Open Science Resources From The Reef and Surface Ocean Ecosystems

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OPEN Open science resources from the

Data Descriptor Tara Pacific expedition across coral
reef and surface ocean ecosystems
Fabien Lombard et al.#
The Tara Pacific expedition (2016–2018) sampled coral ecosystems around 32 islands in the
Pacific Ocean and the ocean surface waters at 249 locations, resulting in the collection of
nearly 58 000 samples. The expedition was designed to systematically study warm-water
coral reefs and included the collection of corals, fish, plankton, and seawater samples for
advanced biogeochemical, molecular, and imaging analysis. Here we provide a complete
description of the sampling methodology, and we explain how to explore and access the
different datasets generated by the expedition. Environmental context data were obtained
from taxonomic registries, gazetteers, almanacs, climatologies, operational biogeochemical
models, and satellite observations. The quality of the different environmental measures has
been validated not only by various quality control steps, but also through a global analysis
allowing the comparison with known environmental large-scale structures. Such publicly
released datasets open the perspective to address a wide range of scientific questions.
Background & Summary

Marine ecosystems are facing numerous perturbations either of seasonal, climatic, or biological origin which are
now further amplified by perturbations due to anthropogenic activities. The resilience of marine ecosystems to
perturbations is a general concern, especially when providing ecosystem services and supporting human activities.
Tropical coral reefs maintain important ecological services such as fisheries, tourism, or coastal protection, but are
also among the most sensitive ecosystems to environmental changes1,2. The health of stony corals, the foundation
species of reef ecosystems, is not only governed by the environment, but also by the composition of the holobiont
and its symbiotic interactions encompassing a wide range of eukaryotic organisms (e.g., crustaceans, molluscs,
fishes), endosymbiotic microalgae, bacteria, fungi, and viruses3. In the open sea, coral ecosystems are associated
with islands and participate in their long-term ecological and geological resilience. Coral ecosystems are hotspots
of biological activities and energy flux that have a strong effect on the open sea through nutrient enrichment that
could propagate in the open ocean, supporting fisheries or biogeochemical fluxes in other marine ecosystems4,5.
However, a more complete understanding of how coral ecosystems are reacting to environmental stressors
is complicated as multiple spatial (from microscale to mesoscale) and temporal (from minutes, day, seasons, or
decades) scales are involved, as well as various biological complexity levels (from molecular, genetic, physiolog-
ical to ecosystem) . Monitoring ecosystems features at large biological, spatial, and temporal scales is very chal-
lenging. An alternative is to use “space-for-time” substitutions which assumes that processes observed at various
static spatial scales could reflect what could happen if the same ecological forcing happens at various temporal
scales6. Historically, this method was used for centuries, for example when Charles Darwin used it to describe
the development of islands from barrier reefs, fringing reefs to atolls7. This method is still commonly used in
ecology, notably when species distribution8 or even diversity9 are modelled using niche models.
This type of approach is often limited by the compatibility between datasets, where many observations often
originated from separate studies with heterogeneous protocols, methods, or measurements. In this respect, large
global expeditions have often paved the way to major scientific breakthroughs from the early expeditions con-
ducted by the Beagle or HSM challenger to the more recent Malaspina or Tara Ocean expeditions10–12.
The Tara Pacific expedition has applied a pan-ecosystemic approach on coral reefs and their surrounding
waters at the entire ocean basin scale throughout the Pacific Ocean13. The aim is to provide a baseline reference
#
A full list of authors and their affiliations appears at the end of the paper.
Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 1

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of coral holobiont genomic, transcriptomic, and metabolomic diversity spanning from genes to organisms
and their interactions with the environment. Tara Pacific focused on widely distributed organisms, two scler-
actinian corals (Pocillopora meandrina and Porites lobata), one hydrocoral (Millepora platyphylla), and two reef
fishes (Acanthurus triostegus and Zanclus cornutus) together with their contextual biological (plankton) and
physicochemical environment14.
The collaboration of more than 200 scientists and participants during this expedition made it possible to
sample coral systems across 32 islands (102 sites), together with 249 oceanic stations, resulting in a collection of
57 859 samples encompassing the integral study of corals, fishes, plankton, and seawater. As with previous Tara
expeditions15, organizing and cross-linking the various measurements is a stepping-stone for true open access
science resources following FAIR principles (Findable Accessible Interoperable and Reusable16). In this effort,
the strategy adopted by Tara Pacific is to provide open access data and early and full releases of the datasets once
validated or published. Such an approach ensures a long-lasting preservation, discovery, and exploration of data
by the scientific community, which will lead to new hypotheses and emerging concepts.
Here, we present an overview of the sampling strategy used to collect coral holobiont specimens in con-
nection with its local, large scale, or historical environment. We also provide a critical assessment of the envi-
ronmental context. We provide the full registries describing the geospatial, temporal, and methodological
information for every sample and connect it to the various sampling events or stations. Extensive environmental
context is also provided at the level of samples or stations. Such registries and environmental context collections
are essential for researchers to explore the Tara Pacific data and will be updated and complemented when addi-
tional datasets will be released to the public. Throughout the entire text, terms stated [within brackets] refer to
the terms used within the registry or in environmental context datasets.
Methods
Sampling locations. Tara Pacific deployed a standardized sampling and analysis protocol to offer a com-
parative suite of samples covering the widest environmental envelope while optimizing cruising and sampling
time over the 2.5 years of the sampling effort. Protocols and global objectives of the Tara Pacific expedition were
previously detailed for coral samples13 and are detailed here in connection with the sample registry. Similarly, pro-
tocols and global objectives for ocean and atmosphere sampling were previously described14 for the 249 stations
sampled during daytime (noted [OA001] to [OA249]; night-time sampling between stations and other non-sys-
tematic sampling events were noted [OA000]).
A set of 32 island systems (noted [I01] to [I32] in the registry; Table 1, Fig. 1) were targeted to cover the
widest range of conditions possible, from temperate latitudes to the equator, from the low diversified system of
the eastern Pacific to the highly diverse western Pacific warm pool17. The variety of coral reef systems explored
includes continental islands, remote volcanic islands up to atolls, with varying island sizes or human populations
(Table 2). Generally, 3 sites ([S01] to [S03]) per island were selected to conduct the full sampling strategy within
4 days. Occasionally only 2 or up to 5 sites were selected (Table 1).
Sampling coral reef systems. The sampling event sequence and protocols were performed consistently
over the whole expedition. Sampling was conducted following the same procedure, approximate timing, and
articulated around the same standardized “sampling events” (Fig. 2) which allowed the same collection of samples
with a standardized protocol (Table 3). On rare occasions, the timing and protocols were adapted due to sailing
conditions and to fit the schedule. Sampling events are characterized by their mode of sampling, which could be
either indirectly from Tara’s dinghy [ZODIAC] or directly either using scuba-diving ([SCUBA]) or snorkeling
([SNORKEL]). In addition, the sampling device and strategy are included in the sample identifier.
The first set of sampling events (usually in the morning) was mostly devoted to the sampling event
[SCUBA-3X10] to sample coral colony fragments. In the meantime, another team pumped underwater, with the
[SCUBA-PUMP] to collect coral surrounding water ([CSW]), while the third team snorkeled to capture a total
of 10–15 fish using a speargun ([SNORKLE-SPEAR]). A small CTD probe (Castaway CTD) was also deployed
from the dinghy down to the reef (generally ~5 to 10 m) to record temperature and conductivity profiles.
The second set of sampling events (usually in the afternoon) was devoted to a survey of coral diversity
([SCUBA-SURVEY]) concurrently with sampling surface water for biogeochemistry ([ZODIAC-NISKIN]),
plankton in the size-fractions smaller than 20 µm ([ZODIAC-PUMP]), and plankton in the size-fractions
between 20 to 2000 µm ([SCUBA-NET-20]). Finally, over a last dive a coral core was recovered over a large col-
ony of Porites sp or Diploastrea sp ([SCUBA-CORER]).
Sampling coral colonies [SCUBA-3X10]. During this typical sampling event, a total of 30 coral colonies [C001]
to [C030], including 10 colonies for each of the 3 target species (Pocillopora meandrina, Porites lobata, and
Millepora platyphylla) were sampled. Each colony was first photographed ([PHOTO]) using a 20 cm quadrat as
a scale, their depth recorded and then sampled to collect about 70 grams of each coral by mechanical fragmen-
tation using hammer and chisel. Fragments were placed in Ziploc bags labelled by colony ID and brought back
to the boat.
Sampling coral surrounding water [SCUBA-PUMP] and [ZODIAC-NISKIN]. Two Pocillopora meandrina coral
colonies [C001] and [C010] were marked with small buoys, and [CSW] samples were collected as close as pos-
sible to the coral colony before the actual SCUBA-3X10 sampling to avoid contamination of the water sam-
ples with fragments or tissues released during the mechanical fragmentation of coral colony. Then, water was
pumped using a manual membrane pump onboard Tara’s dinghy that was stationary above the coral colony.
A scuba diver was holding a clean water tubing next to the colony while the operator onboard the dinghy was

Island code isl_name (s) Archipelagos/synonym names Country latitude longitude station nb
I01 Chapera/Mogo Mogo/Bartolome Islas de las Perlas Panama 8.4061 −79.0605 3
I02 Brincaco/del canal de Afuerta/Jicarita Coiba Panama 7.4667 −81.7833 3
I03 Malpelo Colombia 4 −81.6081 2
I04 Rapa Nui Easter Island Chile −27.1167 −109.367 4
I05 Ducie Pitcairns United Kingdom −24.6833 −124.783 4
I06 Tenoko/Tekava/Kamaka Gambiers France −23.14 −134.94 3
I07 Moorea French Polynesia France −17.5333 −149.833 3
I08 Aitutaki Cook New Zeland −18.8561 −159.785 3
I09 Niue Niue −19.05 −169.917 3
I10 Upolu Samoa Samoa −13.5833 −172.333 4
I11 Futuna Futuna/Horn Islands France −14.2833 −178.15 3
I11 Alofi Futuna/Horn Islands France
I12 Tuvalu Tuvalu −8.5067 179.0979 4
I13 Abaiang Kiribati Kiribati 1.4167 173 3
I14 Chuuk Micronesia Micronesia 7.4167 151.7833 3
I15 Guam USA 13.5 144.8 3
I16 Chichi Jima Ogasawara Japan 26.9981 142.2181 3
I17 Sesoko Okinawa Japan 26.4794 127.9278 3
I18 Fiji Fiji −18 179 3
I19 Heron Australia −23.4385 151.9084 4
I20 Chesterfield France −19.332 158.4727 3
I21 New Caledonia France −22.4973 166.4787 3
I22 Guadacanal/Njurokamo/Njapuna Solomon Solomon Islands −8.5672 158.5733 3
I23 Milney Bay Papua New Guinea −9.2684 151.4979 3
I24 Kimbe Bay Papua New Guinea −5.2801 150.1162 3
I25 Hellen Reef//Tobi/Merir/Pulo Anna/Soronsol Palau South islands Palau 2.890117 131.7944 5
I26 Babeldaob Palau Palau 7.344777 134.4888 3
I27 Hong Kong Hong Kong 22.63486 114.1022 2
I28 Taiwan Taiwan 22.06 121.33 3
I29 Oahu Hawaii USA 21.43421 −157.739 3
I30 Isla Cerralvo/Los Frailes//Bahía Chilenos Baja California Mexico 24.23236 −109.888 3
I31 Clipperton France 10.26905 −109.203 3
I32 Brincaco/Rancheria/Jicarita/Las Uvas Coiba Panama 8.004 −82.3431 4
Table 1. Summary of the different islands sampled during the Tara Pacific expedition with the associated island
code (I01 to I32), their chosen reference name (in bold) corresponding either to the name of the island or of the
archipelagos.
pumping the water up to the skiff. First, the water collected was used to rinse the pumping system, as well as a
20 µm metallic sieve and the 50 L carboys that will be used to transport the sample [C010]. Then, 50 L of water
was filtered within and around the coral colony onto a 20 µm metallic sieve and directly stored in the dedicated
clean 50 L carboy ([SCUBA-PUMP] for [C010]). When available, two replicates of sediment samples (i.e. sand
[SSED]) were also taken using two 10 mL cryovials near the sampled colony. Finally, the coral colony [C010]
itself was sampled following the [SCUBA-3X10] protocol.
Once the [C010] was sampled, the dinghy was moved on top of colony [C001], where, before any other
sampling, carbonate chemistry and nutrient protocols (using a 5 L Niskin bottle for carbonates [CARB] and
nutrients [NUT]) as well as for [PH] protocols (using 5 mL polypropylene vials and a 50 mL Falcon tube) were
performed. The [PH] was first sampled using two vials (5 mL polypropylene vials for samples), and a falcon
50 mL tube (for later use to rinse the probe) were first lowered closed, opened next to the colony, rinsed with
the [CSW], and closed tightly making sure no bubbles were trapped inside the vials. Next, the Niskin bottle was
immersed open by the diver [ZODIAC-NISKIN], well rinsed along the descent and with the coral surround-
ing water near the targeted colony, and finally closed as close as possible to the colony [C001]. The tubing, the
sieve, a 4 L Nalgene (protected with reflective tape to isolate the sample from sunlight), and the 50 L carboy
dedicated for [C001] were rinsed with the [C001] [CSW]. The 5 L Nalgene bottle was filled with [C001] [CSW]
for high-performance liquid chromatography (HPLC). The 50 L carboy was then filled ([SCUBA-PUMP] for
[C001]) and the sediment samples [SSED] were collected following the same procedure as for [C010]. Finally,
the coral colony [C001] itself was sampled following the same [SCUBA-3X10] protocol. For safety reasons,
carbonate chemistry samples [CARB] could not be preserved with mercury (II) chloride on-board the dinghy
due to its acute toxicity. Hence, the Niskin bottle was sampled on the last colony of the sampling sequence to
minimize the time between sampling and chemical preservation on-board Tara.

Fig. 1 Tara Pacific expedition (2016–2018) sampling map. Map of sampled coral systems (red circles) and
oceanic stations (blue dots). Insert: Example of coral sampling locations around Upolu (Samoa; I10) with
overlaid temperature as recorded by the inline thermosalinograph. The absence of sampling during the return
trip in the Atlantic Ocean is due to bad weather.
Sampling for fish [SNORKLE-SPEAR]. Fish sampling of two target species (Acanthurus triostegus and Zanclus
cornutus) was operated by spear-fishing and snorkeling for a target number of about 10–15 fishes ([F001] to
[Fxxx]) depending on the population present. The targets were speared and immediately stored in labeled indi-
vidual Ziplock bags to avoid contamination between samples and kept inside a floating container to keep them
at water temperature.
Sampling sediments and macroalgae [SCUBA-…]. Sediments and macroalgae samples were sampled when
encountered during the different dives. Sediment samples (i.e. sand [SSED]) were taken using two 10 mL cryo-
vials near the sampled colony. Macroalgae, ideally brown macroalgae with thallus morphology type arbustive,
([MA01]-[MAxx]) were photographed ([PHOTO]) and sampled in individual Ziplock bags when encountered.
Coral biodiversity sampling [SCUBA-SURVEY]. Biodiversity sampling transects were conducted in two
depths-range environments to sample up to 80 coral colonies ([C041] to [C120]) arbitrarily chosen with ideally
up to 40 colonies at a depth of 10–16 m, and up to 40 colonies at a depth of 2–10 m, with an emphasis on sam-
pling across a diverse range of coral hosts at different depths. Two pictures of each colony sampled were taken
([PHOTO]), and small pieces of 1–3 cm2 were sampled using a hammer and a chisel or a bone cutter.
Sampling surface seawater [ZODIAC-NISKIN] and [ZODIAC-PUMP]. In addition to the seawater collected
next to coral colonies explained above, surface ([SRF]) seawater was sampled at 2 m depth using the manual
pump on-board of the dinghy ([ZODIAC-PUMP]). The [SRF] site was chosen to be as close as possible from
the coral colonies sampled in the morning but with enough water depth that the plankton net sample could be
taken at 2 m depth and at least 5 m above the seafloor. When the sampling site was shallower than 7 m, the site
was chosen where these sampling conditions could be met within 100 m around the [CSW] sampling site. The
water collected was treated similarly to the [SCUBA-PUMP] samples, with the difference that 100 L [SRF] water
was collected into two 50 L carboys. The 4 L Nalgene bottles protected from sunlight were also filled with water
at 2 m below the dinghy for HPLC filtrations on-board Tara.
Sampling large size plankton [SCUBA-NET-20]. During this surface water pumping, plankton larger than
20 µm were sampled at 2 m below the sea surface using two small diameter bongo plankton nets with 20 µm
mesh size, attached to an underwater scooter ([SCUBA-NET-20]) and towed for about 15 min at maximum
speed (0.69 ± 0.04 m.s−1). The average maximum speed of the net tow was estimated in Taiwan (island 28 site
03) measuring the time it took the diver with full gear on and the nets attached, to travel between two buoys
separated by a 9-meters line held tight and floating with the current, to avoid any impact of the current. The
measurement was repeated three times facing the current, three times in the same direction as the current, and
five times with the current sideways. Each net was equipped with flowmeters, but the speed of the underwater
scooter was insufficient to trigger their rotation, therefore the time of sampling was precisely timed to estimate
theoretically the volume filtered using the following equations:
Volume filtered = Opening area ∗ Tow speed ∗ Tow duration (1)

With

Island code used name Island type land area (km²) max elevation population density (humans/km2)
I01 Islas de las Perlas continental isl. 332.9 na 4500 13.52
I02 Coiba continental isl. 503 416 0 0
I03 Malpelo island 3.5 320 0 0
I04 Rapa Nui island 164 507 7750 47.26
I05 Ducie atoll 3.90 4 0 0
I06 Gambiers island 31 441 1592 51.35
I07 Moorea island 134 1207 17718 132.22
I08 Aitutaki atoll 16.80 124 2194 130.60
I09 Niue island 260 68 1591 4.60
I10 Upolu island 2944 1113 193483 62.50
I11 Futuna island 46.28 524 3225 69.68
I11 Futuna island 17.78 417 1 0.06
I12 Tuvalu atoll 26 1.80 11342 436
I13 Abaiang atoll 16.40 1.80 5568 339.51
I14 Chuuk island 116.20 238 48651 419
I15 Guam island 549 406 164229 299
I16 Ogasawara island 104 916 2821 27.13
I17 Okinawa island 1201 503 1230000 1024.15
I18 Fiji island 18270 1324 935974 51
I19 Heron atoll 0.29 3.60 na na
I20 Chesterfield atoll < 10 6 0 0
I21 New Caledonia island 18575.50 1629 271407 15
I22 Solomon island 28400 2335 652857 18.10
I23 Milney Bay continent 462840 4509 8300000 14
I24 Kimbe Bay continent 462840 4509 8300000 14
Palau South
I25 island 0.85 6 30 35.29
islands
I26 Palau island 330 242 6000 18.18
I27 Hong Kong continent 1104 957 7466441 6763
I28 Taiwan island 35980 3952 23603049 656
I29 Hawaii island 1545.40 1220 976372 631.79
I30 Baja California continental isl. 143396 3096 712029 0.89
I31 Clipperton atoll 1.70 29 0 0
I32 Coiba continental isl. 503 416 0 0
Table 2. Geological, topological and human population characteristics of the sampled Islands recovered from
various sources.
Opening area = π ∗ net radius 2 (2)

The volume estimated from the flowmeter reading was about 60 times smaller than the volume calculated
theoretically, implying that the flow rate was below the level ensuring proper functioning of the flowmeter. thus,
only the theoretical volume will be used in concentration calculations. After 15 minutes of towing, the divers
surfaced the two nets and the two cod-ends were sieved through a 2000 µm metallic sieve, into a 2 L Nalgene (r)
bottle. The bottle was topped-up with 0.2 µm filtered seawater from the same sampling site and kept at ocean
temperature in a bucket during transportation to Tara. Finally, [PH], [NUT] and [CARB] samples were taken at
2 m depth just before leaving the sampling site following the same protocol than for [CSW] sampling and using
the same cleaned 5 L Niskin bottle for [CARB] and [NUT], and two 5 mL polypropylene vials as well as a Falcon
50 mL tube for [PH].
Sampling coral cores [SCUBA-CORER]. During the last dive, coral cores were sampled ([SCUBA-CORER]) on
Porites colonies previously identified and photographed ([PHOTO]). To prevent contamination with coral frag-
ments and tissues released during coring, two [CARB] samples of seawater were taken (one at the surface and
one close to the coral colony) before coring and using two 500 mL glass stoppered bottles. Grease was applied to
the glass stopper before the dive to allow opening under pressure next to the coral colony. The diver lowered the
bottles closed, opened one at 2 m below the surface, and one next to the coral colony. Another seawater sample
was taken with a 60 mL HDPE plastic bottle at 2 m depth for subsequent analysis of trace isotopes in relation to
the core analysis. Once all seawater was sampled, a 250 mm diameter, 600 round per minute corer from Melun
Hydraulique was used to coral cores ([CORE]). Forty coral skeletal cores (40–150 cm long) were collected from

TARA
TA
RA
TARA
TARA
TARA
TARA
Fig. 2 Schematic overview of the various sampling events conducted during the Tara Pacific expedition while
sampling on coral systems. The different events are represented by the different numbers. (1) [SCUBA-3X10]
and [SCUBA-SURVEY]; (2) [SNORKLE-SPEAR]; (3) [SCUBA-CORER]; (4) [SCUBA-PUMP]; (5) [ZODIAC-
PUMP]; (6) [ZODIAC-NISKIN]; (7) [SCUBA-NET-20].
colonies living between 3 m (Moorea Island-I07) and 20 m (Futuna Islands-I11) depth. From island I19 (Great
Barrier Reef) the same protocols were also carried out on large Diploastrea heliopora colonies when encountered.
Samples processing. Benthic samplesOnce back onboard Tara, the material collected during each sampling
event was immediately processed into various samples. Samples were labeled with their target analysis (e.g.
sequencing ([SEQ]), imaging, microscopical or morphological inspection ([IMG]) or biogeochemical measure-
ments ([BGC])).
Coral samples obtained from [SCUBA-3X10] events were immediately sorted and separated using bone
cutters, in several sub-samples usually labeled with the amount of material used or with the targeted analysis
(Table 3). [CS4] and [CS4L] samples containing ~4 g of coral material, were stored at −20 °C in 15 ml Falcon
tubes and 6 ml of DNA/RNA Shield (Zymo Research, Irvine, CA, USA) for subsequent metabarcoding, metagen-
omic and metatranscriptomic analyses. [CS4L] only differs from [CS4] by the addition of lysing matrix beads.
[CS10] and [CS40] samples, that contain respectively 10 g and 40 g of coral material, were stored in Whirlpak
sample bags, immediately flash frozen in liquid nitrogen, and kept at −20 °C. These samples are intended for
®
subsequent metabolomic analysis for [CS10], physiologic/stress biomarkers (symbiont and animal biomasses,
antioxidant capacity and protein damages) and telomeric DNA length for [CS40]. Morphological taxonomic
identification [CTAX] samples were performed by drying 5 g of material in 50 ml Falcon tubes, and removing
organic material with the addition of 3–4% bleach solution during approximately 2 days. After discarding the
bleach solution, clean skeletons were preserved dry at room temperature. For histological measurements of
reproduction status [CREP], 5 g of each coral colony was preserved in a 50 ml Falcon tube filled with a 3.5%
formaldehyde solution and stored at room temperature. Lastly, for transmission electron microscopy examina-
tion of coral intracellular details including viruses [CTEM], 0.1 g of coral tissue was preserved with 250 µL 2%
glutaraldehyde and conserved at 4 °C in a fridge.
Macroalgae samples ([MA]), and the seawater collected with them, were firmly shaken to resuspend attached
epiphytic organisms. 20 mL of water was transferred into glass vials and fixed with 2% acidic Lugol and stored at
4 °C for future benthic dinoflagellates identification and counts using microscopy ([BDI]), while 100 mL of each
replicate were filtered onto a 10 µm pore size polycarbonate filter which was flash frozen and preserved in liquid
nitrogen for future metabarcoding analysis ([BDS]).
[SSED] samples were immediately flash frozen when brought back on-board Tara.
About 30 to 40 mL of the seawater that was sampled with the coral fragments of [C001] and [C010] and
transported in the coral individual Ziplock bags were transferred immediately after the dive into 50 mL falcon
tube and stored at water temperature in non-direct ambient light to recover cultures of plankton species closely
associated with coral colonies ([IMG-LIVE]).
When fish were recovered onboard, a [PHOTO] was taken, their sex and length were determined before
taking a sample of skin mucus ([MUC]) by collecting 1 cm2 of skin. The fish were then dissected to recover
about 3 cm long of the final section of the digestive tract ([GT]) that was preserved in 2 mL cryotubes with 1 ml
of DNA/RNA shield and then stored at −20 °C for metagenomic and metabarcoding analyses. One fin sample
([FIN]) was dissected, and preserved into an Eppendorf tube filled with 95° ethanol for population genetic anal-
yses. Lastely, the otolith ([OTO]) was also dissected and stored dry into an Eppendorf tube at room temperature
for later aging of each fish.

Analysis
category Sample type Material sampled
(sample- Amount of conservation
Sample protocol label n (samples) n (rep.) material_label) material Processing container conservative temperature Targeted analysis
MetaB, metaG,
SEQ CS4 2703 1 Coral 4g cut coral parts Falcon 15 ml DNA/RNA shield −20 °C
metaT
DNA/RNA shield
MetaB, metaG,
SEQ CS4L 2651 1 Coral 4g cut coral parts Falcon 15 ml + lysing matrix −20 °C
metaT
beads
SEQ CS10 2738 1 Coral 10 g cut coral parts Whirlpak bag flash frozen −20 °C Metabolomic
biomarkers and
SEQ CS40 2701 1 Coral 40g cut coral parts Whirlpak bag flash frozen −20 °C
telomere length
cut coral parts,
dryied, and morphology,
IMG CTAX 2763 1 Coral 5g Falcon 50 ml Bleach RT
bleach added for taxonomy
few hours
histological
IMG CREP 2649 1 Coral 5g cut coral parts Falcon 50 ml 3.7% formaldehyde RT analysis of
reproduction
transmission
electron
IMG CTEM 2385 1 Coral 0.1 g cut coral parts 2 ml cryotubes 2% glutaraldehyde 4°C
microscopy
analysis
morphology,
IMG PHOTO 10830 2 Coral, Fish — — — — —
taxonomy
coton swab+
SEQ MUC 1059 1 Fish — dissection DNA/RNA shield −20 °C MetaB, metaG
2mL cryotube
SEQ GT 1059 1 Fish — dissection 2 ml cryotubes DNA/RNA shield −20 °C MetaB, metaG
population
SEQ FIN 1059 1 Fish — dissection eppendorf ethanol RT
genetic analyses
IMG OTO 1057 1 Fish — dissection eppendorf — RT aging
SEQ CDIV 2628 1 Coral <0.5 g cut coral parts 2 ml cryotubes DNA/RNA shield −20 °C MetaB, metaG
seawater replaced
with DNA/RNA 15 mL Flacon DNA/RNA shield
SEQ SSED 351 1 Sediment 7.5 ml −20 °C MetaB, metaG
shield or ethanol tubes or ethanol
and homogenized
plastic bublle morphologic and
IMG CORE 92 1 Coral 26–126 cm dried 24–48h RT
wrap isotopic analysis
Trace elements
60 mL HTPE
BGC MTE-LSCE 170 1 Seawater 60 mL — — RT (Li, Bo) isotopes
vial
measurements
5 ml plastic
BGC PH 364 2 Seawater 5 mL analysed onboard — — pH measurments
vial
500 mL glass Carbonate system
BGC CARB 364 1 Seawater 500 mL — Hg2Cl2 RT
bottle measurements
benthic water from 20 ml
microscopic
IMG BDI 152 1 dinoflagellates (on 20mL shaken scintillation 2% acidic lugol 4 °C
count
brown algae) macroalgae vials
45 mm 10µm
benthic water from
PC filter MetaB, metaG,
SEQ BDS 124 1 dinoflagellates (on 100 mL shaken flash frozen LN
stored in metaT
brown algae) macroalgae
cryotubes
filtered on a
25mm-diameter, 1.5 ml HPLC pigment
BGC HPLC 944 2 Water, pigments 2L flash frozen LN
0.7-µm-pore glass cryotubes analysis
fiber filter
filtered through a
0.45 µm-pore size 20 mL
macronutients
BGC NUT 862 2 Seawater 20 mL cellulose acetate polyethylene — −20 °C
dosing
membrane with a vials
syringe
Plankton 5 mL MetaB, metaG,
SEQ S023 1104 2 50 L Filtration flash frozen LN
(0.2–3 µm) cryotubes metaT
Plankton 5 mL MetaB, metaG,
SEQ S320 1086 2 50 L Filtration flash frozen LN
(3–20µm) cryotubes metaT
FeCl3
Water, Viruses 5 mL
SEQ S<02 874 2 10 L precipitation and — 4 °C Sequencing
(<0.2µm) cryotubes
filtration
Water, membranes 50 mL Falcon
SEQ S<02> 127 1
vesicles(<0.2µm) 80 L
— −20 °C Sequencing
tube
600μl of 48%
5 mL single cells
IMG-SEQ SCG 1056 1 Plankton 4 ml — Glycine Betaine, LN
cryotubes sequencing
flash frozen
Continued

Analysis
category Sample type Material sampled
(sample- Amount of conservation
Sample protocol label n (samples) n (rep.) material_label) material Processing container conservative temperature Targeted analysis
15μL
mix and incubate Glutaraldehyde
IMG FCM 1078 2 Plankton 1.48 5mL 2 ml cryotubes LN Flow cytometry
15min at RT 25%/PoloXamer
10%; flash frozen
filtered onto
47mm 0.22µm Scaning electron
IMG SEM 566 1 Plankton 500 mL petrislides — RT
PC membranes, microscopy
dryed 2h at 50°C
incubate 1–24 h
with PFA 10x;
filter on 25mm,
Fluorescent in
IMG-SEQ FISH 562 1 Plankton 225 mL 0.22µm PC petrislides — −20 °C
situ hybridation
filter, rinse with
ethanol, dry for
5–10 minutes
5 mL
filtered onto
1L (250ml cryotubes MetaB, metaG,
SEQ S20 714 2 Plankton 47mm 10µm PC flash frozen LN
per filter) (two filters per metaT
membranes
tube)
5mL of 10%
paraformaldehyde High througput
50 mL Flacon
IMG H20 422 1 Plankton 45 mL — and 500μl of 4 °C confocal
tubes
glutaraldehyde 25% microscopy
EM grade
analysed using
Quantitative
IMG LIVE20 358 1 Plankton 50 mL Flowcam — — —
imaging analysis
onboard
concentrated
onto 20µm sieve,
stored in ethanol
100– 15 mL Flacon 95% mollecular single cells
IMG-SEQ E20 444 1 Plankton during 24h before −20 °C
250 mL tubes grade ethanol sequencing
seiving again
to change the
ethanol
600μl of 48%
5 mL single cells
IMG-SEQ SCG20 212 1 Plankton 4 mL — Glycine Betaine, LN
cryotubes sequencing
flash frozen
salinity
BGC SAL 50 1 Seawater 250 ml RT
measurements
concentrated
onto 20µm sieve,
50 mL Flacon 1mL of acidic Lugol microscopic
IMG L20 243 1 Plankton 250 mL resuspended 4 °C
tubes solution observations
using filtered sea
water
1mL of acidic
formalin 37%,
50 mL Flacon and filled up microscopic
IMG F20 240 1 Plankton 45 mL — RT
tubes to 50mL with observations
sodium teraborate
decahydrate buffer
concentrated
onto 200 µm
sieve,
resuspended
250 mL
using filtered 30mL of 37% Quantitative
IMG F300 510 1 Plankton 1L double closure RT
sea water formalin solution imaging analysis
bottles
with sodium
teraborate
decahydrate
buffer
prefiltered onto
5 mL
2mm metallic
1L(250 mL cryotubes MetaB, metaG,
SEQ S300 603 2 Plankton sieve, filtered flash frozen LN
per filter) (two filters per metaT
onto 47mm 10µm
tube)
PC membranes
microscopic
IMG AI 1323 1 Aerosols ~21.6 m3 — petrislide dried RT
observations
SEQ AS 1300 1 Aerosols ~21.6 m3 — 2 ml cryotubes flash frozen LN MetaB
MetaB and
SEQ-BGC ABS 1306 2 Aerosols ~21.6 m3 — 2 ml cryotubes flash frozen LN
biogeochemistry
acid cleaned
125 mL low Trace metal
BGC MTE-USC 249 1 Seawater 125 mL — — RT
density PET analysis
bottle
Table 3. Correspondence between samples types and their associated events and a summary of the protocol used
and targeted analysis. RT: Room temperature, LN: Liquid Nitrogen, MetaB: metabarcoding, MetaG: metagenomic,
MetaT: metatranscriptomic, PC: Polycarbonate, PET: Polyethylene.

Coral samples obtained from [SCUBA-SURVEY] were collected for symbionts and coral diversity analysis
([CDIV]) using different marker genes (metabarcoding, 18 S, 16 S and ITS2). About 0.5 g of material was pre-
served with DNA/RNA shield and stored into 2 mL cryotubes at −20 °C.
Finally, samples collected during [SCUBA-CORER] events were also processed and stored onboard Tara.
The [CORE] were rinsed with freshwater, air dried for 24–48 h before being wrapped into a plastic bubble wrap
for sclerochronological and geochemical analysis, to recover historical water biogeochemical properties. The
[PH], [CARB] and [MTE-LSCE] samples associated with the coral core [CORE] were processed following the
same protocol than the water samples collected with the [SCUBA-PUMP] and [ZODIAC-PUMP] (explained in
section 2.2.2), with the exception that the [CARB] and [MTE-LSCE] samples were already stored in their final
container during sampling on the dinghy.
Water samples for biogeochemistryThe [PH] was measured from the two replicates 5 mL polypropylene vials
onboard Tara using an Agilent Technologies Cary 60 UV-Vis Spectrophotometer equipped with an optical fiber.
The detailed protocol was previously described14, but briefly, the 5 mL vials and the 50 mL falcon tube were kept
closed and acclimated to 25 °C for 2–3 h. Absorbance at specific wavelengths was then read before and after the
addition of 40 µL meta-Cresol Purple dye to each 5 mL vial. The probe was rinsed between each measurement
using the 50 mL falcon tube containing the same seawater as the 5 mL vials samples. TRIS buffer solutions18
were measured regularly along the cruise to validate the method and correct for potential drifts of pH of the dye
solution.
The Niskin bottles of the morning ([CSW] for [C001] colony) and afternoon ([SRF]), carefully kept closed
since sampling on the dinghy, were each used to rinse and fill one 500 mL glass stoppered bottle on Tara. Some
grease was applied to the glass stopper, and bottles were filled with water samples leaving 2 mm of air below
the bottom of the bottleneck. Note that the [CARB] samples associated with the [CORE] samples were already
stored in their final container and grease was already applied to the glass stopper before the dive. The water level
of these samples was simply adjusted to 2 mm below the bottleneck. All [CARB] samples were immediately poi-
soned with 200 µL of saturated mercury (II) chloride solution (HgCl2) and stored at room temperature.
The Niskin water was then used to rinse and fill up trace element samples in 60 mL HDPE plastic bottles
[MTE-LSCE]. These samples were stored at room temperature and used to confirm the absence of local influ-
ence on Li and B isotopic signals. Similar to [CARB] associated with [CORE] samples, the [MTE-LSCE] samples
associated with [CORE] samples were already stored in their final containers, therefore, were just stored at room
temperature.
The water remaining from the Niskin bottle, sampled in the morning ([CSW] for [C001] colony) and the
afternoon ([SRF]), was used to prepare macronutrient samples ([NUT]). A 50 mL syringe was rinsed with the
sampled seawater three times. A filter 0.45 µm-pore size cellulose acetate membrane was then connected to the
syringe and ~20 mL of sample water was run through it to rinse the filter. Once the syringe, filter and vials were
properly rinsed twice, two 20 mL polyethylene vials were filled running the sampled water through 0.45 µm-pore
uptidisc syringe filter. Nutrient samples were stored vertically at −20 °C.
Two replicates of two liters of seawater sampled in the 4 L Nalgene bottle from the [SCUBA-PUMP] and
[Zodiac-PUMP] events, were filtered onto 25 mm-diameter, 0.7 µm-pore glass fiber filters (Whatman GF/F) and
immediately stored in liquid nitrogen for later High-Performance Liquid Chromatography ([HPLC]) analysis
to obtain pigments concentration.
Water samples for genomics and imageryThe water collected during the [SCUBA-PUMP] and [Zodiac-PUMP]
events was treated similarly, with the only difference that while [Zodiac-PUMP] samples were treated in dupli-
cates, the two 50 L samples collected during [SCUBA-PUMP] correspond to [C001] and [C010] colonies. This
applies only for sequencing samples ([SEQ-S]), while all other samples were taken in duplicates. Additionally,
all genomic samples were processed to be as comparable as possible with previous existing samples from Tara
Oceans12,15.
As soon as back on-board Tara, the water collected was used to rinse and fill one (for each [CSW]) or
two (for [SRF]) 50 L carboy and two 2 L Nalgene(r) bottles. The content of the 50 L carboys was immedi-
ately size-fractionated by sequential filtration onto 3 µm-pore-size polycarbonate membrane filters and 0.22
µm-pore-size polyethersulfone Express Plus membrane filters. Both were placed on top of a woven mesh spacer
Dacron 124 mm (Millipore) and stainless-steel filter holder “tripods” (Millipore). Water was directly pumped
from the 50 L with a peristaltic pump (Masterflex), and separated into samples that contain particles from
3–20 µm ([S320]) and 0.2–3 µm ([S023]) for latter sequencing. To ensure high-quality RNA, the filtering of the
first replicate ([C001] for [CSW] samples and any of the two 50 L carboys for [SRF]) were stopped after 15 min-
utes of filtration while the second was continued for the full volume (or a maximum of 60 min) to maximize
DNA yield. Filters were folded into 5 mL cryovials and preserved in liquid nitrogen immediately after filtration.
During this filtration 10 L of 0.2 µm filtered water ([S < 02]) was collected from each replicate, 1 mL of FeCl3
solution was added to flocculate viruses19 for 1 hour. This solution was then again filtered onto a 1 µm-pore-size
polycarbonate membrane filter using the same filtration system as for [S320] [S023]. Filters were then stored in
5 mL cryotubes and stored at 4 °C for later sequencing of viruses. The 80 L remaining of 0.22 µm prefiltered water
was used to filter membranes vesicles ([S < 02 > ]) using an ultrafiltration Pellicon2 TFF system by keeping the
pressure below 10 psi until the concentrate was reduced to a final volume of 200–300 mL. This sample was fur-
ther concentrated using a Vivaflow200 TFF system at a recirculation rate of 50–100 mL/min and less than one
bar of pressure until obtaining a final sample of 20 mL. Flushing back the system usually brings this volume to
up to 40 mL which was stored in a 50 mL Falcon tube at −20 °C.
Two 4 mL samples were taken from the 2 L Nalgene bottles, and stored into 5 ml cryotubes fixed with 600 μl
of 48% Glycine Betaine and directly flash-frozen for later single cells genomic analysis ([SCG]). For flow
cytometry cell counting ([FCM]), two replicates of 1.485 mL of sampled water were placed into 2 mL cryotubes
pre-aliquoted with 15 μL of fixative composed of Glutaraldehyde (25%) and PoloXamer (10%). Tubes were then

mixed gently by inversion, incubated 15 min at room temperature in the dark before being flash-frozen, and
kept in liquid nitrogen. For scanning electron microscopy ([SEM]), 500 mL of water was filtered onto a 47 mm,
0.22 µm pore size, polycarbonate filter, placed in a petri slide, dried for two hours at 50 °C and conserved at room
temperature. Fluorescence In Situ Hybridization ([FISH]) samples were prepared by adding 225 mL of seawa-
ter into a 250 mL plastic vial containing 25 ml of 10xPFA. The samples were incubated at 4 °C before filtration
onto two 25 mm 0.22 µm pore size polycarbonate filters, rinsed with ethanol, placed in petri slides, dried for
5–10 minutes before being stored at −20 °C.
Samples collected during the [SCUBA-NET-20] were fractionated for sequencing and imaging needs. One
litre of the sample collected was filtered onto four 47 mm, 10 µm pore size, polycarbonate membranes (250 mL
each). Filters were then placed into 5 mL cryotubes, flash-frozen, and stored in liquid nitrogen for later sequenc-
ing ([S20]). 45 mL was subsampled into a 50 ml Falcon tube, fixed with 5 mL of 10% paraformaldehyde and
500 μl of glutaraldehyde 25% EM grade, and stored at 4 °C for future high-throughput confocal microscopy
([H20]; e.g.20). 4 mL was stored in 5 mL cryotubes, fixed with 600 µl of 48% glycine betaine, immediately flash
frozen and kept in liquid nitrogen for single cell genomics ([SCG20]). Another sample for single cell sequencing
stored in ethanol ([E20]) was done by filtering 100 to 250 mL of the sample onto a 20 µm sieve and re-suspended
in EM grade ethanol for 24 h at 4 °C. After incubation, the sample was sieved a second time to remove any trace
of seawater, re-suspended with EM grade ethanol into 15 mL falcon tube, and stored at −20 °C. Finally, a 50 mL
sample was directly imaged live onboard ([LIVE20]) using a FlowCam21 Benchtop B2 series equipped with a 4x
lens and processed using the auto-image mode.
Oceanic sampling. To obtain both a large scale and local (around coral reef island) environmental charac-
terization, a comprehensive set of physical, chemical and biological properties of the sea surface ecosystem were
recorded while cruising. This sampling scheme was framed to be compatible with the previous Tara Ocean expe-
dition measurements12,15, but also to provide a continuity with water samples conducted directly on the coral reef.
Furthermore, while the biology and ecology of surface ecosystems remain largely unknown, they are an essential
component of air-land-sea exchanges and are subjected to numerous hydrological, atmospheric, physical and
radiative constraints22 and are therefore at the frontline of climate change and pollution.
The main goals and general overview of this sampling are already described14,23 and will be briefly presented
here in the context of the different sampling events and samples that were generated. Measurements and samples
could be separated into two types: i. local samples originating from a local sampling event, and ii. autonomous
high frequency continuous measurements of atmospheric and surface seawater properties (e.g., per minute
averages of higher frequency measurements). In the case of the discrete water sampling, the different sampling
events were either attributed to a station (noted [OA001] to [OA249]) if they were conducted in a reasonably
short time lapse (>75 km away, or >0.25 days away from a group of OA Events), or noted [OA000] otherwise.
Similarly, every OA station located within 200 nautical miles (370 km) of island were annotated with that Island
label, i.e. the sampling-design-label of the corresponding OA Events and OA Samples is [OA###-I##-C000]. The
continuous sampling was conducted as follows: a. surface seawater measurements were performed by pumping
water continuously through the boat hull ([INLINE-PUMP]) at ~1.5 m depth, b. light and atmosphere proper-
ties were measured 5 m above the sea level ([PAR + BATOS]), and c. aerosols were sampled by pumping air on
top of the mast ([MAST-PUMP]) at ~27 m (15 m during the first trans-Atlantic transect prior to May 2016).
Sampling events. Sampling was organized following several successive events, generally at daily frequency,
in the morning. Water collection while cruising was carried out by a custom-made underway pumping sys-
tem nicknamed the [DOLPHIN] connected by a 4 cm diameter reinforced tubing to a large volume industrial
peristaltic pump (max flow rate = 3 m3 h−1) on the deck. The system was equipped with a metallic pre-filter of
2 mm mesh size, two debubblers, and a flowmeter to record the volume of water sampled. Unfiltered water was
collected first for a series of protocols, water was prefiltered using a 20 µm sieve to rinse and fill two 50 L. Both
unfiltered seawater use and 20 µm filtered seawater were labelled as [CARBOY]. To collect larger plankton, water
was pumped from the DOLPHIN into a 20 µm net fixed on the wetlab’s wall ([DECKNET-20]) for 1 to 2 hours
depending on biomass concentration simultaneously to a net tow using a “high speed net” ([HSN-NET-300]).
The HSN was equipped with 300 µm mesh sized net and designed to be efficient up to 9 knots. It was towed
from 60 to 90 minutes depending on the plankton density. Near islands and in the Great Pacific Garbage Patch, a
Manta net ([MANTA-NET-300]) with a 0.16 × 0.6 m mouth opening with a 4 m long net with 300 µm mesh size
was used concurrently at a maximum speed of 3 knots. Finally, trace metal samples ([MTE-USC]) were collected
from the bow using a metal-free carbon fibber pole [HANDHELD-BOW-POLE] on which a plastic fixation have
been added to insert a 125 mL low density polyethylene bottle (LDPE) which was previously pre-washed on land
and stored individually in separate Ziploc bags. To avoid contamination from the boat, samples were hand held
collected, wearing polyethylene gloves, while cruising upwind on the bow of the boat (i.e., before the boat got in
contact with the collected water; Fig. 3).
Samples processing. Water, plankton and aerosols samples collected in the vicinity of islands and from the open
sea were processed as much as possible following similar protocols than on islands. Samples collected both on
islands and in open sea are marked with asterisks* here, and only the few differences in protocols will be noted.
From Dolphin, unfiltered waterUnfiltered seawater collected from the [DOLPHIN] was used to process several
samples for biogeochemical purposes ([BGC]). For every station, samples were collected for nutrients [NUT]*,
[PH]* measurements and pigments analysis by [HPLC]*. Salinity [SAL], carbonates ([CARB]*) and trace ele-
ments [MTE-LSCE]* were sampled on a weekly basis. [SAL] samples were done by sampling 250 mL of seawater
in a 250 mL hermetically sealed glass bottle.

Fig. 3 Schematic overview of the various sampling events conducted during the Tara Pacific expedition while
sampling on oceanic stations. The different events are represented by the different numbers. (1) [INLINE-
PUMP]; (2) [MAST-PUMP]; (3) [DOLPHIN] pumped water that is either used (4) [RAW], filtered at 20 µm
to fill two 50 L (5) [CARBOY], or filtered though (6) [DECKNET-20]; (7) “high speed” [HSN-NET-300] or
[MANTA-NET-300] plankton nets; (8) [HANDHELD-BOW-POLE].
From Dolphin, pre-filtered waterThe two 50 L carboys of 20 µm prefiltered seawater were used to produce size
fractionated samples for genomic analyses ([S320]* [S023]* [S < 02]*). The same pre-filtered seawater was sam-
pled for flow cytometry cell counting ([FCM]*) and single cell genomic ([SCG]*).
From Dolphin-DecknetOnce the [DECKNET-20] time limit reached (between 1 and 2 hours), the flow was
stopped and the net was carefully rinsed with 0.2 µm filtered seawater. The plankton sample was then transferred
to a 2 L Nalgene bottle and completed to 2 L with 0.2 µm filtered seawater. The sample was homogenized by
repeated smooth bottle flips and split into four 250 mL subsamples for [S20]*, one 250 mL sample for [E20]*,
one 250 mL sample for [LIVE20]*, and one 45 mL sample for [H20]*. In addition to these already described
protocols, one 250 mL sample was also taken for [L20], for which the seawater was drained using a 20 µm sieve
and the plankton was transferred in a 50 mL Falcon tube and fixed with 1 mL of acidic lugol solution for latter
microscopic observations. Finally, a 45 mL sample was taken for [F20], transferred in a 50 mL Falcon tube and
fixed with 1 mL of 37% formalin solution and completed to 50 mL with sodium tetraborate decahydrate buffer
solution for latter microscopic observations.
From HSN/Manta netsOnce recovered, samples collected both by the HSN net and the Manta net followed the
same procedure. The net was carefully rinsed from the exterior to drain organisms into the collector. Its content
was transferred using 0.2 µm filtered sea water in a 2 L Nalgene Bottle and completed to 2 L. The sample was then
homogenized and split in two 1 L samples. The first half was prefiltered onto a 2 mm metallic sieve and filtered
onto four 47 mm 10 µm pore size polycarbonate membranes (250 mL each). Filters were then placed into 5 mL
cryotubes, flash frozen and conserved in liquid nitrogen for latter sequencing ([S300]). The second fraction was
concentrated onto a 200 µm sieve and resuspended in a 250 mL double closure bottle using filtered seawater
saturated with sodium tetraborate decahydrate, fixed with 30 mL of 37% formalin solution and stored at room
temperature for latter taxonomic and morphological analysis using imaging methods ([F300]).
From Mast-pumpAerosols pumped through one of the ([MAST-PUMP]) inlets were channelled through a con-
ductive tubing of 1.9 cm inner diameter to four parallel 47 mm filter holders installed in the rear hold using a
vacuum pump (Diaphragm pump ME16 NT, VACUUBRAND GmbH & Co KG, Wertheim, Germany) at a
minimum flow rate of 30 lpm (20 lpm prior to May 2016). Three filter holders were equipped with 0.45 µm pore
size PVDF filters for latter aerosol sequencing ([AS]) and biogeochemical analysis together with sequencing
([ABS]), while the fourth one was a 0.8 µm pore size polycarbonate filter for later aerosol imaging ([AI]) analysis

using scanning electron microscope. Twice a day (12 h pumping periods), at approximate dusk and dawn, those
filters were changed, [AS] and [ABS] filters were placed into 2 mL cryotubes (2 filters for each [ABS] sample) and
immediately flash frozen while [AI] filters were packaged in sterile PetriSlide preloaded with absorbent pads and
stored dry at room temperature.
Continuous measurements. As previously described (see14,23), a comprehensive set of sensors were combined to
continuously measure several properties of the water but also atmospheric aerosols and meteorological condi-
tions. All sensors were interfaced to be synchronized with the ship’s GPS and synchronized in time (UTC time).
Surface seawater was pumped continuously through a hull inlet located 1.5 m under the waterline using a mem-
brane pump (10 LPM; Shurflo), circulated through a vortex debubbler, a flow meter, and distributed to a num-
ber of flow-through instruments. A thermosalinograph [TSG] (SeaBird Electronics SBE45/SBE38), measured
temperature, conductivity, and thus salinity. Salinity measurements where intercalibrated against unfiltered sea-
water samples [SAL] taken every week from the surface ocean, and corrected for any observed bias. Moreover,
temperature and salinity measurements were validated against Argo floats data collocated with Tara. A CDOM
fluorometer [WSCD] (WETLabs), measured the fluorescence of coloured dissolved organic matter [fdom]. An
[ACS] spectrophotometer (WETLabs) measured hyperspectral (4 nm resolution) attenuation and absorption
in the visible and near infrared except between Panama and Tahiti where an AC-9 multispectral spectropho-
tometer (WETLabs) was used instead. A filter-switch system was installed upstream of the [ACS] to direct the
flow through a 0.2 µm filter for 10 minutes every hour before being circulated through the [ACS] allowing the
calculation of particulate attenuation [ap] and absorption [cp], by removing the signal due to dissolved matter,
drift, and biofouling24. From November 13, 2016 to May 6, 2017, a backscattering sensor [BB3] (WETLabs
ECO-BB3) in a flowthrough chamber (BB-box) was added to the underway system, upstream of the switch sys-
tem, to measure the volume scattering function [VSF] at 124° and 3 wavelengths (470, 532, 650 nm) and estimate
the backscattering coefficient [bbp]. From May 7th 2017 to the end of the expedition, the BB-box and the [BB3]
were moved downstream of the filter-switch system to run 0.2 µm filtered seawater for 10 minutes every hour in
order to remove the biofouling signal and improve [bbp] estimations. Chlorophyll a content [chl] was estimated
from [ap]25 and [cp] (when [cp] is hyperspectral26), as well as other pigments (when [ap] is hyperspectral27). The
[chl] estimated from [ap] was then calibrated against the [HPLC] [chl]25. The particulate organic carbon con-
centration [poc] was estimated both using an empirical relation28 between measured [poc] and measured [cp],
or applying an empirical relation between measured [poc] and [bbp]29. Phytoplankton organic carbon [cphyto]
was estimated by an empirical relationship with [bbp]30. An indicator for size distribution of particles between
0.2 and ~20 µm [gamma] was calculated from [cp]31. A brief description of the methods to analyse, calibrate,
correct, and estimate bio-optical proxies are detailed in the section Technical Validation and more extensively
explained in each processing report attached with the dataset.
An Equilibrator Inlet Mass Spectrometer [EIMS] (Pfeiffer Vacuum Quadrupole 1–100 amu) measured the
Oxygen to Argon ratio in percent [o2ar], coupled with an optode (Aanderaa optode 4835) measuring oxy-
gen concentration in the seawater [O2]. Concurrently with samples collected through the [MAST-PUMP], two
instruments were installed aboard Tara to measure the size distribution and abundance of atmospheric aerosol
particles: a scanning mobility particle sizer ([SMPS], SMPS-C GRIMM Aerosol Technik Ainring GmbH & Co.
KG, Ainring, Germany) measuring particles in the size range 0.025–0.70 µm, and an optical particle counter
([EDM]; EDM180 GRIMM Aerosol Technik Ainring GmbH & Co. KG, Ainring, Germany) measuring all par-
ticles in the size range 0.25–32 µm. The SMPS was set to perform a full scan of particle distribution every 5 min
and the EDM produced a particle size distribution every 60 s. Data provided from [EDM] includes both the total
particle concentration (nb cm−3) in the size range 0.25–32 µm every 60 seconds, and through a second dataset
averaged every 30 minutes, both the particle concentration (nb cm−3) together with its normalized size distri-
bution (dN/dlogDp (nb cm−3), i.e., the concentration divided by the log of the size width of the bin),while data
from [SMPS] are averaged at the hour scale and provided both at the scale of particle concentration (nb cm−3)
together with its normalized size distribution (dN/dlogDp (nb cm−3)).
Together with navigation data such as speed over ground [sog] and course over ground [cog] meteorological
station (BATOS-II, Météo France) measured air temperature, relative humidity, and atmospheric pressure at
7 m above sea level. True and apparent wind speed and direction was measured at about 27 m above sea level. In
October 2016 a Photosynthetically Active Radiation [par] sensor (Biospherical Instruments Inc. QCR-2150) was
mounted at the stern of the boat (~5 m altitude).
Data Records
The full collection of datasets has been deposited either at Pangaea or at Zenodo depending on their nature, but
also on the likelihood to be updated.
Provenance metadata. Tara Pacific datasets are articulated around a consistent set of provenance metadata
that provide temporal (UTC date and time) and spatial (latitude, longitude, depth or altitude) references as well as
annotations about environmental features and place names, using controlled vocabulary from the environmental
ontology (https://www.ebi.ac.uk/ols/ontologies/envo) and the marine regions gazetteers (https://www.marinere-
gions.org/). These metadata are available at three granular levels: sampling stations and sites, sampling events, and
samples collected at a specific depth.
A [sampling-design-label] is provided to facilitate the identification and integration of data that originate
from the same open ocean station (OA###), island (I##), site (S##) or coral colony (C###), and hence share
provenance and environmental context. For example, data originating from coral colony number twelve on
the second site of the fourth island visited by Tara will bear the sampling design label OA000-I04-S02-C012.
Similarly, data collected at station number 99 in the middle of the Pacific Ocean will bear the sampling design

label OA099-I00-S00-C000, and data collected at open ocean station number 41 within 200 nautical miles of
island number four will bear the sampling design label OA041-I04-S00-C000.
Each sample is also characterized by its sampling event which have several properties such as its date and
time (UTC) of sampling ([sampling-event_date_time-utc]), the type of event from which the sample originates
([sampling-event_device_label]), the material sampled ([sample-material_label]; see Table 3), the protocol used
([sampling-protocol_label]; see Table 3) and finally the barcode attributed to the final sample obtained and
replicated on the logsheets ([sample-storage_container-label]). Finally, each sample, in addition to its original
barcode was characterized by an event label and a sample label composed of that previous information such as:
Sample label: TARA_SAMPLE_[sampling-event_date_time-utc]_[sampling-design label]_
[sampling-environment_feature_label]_[sample-material_label]_[sampling-protocol_label]_[sample-storage_
container-label]
Event label: TARA_EVENT_[sampling-event_date_time-utc]_[sampling-design label]_
[sampling-day-night_label]_[sampling-environment_feature_label]_[sample-material_label]_
[sampling-protocol_label]_[sample-storage_container-label]
The provenance context of all samples collected during the Tara Pacific Expedition is available as a single
UTF-8 encoded tab-separated-values file, in open access at Zenodo and replicated in part at BioSamples (XYZ).
In addition to georeferences and place names, the provenance metadata includes sample unique identifiers, tax-
onomic annotation from NCBI, and links to sampling logsheets and campaign summary reports.
Additionally, the full repository containing the campaign summary reports, sampling authorisations, logs-
heets and the full record of coral images could be consulted on Pangaea (https://store.pangaea.de/Projects/
TARA-PACIFIC/). The full list of sampling events is consultable on the following dataset 32: https://doi.
org/10.1594/PANGAEA.944548.
Environmental context for data analysis. Rich collection of environmental parameters collected from
either samples, on-board measurements, satellite imagery, operational models or even calculated from astro-
nomical atlas were compiled and made available for further analysis. These environmental measurements were
provided in a multi-layered way in open access to either Pangaea or Zenodo (Tables 4 and 5), depending on
the potentiality to require updates, with (1) raw measurements at the measure level for both physical samples
or for on-board continuous measurements, accompanied with their quality check flags (2) a combined version
regrouping all measurement at the sampling event level and adding satellite imaging and results obtained from
operational models. (3) This latter was propagated, together with all measurements done on samples, to provide
an environmental context to every collected samples belonging to the same station, but by also providing indices
of the spatial ([dxy]), temporal ([dt]) and vertical ([dz]) discrepancies between the various measures and the
designed sample and their variability (as assessed by mean, standard deviation, number of measures and 5, 25, 50,
75, 95 percentiles when possible); (4) a simplified version at the site level where all synonym measurements were
cross-compared and chosen by level of quality. (5) At the scale of the site level, a series of Lagrangian and Eulerian
diagnostics were calculated using satellite-derived and modelled velocity fields, providing multiple information
on water mass transport and mixing (6) Finally, and for coral sites only, historical data of temperature were
extracted (see (6) Historical data on coral sites) from satellite imagery to provide an historical overview of past
heatwave experienced by the sampled coral reefs (since 2002 up to the sampling date).
Raw measurements from samples or sensors. From sensors, the measurements were standardized at the
minute scale when possible (including standard deviation and the number of observations within the min-
ute when available) and accompanied with their UTC time and GPS position. These data sets regroup data
obtained from the [TSG] the [ACS] the [WSCD] the [BB3] the [EIMS] the [optode], the [EDM], the [SMPS],
the [PAR] and the navigation data. These are available as ten distinct data sets, one for each package of sen-
sors. Similarly, measurements made from discrete samples collected on board Tara (see Methods Section 3.3),
together with quality assessment flags, are provided as six distinct data sets, one for each type of analysis ([NUT],
[MTE-USC], [CARB], [FCM], [HPLC], and [CTD]). For [CARB], additional parameters of the carbonate sys-
tem were calculated with CO2SYS.m v3.1.133 using in situ temperature, total alkalinity, total dissolved inorganic
carbon, salinity, phosphate and silicate concentrations as inputs together with recommended parameters34–37
(K1K2 = 10; KSO4 = 3; KF = 2; BOR = 2). Data sets are available in open access at the Data Publisher for Earth
& Environmental Science PANGAEA.
Combined version at the event level. A compilation of all environmental measures obtained during a given
sampling event was produced by compiling the boat’s sensor data available during the time-lapse of the station
and measurements originating from satellite imagery (MODIS-AQUA satellite - Level 3 mapped product, 8-day
average, 4 km resolution) recovered using OpenDAB protocols at https://oceandata.sci.gsfc.nasa.gov. The zone
corresponding to the station position and date was recovered either by taking a two-pixel buffer around the
given location (total zone being a 5 by 5 pixels square of 20 km side) and in order to propose an alternative meas-
ure in the inevitable case where clouds were present an alternative 12-pixels buffer was taken (total zone being a
25 by 25 pixels square of 100 km side).
The corresponding variables recovered are chlorophyll a38 (OCx algorithm39, [Chl_Sat]; mg m−3), the sea
surface temperature40 (4 µm shortwave algorithm; [T_Sat]; °C), daily mean photosynthetically available radia-
tion at the ocean surface41 ([PAR_Sat]; Einstein m−2 d−1), concentration of particulate inorganic carbon42 ([PIC_
Sat]; mol m−3), concentration of particulate organic carbon43 ([POC_Sat]; mol m−3), the diffuse attenuation
coefficient for downwelling irradiance at 490 nm44 ([Kd490_Sat] related to light penetration in water column
modified by particulate matter; m−1), and the particulate backscattering coefficient at 443 nm derived from the

Number of
Name measurements Variables Link/doi Reference
Raw continuous measurements
TSG >590 000 T, S https://doi.org/10.1594/PANGAEA.943675 67
Aerosols concentration (0.25–32 µm) https://doi.org/10.1594/PANGAEA.943694 81

EDM >15 000
1 min and 30 min versions https://doi.org/10.1594/PANGAEA.943691 82
EIMS >230 000 O/Ar ratio https://doi.org/10.1594/PANGAEA.943714 83
Optode >280 000 Oxygen concentration https://doi.org/10.1594/PANGAEA.943790 82
Navigation >1 271 000 Navigation and Meteo https://doi.org/10.1594/PANGAEA.944365 84
ACS >411 000 Chla, phytoplankton size, POC https://zenodo.org/record/6449893 85
BB3 >350 000 Backscattering, phytoplankton carbon https://doi.org/10.1594/PANGAEA.943793 86
WSCD >553 000 relative DOM fluorescence (sd, n) https://doi.org/10.1594/PANGAEA.943739 87
PAR >830 000 Photosynthetically active radiations (sd, n) https://doi.org/10.1594/PANGAEA.943740 88
Aerosols concentration, particle size

SMPS >4600 https://doi.org/10.1594/PANGAEA.943856 89
distribution (25–685 nm), sd
Raw discrete measurements
NUT 849 NO2, NO3, PO4, Si(OH)4 https://doi.org/10.1594/PANGAEA.944289 90
MTE-USC 523 Fe, Zn, Cd, Ni, Cu, Pb, Mn https://doi.org/10.1594/PANGAEA.944395 91
Alkalinity, Carbonates, pH, pCO2, fCO2,

CARB 325 [HCO3−], [CO32−], CO2, Ω-Calcite, https://doi.org/10.1594/PANGAEA.944420 92
Ω-Aragonite
Pico-, Nano-, Picoplankton abundance and
FCM 1041 https://doi.org/10.1594/PANGAEA.944490 93
scattering
HPLC 551 Pigment concentrations https://doi.org/10.1594/PANGAEA.944281 94
T,S, conductivity, conductance, density,

CTD 4246 https://doi.org/10.1594/PANGAEA.943869 95
sound velocity, depth, pressure
Table 4. Data sets providing the environmental context for future analysis and provided as raw measurements
by sensors and from samples.
Garver-Siegel-Maritorena algorithm45 ([GSM_Sat] which gives a good indication of the concentration of sus-
pended organic and inorganic particles such as sediments in the water; m−1).
This compilation of environmental data at the scale of the event was further enriched using data from reana-
lyzed (ie. forced with observations) operational models obtained from Copernicus Marine Services (GLOBAL_
REANALYSIS_PHY_001_03046, daily mean for sea surface height, salinity, temperature, current speeds, mixed
layer depth; GLOBAL_REANALYSIS_BIO_001_02947 daily mean for Chl a, phytoplankton carbon, O2, NO3,
PO4, SIOH, Fe concentrations, Primary production, pH and CO2 partial pressure and GLOBAL_REANALYSIS_
WAV_001_032-TDS48 for sea surface waves) but also using almanach49,50 to calculate essential sun and moon
parameters (position, rises and sets, phase, etc).
Environmental context at the granularity of samples. The environmental context of all samples collected during
the Tara Pacific Expedition is available together with the provenance file in open access at Zenodo. The environ-
mental context of each sample is provided based on environmental data sets described above for continuous and
discrete measurements, as well as those generated from almanacs, satellite imagery and models.
Environmental context is provided in eleven UTF-8 encoded tab-separated-values files, all with the same
structure, but each providing a different statistic: number of values (n), mean value (mean), standard deviation
(stdev), 05, 25, 50, 75 and 95 percentiles (P05, P25, P50, P75, P95), lag in time (dt), i.e. difference between the
collection date/time of the sample and that of the environmental context provided, lag in horizontal space (dxy),
i.e. distance between the collection location of the sample and that of the environmental context provided, and
lag in vertical space (dz), i.e. difference between the collection depth/altitude of the sample and that of the envi-
ronmental context provided.
Missing value terms are: “nav” = not-available, i.e. the expected information is not given because it has not
been collected or generated; “npr” = not-provided, i.e. the expected information has been collected or gener-
ated but it is not given, i.e. a value may be available in a later version or may be obtained by contacting the data
providers; “nac” = confidential, i.e. the expected information has been collected or generated but is not available
openly because of privacy concerns; “nap” = not-applicable, i.e. no information is expected for this combination
of parameter, environment and/or method, e.g. depth below seabed cannot be informed for a sample collected
in the water or the atmosphere
Simplified version at site level. In some cases, certain parameters were not available at specific sampling sites
due to technical issues or sensor availability, however, various basin scale studies and statistical tests require a
complete dataset for all sampled sites. During the Tara Pacific expedition, many parameters were concurrently
measured in-situ, estimated from remote sensing and/or modelled. For instance, sea surface temperature was
measured on the boat using the thermosalinograph included in the underway system, but also with satellite

Number of
Name measurements Variables Link/doi Reference
Environmental context at the granularity of sampling events
Inline all Inline data with n, sd, quartiles, local sun/moon set/rize,
sensors + Almanach + Copernicus + Modis 4155 local zenith, nutrients, hydrology, plancton quantities, Chla, https://zenodo.org/record/6445609 96
Aqua (2 and 12 pixels around) PAR, PIC, POC, T, GSM, KD490 (with n, sd, and quartiles)
Provenance metadata and environmental context at the granularity of samples
georeference, sample unique identifier, logsheet links,
Sample provenance 57859
environmental features and place names
https://zenodo.org/record/6299409 97
mean and std + dt, dx, dz from sampling timing, position and
All previous variables extracted at event level 57859
depth
Environmental context at the granularity of sampling stations
all event level variables 655 intercalibrated and combined version https://zenodo.org/record/6474974 98
Lagrangian Descriptors 246 Eulerian and Lagrangian diagnostics of water dynamic https://zenodo.org/record/6453376 99
Environmental context at the granularity of coral sampling sites

historical heat and cold stress indicators 113 TSA, DHW, recovery time etc…
https://zenodo.org/record/6499374 100
raw time series >6000 × 113 SST at 1, 3 and 9 pixels, seasonal average, DHW, DCW
Reefcheck bleaching occurence 106 Bleaching (% of colony or % of population) https://zenodo.org/record/6511406 101
Photo annotations
5606 photo, identification to the genus level, algal contact (genus of algae),
Qualitative photographic annotations https://zenodo.org/record/6364768 102
2216 colonies presence of boring organisms (type), contact with sediment,
Taxonomic annotations of coral diversity 18 S based taxonomic annotations, corresponding https://zenodo.org/record/6327048

2470 103
(CDIV) surveys morphological annotation based on photo https://ecotaxa.obs-vlfr.fr/prj/4176
Table 5. Data sets providing the provenance and the environmental context for future analysis and provided
aggregated at the sample, event and site levels.
and estimated from a model. Each of these three modes of acquisition have their caveat and accuracy, however,
within a certain confidence interval, missing in-situ data can be replaced by its remotely sensed or modelled
equivalent. We provide here a simplified version at the sampling site level by replacing missing in-situ data by
their closest and most accurate satellite or modelled equivalent. In each case, in-situ data was considered as the
most accurate source of data, with a preference to HPLC pigments analysis followed by measurements done
by the ACS, while satellite and modelled data were used only if in-situ data was not available. We evaluated the
accuracy of ACS and of each satellite and modelled datasets by linear regressions with their in-situ counterparts.
A bias of the modelled or satellite data was identified when the slope of the regression was different to 1 and/or
an intercept was different to 0. The satellite and modelled data were forced to match the in-situ data by dividing
by the slope and subtracting the intercept. This is the case for SST. When large bias persisted between matchups
with observations, the corrected data was not used to replace missing in-situ data. This is the case for chl. The
same approach was then applied to fill missing data with modelled values (MERCATOR-Copernicus).
A correction for the bias in the following variable was applied for SST, SSS, PO4, and SiOH. As previously
done, if large bias persisted between observations and corrected data, they were not used to replace missing
in-situ data. This is the case for chl, NO3, and Fe.
The [MTE] samples were sometimes sampled in the afternoon instead of the morning alongside all the other
water samples, thus were located in between two sampling stations. These [MTE] samples could not be assigned
to a sampling station following the criterion presented in the section 3, therefore, the missing values of the cor-
responding morning stations were interpolated linearly.
The same approach was used for pH measurements, with a preference from measurements provided by
total carbonate system quantifications, followed by direct pH measurements and then modeled values
(MERCATOR-Copernicus).
Lagrangian and Eulerian diagnostics. In order to provide a description of the dynamical properties of the
water masses sampled, different Eulerian and Lagrangian diagnostics were calculated. Here, we report a general
description of the information each of them provides. In the next subsection, we provide the details of how they
were calculated for each station.
The following Eulerian diagnostics were calculated: Absolute velocity ([Uabs], m s−1): sqrt(u2 + v2), where u
and v are the zonal and meridional components of the horizontal velocity field used (described below); Kinetic
energy ([Ekin], m2.s−2): 0.5*(u2 + v2); Divergence ([EulerDiverg], d−1): du/dx + dv/dy; Vorticity ([Vorticity],
d−1): dv/dx - du/dy; Okubo-Weiss ([OW], d−2): s2-vorticity2, where s2 is (du/dx-dv/dy)2 + (dv/dx + du/dy)2. If
negative, it indicates that the station sampled was inside an eddy.
The following Lagrangian diagnostics were calculated: Finite-Time Lyapunov Exponents ([Ftle], d−1,
Shadden et al., 2005): it indicates the rate of horizontal stirring, and it is a means to quantify the intensity of
turbulence in a given region. FTLE are commonly used to identify Lagrangian Coherent Structures, i.e. bar-
riers to transport. In this case, a strong FTLE value indicates a region separating water masses which were far
away backward in time. Lagrangian betweenness51 ([betw], adimensional): this diagnostic draws inspiration
from Lagrangian Flow Network Theory52. It can identify regions which act as bottlenecks for the circulation,

in that they receive waters coming from different origins, and that are then spread over several different desti-
nations. These can represent possible hotspots driving biodiversity51. Lagrangian Divergence53 ([LagrDiverg],
d−1). This diagnostic was calculated by integrating the Eulerian divergence along the backward trajectories. If
positive, it indicates a water mass that, during the previous days, was subjected to a strong divergence, thus to a
possible upwelling. If negative, it indicates a strong convergence, thus possible downwelling. Retention Time54
([RetentionTime], d). This diagnostic indicates how many days a water mass has spent inside an eddy in the
previous period. If the water mass is outside an eddy, then its retention time is set to zero.
Extraction of the Eulerian and Lagrangian diagnosticsFor each of the 246 stations sampled, we proceeded as
follows.
We identified the water mass sampled at the given station. This was considered as a stadium shape with
the two semi-circles centered on the starting and ending points of the transect, respectively. The radius of the
stadium semi-circles was considered 0.1°, which is in accordance with previous studies51,55,56. The stadium was
filled with virtual particles separated by 0.01°.
For each virtual particle inside the stadium shape, we calculated a Eulerian or Lagrangian diagnostic
(described above). The Eulerian diagnostics were extracted directly from the velocity field of the day of sam-
pling. Concerning the Lagrangian diagnostics, these were obtained by advecting the virtual particle backward
in time for an amount of time τ from the day of sampling day_S. For the Lagrangian betweenness, the advection
was performed between day_S + τ/2 and day_S-τ/2, so that the advective time window was centered on the
sampling day (details in51).
For the Lagrangian diagnostics, we used the following advective times τ: 5, 10, 15, 20, 30, and 60 days. The
only exception is the retention time, which, by construction, was calculated only with the largest advective time,
namely τ = 60 days.
Once that, a given diagnostic (Eulerian or Lagrangian) was calculated for all the virtual particles filling the
stadium shape, we calculated the mean value, and the 25, 50, and 75 percentiles. The percentiles were calculated
in order to quantify the spatial variation of the diagnostic inside the stadium shape. Therefore, we associated
each station with four values (mean, 25, 50, and 75 percentiles) of a given diagnostic.
Furthermore, two different velocity fields were used, which are described as follows.
Velocity fields and trajectory calculationBoth the velocity fields were downloaded from E.U. Copernicus
Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). The first velocity field used
was MULTIOBS_GLO_PHY_REP_015_00457 [GlobEkmanDt]. This was produced by combining the altim-
etry derived geostrophic velocities and modelled Ekman surface currents. It had a spatial resolution of 0.25°
and a temporal resolution of one day. The second velocity field was GLOBAL_REANALYSIS_PHY_001_03046
[GloryS12]. It was obtained by a NEMO model assimilating altimetry and other observations. It had a spatial
resolution of 1/12° and a temporal resolution of 1 day.
Historical climate data and indices for climate variability for coral collection sites. It’s becoming increasingly clear
that stress resilience, in particular thermal tolerance, is shaped not only by maximum monthly mean temperatures
(MMMs), but also by long-term and short-term climate variability, even at the scale of reefs58–60. In order to pro-
vide an overview of past climate variability and marine heatwaves experienced by corals sampled at each site, we
built a high-resolution historical dataset that spans from 2002 to each sites’ sampling date. Ocean skin tempera-
ture (11 and 12 µm spectral bands longwave algorithm) was extracted from 1 km resolution level-2 MODIS-Aqua
and MODIS-Terra from 2002 to the sampling date and from level-2 VIIRS-SNPP from 2012 to the sampling date.
Day and night overpasses were used to maximize data recovery. Following recommendations from NASA Ocean
Color (OB.DAAC), only SST products of quality 0 and 1 were used. The 9 closest pixels to the sampling sites of
each scene were extracted. All the extracted pixels from the 3 platforms were then averaged daily to obtain daily
SST averages and standard deviations time series for each sampling site, from 2002 to the sampling date.
Each time series was first averaged on a Julian day basis to provide a seasonal average. This yearly seasonal
average was triplicated and concatenated into a 3−year seasonal cycle to apply a digital low pass filter on the
middle year without generating artefacts. A digital low pass filter (filter order 3, pass band ripple 0.1; “filfilt”
function in matlab) with 36 Julian days windows was applied to the concatenated time series to remove high
frequency noise. The middle year was then extracted from the concatenated time series to recover the seasonal
cycle. The sea surface temperature anomaly was calculated as the SST minus the seasonal cycle over the full time
series. Considering the short periods of missing data (mean of the 95th percentile of the duration of consec-
utive days with missing data: 9.8 ± 4.1 days), the missing values in the SST and SST anomaly time series were
linearly interpolated in order to calculate thermal stress indices. The SST anomaly frequency was calculated
as the number of days over the past 52 weeks when the SST anomaly is greater than or equal to 1 °C. Thermal
stress indices relevant to coral reef health were then calculated using methodology developed for the Coral
Reef Temperature Anomaly Database (CoRTAD)60 (Table 6). Events of cold temperature accumulation were
also reported to cause bleaching and mortality61,62, therefore, the same set of indices were calculated for cold
stress adapting the CoRTAD method, but using the minimum weekly climatologies (Table 6). Further to that,
we checked for previous occurrences of bleaching events at sampled reef sites by matching island coordinates to
the Reef Check dataset (reefcheck.org) obtained from Sully et al.58,63. For each Tara Pacific island, coordinate we
determined that Reef Check site that was closest (in terms of distance in km) and considered only Reef Check
data that was within a 10 km circumference.
A condensed table containing single values associated with each sampling site was created extracting the min-
imum, maximum, sum, averages, standard deviations, and value recorded at the sampling day of each of these
indices (detailed in the readme file provided with the dataset). Additional metrics of the last heating and cooling
events as well as the time of recovery is also provided to represent the state of thermal stress at the day of sampling.

Name Acronym Description Reference

SST daily average 9 pixels [sst_mean_9pixel] Daily average of the 9 closest pixels around the sampling site
Seasonal average 9 pixels [seasonal_average_9pixel] Seasonal average SST calculated from 2002 to the sampling date
SST anomaly 9 pixels [SST_anomaly_9pixel] SST anomaly calculated as: sst_mean_9pixel minus seasonal_average_9pixel
SST daily average interpolated [SST_mean_interpl] SST daily average with missing values interpolated linearly
SST anomaly interpolated [SST_anomaly_interpl] SST anomaly with missing values interpolated linearly
SST anomaly frequency [SST_anomaly_freq] number of days in the past 52 weeks when SST_anomaly_interpl > = 1 °C CoRTAD
Heat Thermal Stress Anomaly [TSA_heat] Daily SST average interpolated minus the maximum weekly climatology CoRTAD
TSA heat frequency [TSA_heat_freq] number of days in the past 52 weeks when TSA_heat > = 1 °C CoRTAD
TSA degree heating week [TSA_DHW] sum of the past 12 weeks when TSA_heat is greater than or equal to 1 °C CoRTAD
TSA degree heating week frequency [TSA_DHW_freq] number of days in the past 52 weeks when TSA_DHW is greater than or equal to 1 °C CoRTAD
Cold Thermal Stress Anomaly [TSA_cold] Daily SST average interpolated minus the minimum weekly climatology Custom
TSA cold frequency [TSA_cold_freq] number of days in the past 52 weeks when TSA_cold < = −1 °C Custom
TSA degree cooling week [TSA_DCW] sum of the past 12 weeks when TSA_cold is lower than or equal to −1 °C Custom
TSA degree cooling week frequency [TSA_DCW_freq] number of days in the past 52 weeks when TSA_DCW is lower than or equal to 1 °C Custom
Table 6. Description of historical SST values and thermal stress indices calculated following CoRTAD60
method and modified to also represent cooling events.
Coral photographic resources and annotations. The [PHOTO] resource consists of two datasets. The first,
obtained from the [SCUBA-3X10] protocol, was annotated for genus validation, gross morphological character-
istics of the colony, algal contact, presence of boring organisms, sediment contact, predation, and health factors
(such as presence of disease and coloration). The acquisition protocol of these annotations is described below.
This dataset is also used for the description of morphotypes within each genus for taxonomic annotation in com-
bination with genetic data. The second dataset, obtained following [SCUBA-SURVEY] protocol was used for the
taxonomic annotation (as close to genus level as possible) of the coral host of the [CDIV] samples. Of a total of
2,470 CDIV samples, 1711 samples had one or more pictures associated (3,085 total pictures), 759 samples had
no photos. Overall, 11,460 coral photographs were generated and annotated allowing for a permanent record of
all colonies sampled. All [PHOTO] were transferred to EcoTaxa64.
(1) Manual Annotations of in situ colony (CO) photos:
Photo analysis for the genus validation and environmental context was conducted using Matlab with code
developed and written specifically for the Tara Pacific Expedition65. Photos were annotated individually, and
annotations were conducted from January to April 2020. To prevent observer bias, photos were randomized, and
the annotator was blind to any information regarding the location or the sampling site. The analysis included 1)
identification to the genus level, 2) algal contact with types of algal genus if identifiable (Halimeda, Turbinaria,
Dictyota, Lobophora, Crustose Coraline Algae (CCA), Sargassum, Galaxaura, other), 3) presence of boring
organisms with types if identifiable (Bivalve, Spirobranchus, Tridacna, Urchin, Other Polychaete, Sponge, and
Other), 5) contact with sediment (sand), 6) presence of predation marks. Most annotations were boolean oper-
ators (yes/no) with identifications added if possible. Several indicators of coral health were also annotated such
as if the coral looked unhealthy or showed tissue loss (Yes/No), coloration (light, normal, dark, or bleached),
and presence of a pigmentation response (Yes/No). If a pigmentation response was present, the annotator was
prompted to determine if it was trematodiasis (Yes/No). Finally, additional notes included but were not limited
to the quality of the photo (blurry, poor visibility, coloration), contact with neighbouring hard or soft coral col-
onies, fish presence in the photograph, snail(s), or hermit crab(s) on the coral, an object in the photograph, etc.
(2) Taxonomic annotations of coral diversity (CDIV) surveys:
All images imported in EcoTaxa have been identified at the genus level by taxonomic experts, and crosslinked
with genomic identification from metabarcoding based on the V9 region of the 18 S rDNA. Analysis of the 18 S
marker aimed to generate coral host taxonomic annotations to the level of genus for every sample. The annota-
tion was generated based on each sample’s most abundant 18 S sequence by aligning to the NCBI ‘nt’ database
with taxonomic labels. A ‘lowest common ancestor’ approach was used when there were multiple best hits. These
alignment-based annotations were verified phylogenetically (i.e. taxonomic similarity agreed with sequence
similarity). More than half of the samples were not annotated at genus or better level using this approach, due to
the lack of resolution of the 18 S V9 marker. Where available, host taxonomic assignments were based on photo
annotations. Otherwise, 18S-based annotations were used.
Technical Validation
Numerous steps of quality control were operated at different levels of acquisition to ensure good quality of the
different datasets and may vary depending on the type of measurement operated and if it originates from sensors
on-board or from samples.
Inline measurements, models, and satellite data validity. [PAR] measurement validity was checked
by first removing physically wrong data (ie. values greater than 0.45 μE cm−2 sec−1 or lower than 0 μE cm−2 sec−1)
and compared with clear sky matchup measurements from MODIS-Aqua & Terra. Comparison confirmed the
good agreement between datasets but also the absence of sensor drift. Temperature and salinity were acquired by
the [TSG]. The quality of the whole time series was manually checked, and the temperature validity was assessed

by comparing the temperature reading of the two sensors placed at two different places along the inline system.
Potential drifts of the temperature sensor was investigated by comparing the temperature time series with satel-
lites’ sea surface temperature. Salinity measurements where intercalibrated against unfiltered seawater samples
[SAL] taken every week from the surface ocean, and corrected for any observed bias. Moreover, temperature
and salinity measurements were validated against Argo floats data collocated with Tara. The [ACS] absorption
and attenuation signal due to dissolved matter, drift, and biofouling were estimated between two filter events
by interpolating filtered water absorption and attenuation following the shape of the [fdom] from the [WSCD],
when available. This method improves data quality in case of strong variation of dissolved matter absorption that
the frequency of filter event would not capture properly (e.g. approaching coastal waters or entering a lagoon).
When [fdom] data was not available, the filtered absorption and attenuation were linearly interpolated between
filter events before being removed from the total absorption and attenuation. From November 13, 2016 to May 6,
2017, the [BB3] was located upstream of the switch system, thus measured total (non-filtered) water all the time.
During this period, the volume scattering coefficient of seawater was removed from the raw data counts to obtain
the particulate backscattering coefficient [bbp]. The biofouling and instrument drift were estimated comparing
values before and after each cleaning events. The biofouling was estimated between two cleaning events by fitting
an exponential or linear model to the raw data before removing it from the signal. We advocate to use this period
with caution as the data was corrected with theoretical assumptions (i.e. pure seawater scattering and linear or
exponential biofouling) that may differ from reality. From May 7th 2017 to the end of the expedition, the [BB3]
was located downstream of the filter-switch system so that, like for the [ACS] processing, the biofouling signal
could be estimated and removed between two filter events and [bbp] quality improved. The correspondence
between total particulate scattering [bp] estimated from the [ACS] and [bbp] was investigated for the whole
expedition. [bbp] values were discarded when [bbp]/[bp] was unusually low ( < 0.002; see range of [bbp]/[bp] in
natural waters66). A similar modelling and correction for biofouling than the one performed for the [BB3] was
applied to the [WSCD] data. The [PAR], [TSG], [BB3], [ACS], and [WSCD] data were processed following the last
recommendations for processing inline24, using custom software available at https://github.com/OceanOptics/
InLineAnalysis. The entire time series of measurement were automatically QC to remove artifacts and manually
checked and QC for obviously inaccurate measurements due to saturated sensor, low flow rate, bubbles, or poor
filtered seawater measurements. The full processing and QC procedure and reports could be accessed together
with each dataset.
Sample measurements technical validation. For nutrients [NUT] samples a quality check was done
in several steps. First a visual inspection was done to determine if samples were overfilled or not frozen vertically
which may induce sample leakage during the frosting procedure. Secondly any readings too close to detection
limits or when duplicate measurements differed by more than 10% were flagged. In this last case, when the differ-
ence between two values of the same sample is greater than 10%, it is considered that the high value is not accept-
able and is not reported. Finally, the overall quality of the dataset was established by comparing measurements
values with Copernicus Marine Services modelling outputs.
For trace metals ([MTE-USC]), any samples in which concentrations were close to detection limits were
flagged. A standard produced by the GEOTRACES program (coastal surface seawater standard) was included in
each sample run. If the metal concentrations of the standard were outside the GEOTRACES community consen-
sus values, the sample run was rejected. Trace metal concentrations had an average error of 5%.
[HPLC] samples were analysed as described in Ras et al. 2008. All pigments peaks were inspected and quality
controlled as good, acceptable or qualitative. Any measurements below detection limits were disregarded.
[FCM] samples were analysed with a FACS Canto II Flow Cytometer equipped with a 488 nm laser67 and
every measurement where cell populations were either complicated, needed manual curation or were impossible
were flagged.
Nets collection validity. To estimate the technical validity of the different nets collection we analysed the
raw abundance of living organisms collected conjointly by the [HSN-NET-300] and [MANTA-NET-300] at the
same stations, but sequentially in time. Indeed [MANTA-NET-300] is operated at different speeds (3 knots maxi-
mum) compared to [HSN-NET-300] (9 knots maximum) and therefore were not deployed simultaneously. Manta
nets are commonly used and recognized as a reference type of net while investigating surface plankton68–70 and
we therefore used a set of 24 stations where both were deployed concurrently to estimate the efficiency of the
[HSN-NET-300]. For this [F300] samples collected by both nets were imaged using the ZooScan71 to obtain
images of each object collected. Images were then transferred to EcoTaxa64 and sorted taxonomically to the deep-
est taxonomic level possible. All results were used to calculate the normalized biovolume size spectra72 (NBSS) of
living organisms for both nets, which is an analogue to abundance per size categories. This NBSS spectra allows
investigating the potential under- or over-sampling while investigating it over various sizes of organisms. The
NBSS of both nets were giving about the same order of magnitudes of abundances (Fig. 4A) and when inspected
along the size spectra between pairs of observations (Fig. 4B) they did not differ largely from 1:1 in 13 cases over
the different deployments. A large variability between nets could however be observed at a few stations which
could possibly be caused by local plankton patchiness73 resulting in more variability for [HSN-NET-300] and
less for [MANTA-NET-300] due to larger sampling volume. Overall, we can conclude that [HSN-NET-300] and
[MANTA-NET-300] are collecting plankton with a relatively similar efficiency even if the larger sampling volume
of [MANTA-NET-300] allows a better collection of larger, rare, organisms, as seen from spectra extending to
larger sizes (Fig. 4A). Nevertheless, these results show that the use of [HSN-NET-300] may be really useful for
underway zooplankton sampling in the situations when it is not possible to stop the ship for regular sampling or
on ships of opportunity.

2 16
10
HSN
Manta 14
12
10 1
NBSS HSN / NBSS Manta

10
NBSS (mm3 /mm 3 /m 3 )
equivalent abundance 8
0 6
10
2
-1
10
0
-2
-2 -4
10
10 3 10 4
oa 6
oa 9
oa 2
oa 5
oa 7
oa 9
0
oa 2
oa 3
oa 1
oa 2
oa 3
oa 4
oa 8
oa 9
oa 0
oa 3
oa 4
oa 5
oa 6
oa 7
oa 8
oa 9
oa 0
07
08
09
12
12
13
07
07
04
04
04
04
04
04
05
05
05
05
06
06
06
06
07
07
oa
station
Fig. 4 Technical validation of net sampling. Comparison of normalized biovolume size spectra (NBSS) of living
organisms sampled with [HSN-NET-300] and [MANTA-NET-300] over a set of 24 stations where both were
deployed together. From both NBSS, a sampling efficiency of the HSN net compared to the MANTA net was
calculated as a mean and standard deviation over all the size classes considered.
Overall biogeochemical data validity. To assess the overall quality and homogeneity of the collected
environmental parameters, we conducted a quick multivariate exploration of the dataset to compare it with
known biogeography of biogeochemical provinces74,75 and their associated biogeochemical signatures. For this,
we first used data simplified at the site version (see section 4 of Data records), selected only datasets providing a
full overview over the geographical range of the expedition, used a box-cox transformation and centred-reduced
each variable to equally consider those. This dataset was then analysed through a PCA analysis (Fig. 5). The 3 first
components of the PCA analysis were recovered to code for a RGB (red, green, blue) color-coding of each station
and better visualize the biogeochemical signature of the station on a map. Finally, those were compared with
known biogeochemical provinces extracted from75. Despite the different temporal resolution between instanta-
neous sampling and biogeochemical provinces representing a consensus over several years and seasons, we can
see that the main biogeochemical provinces (and associated macroscale oceanic features) as well as their pro-
gressive boundaries are well captured by our sampling scheme. Among the notable features, the western Pacific
coast of Americas are marked by a strong upwelling signature (with high amount of nutrients and trace metals),
the southern Pacific gyre with a high salinity but a low iron and silicate concentration, the central Pacific zone is
characterized by high temperature, light and sea surface height, small phytoplankton size (high gamma), with low
chlorophyll a and low NO3 and trace metals (Ni, Cu, Zn, Pb or Cu) concentrations, with the exception of the few
stations centred on the equator which clearly display some indicators of local upwelling such as those potentially
created by the equatorial upwelling. This first overview clearly shows correspondence with known features related
to nutrients and nutrient limitation of plankton, trace metals or even global biogeochemistry76–78 and further
shows that the sampling scheme used allowed to sample corals and plankton across a large variety of environmen-
tal constraints either on oceanographic, climatic or chemical aspects. The same analysis repeated only using sites
realized around islands further confirms this large variety of environmental constraints (Fig. 6). To evaluate the
variety of the past temperature history, and notably the impact of past seasonality and heat/cold waves, we further
reproduced this analysis using historical temperature and heat/cold waves experienced on coral sites. However,
since temperature anomalies and their accumulated degree cooling weeks (DCW) could be negative, only a basic
normalization of data was made since box-cox normalization is not suited for negative values. The first axis of
the PCA separate islands that suffered intense and recurrent heat-waves (positive values) from those that rather
experienced cold-waves (negative values) while the second axis separate cold and highly seasonal islands (positive
values) from islands with warm environments with low seasonality (negative values). This analysis further con-
firms that the selected location also displays a full variety of past history of temperature and heat-waves but also
reflects known geographical patterns of bleaching events58,79.
Usage Notes
We recommend paying close attention to the various quality flags provided with the raw datasets to avoid
using lower quality data if needed. Similarly, to provide the more complete set of observations for each sam-
ple, we provided the lag in time (dt), as well as distance in horizontal (dxy) and vertical (dz) space, between
the collection timing, latitude/longitude and depth/altitude of the sample and that of the environmental con-
text provided. Depending on the scientific question, future users are encouraged to carefully define reasonable
time lag and distances to consider in their study, to avoid including unrealistic associations between samples.
Moreover, we extracted contextual data at the event level to simplify the data extraction task. We also provide
simplified version at the site level by combining and cross-calibrating all similar variables (e.g. using different
sources of SST data to fill gaps of missing data and obtain one merged SST variable). We prioritised observations

A B
1
Curr PIC_Sat
PAR
d
merg
spee
_ Sa
ed_
ent_
d_ n5 Fe
Second Principal Component 12% variance
Si 5
SST
H
O
M
0.5
ge
er
m
NPP u
C
Co59
0 merged_POC
gamma_ACS Pb
_SSS Cd
merged merge Zn
d_ch
l
-0.5
Ni
peed
merged
mergedP
NO
Wind_s
NO
3
MLD
2
_pH
O4
-1
-1 -0.5 0 0.5 1
First Principal Component 24% variance
Fig. 5 Technical validation of the main hydrological and biogeochemical environmental variables compared
with biogeochemical provinces as extracted from75. (A) Environmental data were normalized through a
box-cox normalization and analysed through a PCA analysis to better display their typical environmental
signature. The position of each station in the 3 first axes of the PCA were further used to provide a red blue
green color-coding, allowing to (B) project their environmental signature on a map and compare it with known
biogeochemical provinces.
A B
1
cold std
heat std
SST std
cold meanl
freq std
seaso
an
y
me
SS anomT anomal
q x
fre ma
na std
std
Second Principal Component 18% variance
aly req
an
aly
q me
om ly f
fre q sum
at fre
T SS
an a
0.5
T om
he eat freqq s max

td
SS T an
h ld fre q ax
c o e m
q std coeldat fr W
SS
fre h DH std
e at freq
h DHW sum
DCWmean DHW sum
DHW mean
0 DHW freq mean
DCWsum DHW freq
DHW freq std
DC max
DC W len
DC W fr ght
W eq ma
fre m x
-0.5 q ax
min
std
sea sona min
omaly
sea SST min
SS al ean
son l
T mea
hea mea
SST an
n
t
-1
n
-1 -0.5 0 0.5 1
First Principal Component 29% variance
Fig. 6 Technical validation of the historical SST heatwaves and cold waves parameters compared with
biogeochemical provinces as extracted from75. (A) Environmental data were normalized and analysed through
a PCA analysis to better display their typical environmental signature. Only 50% of the variables having the
more influence on the analysis are displayed here. The position of each station in the 3 first axes of the PCA were
further used to provide a red blue green color-coding, allowing to (B) project their environmental signature on
a map and compare it with known biogeochemical provinces.
originating from in-situ samples over satellite data, and over modelled data (MERCATOR), and evaluated their
correspondence by linear regressions. Potential biases of satellite and modelled data in comparison to in-situ
data were corrected applying the slope and intercept of their linear regression to force satellite and modelled
data to best match in-situ data. Similarly, we also chose to interpolate some environmental variables that were
sampled only few hours before or after the site itself to maximize data recovery for each sampling station. We
acknowledge merging different sources of data can introduce differences in variance depending on the source of
data used, therefore, we encourage the user to cautiously evaluate the relevance of this merged dataset for their
study. Considering the intrinsic heterogeneity of variance between the different datasets, and their potential
non-normal distribution, we recommend using appropriate normalisation methods before any multivariate sta-
tistical analysis. Here we chose to use box-cox transformation and centred-reduced each variable.
In this version of the dataset the satellite data used is 8-days averages while the in-situ measurements are
instantaneous measurements of optical properties averaged over the station sampling period. The 8-days aver-
aging tends to attenuate extreme values and reduces the potential differences between stations. While suited
for macro-ecological processes which depend on large temporal and spatial variations of their environment,
the use of 8-day average satellite products could be inaccurate to study shorter life cycles of the pico-, nano and
micro-plankton.
Moreover, phytoplankton can adjust their light harvesting pigment concentrations according to light expo-
sure, nutrient availability and temperature. These variations are negligible over periods shorter than a day
but can become significant over 3–5 days80,referencestherein. Therefore, we advise the users to cautiously use
the merged bio-optical variables of this dataset and to verify its compatibility with the research question and
potentially replace this 8-day average with shorter time observations if available. As presented in section “3.3.
Continuous measurements”, the [poc] was estimated from the underway system, both using the measured [cp]28,

and [bbp]29. The [BB3] sensor have a low signal-to-noise ratio due to its high sensitivity to bubbles in the water
line and to accumulation of particles in the sensor, therefore, the [poc] estimated from the [BB3] was used to fill
the missing [poc] estimated from the [ACS]. When the [bbp] from the [BB3] was used to estimate [POC], the
[bbp] values from the 470 nm wavelength were prioritized over the 532 nm wavelength and 650 nm wavelength
and the same merging method was applied to correct for bias between [poc] estimated from the [ACS] and the
[BB3], and between wavelength of the [BB3].
Code availability
The different codes used to process the different datasets are indicated within the text and are repeated here and
includes:
-Inline optical processing (https://github.com/OceanOptics/InLineAnalysis)
-Satellite products used38,40–45
-Mercator products46–48,57 used.
-Astronomical almanac to calculate sun/moon position and day-nights parameters from sites positions and
time49,50.
-Additional parameters of the carbonate system were calculated with CO2SYS.m v3.1.133 using in situ tempera-
ture, total alkalinity, total dissolved inorganic carbon, salinity, phosphate and silicate concentrations as inputs
together with recommended parameters34–37 (K1K2 = 10; KSO4 = 3; KF = 2; BOR = 2).
-Ecotaxa64 server github (https://github.com/ecotaxa/ecotaxa).
-EcoTaxa data processing (https://github.com/ecotaxa/ecotaxatoolbox)
-Morphological qualitative annotations65.
Received: 24 May 2022; Accepted: 10 October 2022;

Published: xx xx xxxx
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94. Dimier, C., Ras, J. & Uitz, J. Pigment concentration database of the sea surface layer collected during the Tara Pacific Expedition
95. Bourdin, G. et al. CTD dataset collected during the Tara Pacific Expedition 2016–2018 using a castaway CTD, PANGAEA, https://
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96. Lombard, F. et al. Environmental data at the sampling event level collected with Inline instruments, almanach, models and satellites
during the Tara Pacific Expedition 2016–2018, Zenodo, https://doi.org/10.5281/zenodo.6445609 (2022).
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98. Bourdin, et al. Environmental context observed during the Tara Pacific Expedition 2016–2018, simplified version at site level,
Zenodo, https://doi.org/10.5281/zenodo.6474974 (2022).
99. Baudena, A., Da Silva, O. & Lombard, F. Eulerian and Lagrangian diagnostics of the dynamical properties of the water masses
sampled during the Tara Pacific Expedition 2016–2018, Zenodo, https://doi.org/10.5281/zenodo.6453376 (2022).
100. Bourdin, G. et al. Historical Sea Surface Temperature (SST) data and thermal stress indices of the Tara Pacific Expedition’s coral
reef sampling sites, from May 1st 2002 to August 31st 2018, Zenodo, https://doi.org/10.5281/zenodo.6499374 (2022).
101. Voolstra, C. R. et al. TARA Pacific Bleaching Prevalence of Sampling Sites (Islands), Zenodo, https://doi.org/10.5281/
zenodo.6511406 (2022).
102. Clampitt, M. et al. Tara Pacific Qualitative Photo Annotations, Zenodo, https://doi.org/10.5281/zenodo.6364768 (2022).
103. Hume, B. C. C. et al. TARA Pacific CDIV cnidarian host taxonomic annotation release version 1_1, Zenodo, https://doi.
org/10.5281/zenodo.6327048 (2022).
Acknowledgements
We are keen to thank the commitment of the people and the following institutions for their financial and
scientific support that made this singular expedition possible: CNRS, PSL, CSM, EPHE, Genoscope/CEA, Inserm,
Université Côte d’Azur, ANR, agnès b., UNESCO-IOC, the Veolia Environment Foundation, Région Bretagne,
Billerudkorsnas, Amerisource Bergen Company, Lorient Agglomeration, Smilewave, Oceans by Disney, the Prince
Albert II de Monaco Foundation, L’Oréal, Biotherm, France Collectivités, Fonds Français pour l’Environnement
Mondial (FFEM), the Ministère des Affaires Européennes et Etrangères, the Museum National d’Histoire
Naturelle, Etienne BOURGOIS, the Tara Ocean Foundation’s teams and crew. Tara Pacific would not exist without
the continuous support of the participating institutes. The authors also particularly thank Serge Planes, Denis
Allemand and the Tara Pacific consortium. This study has been conducted using E.U. Copernicus Marine Service
Information and Mercator Ocean products. We acknowledge funding from the Investissement d’avenir project
France Génomique (ANR-10-INBS-09). FL is supported by Sorbonne Université, Institut Universitaire de France
and the Fondation CA-PCA. The in-line and atmospheric optics dataset was collected and analysed with support
from NASA Ocean Biology and Biogeochemistry program under grants NNX13AE58G and NNX15AC08G and
the HPLC processing under NSF award 2025402 to University of Maine. JMF is supported by a research grant
from Scott Eric Jordan and the Bernard & Norton Wolf Family Foundation. NCo was supported by a grant from

the Simons Foundation/SFARI (544236). Work from MC, RMM, ER, DF, PF, EG is supported by the French
Government (National Research Agency, ANR) through the grant “Coralgene” ANR-17-CE02-0020 as well as the
“Investments for the Future” programs LABEX SIGNALIFE ANR-11-LABX-0028 and IDEX UCAJedi ANR-15-
IDEX-01. NC and YL were supported by the “Laboratoire d’Excellence” LabexMER (ANR-10-LABX-19) and co-
funded by a grant from the French government under the program “Investissements d’Avenir”. FL, SP, CdV and
ZM are funded by the European Union’s Horizon 2020 research and innovation programme “Atlantic Ecosystems
Assessment, Forecasting and Sustainability” (AtlantECO) under grant agreement No 862923. ED and FL have
been supported by the French National Research Agency under the grant number ANR-22-CE02-0025 (project
COR-Resilience). FL and ZM are supported by the Belmont Forum project “World Wide Web of Plankton Image
Curation” (WWWPIC, grant No ANR- 18-BELM-003). The support of Pr. Alan Fuchs, President of CNRS, was
crucial for the success of the surface sampling undertaken during the Tara Pacific expedition. We thank A. Gavilli
from TECA Inc. France, and E. Tanguy and D. Delhommeau from the Institut de la Mer, Villefranche-sur-Mer
for the helpful collaboration in the conception of the High Speed Net and the Dolphin systems. The AT and CT
data were analysed at the SNAPO-CO2 service facility at LOCEAN laboratory and supported by CNRS-INSU
and OSU Ecce-Terra. We thank the EMBRC platform PIQv for image analysis and CCPv for samples storage and
supported by EMBRC-France, whose French state funds through the ANR within the Investments of the Future
program under reference ANR-10-INBS-02. The Tara Pacific expedition would not have been possible without
the participation and commitment of over 200 scientists, sailors, artists and citizens (see https://zenodo.org/
record/3777760#.YfEEsfXMLjB). This publication is number 20 of the Tara Pacific Consortium.
Author contributions
Conceptualization and methodology: F.L., G.B., S.P., E.B., N.C., E.D., J.M.F., S.G.J., I.K., M.L.P., J.P.M.P. G.R., S.R.,
E.R., A.V., C.R.V., B.B., C.B., D.F., P.F., P.E.G., E.G., S.R., S.S., O.T., R.T., R.V.T., P.W., D.Z., D.A., S.P., M.B.S., C.d.V.,
E.B., G.G. Sample collection: F.L., G.B., S.P., S.A., E.B., P.C., O.D.S., E.D., A.E., J.M.F., J.F.G., B.C.H., Y.L., R.M.,
D.A.P., M.L.P., J.P., G.R., S.R., E.R., C.R.V., G.I., D.F., P.F., P.E.G., E.G., S.R., S.S., O.T. R.V.T., P.W., D.Z., D.A., S.P.,
C.d.V., E.B., G.G.. Samples analysis and data analysis: F.L., G.B., S.A., E.B., N.C., M.C., P.C. C.D., E.D., A.E., J.F.,
J.M.F., J.F.G., B.C.H., L.J., S.G.J., R.L.K., Y.L., D.M., R.M., Z.M., N.M., D.A.P., M.L.P., M.P., J.R., G.R., S.R., E.R.,
C.R.V., B.B. Data production (models/satellites): F.L., G.B., A.B., O.D.S., C.R.V. Data Curation and validation: F.L.,
G.B., S.P., A.B., M.C., O.D.S., C.D., E.D., A.E., J.F., J.M.F., L.J., S.G.J., R.L.K., I.K., Y.L., D.M., R.M., Z.M., N.M.,
M.P., J.R., G.R., E.R., A.V., C.R.V. Funding Acquisition: F.L., N.C., P.C., E.D., J.F., J.M.F., J.F.G., S.G.J., I.K., D.M.,
N.M., M.L.P., M.P., G.R., E.R., A.V., C.R.V., B.B., C.B., D.F., P.F., P.E.G., E.G., S.R., S.S., O.T., R.T., R.V.T., P.W., D.Z.,
D.A., S.P., C.d.V., E.B., G.G.. Project Administration and supervision: F.L., S.P., S.A., E.B., J.M.F., E.R., C.R.V.,
C.M., B.B., C.B., D.F., P.F., P.E.G., E.G., S.R., S.S., O.T., R.T., R.V.T., P.W., D.Z., D.A., S.P., M.B.S., C.d.V., E.B., G.G..
Visualization: F.L., G.B., Z.M.. Writing – Original Draft Preparation: F.L., G.B., S.P., S.A., A.B., E.B., N.C., M.C.,
O.D.S., E.D., J.F., J.M.F., S.G.J., R.L.K., R.M., Z.M., C.RV. All authors have read and reviewed the manuscript.
Competing interests
The authors declare no competing interests
Additional information
Correspondence and requests for materials should be addressed to F.L.
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© The Author(s) 2022
Fabien Lombard 1,2,3 ✉, Guillaume Bourdin 1,4, Stéphane Pesant5, Sylvain Agostini6,

Alberto Baudena 1, Emilie Boissin7, Nicolas Cassar 8,9, Megan Clampitt10,11,12,
Pascal Conan13,14, Ophélie Da Silva1, Céline Dimier1, Eric Douville15, Amanda Elineau1,
Jonathan Fin16, J. Michel Flores17, Jean-François Ghiglione 13, Benjamin C. C. Hume 18,
Laetitia Jalabert1, Seth G. John 19, Rachel L. Kelly19, Ilan Koren 17, Yajuan Lin8,9,20,
Dominique Marie21, Ryan McMinds 10,22,23, Zoé Mériguet1, Nicolas Metzl 16,
David A. Paz-García 24, Maria Luiza Pedrotti1, Julie Poulain 25, Mireille Pujo-Pay 13,
Joséphine Ras1, Gilles Reverdin16, Sarah Romac 2,21, Alice Rouan10,11,12, Eric Röttinger 10,11,12,

Assaf Vardi 26, Christian R. Voolstra 18, Clémentine Moulin27, Guillaume Iwankow7,

Bernard Banaigs7, Chris Bowler 2,28, Colomban de Vargas2,21, Didier Forcioli10,11,12,
Paola Furla10,11,12, Pierre E. Galand 2,29, Eric Gilson10,11,12,30, Stéphanie Reynaud12,31,
Shinichi Sunagawa 32, Matthew B. Sullivan33, Olivier P. Thomas 34, Romain Troublé27,
Rebecca Vega Thurber23, Patrick Wincker 25, Didier Zoccola 12,31, Denis Allemand 12,31,
Serge Planes7, Emmanuel Boss 4 & Gaby Gorsky1,2
1
Sorbonne Université, Laboratoire d’Océanographie de Villefranche, UMR 7093, CNRS, Institut de la Mer de
Villefranche, 06230, Villefranche sur mer, France. 2Research Federation for the study of Global Ocean Systems
Ecology and Evolution, FR2022/Tara GOSEE, 75000, Paris, France. 3Institut Universitaire de France, 75231, Paris,
France. 4School of Marine Sciences, University of Maine, Orono, Maine, 04469, USA. 5European Molecular Biology
Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD,
UK. 6Shimoda Marine Research Center, University of Tsukuba, 5-10-1, Shimoda, Shizuoka, Japan. 7PSL Research
University: EPHE-UPVD-CNRS, USR 3278 CRIOBE, Laboratoire d’Excellence CORAIL, Université de Perpignan,
52 Avenue Paul Alduy, 66860, Perpignan, Cedex, France. 8Nicholas School of the Environment, Duke University,
Durham, NC, USA. 9Laboratoire des Sciences de l’Environnement Marin, UMR 6539 UBO/CNRS/IRD/IFREMER,
Institut Universitaire Européen de la Mer, Brest, France. 10Université Côte d’Azur, CNRS, INSERM, Institute for
Research on Cancer and Aging, Nice (IRCAN), Nice, France. 11Université Côte d’Azur, Institut Fédératif de Recherche
- Ressources Marines (IFR MARRES), Nice, France. 12LIA ROPSE, Laboratoire International Associé Université Côte
d’Azur - Centre Scientifique de Monaco, Nice, Monaco. 13Sorbonne Université, CNRS, Laboratoire d’Océanographie
Microbienne, LOMIC, 66650, Banyuls Sur Mer, France. 14Sorbonne Université, CNRS, OSU STAMAR - UAR2017,
75252 Paris, France. 15Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ,
Université Paris-Saclay, Gif-sur-Yvette, France. 16Laboratoire LOCEAN/IPSL, Sorbonne Université-CNRS-IRD-
MNHN, Paris, 75005, France. 17Weizmann Institute of Science, Department of Earth and Planetary Sciences,
Rehovot, Israel. 18Department of Biology, University of Konstanz, Konstanz, Germany. 19Department of Earth
Science, University of Southern California, Los Angeles, CA, USA. 20Environmental Research Center, Duke Kunshan
University, Kunshan, China. 21Sorbonne Université, CNRS, Station Biologique de Roscoff, UMR 7144, AD2M, Roscoff,
France. 22Université Côte d’Azur, Maison de la Modélisation, de la Simulation et des Interactions (MSI), Nice, France.
23
Department of Microbiology, Oregon State University, Corvallis, OR, USA. 24Centro de Investigaciones Biológicas
del Noroeste (CIBNOR), La Paz, Baja California Sur, 23096, México. 25Génomique Métabolique, Genoscope, Institut
François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Evry, France. 26Weizmann Institute of Science,
Department of Plant and Environmental Science, Rehovot, Israel. 27Tara Ocean Foundation, Paris, France. 28Institut
de Biologie de l’Ecole Normale Supérieure, Ecole Normale Supérieure, CNRS, INSERM, Université PSL, Paris, France.
29
Sorbonne Université, CNRS, Laboratoire d’Ecogéochimie des Environnements Benthiques, UMR 8222, LECOB,
Banyuls-sur-Mer, France. 30Department of Medical Genetics, CHU, Nice, France. 31Centre Scientifique de Monaco,
8 Quai Antoine Ier, MC-98000, Antoine, Monaco. 32Department of Biology, Institute of Microbiology and Swiss
Institute of Bioinformatics, ETH Zürich, Zurich, Switzerland. 33Department of Microbiology and Department of Civil,
Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH, USA. 34School of Biological
and Chemical Sciences, Ryan Institute, University of Galway, University Road, Galway, Ireland. ✉e-mail: fabien.
lombard@imev-mer.fr

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Background & Summary

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 1

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 2

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 3

Volume filtered = Opening area ∗ Tow speed ∗ Tow duration (1)

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 4

Opening area = π ∗ net radius 2 (2)

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 5

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 6

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 7

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 8

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 9

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 10

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 11

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 12

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 13

Aerosols concentration (0.25–32 µm) https://doi.org/10.1594/PANGAEA.943694 81

EIMS >230 000 O/Ar ratio https://doi.org/10.1594/PANGAEA.943714 83

Optode >280 000 Oxygen concentration https://doi.org/10.1594/PANGAEA.943790 82

Navigation >1 271 000 Navigation and Meteo https://doi.org/10.1594/PANGAEA.944365 84

ACS >411 000 Chla, phytoplankton size, POC https://zenodo.org/record/6449893 85

BB3 >350 000 Backscattering, phytoplankton carbon https://doi.org/10.1594/PANGAEA.943793 86

WSCD >553 000 relative DOM fluorescence (sd, n) https://doi.org/10.1594/PANGAEA.943739 87

PAR >830 000 Photosynthetically active radiations (sd, n) https://doi.org/10.1594/PANGAEA.943740 88

Aerosols concentration, particle size

MTE-USC 523 Fe, Zn, Cd, Ni, Cu, Pb, Mn https://doi.org/10.1594/PANGAEA.944395 91

Alkalinity, Carbonates, pH, pCO2, fCO2,

T,S, conductivity, conductance, density,

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 14

Environmental context at the granularity of coral sampling sites

Taxonomic annotations of coral diversity 18 S based taxonomic annotations, corresponding https://zenodo.org/record/6327048

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 15

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 16

Name Acronym Description Reference

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 17

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 18

NBSS HSN / NBSS Manta

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 19

he eat freqq s max

sea SST min

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 20

Received: 24 May 2022; Accepted: 10 October 2022;

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 21

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 22

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 23

© The Author(s) 2022

Fabien Lombard 1,2,3 ✉, Guillaume Bourdin 1,4, Stéphane Pesant5, Sylvain Agostini6,

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 24

Assaf Vardi 26, Christian R. Voolstra 18, Clémentine Moulin27, Guillaume Iwankow7,

Scientific Data | (2023) 10:324 | https://doi.org/10.1038/s41597-022-01757-w 25

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