# Pathways for converting zooplankton traits to ecological insights are paved with findable, accessible, interoperable, and reusable (FAIR) data practices

Jessica Titocci, Patrick Pata, Tiziana Durazzano, Sakina-Dorothée Ayata, Corentin Clerc, Astrid Cornils, Patrick Duffy, Adam Greer, Claudia Halsband, Ryan Heneghan, et al.

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Jessica Titocci, Patrick Pata, Tiziana Durazzano, Sakina-Dorothée Ayata, Corentin Clerc, et al. Pathways for converting zooplankton traits to ecological insights are paved with findable, accessible, interoperable, and reusable (FAIR) data practices. ICES Journal of Marine Science, 2025, 82 (2), 10.1093/icesjms/fsaf017. hal-05037181

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# Pathways for converting zooplankton traits to ecological insights are paved with findable, accessible, interoperable, and reusable (FAIR) data practices

Jessica Titocci ID<fnref n="1" />,*,‡, Patrick R. Pata ID<fnref n="2" />,<fnref n="3" />,*,‡, Tiziana Durazzano ID<fnref n="4" />, Sakina-Dorothée Ayata ID<fnref n="5" />,<fnref n="6" />, Corentin Clerc ID<fnref n="5" />,<fnref n="7" />, Astrid Cornils ID<fnref n="8" />, Patrick Duffy ID<fnref n="9" />, Adam T. Greer ID<fnref n="9" />, Claudia Halsband ID<fnref n="10" />, Ryan F. Heneghan ID<fnref n="11" />, Anaïs Lacoursière-Roussel ID<fnref n="12" />, Fabien Lombard ID<fnref n="6" />,<fnref n="13" />, Sanna Majaneva ID<fnref n="14" />, Evgeny A. Pakhomov ID<fnref n="2" />,<fnref n="3" />, Carolina Reis ID<fnref n="15" />,<fnref n="16" />, Sinja Rist ID<fnref n="17" />, Alix C.M. Rommel ID<fnref n="18" />, Teresa Silva ID<fnref n="19" />, Lars Stemmann ID<fnref n="6" />,<fnref n="20" />, Kevin Ugwu ID<fnref n="21" />, Alberto Basset ID<fnref n="22" />, Ilaria Rosati ID<fnref n="1" />, Kieran J. Murphy ID<fnref n="23" />, Brian P.V. Hunt ID<fnref n="2" />,<fnref n="3" />

## Abstract

The use of trait-based approaches and trait data in zooplankton ecology is rapidly growing to better understand and predict the patterns of zooplankton distributions and their role in aquatic ecosystems and biogeochemical cycles. Although the number of zooplankton trait-based studies and available trait datasets is increasing, several challenges remain for the findability, accessibility, interoperability, and reusability (FAIR) in trait-based approaches that, if unaddressed, may stifle progress in this research area. Here, we review recent applications of trait-based approaches in zooplankton research and summarize the currently available trait data resources. To realize the potential of trait-based approaches to resolve ecological roles of zooplankton, datasets and approaches must adhere to FAIR principles. We provide recommendations and pathways forward to ensure FAIRness while highlighting the importance of collaborative efforts. These practical and easily implementable strategies will enhance the FAIRness of trait data, ultimately advancing zooplankton ecological research and connecting these findings to aquatic ecosystem functioning.

**Keywords:** trait; zooplankton; FAIR; trait-based approaches

<footnote n="1">¹National Research Council (CNR), Research Institute for Terrestrial Ecosystems (IRET), Lecce, 73100, Italy</footnote>

<footnote n="2">²Institute for the Oceans and Fisheries, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada</footnote>

<footnote n="3">³Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada</footnote>

<footnote n="4">⁴Department of Arctic and Marine Biology,UiT The Arctic University of Norway, Tromsø, 9037, Norway</footnote>

<footnote n="5">⁵Sorbonne Université, CNRS, IRD, MNHN, Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques, LOCEAN-IPSL, Paris, 75005, France</footnote>

<footnote n="6">⁶Institut universitaire de France (IUF), Paris, 75005, France</footnote>

<footnote n="7">⁷Institute of Biogeochemistry and Pollutant Dynamics. ETH Zürich, Zürich, 8092, Switzerland</footnote>

<footnote n="8">⁸Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany</footnote>

<footnote n="9">⁹Skidaway Institute of Oceanography, University of Georgia, Savannah, GA, 31411, United States</footnote>

<footnote n="10">¹⁰Akvaplan-niva, Fram Centre, Tromsø, 9007, Norway</footnote>

<footnote n="11">¹¹Australian Rivers Institute, School of Environment and Science, Griffith University, Nathan, QLD 4111, Australia</footnote>

<footnote n="12">¹²St. Andrews Biological Station, Fisheries and Oceans Canada, St. Andrews, New Brunswick, E5B 0E4, Canada</footnote>

<footnote n="13">¹³Sorbonne Université, Laboratoire d'Océanographie de Villefranche, UMR 7093, CNRS, Institut de la Mer de Villefranche, Villefranche sur mer, 06230, France</footnote>

<footnote n="14">¹⁴Akvaplan-niva, Trondheim, 7010, Norway</footnote>

<footnote n="15">¹⁵Marine Biotechnology Post-Graduation Program (PPGBM), IEAPM/UFF, Niterói, Rio de Janeiro, 24020-141, Brazil</footnote>

<footnote n="16">¹⁶Flanders Marine Institute (VLIZ), Jacobsenstraat 1, 8400 Ostend, Belgium</footnote>

<footnote n="17">¹⁷National Institute of Aquatic Resources (DTU Aqua), Technical University of Denmark, Henrik Dams Allé, 2800 Kgs. Lyngby, Denmark</footnote>

<footnote n="18">¹⁸Scottish Oceans Institute, University of St Andrews, Gatty Marine Laboratory, St Andrews KY16 8LB, United Kingdom,</footnote>

<footnote n="19">¹⁹Marine and Freshwater Research Institute (MFRI), Fornubúðir 5, 220 Hafnafjörður, Iceland</footnote>

<footnote n="20">²⁰Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche (LOV), Villefranche-sur-Mer, 06230, France</footnote>

<footnote n="21">²¹Man-Technology-Environment Research Centre (MTM), Örebro University, 70182, Örebro, Sweden</footnote>

<footnote n="22">²²Department of Biological and Environmental Sciences and Technologies (DiSTeBA), University of Salento, Lecce, 73100, Italy</footnote>

<footnote n="23">²³The Australian Centre for Excellence in Antarctic Science, University of Tasmania, Hobart, 7001, Australia</footnote>

<footnote>*Corresponding authors. Jessica Titocci, National Research Council (CNR), Research Institute for Terrestrial Ecosystems (IRET), Lecce, 73100, Italy. E-mail: jessicatitocci@cnr.it; Patrick Pata, Institute for the Oceans and Fisheries, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada. E-mail: p.pata@oceans.ubc.ca.</footnote>

<footnote>‡These two authors contributed equally to this work.</footnote>
## Introduction

Zooplankton are functionally and biologically diverse components of aquatic ecosystems that link primary producers to higher trophic levels and influence many ecological processes, including energy and matter flows through food webs, biogeochemical cycles, and carbon sequestration (Steinberg and Landry 2017). They span a range of sizes from unicellular microzooplankton to large gelatinous animals and include the early life stages of most aquatic animals. The majority of zooplankton observational studies maximize the resolution of taxonomic identification (i.e. to the genus or species level), which is foundational for documenting biodiversity and describing its biogeography and community associations. However, while taxonomy and community associations can be used to infer ecological interactions, they do not explicitly address ecological processes and functions. Complementing taxonomy with information on traits, which are commonly shared by multiple species, allows for the more direct analysis of ecologically relevant processes, which have the potential of scaling up to ecosystem functioning.

The trait-based approach shifts the focus from the taxonomic identity of individuals to the traits that describe individuals. Various definitions of the term “trait” have been put forward, from narrow and study specific to broader, with the latter approach including the widest swath of trait-based information that can be gleaned from different approaches and sampling devices. In alignment with common usage in trait-based research, Dawson et al. (2021) defined a ‘trait’ as ‘a measurable characteristic (morphological, phenological, physiological, behavioural, or cultural) of an individual organism that is measured at either the individual or other relevant level of organisation’. While the definition implies that traits are quantitative variables (e.g. predator–prey size ratio, body mass, carbon content), qualitative or categorical traits (e.g. feeding mode or reproductive strategy) are also frequently used to describe organisms. A more specific term is ‘functional trait’, which implies that the trait directly influences individual Darwinian fitness through processes of growth, reproduction, or survival (Violle et al. 2007, Demetrius and Ziehe 2007).

Ecological research can regularly involve organismal characteristics or traits, but in the past three decades, studies with a trait-based focus have proliferated (Green et al. 2022). The theories and tools surrounding the trait-based approach formally emerged from terrestrial plant ecology, which is the domain that still dominates the field (Green et al. 2022). Nonetheless, the use of trait-based approaches has revolutionized the mechanistic understanding of ecological systems in both terrestrial and aquatic realms (Meunier et al. 2017, Martini et al. 2021, Green et al. 2022). Reviews describing trait-based approaches particularly on zooplankton have provided frameworks in linking traits to organismal and ecosystem functions (Litchman et al. 2013, Hébert et al. 2016a, Martini et al. 2021). Data on zooplankton traits have long been collected and compiled before the field of trait-based approaches was formalized, but it is only recently that these were integrated into standardized digital formats (Pata and Hunt 2023). The trait-based approach has led to significant insights in zooplankton ecology (see the section “Trait-based approaches”) and resources on zooplankton trait data (see the section ‘Trait data resources’).

Trait-based approaches have widespread advantages and applications, as well as open challenges across different ecological scales and domains (Fig. 1). The use of traits can signif-

icantly simplify zooplankton community structure and overcome the lack of data or the varying resolution of taxonomic identification. This provides a common currency for comparing the functional characteristics of taxonomically diverse and different regions. Although for some questions, it is ideal for trait data to be measured in tandem with organismal sampling, trait-based approaches can also be applied retrospectively to existing community datasets and can be updated when more trait data become available, potentially from different sampling devices and observational scales not present in the original dataset (e.g. Benedetti et al. 2023). More importantly, trait-based approaches assign organismal traits to ecological functions that can be scaled up to processes at the ecosystem level, which allows for the development of mechanistic explanations and models (Kiørboe et al. 2018).

Despite existing frameworks and data for zooplankton ecology, widespread applications of trait-based approaches are limited due to the diversity of standards and methods for acquiring, organizing, and describing zooplankton trait data. Furthermore, data types, formats, and management practices vary among research groups and the systems they work in. Advances in trait-based zooplankton research will therefore be linked to improved findability, accessibility, interoperability, and reusability (FAIR) of trait data (Wilkinson et al. 2016). Adhering to FAIR principles will facilitate common standards and practices when working with trait data, which will promote further innovation. FAIR trait data practices are critical to improving the longevity and reach of zooplankton data beyond project-specific study periods and geographies, potentially leading to innovative strategies to monitor, model, and predict impacts such as those caused by global warming and biodiversity loss.

Recent reviews articulate the benefits of FAIR data (Wilkinson et al. 2016), trait datasets (e.g. Keller et al. 2023, Morim et al. 2023), and trait-based approaches (e.g. Martini et al. 2021).

Here, we highlight the strengths, applications, and challenges of trait-based approaches and the adoption of FAIR principles in zooplankton research. Finally, we outline pathways to improve FAIR trait-based approaches that promise to advance zooplankton ecology. The ideas presented in this paper emerged from the workshop *Approaches towards findable, accessible, interoperable and reusable (FAIR) zooplankton trait data as stepping stones to improved functional ecology* held during the 7th ICES-PICES Zooplankton Production Symposium in March 2024 in Hobart, Tasmania.

## Landscape of what we know on zooplankton trait-based approaches and data resources

### Trait-based approaches

Several studies have used trait-based approaches in zooplankton research, highlighting the breadth of applications that have already been implemented (Table 1). Trait-based approaches have been applied in empirical studies at different levels of biological organization and from the local to global scale. At the individual level, traits were used to demonstrate trade-offs that govern organismal processes (e.g. Kiørboe and Hirst 2014). At the community level, trait-based approaches have characterized the functional composition and diversity of zooplankton (e.g. Pomerleau et al. 2015). Studies that explicitly link traits to biodiversity and ecosystem functioning
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Characteristics of trait-based approaches in zooplankton ecology listed as advantages, challenges, and methodological or ecological applications. Zooplankton vectors adapted from Pata, P (2023). Marine organism line art. https://doi.org/10.5281/zenodo.13685461.</figcaption></figure>

<table><caption>**Table 1.** Examples of applications of trait-based approaches in zooplankton ecology categorized thematically based on the study topic.</caption><thead><tr><td>Category</td><td>References</td></tr></thead><tbody><tr><td>Individual-level analysis, trade-offs between traits</td><td>Kiørboe (2008), Gorokhova et al. (2013), Bianco et al. (2014), Kiørboe and Hirst (2014), Kiørboe et al. (2015)</td></tr><tr><td>Population-level analysis</td><td>García et al. (2007), Frances et al. (2021), Ilić et al. (2021), Shaw et al. (2021)</td></tr><tr><td>Community-level analysis</td><td>Litchman et al. (2013), Pomerleau et al. (2015), Romagnan et al. (2015), Benedetti et al. (2016, 2023), Hébert et al. (2016b), Teuber et al. (2019), Becker et al. (2021), Vilgrain et al. (2021), Cornils et al. (2022), Feuilloley et al. (2022), Li et al. (2022), Beck et al. (2023), Perhirin et al. (2024)</td></tr><tr><td>Biodiversity and ecosystem functioning</td><td>Barnett et al. (2007), Hébert et al. (2016a), St.-Gelais et al. (2023)</td></tr><tr><td>)M)modeling biogeography</td><td>Barton et al. (2013), Brun et al. (2016), Prowe et al. (2019), Drago et al. (2022)</td></tr><tr><td>Modelling food webs, including size-based ecosystem models</td><td>Heneghan et al. (2016), Prowe et al. (2019, 2020, 2023), Everett et al. (2022), Negrete-García et al. (2022), Clerc et al. (2023)</td></tr><tr><td>Modeling biogeochemistry</td><td>Renaud et al. (2018), Archibald et al. (2019), Luo et al. (2020), Wright et al. (2021), Serra-Pompei et al. (2022), Pinti et al. (2023), Clerc et al. (2024)</td></tr></tbody></table>

This table is an open access source that can be updated regularly, and we invite all zooplankton researchers to contribute to its future version at this link https://osf.io/gqu53.
were explored for zooplankton (Hébert et al. 2016a, St.-Gelais et al. 2023), yet linking traits to the effects of multiple anthropogenic stressors on biodiversity (Butt et al. 2022) needs to be further developed for zooplankton. Models that capture the functional diversity of zooplankton have begun to look into the climate change impacts on ecosystem functioning as modulated by traits (Clerc et al. 2023, Heneghan et al. 2023). By focusing on the individual-level processes determined by traits, ecosystem models can mechanistically link functional groups and environmental drivers. Moreover, applications of trait data to global models that have synthesized zooplankton occurrence and biomass data lead to new insights into the drivers of macroscale distribution of traits and the resulting ecosystem functions (Prowe et al. 2019, Drago et al. 2022, Benedetti et al. 2023).

Zooplankton trait data are also essential when building, parameterizing, and validating models. Traits help to simplify the complexity of ecosystems and facilitate the representation of a broad range of organisms in mechanistic ecosystem models, across large ranges of traits and functional trait trade-offs (e.g. Prowe et al. 2019, Heneghan et al. 2020). Trait-based modeling also improves the connection between the representation of zooplankton physiology and biogeochemical processes, especially with regard to carbon cycling (Stemmann and Boss 2012, Renaud et al. 2018, Serra-Pompei et al. 2022). Poor resolution of zooplankton traits in biogeochemical models has been identified as the largest source of uncertainty in projections of climate impacts on the carbon cycle (Rohr et al. 2023). Increasing the number of empirical trait values, therefore, is critical to improving parameterization and validation of these models. Strategies to improve zooplankton representation in models include using functional types and continuous trait distributions (e.g. Wright et al. 2021, Clerc et al. 2023, Heneghan et al. 2023).

Recent advancements in technologies and tools for sampling, such as high-throughput sequencing, environmental DNA (eDNA), biochemical tracers, and advanced imaging techniques, have significantly enhanced trait measurement capabilities. These tools enable more comprehensive and near-real-time monitoring across zooplankton phyla, large size ranges, and gradients of trait distributions, likely at finer spatial and temporal scales than previously possible (Martini et al. 2021). Monitoring biodiversity using DNA metabarcoding and eDNA-based techniques is valuable for assessing species, especially by revealing the hidden biodiversity for species richness. Associating biodiversity with trait distributions is possible but is limited by the reliability of the species' relative abundance estimates based on DNA reads. These molecular tools also allow sampling in a broader range of habitats, especially in remote and seasonally ice-covered areas that are not easily accessible by traditional net sampling (Thomsen and Willerslev 2015, Deiner et al. 2017, Lacoursière-Roussel et al. 2018). Additionally, biochemical tracers, including stable isotopes and fatty acids, can provide information for traits related to feeding behaviour and nutritional quality and insights into dietary relationships and the trophic roles of zooplankton in the food web (Laakmann and Auel 2010, Visconti et al. 2018).

Plankton imaging devices, similar to molecular approaches, are becoming widespread and have a variety of technical approaches for sampling the full spectrum of plankton size classes (Romagnan et al. 2015, Lombard et al. 2019, Greer et al. 2020). Many morphological traits that are difficult or

labour-intensive to quantify with microscopy can be measured automatically with *in situ* or benchtop imaging methods (Irisson et al. 2022, Orenstein et al. 2022), albeit at the expense of lower taxonomic resolution. For example, some morphological traits can be directly measured on the images (e.g. size, shape, colour, presence of elongations/spines), while others can be inferred by the values of the measured traits and related to behavioural or physiological characteristics and the life stage category such as lipid content, gonadal maturity, and feeding activity (Vilgrain et al. 2021, Orenstein et al. 2022, Maps et al. 2024). *In situ* imaging systems can also produce trait data at finer spatial scales (~1–10 m) and overcome detection biases due to the fragility of some zooplankton (Biard et al. 2016), and are thus able to investigate habitat transition zones that are difficult to capture with coarser traditional sampling methods (Greer et al. 2015, McManus et al. 2021). Imaging systems can substantially reduce the cost and duration of data analysis, allowing the study of time series of morphological traits over long time intervals or at a high frequency (Feuilloley et al. 2022, Beck et al. 2023).

## Trait data resources

The use of trait-based approaches is facilitated by the extensive resources publicly available online for trait data collection, curation, processing, and analysis. Extensive digitization efforts over the past decade have enabled the integration of historical data from original species descriptions, research articles, and field studies into the compilation of many organismal trait databases and several comprehensive online resources dedicated to the study of zooplankton traits (Supplementary Table). Some of the early databases with zooplankton trait data have focused on copepods (Benedetti et al. 2016, Hébert et al. 2016b, Brun et al. 2017, Razouls et al. 2005–2024). Recently, these databases were harmonized along with other datasets and publications to include a broader range of zooplankton taxonomic groups and >50 traits in the ‘Global Zooplankton Trait Database’ (Pata and Hunt 2023). This harmonized database is focused on marine species only and is mostly limited to holoplanktonic mesozooplankton. Trait data for some meroplankton and micronekton are available in other databases that do not explicitly focus on zooplankton (Madin et al. 2016, Gleiber et al. 2024, Degen and Faulwetter 2019, Faulwetter et al. 2014), while trait data for freshwater zooplankton are stored in separate databases (Barnett et al. 2007, Hébert et al. 2016b).

Although broader in scope, other valuable resources and repositories of zooplankton trait data include the Encyclopedia of Life (EOL), TraitBank (Parr et al. 2016), the Marine Traits Portal of the World Register of Marine Species (WoRMS), SeaLifeBase, and the Global Biodiversity Information Facility (GBIF). These resources primarily focus on taxonomic information, but also contain detailed entries for many zooplankton species with some trait information and extensive occurrence data that can be used to infer traits related to distribution and environmental preferences or to collate and incorporate trait data (Hébert et al. 2016a). Moreover, several zooplankton trait resources can be extracted by consulting datasets archived among the main biodiversity and marine data portals (e.g. PANGAEA, OBIS).

The development of comprehensive molecular databases (Wang et al. 2009, Sayers et al. 2020) and repositories
such as GenBank, Barcode of Life Data Systems (BOLD, doi.org/10.17616/R3PP7J), and MetaZooGene Atlas and Database (Bucklin et al. 2021, O'Brien et al. 2024) provide an additional source of taxonomic data that can be integrated with trait data and facilitate interspecies comparisons and the linkage of genetic information with phenotypic traits. Another emerging trait data resource is Ecotaxa (https://ecotaxaobs.vlfr.fr/), the largest database of sorted plankton images containing ~450 million images, which is dedicated to the taxonomic identification of images of plankton and includes measurements of morphological traits (Picheral et al. 2017).

Collectively, these resources support the advancement of trait-based research in zooplankton ecology by providing readily accessible information, courses, and documentation of FAIR data practices to researchers worldwide. The repositories also have implemented quality assurance (QA) and quality control (QC) measures to maintain high standards of data submission. Users collaborate through defined responsibilities shared among data providers, data curators, and reviewers, and provide direct support and guidance to facilitate data preparation and submission. This ensures rigorous QC through evaluation processes that may include pre-entry checks, formal criteria assessments, and content review. However, trait data resources remain decentralized and widely dispersed across the Web, which makes it challenging to develop robust information systems to aggregate and disseminate data according to FAIR principles. To date, there has been no specific online repository dedicated to zooplankton traits. Nevertheless, the Open Traits Network (OTN) represents one of the major efforts to provide a centralized hub for trait-related data (https://opentraits.org). It includes a collection of trait datasets, often specific to a particular taxonomic domain or biogeographic region, with some datasets including zooplankton taxa. Similarly, a catalogue of trait databases for a plethora of aquatic organisms, including zooplankton, has been provided in Martini et al. (2021).

As more trait data become available, collaborative projects and initiatives are establishing standardized methods for producing FAIR trait data (Gallagher et al. 2015, Schneider et al. 2019, Keller et al. 2023). In recent years, particular attention has been paid to addressing issues of heterogeneity in terminologies of trait data in units or categorical variables by applying standardized definitions. One of the most important examples of improving the annotation, standardization, and interoperability of trait data and metadata is the Ecological Trait-data Standard (ETS) vocabulary (Schneider et al. 2019). The ETS vocabulary is a single resource terminology that provides a starting point for the development of a common language and terminology around traits and trait-based research across disciplines. Specifically, for zooplankton, the Zooplankton Trait Thesaurus (https://ecoportal.lifewatch.eu/ontologies/ZOOPLANKTRAITS) was initiated to make zooplankton-related trait terminologies machine-readable and actionable, to ensure data consistency, and to promote zooplankton data harmonization and integration across studies. This resource provides standards mainly for morphological traits and is currently being improved and extended to include standards for physiological, behavioural, and life-history traits. Furthermore, this thesaurus has been merged with the Phytoplankton Trait Thesaurus (Rosati et al. 2017), the Macroalgae Trait Thesaurus, and the Fish Trait Thesaurus in a unique resource: the Traits Thesaurus (https://www.doi.org/10.48373/sa6p-ta25).

The ultimate goal of this Traits Thesaurus is to interact with the ETS and to be linked and aligned with other existing biodiversity and trait data terminology initiatives within The Biodiversity Information Standards (https://www.tdwg.org/). Establishing clear trait definitions and semantically annotating trait terminologies with rich metadata using controlled vocabularies is essential for standardizing traits and associated information, such as units of measurement, the location or environment where the trait was measured, the level of measurement (e.g. individual or species), and the protocol or instrument used. This limits confusion during data aggregation and allows for accommodating multiple trait records for a single species, which facilitates documenting intraspecific variation.

Recently, the identification of zooplankton and the collection of trait-based measurements have seen significant advances through the integration of image processing pipelines, machine learning applications, and various analytical tools, Web services, and open-source code and software (Supplementary Table). Among these, Ecotaxa is a valuable example of an interactive database that allows collaborative image processing and classification according to a universal taxonomy and machine learning techniques to automate the identification process, thereby reducing manual effort and improving data consistency. Ecotaxa uses the UniEuk taxonomic framework, which is based on curated molecular phylogenies and has established new standards relevant to marine biodiversity image networks (Irisson et al. 2022, Martin-Cabrera et al. 2022). Other openly available analytical services for zooplankton trait data acquisition, processing, analysis, and modelling include The LifeWatch Data Explorer, Plankton Toolbox, Plankton Identifier, Plankton Lifeform Extraction Tool, and Plankton Inversion Model (Supplementary Table). The Zoo and Phytoplankton EOV Product from the Blue-Cloud VRE and the Plankton Genomics VLab are examples of Virtual Lab (VLab) and VREs that promote e-Science and collaborative research (Supplementary Table). They do so through platforms that provide integrated access to data, computational resources, and analytical workflows for interpolating sparse *in situ* measurements and modelling phytoplankton-zooplankton interactions, and for in-depth assessment of plankton distributions by mining biomolecular, imaging, and environmental data. All of these data resources and Web services have the potential to greatly motivate researchers to organize, share, and use trait data, fostering the adoption of common protocols and analytical processes in accordance with the FAIR principles.

## Existing challenges in zooplankton trait-based approaches

Despite the growing interest in trait-based approaches in zooplankton ecology, their application in fundamental and generalizable ecological research is limited by (i) data availability, (ii) data FAIRness, and (iii) some inherent shortcomings and uncertainties in recent implementations of trait-based approaches.

### Trait data availability

There are significant gaps in the taxonomic coverage of zooplankton trait information where most available data are extracted from morphological information (e.g. size, shape, body mass) or categorical behavioural traits, with less avail-
able information on physiological, biochemical, and life-
history traits (Pata and Hunt 2023). The existing databases
mainly focus on crustaceans, with other taxa, such as pelagic
cnidarians, ctenophores, and tunicates, largely absent. Impor-
tant zooplankton groups that need to be incorporated into or
linked with global zooplankton trait databases include mero-
plankton, microzooplankton, macrozooplankton, and fresh-
water species. Additionally, several important traits, such as
growth rates, clearance rates, reproduction frequency, vertical
migration, motility, and size at maturation, are largely missing
from the available databases even for relatively well-studied
species.

A strategy for resolving the gaps in trait data availability
is to use available data as proxies for the traits of interest or
estimating trait values through broad taxonomic generaliza-
tions, allometric scaling equations, or imputation (Litchman
et al. 2021, Thorson et al. 2023). Standardization of these es-
timation procedures is necessary, with recommendations pro-
vided by de Bello et al. (2021). The accuracy of these vari-
ous methods in estimating zooplankton traits was found to
be strongly dependent on the number of existing trait records
(Pata and Hunt 2023). Thus, acquiring new trait data is
essential.

It is not clear whether the limitations on zooplankton trait
data's findability and accessibility stem from actual gaps in
data availability or from the scarcity of FAIR zooplankton
trait data. At the same time, zooplankton trait data that are
not available in public open data repositories but are stored
in log books or on computer hard drives and discs in research
laboratories need to be identified, digitized, and uploaded to
appropriate data repositories. Furthermore, trait data can also
be 'hidden' in publications that collect these data for other
purposes and are therefore not incorporated in trait databases.
However, mining for all the hidden data would require ex-
tensive manual effort to harmonize the datasets and integrate
them into trait data repositories.

Trait data FAIRness

Beyond the challenges in data availability, the majority of
available zooplankton trait data are decentralized, and, cur-
rently, there is no single access point or repository specifically
tailored for accessing zooplankton trait data and metadata. As
a result, many available data exist primarily as trait datasets
attached to publications or uploaded to general-purpose data
repositories such as Figshare, Zenodo, ResearchGate, and
Data Dryad. Although these general-purpose repositories are
open source and provide DOIs, they typically allow data to be
archived with minimal standards for metadata documentation
and data interoperability, resulting in variable tabular struc-
tures and labelling conventions for trait variables. Therefore,
they often do not fully meet the requirements of FAIR prin-
ciples. Beyond general-purpose data repositories, zooplank-
ton trait data and metadata are also often scattered across
multiple domain-specific repositories for marine and freshwa-
ter systems (Supplementary Table) or available through insti-
tutional websites and e-Science research infrastructure (e.g.
LifeWatch, Long Term Ecological Research Network, Euro-
pean Marine Biological Resource Centre, European Multidis-
ciplinary Seafloor and water column Observatory). In addi-
tion, FAIR data practices and data quality assurance mea-
surements are still not consistent across research data host-
ing centres and vary considerably depending on the reposi-

tory used (Kindling and Strecker 2022). Thus, the scattered
data are complicated to extract and subsequently reuse. This
issue is exacerbated by the occurrence of multiple parallel
and concurrent efforts that may share the same objectives
but employ their own approaches to data FAIRness and data
and metadata quality assurance, resulting in differences in the
metadata schemas, data formats, standards used, and indi-
vidual approaches to QA/QC assessment. Currently, the lack
of common agreement, linkage, and interoperability among
these data resources underscores the urgent need for align-
ment and harmonization. Moreover, similar to the peer review
of research papers, research on the assessment and certifica-
tion of data repositories is needed to improve the value of data,
policies, procedures, and services for data management and
sharing.

Another source of misunderstanding that limits data FAIR-
ness is, fundamentally, in the definition of what a trait is,
which previous studies have attempted to constrain (Violle
et al. 2007). Trait-based approaches include a heterogeneity in
the sources of trait observations and in the level of biological
organization in which traits were measured. Thus, practically,
ecologists use varying, although closely related, definitions of
what a trait is (Dawson et al. 2021). Beyond resolving the term
'trait', the terminologies and units of the traits themselves are
context-dependent and remain challenging to standardize and
decode.

In recent years, the DarwinCore (DwC) standard (Wieczorek et al. 2012) and DwC-Archive have gained popularity for biodiversity data. However, the recommended data architecture was created with the intention of harmonizing occurrence data and does not provide standards for trait data. The 'Ecological Trait-data Standard' (ETS; Schneider et al. 2019) addresses this gap, providing a vocabulary for trait datasets with essential terms such as 'trait name', 'trait value', and 'trait units'. The ETS, however, does not offer standardized descriptions for trait names and units, which can be domain specific. There are currently a number of glossaries aimed at standardizing terminology and facilitating communication between trait-based research communities (e.g. Marine Species Traits, BIOTIC, ICES vocabulary, Ecotaxonomy). However, a common problem is that many of these glossaries often lack the ability to create linked data, as they do not provide Uniform Resource Identifiers (URIs) for terms that are required to connect between different datasets and resources (Parr et al. 2016, Schneider et al. 2019). This limitation in interoperability hinders their usefulness in automated systems and data integration efforts and is further exacerbated by the lack of alignment between the semantic resources mentioned previously.

Another key challenge when integrating and ensuring the
FAIRness of zooplankton trait data lies in the quality of the
metadata associated with the collected trait data, which can be
widely heterogeneous (e.g. various levels of taxonomic speci-
ficity, non-systematic information about the location and time
of sampling). Due to the rapid changes in marine environ-
ments caused by climate change, harmonized metadata are
needed to evaluate and establish trait baselines, as well as to
track the changes over time and to test their robustness. In
particular, the recurrent absence of spatial metadata for zoo-
plankton traits poses difficulties for understanding ecological
and biogeographical patterns and limits the ability to relate
trait variations with environmental conditions.
## Trait-based approaches

Trait selection is limited by the amount of available information from literature and the cost of making new trait measurements. Reliance on analysing only the functional traits that are deemed to have sufficiently available information introduces a risk of oversimplification that may fail to capture the functional diversity or ecological process of interest. One outcome of this is focusing mainly on some easily measurable traits such as size, shape, and body mass (or biovolume), and putting less emphasis on behavioural or physiological traits, which may require more detailed analyses and more specific equipment to obtain accurate and reliable trait data (Petchey and Gaston 2006). To address this, we recommend identifying the relevant traits *a priori* and explicitly linking these to the associated organismal or ecosystem processes of interest. This approach is consistent with the work already undertaken within the Essential Ocean Variables (EOVs) and the Essential Biodiversity Variables (EBVs), which provide international coordination on best practices for observing and producing data (Muller-Karger et al. 2018). The EOV and EBV frameworks demonstrate the value of recommended and commonly agreed standards and protocols for data collection and management at the international level to ensure interoperability and integrability of data from local to global scales. Furthermore, since trait information could be derived from various levels of biological organization, focusing only on taxonomic level trait values (e.g. using the same trait value for all individuals of the same species) will effectively miss the intrataxa variability. This presents a particular challenge when trait plasticity is ecologically relevant, whether from developmental changes in life history, regional variability in environmental conditions, or from the long-term responses to environmental changes. The appropriateness of trait records obtained from data compilations needs to be evaluated based on the ecological context for when the trait was measured, which are ideally provided by the metadata. This would be consistent with FAIR data practices necessary for utilizing trait data to address ecological questions.

The selection of measured or applied traits can affect estimates and applications of trait data (de Bello et al. 2021). For practical reasons, researchers tend to focus on understanding the ecosystem effects of a single or a few traits. The use of a 'representative trait' approach, often involving a single trait (i.e. size), may oversimplify the interpretation of these ecological aspects. For instance, recent work documenting morphological traits of mesozooplankton revealed that carbon export may be more influenced by body transparency (i.e. more transparent individuals are often gelatinous) than by their size (Perhirin et al. 2024). Instead, measuring more traits may better represent the functional diversity and variation of the community (Maire et al. 2015), although measuring a large number of traits at different levels of ecological complexity quickly becomes impractical, requiring increased sampling effort and costs for analysis. Traits are, however, known to intersect in the organismal processes they capture and so identifying the commonalities between traits in the statistical trait space (Vilgrain et al. 2021) would be useful for trait selection.

Currently, the relationships between traits and associated ecological functions are poorly investigated and understood (Degen et al. 2018). Furthermore, the trade-offs between resource acquisition and defence govern the diversity of communities, as they allow the coexistence of many trait configurations

with similar fitness (Hébert et al. 2017). The execution of any one of these functions, however, may conflict with the others, as they cannot all be maximized simultaneously (Bremner et al. 2006). Thus, quantifying the risks and trade-offs associated with key traits is necessary to predict the morphology, behaviour, and physiology that optimizes the fitness of an organism in any environment (Violle et al. 2007), which requires cross-disciplinary investigations.

Finally, an important challenge to address in zooplankton ecology is understanding how zooplankton traits and processes contribute to and interact within the overall food web, from viroplankton to large marine mammals. Other than body size, there are traits that are common across trophic groups (Litchman et al. 2021) and the use of these cross-trophic level traits will stimulate trait-based research on entire ecosystems (Martini et al. 2021). Moreover, it will be worthwhile to learn from other ecological domains, such as terrestrial plants, that may have led to the development of trait-based approaches in terms of trait data infrastructure, forming expert networks, and applying traits for predictive studies (Green et al. 2022).

## Future directions

Although trait-based approaches have been used in ecological research for some time, coordinated efforts in trait data collection, harmonization, and analysis are needed to realize the full potential of zooplankton trait-based approaches. Broadly, future zooplankton trait-based research involves the need to incorporate new sources of trait data and facilitate how trait-based studies utilize common standards, practices, and technologies for FAIR data to achieve a more cohesive and comprehensive understanding of zooplankton trait-based knowledge. We believe that several steps are necessary to coordinate and promote the collection and standardization of zooplankton trait data and the increase of FAIR zooplankton trait data (Fig. 2).

## Enabling zooplankton trait data findability and accessibility

Continuing to collect zooplankton trait data from field and laboratory studies, as well as finding and synthesizing trait records from literature into existing databases, is an ongoing effort that will advance trait-based approaches. This process involves digitizing existing data and metadata, extracting information from papers and grey literature, and encouraging potential data holders to standardize and share their data in domain-specific repositories through persistent identifiers (PIDs). It is essential to establish structured connections between zooplankton data and related information through high-quality and rich metadata. This includes detailed descriptions of the methodology, environmental conditions of the trait measurements, data provenance, and other relevant attributes. For image data, assigning a unique identifier to high-resolution images will allow for potential revisions or additions to the trait measurements. When publishing, it is crucial to share data and metadata openly through domain-specific repositories rather than in supplementary materials, ensuring that the data and metadata have a PID. Additionally, the published data need to be associated with a clear usage licence. The high volume and diversity of trait data generated by different systems and research groups underscore the critical
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Summary of next steps to achieve FAIRness in zooplankton trait-based research. Recommendations are listed thematically for next steps related to FAIR data practices, collaboration within the scientific community, methods in trait data analysis, and expanding trait data collection. Sources: Illustration of the UMCES research vessel, *RV Rachel Carson*, adapted from Tracey Saxby, Integration and Application Network (ian.umces.edu/media-library), CC BY-SA 4.0. Zooplankton vectors adapted from Pata, P (2023). Marine organism line art. https://doi.org/10.5281/zenodo.13685461.</figcaption></figure>

importance of reproducible and platform-independent data management strategies.

To support these efforts, a dedicated data rescue working group should be established, potentially within the ICES framework, to facilitate connections and common practices and to provide technical support in adjusting existing data to fit the structure of databases. The contributions of data providers to making data FAIR and harmonized must be acknowledged, credited, and incentivized, thereby increasing the value of data reuse and promoting even more the adoption of FAIR data management practices. Additionally, centralized

databases such as the Plant Trait Database (TRY, https://www.try-db.org/de/TabDetails.php) and FishBase (https://fishbase.org) have demonstrated that providing accessible resources in a single access point facilitates innovation in terrestrial plant and fish functional ecology. Since there is currently no specific online repository dedicated to zooplankton traits, the zooplankton research community must decide on a suitable path forward. Options include improving data FAIRness across existing repositories by aligning and standardizing FAIR data management practices along the different repositories or developing a centralized trait database or data portal specific
for zooplankton traits. A centralized platform has the poten-
tial to benefit zooplankton ecologists by facilitating the find-
ability, accessibility, and integration of zooplankton trait data
into their studies. However, managing a centralized repository
is challenging and requires consistent funding to remain oper-
ational and ongoing investment in technical digital infrastruc-
ture, staff, and user support to maintain their functionality.
Collaborations with digital research infrastructure, academic
institutions, funding bodies, and data-sharing consortia can
help ensure the long-term sustainability of the repository.

Enabling zooplankton trait data interoperability

Even if trait data are available in open data repositories, data interoperability seems to be the most challenging aspect, especially among data generated by different devices and research groups (Bi et al. 2024). A critical first step in adhering to FAIR principles is the need for semantic and syntactic data standardization in zooplankton ecology. This includes standardizing data and metadata from disparate trait data collection systems into interoperable and reusable formats and adopting widely used protocols and manuals for zooplankton trait measurements (e.g. Harris et al. 2000). For this reason, zooplankton ecologists must achieve a general agreement on the definitions of specific traits and how these are measured. This would require developing agreed-upon trait terminologies and trait measurement protocols and the need for more Linked Open Data (LOD) for sharing machine-readable and actionable interlinked data on the Web. This should be achieved by the improved use of existing standards and controlled vocabularies for biodiversity and trait data annotation (Supplementary Table). Although initial steps have been taken to include species traits as EBVs, and several datasheets, templates, and guidelines for trait data collection have been proposed (Muller-Karger et al. 2018), progress is slow for integrating and operationalizing species traits in global monitoring. This is due partly to the challenges highlighted in this paper, such as the lack of a clear consensus on what is considered a 'trait' and insufficient standardization of trait data and metadata from data providers. In this context, developing tools to assist in aligning datasets to data templates and in mapping the measured traits to trait-based semantic resources remains a useful and ongoing task. These tools should include software for Web-based access to trait data and semantic Web standards.

Pre-configured spreadsheet templates for capturing trait
data may be an efficient way to identify and share a con-
sistent structure and terminology extracted from controlled
vocabularies (i.e. thesauri, ontologies) for trait data collec-
tion that can be used in workflow analyses through Web ser-
vices and Virtual Research Environments (VREs) . Similar
initiatives already exist for occurrence data and phytoplank-
ton trait data (i.e. PhytoplanktonData Template, https://www.
phytovre.lifewatchitaly.eu/phyto-data-template/). For plank-
ton imaging data management, best practices and recommen-
dations have already been individuated among EMODnet Bi-
ology, OBIS, and EurOBIS networks (Martin-Cabrera et al.
2022) and some specific data templates and standard termi-
nologies have been suggested (De Pooter et al. 2017). How-
ever, usually, data templates have limitations, as they can be
restrictive, inflexible, and may not cover all possible scenarios
exhaustively. An alternative approach is to use template gen-
erators, such as The Nansen Legacy Template Generator for

DwC and CF-NetCDF (Marsden and Schneider 2024), which
enables the addition of terms such as the Climate and Fore-
casting convention standard names and DwC terms to spread-
sheets, allowing scientists to create semantically aware tem-
plates without needing to understand the underlying technol-
ogy. Similar initiatives should be shared in the zooplankton
community to provide open source easily built templates with
standardized structures for trait data collection and acquisi-
tion that is flexible enough to ideally contain the details re-
lated to the trait record (e.g. the prey type and concentrations
used when measuring feeding rates).

Although the use of data templates may be helpful in trait
data harmonization, they have limited applications if data in-
teroperability is weak. Therefore, there is a strong need to
identify mappings among already existing trait-based seman-
tic resources and align these resources. In this context, the
use of semantic technologies that promote automated ontol-
ogy matching approaches is a promising solution to the se-
mantic heterogeneity problem. Moreover, trait-based termi-
nologies included in glossaries and vocabularies with no URIs
should be assigned with URIs to facilitate the sharing and
reuse of LOD on the Web. Thus, in the case of zooplank-
ton data, providing the specific standardized definitions of
traits and units through the Traits Thesaurus will enable an
automated tool to extract relevant parameters from these di-
verse data sources. It is essential that each type of measure-
ment is uniquely identifiable and accessible in a machine-
readable format. The list of possible traits is extensive, yet
many are seldom measured or recorded as continuous data,
but rather as categorical descriptions. With regard to cate-
gorical traits, each value or level of a trait would require
standardized definitions as well. As a result, there is an in-
creasing demand for domain-specific thesauri and ontologies
tailored to address these limitations and improve annotation
and specificity, ensuring machine-readability and actionability.
These actions, aligned with preexisting government-backed
frameworks and product distribution initiatives, should re-
sult as synergistic efforts for enhancing the information avail-
able for existing EBVs. This would also facilitate inclusion
of species traits as EBVs, particularly within the zooplankton
domain.

Enabling zooplankton trait data reusability

Once zooplankton trait data are findable, accessible, and inter-
operable, it is essential to enhance their full reusability by doc-
umenting the analytical workflow. This will ensure that zoo-
plankton trait data are not only accessible but also reusable
in different analytical contexts, thus maximizing the value
of the data collected. Since the handling and processing of
large amounts of zooplankton trait data is difficult to per-
form manually, the development of efficient tools for data in-
tegration, analysis, and modelling, open-source codes, user-
friendly Web services, workflows, and VREs specifically de-
signed for zooplankton trait-based data analysis should be
strongly encouraged by digital research infrastructure and IT
developers. Reusability also includes the appropriate recog-
nition of the data providers and preserving the history of
the PIDs linked to the data. Digital research infrastructure
plays an important role in propagating good practices in data
use and citation through providing user interfaces and tools
that facilitate reusability. In plankton imaging, significant ad-
vances in machine learning and computer vision algorithms
have led to an immense increase in recorded data quantity and quality (Orenstein et al. 2022). These data are now part of large-scale monitoring programmes, such as the Canadian Aquaculture Monitoring Program (Finnis et al. 2023). Efforts are underway to make these images and associated processing algorithms available for broad-scale plankton analyses of taxa and functional traits (Picheral et al. 2017, Drago et al. 2022, Dugenne et al. 2024), with initiatives such as iFDO providing standards and frameworks for FAIR marine images (https://marine-imaging.com/fair/ifdos/iFDO-overview/).

The promotion of open-source codes and software, work-
flows, and pipelines for data processing can support high-
throughput data analyses, and various statistical and compu-
tational techniques and modelling, including machine learn-
ing, and enhance the scalability of research efforts. Dedicated
virtual zooplankton labs and VRE should be promoted as
collaborative spaces where researchers can work together on
zooplankton trait data analysis, sharing tools, resources, and
data, facilitating collaboration and knowledge sharing. In ad-
dition, incorporating more traits into models allows for multi-
platform analyses that comprehensively address trait-based
ecological questions and improve predictive power. These
models can simulate different ecological scenarios and help
predict the effects of environmental changes on zooplankton
communities. By implementing these strategies, the zooplank-
ton community can augment the reusability of zooplankton
trait data, driving advances in ecological research and sup-
porting sustainable ecosystem management.

Collaborating towards FAIR trait-based zooplankton
research

All the steps previously stated depend heavily on support from
the institutions and digital research infrastructure that are rev-
olutionizing data management practices across a range of sci-
entific disciplines. They are essential frameworks that facili-
tate the organization, storage, and dissemination of the vast
amounts of research data produced worldwide. However, the
effectiveness of this infrastructure depends on the commit-
ment of individual researchers to be open and to improve the
FAIRness of their own research, together with the support of
data curators and data managers. The existing digital research
infrastructure could play an important role in providing fund-
ing and professional support in maintaining data centres. In
the future, the funding, development, and promotion of a net-
work of aligned physical and digital research infrastructure
will be a major asset to the trait-based research community.

Strengthening collaboration and networking in zooplank-
ton research is the basis for integrating diverse expertise,
methodologies, and data sources. Stronger networks and col-
laborative efforts will lead to improved data standardization,
and more robust and accurate analytical processes and mod-
els. Collaboration will be facilitated through forming working
groups to establish expert networks with the skills necessary
to navigate the complexities of FAIR data principles. Future
working groups would need to solicit financial support speci-
fying the funds and labour necessary for data management.

Encouraging the development of specialized programmes
that integrate training in computer science, zooplankton ecol-
ogy, and data management is also essential. These actions may
have the potential to cultivate new professionals who have ex-
pertise in both ecological science and data management, fost-
ering dialogue and collaboration and ultimately advancing

research in both fields. Furthermore, implementing workshops
and courses related to open science and zooplankton trait
data management will facilitate the dissemination of FAIR
zooplankton trait data. These initiatives should be promoted
early in scientific careers, even at the undergraduate level, and
within the framework of digital research infrastructure, which
plays a key role in enabling data-driven research by provid-
ing digital resources, tools, and Web services. Moreover, con-
necting with and learning from experts in other domains of
ecology, such as marine microbes, fishes, and terrestrial plants,
who have more extensive experience in organizing trait data
and applying trait-based approaches, would be helpful in ac-
quiring best practices for zooplankton ecology.

Finally, incentives must be generated to foster a culture
of data sharing in the scientific community. Improving the
way we attribute datasets in peer-reviewed research and aca-
demic evaluations is critical. Recognizing and rewarding data
sharing efforts through proper citation and academic credit
can provide avenues for professional advancement for re-
searchers contributing their data to public repositories. By
treating datasets and data papers as research outputs on par
with traditional publications, the scientific community can
foster a more collaborative and transparent research environ-
ment. Future trait data rescue and data standardization activ-
ities should consider how to promote the achievements of the
scientists generating the original trait data.

The pathways forward are paved with FAIR data prac-
tices, and strengthening collaborations among the zooplank-
ton research community with the existing FAIR initiatives and
frameworks is much needed. By adhering to FAIR principles
and practices, trait-based approaches in zooplankton research
hold significant promise for transforming trait data into ac-
tionable knowledge leading to a more holistic understanding
of the critical role of zooplankton in aquatic ecosystems and
biogeochemical cycles. Working towards establishing a con-
solidated network of trait data providers, stewards, and users
will be key in promoting the benefits of FAIR data practices
and in achieving the steps we outlined towards converting
traits to ecological insights.

Acknowledgements

The authors would like to thank all the participants during the
7th ICES-PICES Zooplankton Production Symposium work-
shop on ‘Approaches towards findable, accessible, interoper-
able and reusable (FAIR) zooplankton trait data as stepping
stones to improved functional ecology’ for contributing to the
vibrant discussions that resulted in the ideas featured in this
manuscript. We especially thank Inessa Corney for assisting in
organizing the workshop. We also thank Leen Vandepitte and
Stefanie Dekeyzer for their feedback on the initial version of
the manuscript. The authors thank the three anonymous re-
viewers for their helpful feedback on the previous version of
the manuscript.

Author contributions

J.T. and P.R.P.: conceptualization, writing—original draft, review and editing, project administration, and supervision. T.D.: writing—review and editing and visualization. S-D.A., C.C., A.C., P.D., A.T.G., C.H., R.F.H., A.L-R., F.L., S.M., E.A.P., C.R., S.R., A.C.M.R., T.S., L.S., and K.U.: writing—review and editing. A.B., I.R., K.J.M., B.P.V.H.: conceptualiza-
tion, writing—original draft, review and editing, project administration, and funding acquisition.

## Supplementary data

Supplementary data is available at *ICES Journal of Marine Science* online.

*Conflict of interest:* None declared.

## Funding

P.P. was supported by a Natural Sciences and Engineering Research Council of Canada Doctoral Fellowship, MOPAR grant NCE 2-02-03-038.2, Canadian Space Agency grant FAST 50568, and Brian Hunt's NSERC Discovery Grant RGPIN-2017-04499. J.T. was supported by the project 'Italian Integrated Environmental Research Infrastructure System' (ITINERIS) in the framework of Next Generation EU PNRR—Mission 4 ‘Education and Research’—Component 2: ‘From research to business’—Investment 3.1: ‘Fund for the realisation of an integrated system of research and innovation infrastructures’, Notice 3264/2021, IR0000032, CUP B53C22002150006. C.R. has received funding from European Union's Horizon 2020 Research and Innovation Project Mission Atlantic (grant # 862428) and by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil (CAPES) (Finance Code 001). J.T. and P.P. would also like to acknowledge the support received through the North Pacific Marine Science Organization (PICES) travel grant, which ensured their successful participation at the 7th International Zooplankton Production Symposium in Hobart, Australia.

## Data availability

No new data were generated or analysed in support of this research. Information containing the contents of Table 1 and the supplementary table are archived in this Open Science Framework project link (https://osf.io/gqu53/).

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