 and2020). We chose to average over 10 years at 33-year intervals to compare two
distant periods with the same atmospheric variability, thus removing the possible
effects of interannual variation and allowing better detection of the climatechange signal.
The relative biomass change δB (2050) was computed for the EEZs of Pacific
SIDS and all high-seas areas in the WCPO and EPO (Supplementary Fig. 1).
Sensitivity analyses to explore uncertainty. We analysed the impacts of climatechange on skipjack, yellowfin and bigeye tuna with an ensemble of simulations
focusing on the greatest sources of uncertainty in the NEMO–PISCES variables
and in SEAPODYM (Supplementary Fig. 11 and Supplementary Table 21). The
methods used to explore these uncertainties, and the rationale for these analyses,
are explained in the Supplementary Methods.
Modelling tuna distribution under lower-emissions scenarios. The simulations
based on RCP 8.5 project a redistribution of tuna biomass by 2050 as globally
averaged surface temperature rises to 2 °C above pre-industrial levels by
mid-century. To evaluate possible effects of a lower GHG emission scenario ontuna redistribution, we also estimated the responses of tropical tuna species toconditions similar to RCP 4.5 and RCP 2.6 by 2050.
In the absence of ocean forcings and SEAPODYM outputs for RCP 4.5 and
RCP 2.6, we used estimates based on the RCP 8.5 simulations using a ‘time-shift’
approach68. This method consists of identifying the time segment in RCP 8.5 inwhich a key variable (for example, CO2-equivalent (CO2e)) matches the value
expected for the selected RCP in 2050. Accordingly, we selected the periods in the908

RCP 8.5 curve when total CO2e concentrations in the atmosphere reached thoseprojected for RCP 4.5 and RCP 2.6 in 2050 (Supplementary Fig. 12). On the basisof this method, the equivalent of RCP 4.5 in 2050 is reached in 2037 under RCP
8.5, and the equivalent for RCP 2.6 in 2050 is reached in 2026.
An important assumption of this method is that the dynamical patterncorresponding to a given change of global temperature is independent of the rateof change. This assumption is expected to be met for key features of the tropical
Pacific Ocean because the upper ocean generally responds rapidly to changes
in atmospheric forcing. However, this assumption is unlikely to hold for tuna
population dynamics because interannual variability of tuna biomass is driven bydemographic processes (recruitment and mortality), which are in turn influenced
by environmental variability. Furthermore, due to the slow nature of demographic
processes, the repercussions of environmental variability on tuna population
dynamics are time lagged. For example, there is a time lag of 8 months between
the Southern Oscillation Index and the biomass of young skipjack tuna (aged from
3 to 9 months)17, and a time lag of 12 months between the Southern Oscillation
Index and total biomass of skipjack tuna (Supplementary Fig. 13). When combinedwith the effects of stock–recruitment relationships, and different generation times
between tuna species, the speed and duration of climate change processes may
have a profound effect on tuna biomass. Therefore, due to the rapidly changing
ocean conditions in the RCP 8.5 scenario, the population status of a tuna species
in the second and third decade cannot be assumed to be equivalent to that under ascenario with lower emissions by mid-century.
To address the complications associated with the population dynamics oftuna in a changing environment, we generated synthetic RCP 4.5 and RCP 2.6
2011−2050 time series by recycling the years from RCP 8.5 simulations. Notethat recycling the ‘equivalent’ years from RCP 8.5 simulations to imitate thoseprojected for the RCP 4.5 and RCP 2.6 scenarios involves re-using the same years
multiple times because of their lower rate of change. To avoid looping the forcings
over the same year multiple times, we selected several years around the equivalent
RCP 8.5 year while enlarging the temporal window with increasing differences in
the rates of GHG change between the two scenarios and ensuring that the mean
CO2e within this window was equal to those in the target RCP 4.5 or RCP 2.6
scenario. The inverse mapping of the RCP 8.5 curve from arrays of CO2e values tothe equivalent years in the RCP 8.5 simulation (Supplementary Fig. 14) provided
the selected range of RCP 8.5 years to imitate the RCP 4.5 and RCP 2.6 scenarios.
The NEMO–PISCES model variables from those years were then used to computemonthly climatology for each year of the surrogate RCP 4.5 or RCP 2.6 forcing toprovide smoothed time series of forcing variables over the complete time range.
The temporal evolution of epipelagic ocean temperature is compared for four
climate models and three RCP scenarios in Supplementary Fig. 14.
The biomass changes projected for the three tuna species in 2050 under
RCP 8.5 and under the lower surrogate emissions scenarios were then computed
for all Pacific Island EEZs (Supplementary Fig. 15) following equation (1)(Supplementary Methods). The biomass changes projected under the RCP 4.5
forcing are smaller in magnitude than those for RCP 8.5, demonstrating thatthe effect of climate change is less pronounced in the simulations under thislower-emissions scenario.
The simulations under the surrogate RCP 2.6 forcing did not follow the
expected pattern and were deemed to be too unreliable for use in this study(Supplementary Methods).
Estimating changes in tuna biomass in EEZs and the high seas. For this analysis,
we produced reference biomasses for skipjack, yellowfin and bigeye tuna for theperiod 1979−2010 from quantitative assessment studies using SEAPODYM, which
estimates population dynamics, habitats, movements and fisheries parameterswith an MLE approach (Supplementary Note 7). The fit between observations
and predictions (for catch and catch size frequencies) was used to validate theoptimal solutions of the models within and outside the time window for the model
parameter estimates. The fit was analysed spatially by fishery to ensure that there
were no regional biases. Once the optimal solution was achieved, a final simulation
was made with the same set of parameter estimates but without considering
any fishing, to obtain the unfished biomass dynamics during both the historicalperiod and the projection for the twenty-first century. The differences in unfishedbiomass between the historical period (2001−2010) and projections in 2050 (meanof 2046−2050) for each species were used to compute the weighted mean change
in total tuna biomass in the EEZs of the ten Pacific SIDS, the high-seas areas
shown in Supplementary Fig. 1 and the EEZs of the other Pacific SIDS listed in
Supplementary Table 1 for the RCP 8.5 and RCP 4.5 emission scenarios by 2050.
Estimating changes in catch in EEZs and the high seas. To evaluate the impactsof climate change scenarios on purse-seine fisheries, comparisons were restricted
to the EEZs of the ten tuna-dependent Pacific SIDS and the high-seas areas,
particularly EPO-C (Supplementary Fig. 1).
To estimate the effects of projected changes in biomass of skipjack, yellowfin
and bigeye tuna due to RCP 8.5 and RCP 4.5 on purse-seine catches in the EEZsof Pacific SIDS and in high-seas areas by 2050, in the absence of management
interventions to reallocate catch entitlements to maintain historical access rights
for Pacific SIDS, we assumed that there would be a direct relationship between

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projected changes in biomass and catch. Because purse-seine catches are composedof different proportions of the three tuna species, and because each species is
projected to have a different response to climate change (Fig. 2), changes inpurse-seine catches by 2050 were estimated using the weighted mean response of
the three tuna species to RCP 8.5 and to RCP 4.5. These estimates were derived
from the average relative abundance of each species in purse-seine catches inthe EEZs of the ten Pacific SIDS (Supplementary Table 3) and in high-seas areas(Supplementary Table 4) and the projected percentage change in biomass of eachspecies under each emission scenario (Supplementary Tables 17 and 18).
The weighted average percentage changes in biomass of all tuna species
combined were then applied to the 10-year average (2009−2018) purse-seine
catches from the EEZs of the ten Pacific SIDS and high-seas areas (Supplementary
Tables 3 and 4) to estimate the changes in purse-seine catches for thesejurisdictions by 2050 under RCP 8.5 and RCP 4.5. In the case of Kiribati, which
has three separate EEZ areas (Fig. 1), we estimated the change in catch for each
EEZ area and amalgamated the results to produce the overall estimated change inpurse-seine catch for the country.
The projected percentage change in total purse-seine catch differs fromthe percentage change in total tuna biomass due to variation in the relative
contributions of the three tuna species to total catch and to total biomass.
Estimating the effects of tuna redistribution on economies. To assess the effects
of climate-driven redistribution of tuna on the economies of the 10 Pacific SIDS,
we assumed that estimated changes in purse-seine catch within their EEZs due tothe redistribution of tuna biomass described above would result in a proportionalchange in access fees earned from purse-seine fishing and associated operations.
To estimate the effects of RCP 8.5 and RCP 4.5 on the capacity of Pacific Island
governments to earn access fees from industrial tuna fishing, and the contributionsof these access fees to total government revenue excluding grants (‘government
revenue’), we used annual averages of government revenue, tuna-fishing accessfees earned by the ten Pacific SIDS and the percentage contribution of access feesto government revenue for the period 2015−2018 (Supplementary Table 2) as abaseline. We applied the projected average percentage changes in total purse-seine
catch in each EEZ for RCP 8.5 and RCP 4.5 (summarized in Supplementary Tables
17 and 18) to the average annual access fees received in 2015−2018 by each of
the Pacific SIDS to estimate the change in value of their access fees by 2050 undereach emissions scenario. The change in value of access fees was used to estimate
decreases or increases in government revenue in 2050 relative to 2015–2018 underboth emissions scenarios in US$ and percentage terms, assuming that the relative
contributions of other sources of government revenue remain the same.
The estimated percentage changes in government revenue for each Pacific
SIDS do not account for (1) management responses; (2) variation in the value ofaccess to particular EEZs and the willingness of fleets to pay for this access dueto the effects of changes in tuna biomass on catchability of each species, levelsof fishing effort/catch rates, the price of tuna or cost of landing tuna; and (3) the
impact of tuna redistribution on the degree of control that Pacific SIDS exert overfisheries targeting tuna. The third factor is expected to be particularly important.
For example, substantial movement of tuna from the EEZs of PNA countries
into high-seas areas would be expected to limit the effectiveness of the VDS69 by
reducing the degree of control over the fishery exerted by PNA members.
Overall, it is important to note that the simple approach used to assess thepotential effects of tuna redistribution on government revenue is intended
only to provide indicative information on the magnitude of these impacts. To
obtain robust estimates of climate-driven changes in government revenue, more
complex bio-economic analyses will be required, beginning with, for example, afleet-dynamics analysis to investigate the potential response of purse-seine vessels
to redistribution of tuna and the flow-on effects on access fees.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.

Data availability

The 3D ocean data from the ESMs in netcdf format used to inform the
SEAPODYM modelling are available at http://data.umr-lops.fr/pub/AFCM85/. Allanalysed data on tuna catch and government revenue are included in the publishedAnalysis and the Supplementary Information files.

Code availability

The executable files for SEAPODYM, together with the input files, the outputs andthe SEAPODYM manual, are available on a repository at https://osf.io/qa8w4/.

Received: 19 November 2020; Accepted: 15 June 2021;
Published online: 29 July 2021

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Acknowledgements

This article is dedicated to our co-author Sue Taei, who lost her battle with cancer
recently. Sue was a regional thought leader and was instrumental in helping to design
this study and obtain the resources needed to extend the tuna modelling from exclusive
economic zones to high-seas areas to examine the implications for Pacific Island
economies. We thank T. Ruaia for providing data on access fees; C. Appel, B. Colas,
A. Desurmont and E. Johansen for assistance with figures; and P. Gardiner, S. Kay,
J. Kittinger and P. Obregon for helpful discussions/comments on the draft manuscript.
J.D.B., I.S. and P.L. thank the Moccasin Lake Foundation and the GEF-funded, World
Bank-implemented Ocean Partnerships for Sustainable Fisheries and Biodiversity
Conservation, a sub-project of the Common Oceans ABNJ Program led by UN-FAO,
for financial support for this research and preparation of this article. SEAPODYM
simulations were also partially funded from the European Union’s Horizon 2020 researchand innovation programme COMFORT, under grant agreement no. 820989 (noting that
the European Commission and their executive agency are not responsible for any usethat may be made of the information from the simulations). Q.H., H.H.-D., Y.O. and
K.L.S. acknowledge support from the Nippon Foundation Ocean Nexus Center at the
University of Washington Earthlab.

Author contributions

J.D.B., T.A., Q.H., H.H.-D., G.H., W.M., C.P., Y.O., K.L.S., N.S., S.T. and M.T. designed thestudy during a workshop at the University of Wollongong. O.A., B.C., M.D., M.G., T.G.,
M.L. and C.M. produced the ocean forcings, and I.S. and P.L. modelled the responsesof tuna to ocean warming with SEAPODYM. J.D.B. compiled the main text andSupplementary Information on the basis of contributions from all authors.

Competing interests

The authors declare no competing interests.

Additional information

Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41893-021-00745-z.
Correspondence and requests for materials should be addressed to J.D.B.
Peer review information Nature Sustainability thanks Liam Campling, Kevern Cochraneand the other, anonymous, reviewer(s) for their contribution to the peer review ofthis work.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, aslong as you give appropriate credit to the original author(s) and the source, provide a linkto the Creative Commons license, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commonslicense, unless indicated otherwise in a credit line to the material. If material is notincluded in the article’s Creative Commons license and your intended use is not
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Describe how you pre-defined primary and secondary outcome measures and how you assessed these measures.

Dual use research of concern
Policy information about dual use research of concern

Hazards
Could the accidental, deliberate or reckless misuse of agents or technologies generated in the work, or the application of information presented
in the manuscript, pose a threat to:
No YesPublic healthNational security
Crops and/or livestock
Ecosystems
Any other significant area

Experiments of concern
Does the work involve any of these experiments of concern:
No YesDemonstrate how to render a vaccine ineffective
Confer resistance to therapeutically useful antibiotics or antiviral agents
Enhance the virulence of a pathogen or render a nonpathogen virulent
Increase transmissibility of a pathogen
Alter the host range of a pathogen
Enable evasion of diagnostic/detection modalities
Enable the weaponization of a biological agent or toxin

ChIP-seq

April 2020

Any other potentially harmful combination of experiments and agents

Data deposition
Confirm that both raw and final processed data have been deposited in a public database such as GEO.
Confirm that you have deposited or provided access to graph files (e.g. BED files) for the called peaks.

4For "Initial submission" or "Revised version" documents, provide reviewer access links. For your "Final submission" document,
provide a link to the deposited data.

Files in database submission

Provide a list of all files available in the database submission.

Genome browser session

Provide a link to an anonymized genome browser session for "Initial submission" and "Revised version" documents only, toenable peer review. Write "no longer applicable" for "Final submission" documents.

(e.g. UCSC)

Methodology
Replicates

Describe the experimental replicates, specifying number, type and replicate agreement.

Sequencing depth

Describe the sequencing depth for each experiment, providing the total number of reads, uniquely mapped reads, length of reads andwhether they were paired- or single-end.

Antibodies

Describe the antibodies used for the ChIP-seq experiments; as applicable, provide supplier name, catalog number, clone name, and lot
number.

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May remain private before publication.

Data access links

Peak calling parameters Specify the command line program and parameters used for read mapping and peak calling, including the ChIP, control and index files
used.

Data quality

Describe the methods used to ensure data quality in full detail, including how many peaks are at FDR 5% and above 5-fold enrichment.

SoftwareDescribe the software used to collect and analyze the ChIP-seq data. For custom code that has been deposited into a community
repository, provide accession details.

Flow Cytometry
Plots
Confirm that:
The axis labels state the marker and fluorochrome used (e.g. CD4-FITC).
The axis scales are clearly visible. Include numbers along axes only for bottom left plot of group (a 'group' is an analysis of identical markers).
All plots are contour plots with outliers or pseudocolor plots.
A numerical value for number of cells or percentage (with statistics) is provided.

Methodology
Sample preparation

Describe the sample preparation, detailing the biological source of the cells and any tissue processing steps used.

Instrument

Identify the instrument used for data collection, specifying make and model number.

SoftwareDescribe the software used to collect and analyze the flow cytometry data. For custom code that has been deposited into acommunity repository, provide accession details.

Cell population abundance

Describe the abundance of the relevant cell populations within post-sort fractions, providing details on the purity of thesamples and how it was determined.

Gating strategy

Describe the gating strategy used for all relevant experiments, specifying the preliminary FSC/SSC gates of the starting cell
population, indicating where boundaries between "positive" and "negative" staining cell populations are defined.

Tick this box to confirm that a figure exemplifying the gating strategy is provided in the Supplementary Information.

Magnetic resonance imaging
Experimental design
Indicate task or resting state; event-related or block design.

Design specifications

Specify the number of blocks, trials or experimental units per session and/or subject, and specify the length of each trialor block (if trials are blocked) and interval between trials.

Behavioral performance measures

State number and/or type of variables recorded (e.g. correct button press, response time) and what statistics were used
to establish that the subjects were performing the task as expected (e.g. mean, range, and/or standard deviation acrosssubjects).

April 2020

Design type5

Imaging type(s)Specify: functional, structural, diffusion, perfusion.

Field strength

Specify in Tesla

Sequence & imaging parameters

Specify the pulse sequence type (gradient echo, spin echo, etc.), imaging type (EPI, spiral, etc.), field of view, matrix size,
slice thickness, orientation and TE/TR/flip angle.

Area of acquisition

State whether a whole brain scan was used OR define the area of acquisition, describing how the region was determined.

Diffusion MRI

Used

Not used

Preprocessing
Preprocessing software

Provide detail on software version and revision number and on specific parameters (model/functions, brain extraction,
segmentation, smoothing kernel size, etc.).

Normalization

If data were normalized/standardized, describe the approach(es): specify linear or non-linear and define image types used fortransformation OR indicate that data were not normalized and explain rationale for lack of normalization.

Normalization template

Describe the template used for normalization/transformation, specifying subject space or group standardized space (e.g.
original Talairach, MNI305, ICBM152) OR indicate that the data were not normalized.

Noise and artifact removal

Describe your procedure(s) for artifact and structured noise removal, specifying motion parameters, tissue signals andphysiological signals (heart rate, respiration).

Volume censoring

Define your software and/or method and criteria for volume censoring, and state the extent of such censoring.

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Acquisition

Statistical modeling & inference
Model type and settingsSpecify type (mass univariate, multivariate, RSA, predictive, etc.) and describe essential details of the model at the first andsecond levels (e.g. fixed, random or mixed effects; drift or auto-correlation).

Effect(s) tested

Define precise effect in terms of the task or stimulus conditions instead of psychological concepts and indicate whether
ANOVA or factorial designs were used.

Specify type of analysis:
Statistic type for inference(See Eklund et al. 2016)

Correction

Whole brain

ROI-based

BothSpecify voxel-wise or cluster-wise and report all relevant parameters for cluster-wise methods.
Describe the type of correction and how it is obtained for multiple comparisons (e.g. FWE, FDR, permutation or Monte Carlo).

Models & analysis
n/a Involved in the study
Functional and/or effective connectivity
Graph analysis
Multivariate modeling or predictive analysis

Functional and/or effective connectivity

Report the measures of dependence used and the model details (e.g. Pearson correlation, partial correlation,
mutual information).

Graph analysis

Report the dependent variable and connectivity measure, specifying weighted graph or binarized graph,subject- or group-level, and the global and/or node summaries used (e.g. clustering coefficient, efficiency,etc.).

Multivariate modeling and predictive analysis Specify independent variables, features extraction and dimension reduction, model, training and evaluation
metrics.

April 20206