# Unraveling Major Questions in Micronekton Ecology and Their Role in the Biological Carbon Pump Through Integrative Approaches and Autonomous Monitoring

Pavanee Annasawmy, Guillaume Chandelier, Thomas Le Mézo

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Pavanee Annasawmy, Guillaume Chandelier, Thomas Le Mézo. Unraveling Major Questions in Micronekton Ecology and Their Role in the Biological Carbon Pump Through Integrative Approaches and Autonomous Monitoring. Oceanography, 2025, 38 (1), pp.76-81. 10.5670/oceanog.2025e118. hal-05069012

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UNRAVELING MAJOR QUESTIONS IN MICRONEKTON ECOLOGY
AND THEIR ROLE IN THE BIOLOGICAL CARBON PUMP THROUGH
INTEGRATIVE APPROACHES AND AUTONOMOUS MONITORING

By Pavanee Annasawmy, Guillaume Chandelier, and Thomas Le Mézo

Micronekton consist of crustaceans, cephalopods, gelatinous organisms, and fishes that are 2-20 cm in size (Figure 1). These organisms have unique functional traits that impact their vertical migration patterns and ecosystem processes (Aparecido et al., 2023). Our understanding of their potential carbon transport and sequestration from the epipelagic (upper 200 m) to mesopelagic zones (200-1,000 m) or deeper (e.g., Boyd et al., 2019; Le Moigne, 2019; Cavan et al., 2019) is limited by the tools traditionally used to assess their biomass, diversity, and varied migration patterns (e.g., Annasawmy et al., 2019, 2024; Barbin et al., 2024; Eduardo et al., 2024). These knowledge gaps are notable considering that micronekton are ubiquitous throughout the world ocean.

MAJOR UNCERTAINTIES IN MICRONEKTON
RESEARCH

After 200 years of oceanography, the answers to the fol-
lowing major questions remain incomplete due to the
limitations of existing conventional approaches used to
investigate micronekton such as trawl (and net) sampling
and active acoustics:

- What is the global biomass of micronekton?

* What are their ecological patterns, including species richness, functional diversity, and vertical migration?

- What is their role in the biological carbon pump?

Biomass and biodiversity estimates of mesopelagic
communities in the ocean vary by an order of 5 to 58
(e.g., Kloser et al., 2009; Irigoien et al., 2014; Dornan et al.,
2022) because of net selectivity and catchability limitations

(Annasawmy et al., 2019; Kwong et al., 2022; Barbin et al., 2024) and because active acoustics lack taxonomic resolution and do not give direct biomass measurements. Novel eDNA methods offer the potential to fill in knowledge gaps in micronekton diversity and distribution left by traditional tools. However, variations in eDNA sampling collection and analysis methods may influence results and conclusions (Govindarajan et al., 2023a). To bridge the current methodological gaps and for global-scale comparisons, it is crucial to construct open access micronekton DNA reference libraries, establish consensus on optimal primers for detecting micronekton, and integrate eDNA with active acoustics and trawls.

Although contemporary eDNA methods can detect spe-
cies presence and absence (Govindarajan et al., 2023b),
they often rely on ship-based platforms (similar to ship-
borne active acoustics and nets) that offer only limited tem-
poral and spatial sampling resolutions. When autonomous
platforms are used, knowledge and expertise are currently
restricted to larger, northern institutions, generally limiting
scientists from the Global South from participating as lead-
ers in deep-sea research (Bell et al., 2023). Research on the
biological carbon pump is primarily focused on planktonic
organisms and concentrated in the Global North (Pacific
and Atlantic Oceans), leaving substantial geographical
gaps in the Southern Hemisphere (Kwong et al., 2022; Yang
et al., 2024). Technological and geographical gaps in deep-
sea research constitute major knowledge gaps, resulting in
significant uncertainties regarding the role of micronekton
in the biological carbon pump, and, consequently, in cli-
mate regulation (Pillar et al., 2024).
<figure><img src="image_2.png" /><figcaption>FIGURE 1. Examples of micronekton organisms shown here include mesopelagic fishes, cephalopods, gelatinous organisms, and crustaceans. Photo credit: Gildas Roudaut, Institut de Recherche pour le Développement, Brest, France</figcaption></figure>

## CASE STUDY: PROJECT NEAT AND THE INTEGRATION OF TRADITIONAL AND CONTEMPORARY TOOLS

The project NEAT, funded by the European Marine Research Network (EuroMarine), investigates micronekton ecology in the Western Indian Ocean. Samples were collected in April to May 2022 during the RESILIENCE (fRonts, EddieS and marine Life in the wEstern iNdian oCEan) cruise (Figure 2). The project objective was to integrate the eDNA, acoustic, and trawl data collected to investigate the diversity, abundance, biomass, and distribution of micronekton in the Western Indian Ocean. Migration patterns of the different species were not assessed due to the unbalanced trawl and eDNA sampling designs.

Micronekton specimens were collected using a Mesopelagos trawl net (mean vertical opening: 7 m; horizontal opening: 12 m; mouth area: 65 m²; length: 44 m; mesh nettings: 30 mm at the front and 4 mm at the cod end) towed at a ship speed of approximately 1.2 knots for a fishing duration of 30 min and at a maximum depth of 70 m. Specimens were divided into four broad categories: fishes, crustaceans, cephalopods, and gelatinous. Wet weight

<figure><img src="image_3.png" /><figcaption>FIGURE 2. With land masses shown in gray, red dots indicate Western Indian Ocean sites where samples were collected during the RESILIENCE cruise, plotted on GEBCO's (General Bathymetric Chart of the Oceans) gridded bathymetric dataset (relative depth below the sea surface in meters; GEBCO_2024 grid) in Ocean Data View software (Schlitzer, 2024).</figcaption></figure>

measurements of each broad category were recorded. Organisms are being identified to the lowest taxonomic level possible at the CITEB laboratory (Centre technique, de recherche et de valorisation des milieux aquatiques,
Reunion Island). Organism abundance (A, ind. m⁻²) was calculated using the number of individuals captured in each tow (N), volume of water filtered (VF; m⁻³), and the thickness of the scattering layer (SL thickness of 20 m) according to Kwong et al. (2022):

$$A = \frac{N}{VF} \times SL \text{ thickness.}$$

Dry mass was calculated from wet mass using the percentage water content from each broad category according to Cotté et al. (2022): 75% for fishes and crustaceans, 80% for cephalopods, and 94% for gelatinous organisms. The percentage of C in dry mass was also calculated for each group according to Cotté et al. (2022) as follows: 50% for fishes, 40% for crustaceans, 35% for cephalopods, and 15% for gelatinous organisms. The biomass (mgC m⁻²) was calculated using the total biomass (Bt) of each broad category per net tow, VF and SL thickness according to Kwong et al. (2022):

$$B = \frac{B_t}{VF} \times SL \text{ thickness.}$$

Seawater from the trawl net cod end was filtered on Millipore cellulose filters of 47 mm diameter and 0.45 µm pore size. The filters were stored at -80°C before being analyzed using the “MiFish-E” primer amplifying a 170 bp fragment of the 12S rRNA gene (Miya et al., 2015), and the “m1CO1ntF” primer targeting a 313 bp fragment of the COI (mitochondrial Cytochrome c Oxidase subunit I) gene (Leray et al., 2013). Metabarcoding analyses were conducted at the ADNId laboratory (Montpellier, France). Bioinformatics were conducted using the FROGS pipeline (Escudié et al., 2017; Figure 3). Sequences were clustered into operational taxonomic units (OTUs) using the SWARM v2.1.10 algorithm (Mahe et al., 2015).

In an effort to identify the organisms reflecting the transmitted sound waves, and layers of scattering organisms, a backscatter classification approach based on pairwise frequency differences (S<sub>v18-38</sub>, S<sub>v70-38</sub>, and S<sub>v120-38</sub>), called the Ellipsoid score (Escore) algorithm, was developed and used to classify the multifrequency acoustic data (18, 38, 70, 120 kHz) collected using a shipborne SIMRAD EK80

<figure><img src="image_4.png" /><figcaption>FIGURE 3. Schematic shows apparatus for eDNA field collection and laboratory analyses during the NEAT project. Image credit: Guillaume Chandelier</figcaption></figure>
echosounder (Annasawmy et al., 2024). Annasawmy et al. (2024) describe the Escore algorithm in more detail, and the open access code and manual for its implementation are available through the IRD (Institut de recherche pour le développement) Forge website.

The Escore algorithm classified the RESILIENCE cruise acoustic backscatter into groups corresponding to zooplankton-like organisms (echo-class 1); small gelatinous organisms (e.g., siphonophores with pneumatophores) and small gas-bearing fishes of juvenile stages (echo-class 2); organisms with gas bubbles, siphonophores with pneumatophores, and gas-filled swimbladdered mesopelagic fish (echo-class 3); and siphonophores of small sizes (echo-class 4) (Annasawmy et al., 2024; Figure 4).

Fishes were well represented by the Escore algorithm, trawl relative abundance and biomass estimates, and the trawl net cod end eDNA analyses (Figure 4). Cephalopods, although caught in very low numbers, accounted for the majority of the trawl net biomass estimates. In contrast, crustaceans were abundant in the net but contributed the least to the overall biomass estimates. Cephalopods were underrepresented by the Escore algorithm and minimally detected in the eDNA analyses with the primers used in this study. Crustaceans and gelatinous organisms were also completely overlooked in the metabarcoding

analyses, despite gelatinous organisms being a dominant group detected by both the Escore algorithm and in trawl net samples.

Future work will investigate species richness from the eDNA samples compared to trawl data. Although cephalopods are often underrepresented in acoustics, trawl, and eDNA data, biomass estimates, and bioenergetic and trait-based models of carbon transport (Aumont et al., 2018; Woodstock et al., 2022), they are significant contributors to carbon transport, as this study demonstrates. The use of a cephalopod-specific universal primer set could enhance the detection of cephalopods in eDNA samples. To be able to estimate cephalopod biomass, it is crucial to improve trawl net sampling and acoustic backscatter classification techniques because squids are weak scatterers and distribute sparsely under natural conditions (Chen et al., 2014).

Biomass and abundance estimations from trawls are limited by net efficiency. Future work will refine biomass estimates by classifying captured organisms into acoustic groups based on their backscattering properties using established acoustic backscattering models for "gas-bearing," "fluid-like," and "elastic-shelled" categories. The relative abundance of each group, determined from net catches, will be used to assess their contribution to water

<figure><img src="image_5.png" /><figcaption>FIGURE 4. (a) One nighttime trawl path of the RESILIENCE cruise (magenta) is plotted here showing the echo classes determined by the Escore algorithm and labeled 1 through to 4. Unclassified acoustic backscatter is shown in black. (b) Analysis of the trawl contents revealed these relative biomass and abundance estimates of cephalopods, crustaceans, gelatinous organisms, and mesopelagic fishes. (c) Plots show percentages of rarefied operational taxonomic units from the trawl net cod end sample for each DNA marker.</figcaption></figure>
column backscattering. Sonar observations will permit
the extrapolation of abundances based on actual mea-
sured backscattering levels. Biomass will be estimated by
combining these abundance estimates with the average
wet weight from net catches. Some parameters used to
model the organisms' echoes are not known at the spe-
cies level. The literature offers various hypotheses such as
the shape of gas inclusions, the orientation of organisms,
and the density contrast with the surrounding medium.
To address these uncertainties, scattering models will be
run using bootstrapping (1,000 iterations), randomly vary-
ing these parameters. Accurate biomass estimations are
the major limiting factor in trait-based, bioenergetic, and
ecosystemic models of micronekton carbon transport. The
approach described above will enhance the accuracy of
biomass estimations for micronekton communities, includ-
ing those underrepresented in trawl samples, while identi-
fying sources of variability and quantifying uncertainties, a
necessary first step in quantifying the role of micronekton
in the biological carbon pump.

By integrating eDNA, acoustics, and trawl data, NEAT is
an example of how traditional tools such as active acous-
tics and trawls could be used in conjunction with novel
eDNA methods to provide information on species presence
and absence, abundance, biomass, and distribution in
the deep ocean.

PUSHING MICRONEKTON RESEARCH PAST
THE CURRENT BOTTLENECKS

While NEAT represents a significant step forward in inte-
grating traditional and novel technologies and addressing
key methodological and knowledge gaps, substantial geo-
graphical gaps remain because observations have been
vessel-based in a localized region of the Indian Ocean
and limited to the year 2022. The next step should be to
develop a fleet of cost-effective autonomous underwater
vehicles (AUVs) each equipped with "acoustic intelligence"
(i.e., a computer to process and transmit acoustic data in
real time), a camera, and an autonomous eDNA sampler
for daily monitoring of micronekton (Figure 5). Commercial
autonomous samplers are often not cost-effective due to
high proprietary costs. Our approach aims to bypass the
expense of commercially developed vehicles by investing in
research-driven prototypes that could offer greater sensor
flexibility, autonomy, and spatio-temporal sampling capac-
ity, allowing for tailored solutions that better serve specific
research needs (Le Mézo et al., 2020).

The use of cost-effective AUVs could achieve finer spa-
tial and temporal sampling resolutions by increasing the
density of measurements in areas of interest, allowing
for year-round investigation of diversity and migration

patterns of micronekton taxonomic groups, which has
never before been accomplished. Presently, we do not know
how the biomass, migration patterns, and carbon trans-
port by micronekton varies year-round. Existing sampling
approaches are limited by the high cost of data acquisition,
resulting in small temporal sampling resolution with insuffi-
cient day-night, monthly, seasonal, or multi-year sampling
frequency at any single location done aboard research
vessels. Existing models of carbon transport do not incor-
porate micronekton's migration patterns, accurate biomass
estimates, and diets. Development of cost-effective AUVs
that integrate active acoustics, optical, and eDNA sensors
is crucial for advancing micronekton research in order to
address the major questions related to their biomass, diver-
sity, and migration. Answering these questions is a neces-
sary step before tackling more complex issues, such as their
role in carbon transport out of the euphotic zone.

<figure><img src="image_6.png" /><figcaption>FIGURE 5. Schematic of an idealized autonomous underwater vehicle for micronekton studies.</figcaption></figure>

REFERENCES

Annasawmy, P., J.-F. Ternon, P. Cotel, Y. Cherel, E.V. Romanov, G. Roudaut, A. Lebourges-Dhaussy, F. Ménard, and F. Marsac. 2019. Micronekton distributions and assemblages at two shallow seamounts of the southwestern Indian Ocean: Insights from acoustics and mesopelagic trawl data. *Progress in Oceanography* 178:102161, https://doi.org/10.1016/j.pcean.2019.102161.

Annasawmy, P., G. Roudaut, and A. Lebourges Dhaussy. 2024. Impact of an eddy dipole of the Mozambique channel on mesopelagic organisms, highlighted by multifrequency backscatter classification. *PLoS ONE* 19(9):e0309840, https://doi.org/10.1371/journal.pone.0309840.

Aparecido, K.C., T. Frédou, L.N. Eduardo, M.M. Mincarone, R.S. Lima, M.F.D.S. Morais, and B. Mérigot. 2023. Living in darkness: Functional diversity of mesopelagic fishes in the western tropical Atlantic. *Frontiers in Marine Science* 10:1117806, https://doi.org/10.3389/fmars.2023.1117806.

Aumont, O., O. Maury, S. Lefort, and L. Bopp. 2018. Evaluating the poten-
tial impacts of the diurnal vertical migration by marine organisms on
marine biogeochemistry. *Global Biogeochemical Cycles* 32:1,622–1,643,
https://doi.org/10.1029/2018GB005886.
Barbin, L., A. Lebourges-Dhaussy, V. Allain, A. Receveur, P. Lehodey, J. Habasque, E. Vourey, A. Portal, G. Roudaut, and C. Menkes. 2024. Comparative analysis of day and night micronekton abundance estimates in west Pacific between acoustic and trawl surveys. *Deep Sea Research Part I* 204:104221, https://doi.org/10.1016/j.dsr.2023.104221.

Bell, K.L.C., M.C. Quinzin, D. Amon, S. Poulton, A. Hope, O. Sarti, T.E. Cañete, A.M. Smith, H.I. Baldwin, D.M. Lira, and others. 2023. Exposing inequities in deep-sea exploration and research: Results of the 2022 Global Deep-Sea Capacity Assessment. *Frontiers in Marine Science* 10:1217227, https://doi.org/10.3389/fmars.2023.1217227.

Boyd, P.W., H. Claustre, M. Levy, D.A. Siegel, and T. Weber. 2019. Multi-faceted particle pumps drive carbon sequestration in the ocean. *Nature* 568:327-335, https://doi.org/10.1038/s41586-019-1098-2.

Cavan, E.L., E.C. Laurenceau-Cornec, M. Bressac, and P.W. Boyd. 2019. Exploring the ecology of the mesopelagic biological pump. *Progress in Oceanography* 176:102125, https://doi.org/10.1016/j.pocean.2019.102125.

Chen, G.B., J. Zhang, J. Yu, J.T. Fan, and L.C. Fang. 2014. Hydroacoustic scattering characteristics and biomass assessment of the purple-back flying squid (*Sthenoteuthis oualaniensis*) from the deep water area of the South China Sea. Pp. 49-62 in *Progress in the Application of Acoustics in Inland and Estuarine Fishery Research*. H. Rosenthal, Q. Wei, and P. Bronzi, eds, World Conservation Society, Special Publication no. 6, Books on Demand, Norderstedt, Germany.

Cotté, C., A. Ariza, A. Berne, J. Habasque, A. Lebourges-Dhaussy, G. Roudaut, B. Espinasse, B.P.V. Hunt, E.A. Pakhomov, N. Henschke, and others. 2022. Macrozooplankton and micronekton diversity and associated carbon vertical patterns and fluxes under distinct productive conditions around the Kerguelen Islands. *Journal of Marine Systems* 226:103650, https://doi.org/10.1016/j.jmarsys.2021.103650.

Dornan, T., S. Fielding, S.A. Saunders, and M.J. Genner. 2022. Large mesopelagic fish biomass in the Southern Ocean resolved by acoustic properties. *Proceedings of the Royal Society B* 289:20211781, https://doi.org/10.1098/rspb.2021.1781.

Eduardo, L.N., M.M. Mincarone, T. Sutton, and A. Bertrand. 2024. Deep-pelagic fishes are anything but similar: A global synthesis. *Ecology Letters* 27(9):e14510, https://doi.org/10.1111/ele.14510.

Escudié, F., L. Auer, M. Bernard, M. Mariadassou, L. Cauquil, K. Vidal, S. Maman, G. Hernandez-Raquet, S. Combes, and G. Pascal. 2017. FROGS: Find, Rapidly, OTUs with Galaxy Solution. *Bioinformatics* 34:1-287-1,294, https://doi.org/10.1093/bioinformatics/btx791.

Govindarajan, A.F., A. Adams, E. Allan, S. Herrera, A. Lavery, J. Llopiz, L. McCartin, D.R. Yoerger, and W. Zhang. 2023a. Advances in environmental DNA sampling for observing ocean twilight zone animal diversity. Pp. 80-86 in *Frontiers in Ocean Observing: Emerging Technologies for Understanding and Managing a Changing Ocean*. E.S. Kappel, V. Cullen, M.J. Costello, L. Galgani, C. Gordó-Vilaseca, A. Govindarajan, S. Kouhi, C. Lavin, L. McCartin, J.D. Müller, B. Pirenne, T. Tanhua, Q. Zhao, and S. Zhao, eds, *Oceanography* 36(Supplement 1), https://doi.org/10.5670/oceanog.2023.s1.27.

Govindarajan, A.F., J.K. Llopiz, P.E. Caiger, M.J. Jech, A.C. Lavery, H. McMonagle, P.H. Wiebe, and W. Zhang. 2023b. Assessing mesopelagic fish diversity and diel vertical migration with environmental DNA. *Frontiers in Marine Science* 10:121993, https://doi.org/10.3389/fmars.2023.121993.

Irigoien, X., T.A. Klevjer, A. Røstad, U. Martinez, G. Boyra, J.L. Acuña, A. Bode, F. Echevarria, J.I. Gonzalez-Gordillo, S. Hernandez-Leon, and S. Agusti. 2014. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. *Nature Communications* 5(1):3271, https://doi.org/10.1038/ncomms4271.

Kloser, R.J., T.E. Ryan, J.W. Young, and M.E. Lewis. 2009. Acoustic observations of micronekton fish on the scale of an ocean basin: Potential and challenges. *ICES Journal of Marine Science* 66(6):998-1,006, https://doi.org/10.1093/icesjms/fsp077.

Kwong, L.E., A.A. Bahl, and E.A. Pakhomov. 2022. Variability in micronekton active carbon transport estimates on the Southwest Coast of Oahu using three different sampling gears. *Frontiers in Marine Science* 9:948485, https://doi.org/10.3389/fmars.2022.948485.

Le Mézo, T., G. Le Maillot, T. Ropert, L. Jaulin, A. Ponte, and B. Zerr. 2020. Design and control of a low-cost autonomous profiling float. *Mechanics & Industry* 21:512, https://doi.org/10.1051/meca/2020037.

Le Moigne, F.A.C. 2019. Pathways of organic carbon downward transport by the oceanic biological carbon pump. *Frontiers in Marine Science* 6:634, https://doi.org/10.3389/fmars.2019.00634.

Leray, M., J.Y., Yang, C.P. Meyer, S.C. Mills, N. Agudelo, V. Ranwez, J.T. Boehm, and R.J. Machida. 2013. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. *Frontiers in Zoology* 10:34, https://doi.org/10.1186/1742-9994-10-34.

Mahe, F., T. Rognes, C. Quince, C. De Vargas, and M. Dunthorn. 2015. Swarm v2: Highly-scalable and high-resolution amplicon clustering. *PeerJ* 3:e1420, https://doi.org/10.7717/peerj.1420.

Miya, M., Y. Sato, T. Fukunaga, T. Sado, J.Y. Poulsen, K. Sato, T. Minamoto, S. Yamamoto, H. Yamanaka, H. Araki, and others. 2015. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. *Royal Society Open Science* 2:150088, https://doi.org/10.1098/rsos.150088.

Pillar, H.R., E. Hetherington, L.A. Levin, L. Cimoli, J.M. Lauderdale, J.M.A. van der Grient, K. Johannes, P. Heimbach, L. Smith, C.I. Addey, and others. 2024. Future directions for deep ocean climate science and evidence-based decision making. *Frontiers in Climate Science* 6:1445694, https://doi.org/10.3389/fclim.2024.1445694.

Schlitzer, R. 2024. Ocean Data View, http://odv.awi.de.

Woodstock, M.S., T.T. Sutton, and Y. Zhang. 2022. A trait-based carbon export model for mesopelagic fishes in the Gulf of Mexico with consideration of asynchronous vertical migration, flux boundaries, and feeding guilds. *Limnology and Oceanography* 7(7):1,443-1,455, https://doi.org/10.1002/lno.12093.

Yang, N., D. Jin, and A.F. Govindarajan. 2024. Applying environmental DNA approaches to inform marine biodiversity conservation: The case of the Ocean Twilight Zone. *Marine Policy* 165:106151, https://doi.org/10.1016/j.marpol.2024.106151.

## ACKNOWLEDGMENTS

We thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions. The authors acknowledge the captain, crew, and scientific staff of R/V Marion Dufresne for their assistance during the research cruise RESILIENCE. The chief scientist, Jean-François Ternon (IRD, Sète, France), and co-principal investigators, Margaux Noyon (Nelson Mandela University, South Africa), Steven Herbette (Laboratoire d'Oceanographie Physique et Spatiale, Brest, France) and Pierrick Penven (IRD, Brest, France) are further acknowledged for their support and assistance with biological and environmental data acquisition during the cruise. We extend our gratitude to Arthur Blanluet (University of Queensland, Australia), Anne Lebourges Dhaussy (IRD, Brest, France), and the master's students from the ISblue (Interdisciplinary graduate School for the blue planet) floating university (Université de Bretagne Occidentale, Université du Littoral Côte d'Opale, Université Côte d'Azur) and their coordinators, for their help with the trawl and acoustic data collection. Micronekton taxonomic identification is being conducted by Evgeny Romanov (CITEB, Reunion Island). The RESILIENCE oceanographic cruise was supported by the French National Oceanographic Fleet, by the Belmont Forum Ocean Front Change project, implemented through the French National Research Agency (ANR-20-BFOC-0006-04), by the ISblue project, Interdisciplinary graduate school for the blue planet (ANR-17-EURE-0015) and co-funded by a grant from the French government under the program Investissements d'Avenir embedded in France 2030. The eDNA analyses were partly supported by the French National program LEFE (Les Enveloppes Fluides et l'Environnement) coordinated by the CNRS (Centre national de la recherche scientifique) through a grant awarded in 2022. The eDNA analyses were also supported by EuroMarine Association, through its 2023 funding programme for Cooperation Projects led by Early Career Researchers, managed by OYSTER (Orienting Young ScienTists of EuromaRine).

## AUTHORS

Pavanee Annasawmy (angelee-pavanee.annasawmy@ird.fr), Foundation for Biodiversity Research-Center for the Synthesis and Analysis of Biodiversity (FRB-CESAB), Montpellier, France. **Guillaume Chandelier**, Florida International University, Marine Conservation Ecology Lab, North Miami, FL, USA. **Thomas Le Mézo**, École Nationale Supérieure de Techniques Avancées (ENSTA), LabSTICC M3, Bretagne, Brest, France.

## ARTICLE DOI. https://doi.org/10.5670/oceanog.2025e118