# Metazoan zooplankton in the Bay of Biscay: a 16-year record of individual sizes and abundances obtained using the ZooScan and ZooCAM imaging systems

Nina Grandremy, Paul Bourriau, Edwin Daché, Marie-Madeleine Danielou, Mathieu Doray, Christine Dupuy, Bertrand Forest, Laetitia Jalabert, Martin Huret, Sophie Le Mestre, et al.

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Nina Grandremy, Paul Bourriau, Edwin Daché, Marie-Madeleine Danielou, Mathieu Doray, et al. Metazoan zooplankton in the Bay of Biscay: a 16-year record of individual sizes and abundances obtained using the ZooScan and ZooCAM imaging systems. Earth System Science Data, 2024, 16 (3), pp.1265-1282. 10.5194/essd-16-1265-2024. hal-04556367

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# Metazoan zooplankton in the Bay of Biscay: a 16-year record of individual sizes and abundances obtained using the ZooScan and ZooCAM imaging systems

Nina Grandremy¹,*, Paul Bourriau¹, Edwin Daché², Marie-Madeleine Danielou³, Mathieu Doray¹, Christine Dupuy⁴, Bertrand Forest⁵, Laetitia Jalabert⁶, Martin Huret⁷, Sophie Le Mestre⁷, Antoine Nowaczyk⁸, Pierre Petitgas⁹, Philippe Pineau⁴, Justin Rouxel¹⁰, Morgan Tardivel¹⁰, and Jean-Baptiste Romagnan¹,★

¹DECOD (Ecosystem Dynamics and Sustainability), IFREMER, INRAE, Institut Agro, Nantes, Centre Atlantique, Rue de l’Ile d’Yeu, BP 21105, 44311 Nantes CEDEX 03, France

²Unité Biologie et Ecologie des Ecosystèmes marins Profonds, Laboratoire Environnement Profond, Ifremer Centre Bretagne, ZI de la Pointe du Diable, CS 10070, 29280 Plouzané, France

³Unité DYNECO-PELAGOS, Laboratoire d'Ecologie Pélagique, Ifremer Centre Bretagne, ZI de la Pointe du Diable, CS 10070, 29280 Plouzané, France

⁴BIOFEEL, UMRi LIENSs, La Rochelle University/CNRS, 2, rue Olympe de Gouges, 17000 La Rochelle, France

⁵Laboratoire Hydrodynamique Marine, Unité RDT, Ifremer Centre Bretagne, ZI de la Pointe du Diable, CS 10070, 29280 Plouzané, France

⁶Sorbonne Université, Institut de la Mer de Villefranche, 06230 Villefranche-sur-mer, France

⁷DECOD (Ecosystem Dynamics and Sustainability), IFREMER, INRAE, Institut Agro, Centre Bretagne, ZI de la Pointe du Diable, CS 10070, 29280 Plouzané, France

⁸UMR CNRS 5805 EPOC–OASU, Station Marine d’Arcachon, Université de Bordeaux, 2 Rue du Professeur Jolyet, 33120 Arcachon, France

⁹Departement Ressources Biologiques et Environnement, Ifremer Centre Atlantique, Rue de l’Ile d’Yeu, BP 21105, 44311 Nantes CEDEX 03, France

¹⁰Laboratoire Détection, Capteurs et Mesures, Unité RDT, Ifremer Centre Bretagne, ZI de la Pointe du Diable, CS 10070, 29280 Plouzané, France

★These authors contributed equally to this work.

**Correspondence:** Nina Grandremy (grandremy.n@gmail.com) and Jean-Baptiste Romagnan (jean.baptiste.romagnan@ifremer.fr)

Received: 13 May 2023 – Discussion started: 1 June 2023
Revised: 11 December 2023 – Accepted: 15 December 2023 – Published: 13 March 2024

**Abstract.** This paper presents two metazoan zooplankton datasets obtained by imaging samples collected on the Bay of Biscay continental shelf in spring during the PELGAS (PE Lagique GAS cogne) integrated surveys over the 2004–2019 period. The samples were collected at night with a 200 µm mesh-size WP2 net fitted with a Hydrobios (back-run stop) mechanical flowmeter and hauled vertically from the sea floor to the surface, with the maximum depth set at 100 m when the bathymetry was deeper than this. The first dataset originates from samples collected from 2004 to 2016 and imaged on land with the ZooScan and is composed of 1 153 507 imaged and measured objects. The second dataset originates from samples collected from 2016 to 2019 and imaged onboard the R/V *Thalassa* with the ZooCAM and is composed of 702 111 imaged and measured objects. The imaged objects are composed of zooplankton individuals, zooplankton pieces, non-living particles and imaging artefacts ranging from 300 µm to 3.39 mm in equivalent spherical diameter which were individually imaged,
measured and identified. Each imaged object is geolocated and associated with a station, a survey, a year and other metadata. Each object is described by a set of morphological and grey-level-based features (8 bit encoding, 0 = black, 255 = white), including size, that were automatically extracted from each individual image. Each object was taxonomically identified using the web-based application Ecotaxa with built-in random-forest and CNN-based semi-automatic sorting tools, which was followed by expert validation or correction. The objects were sorted into 172 taxonomic and morphological groups. Each dataset features a table combining metadata and data at individual-object granularity from which one can easily derive quantitative population and community descriptors such as abundances, mean sizes, biovolumes, biomasses and size structure. Each object's individual image is provided along with the data. These two datasets can be used in combination for ecological studies, as the two instruments are interoperable, or they can be used as training sets for ZooScan and ZooCAM users. The data presented here are available at the SEANOE dataportal: https://doi.org/10.17882/94052 (ZooScan dataset, Grandremy et al., 2023c) and https://doi.org/10.17882/94040 (ZooCAM dataset, Grandremy et al., 2023d).

# 1 Introduction

Metazoan planktonic organisms, hereafter referred to as zoo-
plankton, encompass an immense diversity of life forms
which have successfully colonized the entire ocean, from
eutrophic estuarine shallow areas to the oligotrophic open
ocean and from the sunlit ocean to hadal depths. Their body
sizes span 5 to 6 orders of magnitude in length: from µm to
tens of metres (Sieburth and Smetacek, 1978). Zooplankton
plays a pivotal role in marine ecosystems (Banse, 1995). It
transfers the organic matter produced in the epipelagic do-
main by photosynthesis to the deeper layers of the ocean
(Siegel et al., 2016) by producing fast-sinking aggregates
(Turner, 2015) and by diel vertical migration (Steinberg et al.,
2000; Ohman and Romagnan, 2016). Zooplankton therefore
participates in mitigating the anthropogenic carbon dioxide
buildup in the atmosphere that is responsible for climate
change. Moreover, zooplankton is an exclusive trophic re-
source for commercially important fish during their larval
stage, so a shift in zooplankton species or phenology can
have dramatic effects on recruitment (e.g. for North Sea cod;
Beaugrand et al., 2003). In addition, it is a major trophic re-
source for adult small planktivorous pelagic fish known as
forage fish (van der Lingen, 2006). Recent studies suggest
that zooplankton dynamics may have a significant effect on
small pelagic fish population dynamics and individual body
condition (Brosset et al., 2016; Menu et al., 2023) and there-
fore impact wasp-waist ecosystem-based fisheries and socio-
ecosystems that are dependent on those fisheries worldwide
(Cury et al., 2000).

Despite zooplankton being of such global importance in
both climate change effects on ecosystems and the manage-
ment of fisheries (Chiba et al., 2018; Lombard et al., 2019),
it is still technically difficult to monitor compared to other
marine ecological compartments. Zooplankton biomass, di-
versity and spatio-temporal distributions cannot be estimated
from spaceborne sensors, unlike those of phytoplankton
(Uitz et al., 2010), commercial exploitation data of zoo-
plankton do not exist yet, unlike the corresponding data

for fish. One noticeable exception is the CPR Survey net-
work, which enables zooplankton data generation at spatio-
temporal scales that are fine enough to study climate change
and diversity-related zooplanktonic processes (Batten et al.,
2019). Yet, generating zooplankton data often requires ded-
icated surveys at sea, specific sampling instruments and
trained taxonomic analysts. Moreover, besides actual obser-
vation, modelling zooplankton remains a challenging task
due to the diversity of traits, such as life forms, life cycles,
body sizes, and physiological processes, exhibited by zoo-
plankton (Mitra and Davis, 2010; Mitra et al., 2014). How-
ever, over the past 2 decades, the development of imaging
and associated machine-learning semi-automatic identifica-
tion tools (Irisson et al., 2022) has greatly improved the ca-
pability of scientists to analyse long (Feuilloley et al., 2022),
high-frequency (Romagnan et al., 2016) or spatially resolved
(Grandremy et al., 2023a) zooplankton time series as well
as trait-based data (Orenstein et al., 2022). Imaging and ma-
chine learning have particularly enabled the increased devel-
opment of combined size and taxonomy zooplankton ecolog-
ical studies (e.g. Vandromme et al., 2014; Romagnan et al.,
2016; Benedetti et al., 2019). Yet, the use of these machine-
learning tools is not trivial because they require abundant,
scientifically qualified, sensor-specific training image data
(i.e. a learning set and test set; Irisson et al., 2022) and com-
plex hardware and software setups (Panaïotis et al., 2022).
One good example of such an image dataset is the ZooScan-
Net dataset (Elineau et al., 2018), which features an exten-
sive ZooScan (Gorsky et al., 2010) imaging dataset usable
as a training set for ecologists as well as for imaging and
machine-learning scientists.

The objective of this paper is to present two freely avail-
able zooplankton imaging datasets originating from two dif-
ferent instruments, the ZooScan (Gorsky et al., 2010) and
the ZooCAM (Colas et al., 2018). These datasets originate
from the PELGAS (PELagique GAScogne) integrated sur-
vey in the Bay of Biscay (Doray et al., 2018a), a conti-
nental shelf ecosystem supporting major European fisheries
(ICES, 2021). Combined, these datasets make up a 16-year
time series of sized and taxonomically resolved zooplank-
ton, along with context metadata allowing the calculation of
quantitative data, covering the whole Bay of Biscay conti-
nental shelf from the French coast to the continental slope
and from the Basque Country to southern Brittany in spring.
These datasets can be used for ecological studies (Grandremy
et al., 2023a), machine-learning studies and modelling stud-
ies.

## 2 Methods

2.1 Sampling

Zooplankton samples were collected during successive PEL-
GAS integrated surveys carried out over the Bay of Biscay
(BoB) French continental shelf every year in spring from
2004 to 2019 onboard the R/V *Thalassa*. The aim of this
survey is to assess small pelagic fish biomass and moni-
tor the pelagic ecosystem to inform ecosystem-based fish-
eries management. Fish data, hydrology, phyto- and zoo-
plankton samples, and megafauna sightings (marine mam-
mals and seabirds) are concomitantly collected to build long-
term spatially resolved time series of the BoB pelagic ecosys-
tem. The PELGAS sampling protocols combine daytime en-
route data collection (small pelagic fish and megafauna) with
night-time depth-integrated hydrology and plankton sam-
pling at fixed points. Detailed PELGAS survey protocols can
be found in Doray et al. (2018a, 2021). The PELGAS sur-
vey datasets providing hydrological, primary producers, fish
and megafauna data are available as gridded data in the SEA-
NOE data portal (Doray et al., 2018b) at the following link:
https://doi.org/10.17882/53389.

The number of zooplankton samples obtained per year var-
ied between 41 (2005) and 65 (2019) due to adjustments
in the sampling strategy and the weather conditions, with
889 zooplankton samples collected in total. From 2004 to
2006, samples were collected in the southern Bay of Bis-
cay up to the Loire estuary only (Fig. 1). Sampling was car-
ried out in vertical tows performed during the night using
a 200 µm mesh-size WP2 net, generally from 100 m depth
(or 5 m above the seabed) to the surface. In 2004 and 2005,
the targeted maximum sampling depth was 200 m. In 2004,
15 samples were collected deeper than 100 m, among which
11 were deeper than 120 m; in 2005, 20 samples were col-
lected deeper than 100 m, among which 13 were deeper than
120 m. Before 2014, the sampled water volume was esti-
mated by multiplying the cable length by the net opening
surface (0.25 m²), whereas the net has been equipped with a
Hydrobios back-run stop flowmeter since 2014. The samples
originating from the 2004 to 2016 surveys were preserved in
4 % formaldehyde (final concentration) and analysed on land
in the laboratory with the ZooScan, while they have been
analysed live onboard with the ZooCAM since 2016.

## 2.2 Sample processing and analyses

2.2.1 Digitization with the ZooScan

Preserved samples were digitized with the ZooScan (Gorsky
et al., 2010), a flatbed scanner generating 16 bit grey-level
high-resolution images (2400 dpi, pixel size: 10.56 µm, im-
age size: 15 × 24 cm equivalent to 14 200 × 22 700 pixels). It
is well suited for the imaging of preserved organisms rang-
ing in size from 300 µm to several centimetres. The ZooScan
is run by the custom-made, ImageJ-based, ZooProcess soft-
ware, which generates a single large image for each scan that
contains up to 2000 organisms, depending on the size of the
imaged organisms.

Prior to digitization, the seawater and formaldehyde solu-
tion was filtered through a 180 µm mesh sieve into a trash
tank under a fume hood. The organisms were then gently but
thoroughly rinsed with freshwater over the tank in the sieve.
They were then size fractionated with a 1 mm sieve into or-
ganisms larger and smaller than 1 mm. This size-splitting
step is recommended when using the ZooScan as it addresses
the possible bias due to the underrepresentation of large ob-
jects caused by the necessary subsampling. Each size fraction
was subsampled separately with a Motoda splitter to obtain
two subsamples containing 500–1000 objects for the large-
organism size fraction and 1000–2000 objects for the small-
organism size fraction. To mitigate the number of overlap-
ping objects, each subsample was imaged after the manual
separation of objects on the scanning tray, as recommended
in Vandromme et al. (2012). Overall, 699 samples were dig-
itized following this protocol, corresponding to 1397 scans
(one sample was not divided into size fractions as it did not
contain organisms larger than 1 mm).

2.2.2 Digitization with the ZooCAM

The ZooCAM is an in-flow imaging instrument designed to digitize preserved as well as live zooplankton samples onboard, immediately after net collection (Colas et al., 2018). The ZooCAM features a cylindrical transparent tank in which the zooplankton sample is mixed with filtered seawater. Depending on the richness of the sample and the subsampling (if necessary), the volume of seawater can be adjusted between 2–7 L. The organisms were pumped at 1 L min⁻¹ from the tank to a flow cell inserted between a CCD camera (pixel size: 10.3 µm) and a red LED flashing device, where they were imaged at 16 fps. Given the flow cell volume, the size of the field of view, the imaging frequency and the flow rate, all the seawater volume containing the organisms was imaged (Colas et al., 2018). Before all the initial volume was imaged, the tank and the tubing were carefully and thoroughly rinsed with filtered seawater to ensure the imaging of all the organisms poured into the tank. For each sample, the ZooCAM generates a stack of small-size (~1 MB) raw images that are subsequently analysed with the ZooCAM software. Depending on the initial water content of the tank and
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Metazoan zooplankton sampling locations during the PELGAS cruises in the Bay of Biscay from 2004 to 2019. The years with the poorest coverage are 2005 and 2006, with 41 and 43 sampling stations respectively, and the years with the best coverage are 2015, 2017 and 2019, with 64, 64 and 65 sampling stations respectively.</figcaption></figure>

the rinsing, a ZooCAM run can generate up to 10 000 raw
images from which the individual organism vignettes will be
extracted. A ZooCAM run on a live sample often generates
up to 5000–10 000 vignettes of individual organisms. It is
very important to subsample the initial samples with a di-
chotomic splitter (a Motoda splitter was used here) to ensure
that the object concentration in subsamples is low enough to
reduce the risk of imaging overlapping objects and avoid any
dependency on the water volume imaged when reconstruct-
ing quantitative estimates of zooplankton, as the initial and

rinsing volumes are variable. Overall, 190 samples were dig-
itized live onboard with the ZooCAM.

## 2.3 Image processing

Both instruments generate grey-level working images (8 bit
encoding, 0 = black, 255 = white). In both cases, image
processing consisted of (i) a “physical” background homog-
enization in which an empty background image was sub-
tracted from each sample image (one for ZooScan and as
many as there were raw images for ZooCAM), (ii) a thresholding of each raw image (the threshold value was 243 for ZooScan and 240 for ZooCAM), and (iii) the segmentation of each object imaged. The ZooProcess software was set to detect and segment objects with an area equal to or larger than 631 pixels, whereas the ZooCAM software was set to detect objects with an area equal to or larger than 667 pixels, which in both cases equals 300 µm equivalent spherical diameter (ESD), or a biovolume of 0.014 mm³ (using a spherical biovolume model; Vandromme et al., 2012).

Morphological features were then extracted for each detected object. Features generated by the ZooScan are defined in Gorsky et al. (2010) and those generated by the ZooCAM are defined in Colas et al. (2018). ZooScan images were processed with the ZooProcess v7.39 (4 October 2020) open-source software. ZooCAM images were processed with the proprietary ZooCAM custom-made software, which uses the MIL (Matrox Imaging Library, Dorval, Québec, Canada) as the individual-object processing kernel. Each detected object was finally cropped from the working sample images and saved as a unique labelled vignette in a sample-specific folder, along with a sample-specific single text file containing the object features arranged as a table with objects arranged in lines and features in columns.

## 2.4 Touching objects

ZooProcess features a tool that enables the digital separation of possibly touching objects in the final image dataset for each sample. As touching objects may impair abundance and size-structure estimations (Vandromme et al., 2012), remaining touching objects were searched for in the individual vignettes from the ZooScan and were digitally manually separated with the ZooProcess separation tool to improve the quality of further identifications, counts and the size structure of zooplankton. The ZooCAM software does not offer such a tool.

## 2.5 Taxonomic identification of individual images

All individual vignettes from both instruments were sorted into two instrument-specific separated sets and identified with the help of the online application Ecotaxa (Picheral et al., 2017). Ecotaxa features a random forest algorithm (Breiman, 2001) and a series of instrument-specific tuned spatially sparse convolutional neural networks (Graham, 2014) that were used in a combined approach to predict identifications of unidentified objects. First, an automatic classification of non-identified individual vignettes into coarse zooplankton and non-zooplankton categories was carried out. In both cases (ZooScan and ZooCAM), Ecotaxa hosted instrument-specific image datasets, previously curated and freely available, that were used as initial learning sets. These initial classifications were then visually inspected, manually validated or corrected when necessary, and taxonomically re-

fined when possible. After a few thousand images had been validated in each project, they were used as dataset-specific learning sets to improve the initial coarse automatic identifications. This process was iterated until all the individual vignettes were classified up to their maximum reachable level of taxonomical detail. A subsequent quality check of automatic taxonomic identifications was realized in a two-step process: first, a complete review (validation and/or correction) of all individual automatic identifications was done by Nina Grandremy and Jean-Baptiste Romagnan; then, trained experts reviewed and curated the ZooScan and the ZooCAM datasets (Laeticia Jalabert handled the ZooScan dataset and Antoine Nowaczyk handled the ZooCAM dataset) at the individual level. Although some identification errors may still remain in the datasets, we consider this double check process to be sufficient to provide taxonomically qualified data.

## 2.6 Intercalibration of the two instruments

The two datasets are usable separately. However, considered together, they form a 16-year-long spatio-temporal time series. A comparison study was done to ensure these datasets are homogeneous and can thus be combined for ecological studies (Grandremy et al., 2023b). All the zooplankton samples from year 2016 (61 sampling stations over the whole BoB continental shelf) were imaged with both instruments. In brief, all non-zooplankton and touching-object images were removed from the initial datasets. Then, the interoperable size range was determined with an assessment based on a comparison of the normalized biovolume–size spectra (NB-SSs) for the instruments. This size interval ranges between 0.3–3.39 mm ESD. Finally, the zooplankton communities as seen by the ZooScan and the ZooCAM were compared by taxa and by station using 27 taxonomic groups. Poorly represented taxa as well as non-taxonomically identified objects were not taken into account in the computation of zooplankton variables and in community structure analyses. Both instruments showed similar NB-SS slopes for 58 out of 61 stations and depicted equivalent abundances, biovolumes and mean organism sizes as well as similar community compositions for a majority of the sampling stations. They also estimated similar spatial patterns of the zooplankton community at the scale of the Bay of Biscay. However, some taxonomic groups showed discrepancies between instruments, which originate from differences in the sample preparation protocols before image acquisition, the imaging techniques and quality, and whether the samples were imaged live or fixed. For example, the mineralized protists (Rhizaria here) dissolve in formalin and are considered to be underestimated in preserved seawater samples (Biard et al., 2016). Also, the random orientation of objects in the ZooCAM flow cell leads to a loss of taxonomic identification accuracy due to the difficulty in spotting the specific features needed for the identification (Colas et al., 2018; Grandremy et al., 2023b). This is particularly acute for copepods, for
which the ZooScan seems to provide better identification capabilities for experts, as the organisms are imaged in a lateral view most of the time, whereas the ZooCAM often images them in a non-lateral, randomly oriented view, preventing the visualization of specific features. A detailed discussion about how to explain the discrepancies between the ZooScan and the ZooCAM can be found in Grandremy et al. (2023b). We assume that the two presented datasets form a single 16-year-long spatio-temporal time series of abundances (Fig. 2) and sizes of zooplanktonic organisms (Fig. 3) from which biovolumes, biomasses, the Shannon index (Fig. 4) and the zooplankton community size structure can be derived (Vandromme et al., 2012).

# 3 Datasets

## 3.1 Taxonomic groups and operational morphological groups

The ZooScan dataset is composed of 1 153 507 zooplankton individuals, zooplankton parts, non-living particles and imaging artefacts individually imaged and measured with the ZooScan and ZooProcess (Gorsky et al., 2010) and sorted into 127 taxonomic and morphological groups. The ZooCAM dataset is composed of 702 111 zooplankton individuals, zooplankton parts, non-living particles and imaging artefacts individually imaged and measured with the ZooCAM (Colas et al., 2018) and sorted into 127 taxonomic and morphological or life-stage groups. The total number of different groups identified with both instruments combined is 170, among which 84 are common to both datasets (Table 1), 43 belong to the ZooScan dataset only and 43 others belong to the ZooCAM dataset only (Table 2). The identified groups were divided into actual taxa and operational morphological groups (OMGs). Typically, OMGs are either non-adult life stages of taxa, aggregated morphological groups, or non-living groups (see Tables 1 and 2). Among the groups common to both instruments, 45 are actual taxa and 39 are OMGs (Table 1). Among the ZooScan-only groups, 22 are taxa and 21 are OMGs, and among the ZooCAM-only groups, 18 are taxa and 25 are OMGs (Table 2).

The differences in identified groups, in the taxa/OMGs ratio and in the associated counts arose from several aspects of the data generation. Firstly, the two imaging methods differ in their technical set-up. The main difference is that, on the one hand, fixed organisms are laid down and arranged manually on the imaging sensor and digitized in a lab (i.e. steady 2-D) set-up when using the ZooScan, whereas organisms are imaged live in a moving fluid in a 3-D environment (the flow cell) onboard when digitized with the ZooCAM. Their position in front of the camera may not enable an identification as precise as that achieved when they are laid on the scanner tray (Grandremy et al., 2023b; Colas et al., 2018). Secondly, the datasets are sequential in time: the ZooCAM dataset follows the ZooScan's. Zooplankton communities in the Bay of

Biscay may have changed over time, even if their biomass as aggregated groups shows remarkable space-time stability (Grandremy et al., 2023a). Thirdly, we cannot guaranty that there is no adverse effect on taxonomic identification, as validation involved several experts (Culverhouse, 2007). Although we paid great attention to homogenizing the final detailed datasets, we recommend that taxa and OMGs should be aggregated and the biological resolution should be reduced for ecological studies (Grandremy et al., 2023a, b). Additionally, numerous identified and sorted taxa and OMGs do not belong to the metazoan zooplankton or they are non-adult life stages or parts of organisms. Those were included in the presented datasets because they are always found in natural samples. They need to be separated from entire organisms to ensure that abundance estimations are as accurate as possible, and they must be taken into account to ensure accurate biovolume or biomass estimations. A good example is the siphonophore issue: numerous swimming bells of degraded siphonophore individuals can be found and imaged in a sample. Determining an accurate siphonophore abundance may not be easy, but this could be overcome by considering the biovolume or biomass of siphonophores by adding up the biovolumes or biomasses of the numerous parts of the organisms imaged.

OMG names mainly take the form of two words separated by “_” (underscore). Although we tried to name them as explicitly as possible, a few potentially needed clarifications can be found in Table 3.

## 3.2 Data and images

### 3.2.1 Data

The data are divided into two datasets available as tab-separated files, one for each instrument. Within each dataset, the data is organized as a table containing text data as well as numerical data. Each dataset contains a combination of actual data and metadata at individual-object granularity. For each object, the user can find descriptors originating from the image processing (i.e. features), sampling metadata (i.e. latitude and longitude of the sampling station, date and time of sampling, sampling device, etc.), and sample processing metadata (i.e. subsampling factor, seawater sampled volume, pixel size) in columns and individual objects in lines. The column headers are defined in Tables A1 and A2 for the ZooCAM and ZooScan datasets respectively. The following prefixes enable the segregation of types of data and metadata: (i) “object_”, which identifies variables assigned to each object individually; (ii) “sample_”, which identifies variables assigned to each sample; (iii) “acq_”, which identifies variables assigned to each data acquisition for the same sample (note that this type of variable is only found in the ZooScan dataset, as ZooScan samples were split into two size fractions corresponding to two acquisitions); and (iv) “process_”, which identifies variables describing key image processing
<table><caption>**Table 1.** Taxa and operational morphological groups (OMGs) common to the ZooCAM and ZooScan datasets. Taxa are listed on the left of the table and OMGs are listed on the right of the table. OMG names are spelled as they appear in the datasets. The numbers next to each taxon and OMG are the count and the percentage (%) for each category for each instrument in the whole dataset. Non-zooplanktonic OMGs are highlighted in bold, and genera and species are formatted in italics.</caption><thead><tr><th rowspan="2">Taxon</th><th colspan="2">ZooCAM</th><th colspan="2">ZooScan</th><th rowspan="2">OMG</th><th colspan="2">ZooCAM</th><th colspan="2">ZooScan</th></tr><tr><th>Counts</th><th>%</th><th>Counts</th><th>%</th><th>Counts</th><th>%</th><th>Counts</th><th>%</th></tr></thead><tbody><tr><td>Calanoida</td><td>137 536</td><td>19.58</td><td>149 956</td><td>13.00</td><td><strong>detritus</strong></td><td>105 751</td><td>15.06</td><td>219 541</td><td>19.03</td></tr><tr><td>Oithonidae</td><td>112 977</td><td>16.09</td><td>110 510</td><td>9.58</td><td><strong>diatoma</strong></td><td>36 842</td><td>5.25</td><td>1084</td><td>0.09</td></tr><tr><td>Acartiidae</td><td>30 403</td><td>4.33</td><td>66 353</td><td>5.75</td><td><strong>bubble</strong></td><td>32 563</td><td>4.64</td><td>1112</td><td>0.10</td></tr><tr><td>Temoridae</td><td>13 520</td><td>1.93</td><td>31 335</td><td>2.72</td><td><strong>Noctiluca_Noctiluceaceae</strong></td><td>22 165</td><td>3.16</td><td>20 784</td><td>1.80</td></tr><tr><td>Oncaeidae</td><td>11 843</td><td>1.69</td><td>34 651</td><td>3.00</td><td>other_living</td><td>15 029</td><td>2.14</td><td>5861</td><td>0.51</td></tr><tr><td>Calanidae</td><td>9578</td><td>1.36</td><td>91 513</td><td>7.93</td><td>dead_copepoda</td><td>13 383</td><td>1.91</td><td>17 151</td><td>1.49</td></tr><tr><td>Limacinidae</td><td>8966</td><td>1.28</td><td>6423</td><td>0.56</td><td><strong>fiber_detritus</strong></td><td>13 379</td><td>1.91</td><td>25 124</td><td>2.18</td></tr><tr><td>Appendicularia</td><td>6724</td><td>0.96</td><td>34 027</td><td>2.95</td><td>nauplii_cirripedia</td><td>6766</td><td>0.96</td><td>6008</td><td>0.52</td></tr><tr><td>Cladocera</td><td>5590</td><td>0.80</td><td>18 213</td><td>1.58</td><td>gonophore_diphyidae</td><td>4395</td><td>0.63</td><td>1462</td><td>0.13</td></tr><tr><td>Centropagidae</td><td>4592</td><td>0.65</td><td>14 651</td><td>1.27</td><td>multiple_copepoda</td><td>3740</td><td>0.53</td><td>961</td><td>0.08</td></tr><tr><td><em>Neoceratium</em></td><td>2984</td><td>0.43</td><td>4830</td><td>0.42</td><td>nauplii_crustacea</td><td>3422</td><td>0.49</td><td>10 747</td><td>0.93</td></tr><tr><td>Euchaetidae</td><td>2643</td><td>0.38</td><td>12 957</td><td>1.12</td><td><strong>artefact</strong></td><td>2643</td><td>0.38</td><td>60 718</td><td>5.26</td></tr><tr><td>Metridinidae</td><td>2333</td><td>0.33</td><td>15 081</td><td>1.31</td><td>multiple_other</td><td>1928</td><td>0.27</td><td>10 303</td><td>0.89</td></tr><tr><td>Corycaeidae</td><td>2021</td><td>0.29</td><td>4720</td><td>0.41</td><td>pluteus_echinodermata</td><td>1623</td><td>0.23</td><td>1441</td><td>0.12</td></tr><tr><td><em>Euterpina</em></td><td>1043</td><td>0.15</td><td>2870</td><td>0.25</td><td>calyptopsis_euphausiacea</td><td>1396</td><td>0.20</td><td>3246</td><td>0.28</td></tr><tr><td>Euphausiacea</td><td>889</td><td>0.13</td><td>1195</td><td>0.10</td><td>bivalvia_mollusca</td><td>1324</td><td>0.19</td><td>3766</td><td>0.33</td></tr><tr><td><em>Calocalanus</em></td><td>820</td><td>0.12</td><td>1196</td><td>0.10</td><td>bract_diphyidae</td><td>1315</td><td>0.19</td><td>386</td><td>0.03</td></tr><tr><td>Chaetognatha</td><td>624</td><td>0.09</td><td>7274</td><td>0.63</td><td>cypris</td><td>862</td><td>0.12</td><td>2363</td><td>0.20</td></tr><tr><td>Harpacticoida</td><td>481</td><td>0.07</td><td>1697</td><td>0.15</td><td>nectophore_diphyidae</td><td>839</td><td>0.12</td><td>14 389</td><td>1.25</td></tr><tr><td><em>Obelia</em></td><td>459</td><td>0.07</td><td>1016</td><td>0.09</td><td><strong>egg_actinopterygii</strong></td><td>768</td><td>0.11</td><td>3596</td><td>0.31</td></tr><tr><td>Annelida</td><td>256</td><td>0.04</td><td>2434</td><td>0.21</td><td>tail_appendicularia</td><td>753</td><td>0.11</td><td>11 349</td><td>0.98</td></tr><tr><td>Decapoda</td><td>173</td><td>0.02</td><td>471</td><td>0.04</td><td>cyphonaute</td><td>684</td><td>0.10</td><td>2218</td><td>0.19</td></tr><tr><td><em>Microsetella</em></td><td>116</td><td>0.02</td><td>1169</td><td>0.10</td><td>eudoxie_diphyidae</td><td>501</td><td>0.07</td><td>69</td><td>0.01</td></tr><tr><td>Phoronida</td><td>90</td><td>0.01</td><td>163</td><td>0.01</td><td>larvae_echinodermata</td><td>483</td><td>0.07</td><td>2200</td><td>0.19</td></tr><tr><td>Actinopterygii</td><td>85</td><td>0.01</td><td>2113</td><td>0.18</td><td>part_siphonophorae</td><td>279</td><td>0.04</td><td>12 976</td><td>1.12</td></tr><tr><td>Candaciidae</td><td>70</td><td>0.01</td><td>2773</td><td>0.24</td><td>larvae_annelida</td><td>244</td><td>0.03</td><td>708</td><td>0.06</td></tr><tr><td>Amphipoda</td><td>68</td><td>0.01</td><td>853</td><td>0.07</td><td><strong>egg_sac_egg</strong></td><td>152</td><td>0.02</td><td>394</td><td>0.03</td></tr><tr><td>Tomopteridae</td><td>58</td><td>0.01</td><td>618</td><td>0.05</td><td>zoea_decapoda</td><td>151</td><td>0.02</td><td>1405</td><td>0.12</td></tr><tr><td>Ostracoda</td><td>55</td><td>0.01</td><td>341</td><td>0.03</td><td>cnidaria metazoa</td><td>148</td><td>0.02</td><td>4974</td><td>0.43</td></tr><tr><td>Doliolida</td><td>26</td><td>&lt;0.01</td><td>128</td><td>0.01</td><td>larvae_porcellanidae</td><td>127</td><td>0.02</td><td>2838</td><td>0.25</td></tr><tr><td>Echinodermata</td><td>24</td><td>&lt;0.01</td><td>253</td><td>0.02</td><td>nectophore_physonectae</td><td>106</td><td>0.02</td><td>696</td><td>0.06</td></tr><tr><td>Aetideidae</td><td>15</td><td>&lt;0.01</td><td>75</td><td>0.01</td><td>ctenophora metazoa</td><td>94</td><td>0.01</td><td>126</td><td>0.01</td></tr><tr><td><em>Branchiostoma</em></td><td>15</td><td>&lt;0.01</td><td>210</td><td>0.02</td><td><strong>egg unkn_temp_Engraulidae_temp</strong></td><td>61</td><td>0.01</td><td>192</td><td>0.02</td></tr><tr><td>Thecosomata</td><td>15</td><td>&lt;0.01</td><td>59</td><td>0.01</td><td>part_ctenophora</td><td>30</td><td>&lt;0.01</td><td>319</td><td>0.03</td></tr><tr><td>Heterorhabdidae</td><td>8</td><td>&lt;0.01</td><td>205</td><td>0.02</td><td>tornaria larvae</td><td>21</td><td>&lt;0.01</td><td>83</td><td>0.01</td></tr><tr><td>Pontellidae</td><td>6</td><td>&lt;0.01</td><td>299</td><td>0.03</td><td><strong>egg_other</strong></td><td>17</td><td>&lt;0.01</td><td>2281</td><td>0.20</td></tr><tr><td>Cumacea</td><td>4</td><td>&lt;0.01</td><td>180</td><td>0.02</td><td>megalopa</td><td>6</td><td>&lt;0.01</td><td>460</td><td>0.04</td></tr><tr><td>Mysida</td><td>3</td><td>&lt;0.01</td><td>885</td><td>0.08</td><td><strong>scale</strong></td><td>2</td><td>&lt;0.01</td><td>53</td><td>&lt;0.01</td></tr><tr><td>Eucalanidae</td><td>2</td><td>&lt;0.01</td><td>839</td><td>0.07</td><td>siphonula</td><td>1</td><td>&lt;0.01</td><td>20</td><td>&lt;0.01</td></tr><tr><td>Insecta</td><td>2</td><td>&lt;0.01</td><td>3</td><td>&lt;0.01</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Foraminifera</td><td>1</td><td>&lt;0.01</td><td>384</td><td>0.03</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><em>Haloptilus</em></td><td>1</td><td>&lt;0.01</td><td>5</td><td>&lt;0.01</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Isopoda</td><td>1</td><td>&lt;0.01</td><td>123</td><td>0.01</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Rhincalanidae</td><td>1</td><td>&lt;0.01</td><td>127</td><td>0.01</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sapphirinidae</td><td>1</td><td>&lt;0.01</td><td>21</td><td>&lt;0.01</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><caption>**Table 2.** Taxa and operational morphological groups (OMGs) present in either the ZooCAM dataset or the ZooScan dataset but not both. Taxa and OMGs appearing exclusively in the ZooCAM dataset are listed on the left; those appearing exclusively in the ZooScan dataset are listed on the right. OMG names are spelled as they appear in the dataset. The numbers next to each taxon and OMG are the count and the percentage (%) for each category for each instrument in the whole dataset. Non-zooplanktonic taxa and OMGs are highlighted in bold, and genera and species are formatted in italics.</caption><thead><tr><th colspan="3">ZooCAM</th><th colspan="3">ZooScan</th></tr><tr><th>Taxon/OMG</th><th>Counts</th><th>%</th><th>Taxon/OMG</th><th>Counts</th><th>%</th></tr></thead><tbody><tr><td><strong>lightDetritus</strong></td><td>38 126</td><td>5.43</td><td><strong>badfocusartefact</strong></td><td>34 507</td><td>2.99</td></tr><tr><td>Rhizaria</td><td>13 347</td><td>1.90</td><td>badfocusCopepoda</td><td>11 656</td><td>1.01</td></tr><tr><td>Copepoda X</td><td>6727</td><td>0.96</td><td>Eumalacostraca</td><td>9815</td><td>0.85</td></tr><tr><td><strong>fluffyDetritus</strong></td><td>3589</td><td>0.51</td><td>partCrustacea</td><td>7530</td><td>0.65</td></tr><tr><td>Evadne</td><td>1889</td><td>0.27</td><td>Fritillariidae</td><td>3635</td><td>0.32</td></tr><tr><td>Hydrozoa</td><td>1674</td><td>0.24</td><td>trunkappendicularia</td><td>1210</td><td>0.10</td></tr><tr><td>Poecilostomatoida</td><td>1094</td><td>0.16</td><td>Aglaura</td><td>1113</td><td>0.10</td></tr><tr><td>Rhizaria X</td><td>857</td><td>0.12</td><td>Pleuromamma</td><td>695</td><td>0.06</td></tr><tr><td><strong>Rhizosolenids</strong></td><td>761</td><td>0.11</td><td>partCnidaria</td><td>692</td><td>0.06</td></tr><tr><td>dead_harpacticoida</td><td>528</td><td>0.08</td><td>zoea_galatheidae</td><td>660</td><td>0.06</td></tr><tr><td>gelatinous</td><td>348</td><td>0.05</td><td>pluteus_ophiuroidea</td><td>640</td><td>0.06</td></tr><tr><td><strong>Trichodesmium</strong></td><td>265</td><td>0.04</td><td>Salpida</td><td>470</td><td>0.04</td></tr><tr><td><strong>aggregata</strong></td><td>253</td><td>0.04</td><td>Harosa</td><td>374</td><td>0.03</td></tr><tr><td><strong>feces</strong></td><td>227</td><td>0.03</td><td>tail_chaetognatha</td><td>251</td><td>0.02</td></tr><tr><td><strong>Halosphaera</strong></td><td>193</td><td>0.03</td><td>Euchirella</td><td>239</td><td>0.02</td></tr><tr><td>Podon</td><td>162</td><td>0.02</td><td>protozoea_mysida</td><td>229</td><td>0.02</td></tr><tr><td>Diphyidae</td><td>144</td><td>0.02</td><td>Solmundella bitentaculata</td><td>178</td><td>0.02</td></tr><tr><td>larvae_gastropoda</td><td>116</td><td>0.02</td><td>Peltidiidae</td><td>133</td><td>0.01</td></tr><tr><td><strong>chainlarge</strong></td><td>114</td><td>0.02</td><td>Liriope tetraphylla</td><td>121</td><td>0.01</td></tr><tr><td>veliger</td><td>113</td><td>0.02</td><td>partAnnelida</td><td>121</td><td>0.01</td></tr><tr><td>egg 1 temp_Sardina temp</td><td>100</td><td>0.01</td><td>larvae_crustacea</td><td>114</td><td>0.01</td></tr><tr><td>egg 1 temp_Engraulidae temp</td><td>65</td><td>0.01</td><td>larvae_mysida</td><td>73</td><td>0.01</td></tr><tr><td>Isias</td><td>51</td><td>0.01</td><td>ephyra_scyphozoa</td><td>64</td><td>0.01</td></tr><tr><td>egg 2 3 temp_Sardina temp</td><td>49</td><td>0.01</td><td>actinula_hydrozoa</td><td>49</td><td>&lt;0.01</td></tr><tr><td>Calycophorae</td><td>30</td><td>&lt;0.01</td><td>part_thaliacea</td><td>44</td><td>&lt;0.01</td></tr><tr><td>egg 9 11 temp_Sardina temp</td><td>26</td><td>&lt;0.01</td><td>Atlanta</td><td>43</td><td>&lt;0.01</td></tr><tr><td>egg unkn temp_Sardina temp</td><td>23</td><td>&lt;0.01</td><td>like_laomediidae</td><td>36</td><td>&lt;0.01</td></tr><tr><td>Calocalanus tenuis</td><td>17</td><td>&lt;0.01</td><td>Nemertea</td><td>31</td><td>&lt;0.01</td></tr><tr><td>egg 4 6 temp_Sardina temp</td><td>15</td><td>&lt;0.01</td><td>protozoea penaeidae</td><td>28</td><td>&lt;0.01</td></tr><tr><td>egg 9 11 temp_Engraulidae temp</td><td>14</td><td>&lt;0.01</td><td>Cavoliniidae</td><td>21</td><td>&lt;0.01</td></tr><tr><td>egg 7 8 temp_Engraulidae temp</td><td>13</td><td>&lt;0.01</td><td>Actiniaria</td><td>13</td><td>&lt;0.01</td></tr><tr><td>Enteropneusta_Hemichordata</td><td>12</td><td>&lt;0.01</td><td>pilidium_nemertea</td><td>12</td><td>&lt;0.01</td></tr><tr><td><strong>Chaetoceros sp.</strong></td><td>9</td><td>&lt;0.01</td><td>protozoea SERGESTIDAE</td><td>12</td><td>&lt;0.01</td></tr><tr><td>head_crustacea</td><td>9</td><td>&lt;0.01</td><td>phyllosoma</td><td>8</td><td>&lt;0.01</td></tr><tr><td>Centropages hamatus</td><td>8</td><td>&lt;0.01</td><td>Creseidae</td><td>7</td><td>&lt;0.01</td></tr><tr><td>Thaliacea</td><td>7</td><td>&lt;0.01</td><td>Penaeoidea</td><td>7</td><td>&lt;0.01</td></tr><tr><td>egg 4 6 temp_Engraulidae temp</td><td>6</td><td>&lt;0.01</td><td>Paguridae</td><td>4</td><td>&lt;0.01</td></tr><tr><td>Sphaeronectidae</td><td>4</td><td>&lt;0.01</td><td>larvae_squillidae</td><td>4</td><td>&lt;0.01</td></tr><tr><td>Thalassionema</td><td>4</td><td>&lt;0.01</td><td>Cephalopoda</td><td>3</td><td>&lt;0.01</td></tr><tr><td>egg 2 3 temp_Engraulidae temp</td><td>3</td><td>&lt;0.01</td><td>Cymbulia peroni</td><td>3</td><td>&lt;0.01</td></tr><tr><td>Jaxea</td><td>2</td><td>&lt;0.01</td><td>Nannosquillidae</td><td>2</td><td>&lt;0.01</td></tr><tr><td>Pyrosoma</td><td>1</td><td>&lt;0.01</td><td>Lubbockia</td><td>1</td><td>&lt;0.01</td></tr><tr><td>larvae_ascidiacea</td><td>1</td><td>&lt;0.01</td><td>Monstrilloida</td><td>1</td><td>&lt;0.01</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Gridded maps of total zooplankton abundances (expressed in individuals per cubic metre of sampled seawater) during the PELGAS cruises in the Bay of Biscay from 2004 to 2019. The abundances are well within the range of zooplankton abundances seen over other temperate continental shelves. They exhibit a marked coastal to offshore gradient, with abundances being higher at the coast. Abundances also show an overall increase over the years. The gridding procedure is presented in Petitgas et al. (2009, 2014). See also Doray et al. (2018c) and Grandremy et al. (2023a) for application examples.</figcaption></figure>

features (i.e. pixel size). Those prefixes originate from the use of the Ecotaxa web application to sort and identify the images (Picheral et al., 2017), which promotes this specific formatting. The ZooCAM dataset is arranged as a matrix with 72 columns (variables) × 702 111 rows (individual imaged objects), and the ZooScan dataset is arranged as a matrix with 71 columns (variables) × 1 153 507 rows (individual imaged objects).

Among the 70+ variables, it is worth noting the following ones:

i. **objid**: this is a unique individual object numerical identifier that enables a single data line to be linked to a corresponding single image in the image dataset.

ii. **taxon**: this is the taxonomic or OMG identification of the imaged objects, written as they appear in Tables 1 and 2.

iii. **lineage**: this is the full taxonomic lineage of the taxon. Lineage may be used to aggregate taxa at higher taxonomic levels (respecting taxonomic lineages).

iv. **classif_id**: This is a unique numerical taxon identifier.
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Gridded maps of total zooplankton mean size (expressed in mm equivalent spherical diameter) during the PELGAS cruise in the Bay of Biscay from 2004 to 2019. They exhibit a coastal to offshore gradient as well as a north–south gradient. Mean body sizes are smaller at the coast and usually smaller in the south. In general, mean body sizes show an overall decrease over the years. The gridding procedure is presented in Petitgas et al. (2009, 2014). See also Doray et al. (2018c) and Grandremy et al. (2023a) for application examples.</figcaption></figure>

v. sample_sub_part/acq_sub_part: these are the subsam-
pling ratios for ZooCAM and ZooScan respectively,
which are needed to reconstruct the quantitative esti-
mates of the sample abundances.

vi. sample_fishingvolume/sample_tot_vol: these are the total sampled seawater volumes for ZooCAM and ZooScan respectively, which are needed to normalize the sample concentrations by the seawater volume.

One can therefore calculate quantitative abundance esti-
mates for a taxon in a sample as follows:

$$
\text{ZooCAM: } Ab_{\text{taxon}} = \frac{n_{\text{taxon}} \times \text{sample\_sub\_part}}{\text{sample\_fishingvolume}} \quad (1)
$$

$$
\text{ZooScan: } Ab_{\text{taxon}} = \frac{(n_{\text{taxon}_{\text{acq1}}} \times \text{acq\_sub\_part}_{\text{acq1}}) + (n_{\text{taxon}_{\text{acq2}}} \times \text{acq\_sub\_part}_{\text{acq2}})}{\text{sample\_tot\_vol}}, \quad (2)
$$

where Ab is the abundance in ind m⁻³ and n is the number
of individuals for “taxon”.
<table><caption>**Table 3.** Non-exhaustive list of OMG prefixes, their types (morphological, developmental stage, taxonomic, non-living, or imaging artefact) and their contents.</caption><thead><tr><th>Prefix</th><th>Type</th><th>Content of category</th></tr></thead><tbody><tr><td>bract</td><td>morphological</td><td>single siphonophore bracts</td></tr><tr><td>eudoxie</td><td>morphological</td><td>single siphonophore eudoxid zooids</td></tr><tr><td>gonophore</td><td>morphological</td><td>single siphonophore gonozooids</td></tr><tr><td>nectophore</td><td>morphological</td><td>single siphonophore swimming bells</td></tr><tr><td>trunk</td><td>morphological</td><td>single appendicularian trunks detached from their tails</td></tr><tr><td>tail</td><td>morphological</td><td>tail-shaped part of an appendicularian's or chaetognath's body</td></tr><tr><td>head</td><td>morphological</td><td>individual organisms' heads detached from the body</td></tr><tr><td>part</td><td>morphological</td><td>unidentified body part</td></tr><tr><td>egg sac</td><td>morphological</td><td>detached copepod egg sacs</td></tr><tr><td>like</td><td>morphological</td><td>look alike, but identification is not absolutely certain</td></tr><tr><td>multiple</td><td>morphological</td><td>two or more objects touching each other in the same vignette</td></tr><tr><td>other</td><td>morphological</td><td>non-identified living object</td></tr><tr><td>actinula</td><td>developmental stage</td><td>undefined hydrozoan actinula larval stage</td></tr><tr><td>calyptopsis</td><td>developmental stage</td><td>euphausiacean calyptopsis larval stage</td></tr><tr><td>egg</td><td>developmental stage</td><td>egg larval stage</td></tr><tr><td>ephyra</td><td>developmental stage</td><td>hydrozoan ephyra larval stage</td></tr><tr><td>larvae</td><td>developmental stage</td><td>undefined larval stage</td></tr><tr><td>nauplii</td><td>developmental stage</td><td>crustacean nauplius larval stage</td></tr><tr><td>pilidium</td><td>developmental stage</td><td>free-swimming larvae of a nemertean worm</td></tr><tr><td>protozoea</td><td>developmental stage</td><td>crustacean protozoea larval stage</td></tr><tr><td>pluteus</td><td>developmental stage</td><td>echinoderm pluteus larval stage</td></tr><tr><td>zoea</td><td>developmental stage</td><td>crustacean zoea larval stage</td></tr><tr><td>egg 1 temp</td><td>developmental stage</td><td>clupeid fish embryo developmental stage 1*</td></tr><tr><td>egg 2 3 temp</td><td>developmental stage</td><td>clupeid fish embryo developmental stages 2 and 3 (aggregated)*</td></tr><tr><td>egg 4 6 temp</td><td>developmental stage</td><td>clupeid fish embryo developmental stages 4 to 6 (aggregated)*</td></tr><tr><td>egg 7 8 temp</td><td>developmental stage</td><td>clupeid fish embryo developmental stages 7 and 8 (aggregated)*</td></tr><tr><td>egg 9 11 temp</td><td>developmental stage</td><td>clupeid fish embryo developmental stages 9 to 11 (aggregated)*</td></tr><tr><td>egg unknown</td><td>developmental stage</td><td>unidentified clupeid fish embryo developmental stage*</td></tr><tr><td>Bivalvia</td><td>taxonomic</td><td>small bivalve larvae of unidentified mollusc</td></tr><tr><td>dead</td><td>non-living</td><td>exuvia, carcass, or part of dead body of a copepod</td></tr><tr><td>fiber</td><td>non-living</td><td>fibre-like detritus</td></tr><tr><td>fluffy</td><td>non-living</td><td>very porous detritic particles</td></tr><tr><td>light</td><td>non-living</td><td>very transparent detritic particles</td></tr><tr><td>badfocus</td><td>imaging artefact</td><td>out-of-focus objects</td></tr></tbody></table>

### 3.2.2 Images

There are two sets of individual images sorted into folders by category (Tables 1 and 2) in each dataset. For the ZooCAM only, the images from years 2016 and 2017 contain printed region of interest (ROI) bounding-box limits and text at the bottom of each image and a non-homogenized background within and around the ROI bounding box; images from year 2018 contain a non-homogenized background within the ROI bounding box only; and images from 2019 have a completely homogeneous and thresholded background around the object. These differences arose from successive ZooCAM software updates that did not modify the calculation of an object's features. The ZooScan images all have a completely homogeneous and thresholded background around the object, with no bounding-box limits nor text printed in the im-

ages. All images for the two instruments' datasets have a 1 mm scale bar printed at the bottom left corner.

## 4 Data availability

The ZooScan dataset can be found as the PELGAS Bay of Biscay ZooScan zooplankton Dataset (2004–2016) at the SEANOE data portal at https://doi.org/10.17882/94052; Grandremy et al., 2023c). Individual object images can be freely viewed and explored by anyone using the Ecotaxa web application (https://ecotaxa OBS-vlfr.fr/, Picheral et al., 2017; no registration): search for the project name “PELGAS Bay of Biscay ZooScan zooplankton Dataset (2004–2016)” under the tab “explore images”.

The ZooCAM dataset can be found as the PELGAS Bay of Biscay ZooCAM zooplankton Dataset (2016–2019) at

<footnote>* Clupeid fish embryo developmental stages according to Ahlstrom (1943) and Moser and Ahlstrom (1985).</footnote>
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Gridded maps of total zooplankton Shannon index (calculated on spherical biovolumes) during the PELGAS cruise in the Bay of Biscay from 2004 to 2019. The Shannon index exhibits a coastal to offshore gradient as well as a north-south gradient. The Shannon index is larger at the coast and in the south except in 2014, where it is smaller offshore and in the south. The gridding procedure is presented in Petitgas et al. (2009, 2014). See also Doray et al. (2018c) and Grandremy et al. (2023a) for application examples.</figcaption></figure>

the SEANOE data portal (https://doi.org/10.17882/94040; Grandremy et al., 2023d). Individual object images can be freely viewed and explored by anyone using the Eco-taxa web application (https://ecotaxaobs-vlfr.fr/; no registration): search for the project name “PELGAS Bay of Biscay ZooCAM zooplankton Dataset (2016–2019)” under the tab “explore images”.

Each dataset comes as a .zip archive that contains

- one tab-separated file containing all data and metadata associated with each imaged and identified object

- one comma-separated file containing the name, type, definition and unit of each field (column)

- one comma-separated file containing the taxonomic list of the dataset with counts and the nature of the content of the category

- a directory named *individual_images* containing images of individual objects that are named according to the object id *objid* and sorted into subdirectories according to taxonomic identification, year and sampling station.
5 Concluding remarks

Recent studies showed that small pelagic fish (SPF) commu-
nities have suffered from a drastic loss of condition in the
Mediterranean Sea and in the Bay of Biscay (Van Beveren
et al., 2014; Doray et al., 2018d; Saraux et al., 2019) over
the last 20 years. This loss of condition is expressed in par-
ticular as a constant decrease in SPF size- and weight-at-age
(Doray et al., 2018d; Véron et al., 2020) and is possibly ex-
plained by a change in SPF trophic resource composition,
size and quality (Broset et al., 2016; Queiros et al., 2019;
Menu et al., 2023). Identifying and measuring zooplankton
at appropriate temporal and spatial scales is not an easy task,
but it can be addressed with imaging. These datasets were as-
sembled in an effort to make possible the exploration of the
relationship between the observed dynamics of SPF in the
Bay of Biscay and the dynamics of their main food resource,
metazoan zooplankton. This zooplankton imaging data se-
ries is a significant output of Nina Grandremy's PhD (2019-
2023), and is currently being exploited (Grandremy et al.,
2023a), and the intention is to continue this series and update
it on a yearly basis in the framework of the PELGAS pro-
gram to better understand the underlying processes presiding
over the long-term SPF dynamics. Moreover, those two zoo-
plankton datasets can be associated with the PELGAS survey
datasets published in 2018 (which can also be found at the
SEANOE data portal), which feature hydrological, primary
producers, fish and megafauna data arranged as gridded data
(Doray et al., 2018b). Together, all these datasets allow all of
the pelagic ecosystem compartments to be studied simultane-
ously with a coherent spatial domain (the Bay of Biscay con-
tinental shelf), resolution and time series. Nevertheless, spa-
tial gridding of the data is highly recommended (as presented
in Figs. 2, 3 and 4), since the spatial coverage of the sampling
protocols can vary between years (Fig. 1) within and between
each pelagic ecosystem compartment. A procedure for such
batch data spatial smoothing is presented in, for example, Pe-
titgas et al. (2009, 2014). See also Doray et al. (2018c) and
Grandremy et al. (2023a) for application examples. As sev-
eral descriptors of the spring zooplankton community (abun-
dances, sizes, biovolumes, biomass) can be derived from this
16-year-long spatially resolved time series at several taxo-
nomic levels, these datasets are intended to be used in various
ecological studies that include the zooplankton compartment,
especially in modelling studies, where zooplankton is usually
underrepresented (Mitra, 2010; Mitra et al., 2014). Finally,
these datasets could also be applied as learning datasets when
machine learning is used in plankton studies.
Appendix A

<table><caption>Table A1. ZooCAM dataset column headers – definitions of data and metadata fields.</caption><thead><tr><td>Column name</td><td>Definition</td></tr></thead><tbody><tr><td>object_id</td><td>Name of object and associated image</td></tr><tr><td>objid</td><td>Unique Ecotaxa internal object identifier</td></tr><tr><td>object_lat</td><td>Latitude of sampling</td></tr><tr><td>object_lon</td><td>Longitude of sampling</td></tr><tr><td>object_date</td><td>Date of sampling</td></tr><tr><td>object_time</td><td>Time of sampling</td></tr><tr><td>object_depth_min</td><td>Minimum sampling depth</td></tr><tr><td>object_depth_max</td><td>Maximum sampling depth</td></tr><tr><td>object_taxon</td><td>Taxonomic name</td></tr><tr><td>object_lineage</td><td>Full taxonomic lineage corresponding to the taxon</td></tr><tr><td>classif_id</td><td>Unique Ecotaxa internal taxon identifier</td></tr><tr><td>object_area</td><td>Object's surface</td></tr><tr><td>object_area_exc</td><td>Object surface excluding white pixels</td></tr><tr><td>object_%area</td><td>Proportion of the image corresponding to the object</td></tr><tr><td>object_area_based_diameter</td><td>Object's area-based diameter: 2 × (object_area/pi)<sup>1/2</sup></td></tr><tr><td>object_meangreyimage</td><td>Mean image grey level</td></tr><tr><td>object_meangreyobject</td><td>Mean object grey level</td></tr><tr><td>object_modegreyobject</td><td>Modal object grey level</td></tr><tr><td>object_sigmagrey</td><td>Object grey-level standard deviation</td></tr><tr><td>object_mingrey</td><td>Minimum object grey level</td></tr><tr><td>object_maxgrey</td><td>Maximum object grey level</td></tr><tr><td>object_sumgrey</td><td>Object grey-level integrated density: object_mean × object_area</td></tr><tr><td>object_breadth</td><td>Breadth of the object along the best-fitting ellipsoid's minor axis</td></tr><tr><td>object_length</td><td>Breadth of the object along the best-fitting ellipsoid's major axis</td></tr><tr><td>object_elongation</td><td>Elongation index: object_length/object_breadth</td></tr><tr><td>object_perim</td><td>Object's perimeter</td></tr><tr><td>object_minferetdiam</td><td>Minimum object's Feret diameter</td></tr><tr><td>object_maxferetdiam</td><td>Maximum object's Feret diameter</td></tr><tr><td>object_meanferetdiam</td><td>Average object's Feret diameter</td></tr><tr><td>object_feretelongation</td><td>Elongation index: object_maxferetdiam/object_minferetdiam</td></tr><tr><td>objectCompactness</td><td>Isoperimetric quotient: the ratio of the object's area to the area of a circle with the same perimeter</td></tr><tr><td>object_intercept0</td><td>The number of times that a transition from background to foreground occurs at an angle of 0° for the entire object</td></tr><tr><td>object_intercept45</td><td>The number of times that a transition from background to foreground occurs at an angle of 45° for the entire object</td></tr><tr><td>object_intercept90</td><td>The number of times that a transition from background to foreground occurs at an angle of 90° for the entire object</td></tr><tr><td>object_intercept135</td><td>The number of times that a transition from background to foreground occurs at an angle of 135° for the entire object</td></tr><tr><td>objectConvexhullarea</td><td>Area of the convex hull of the object</td></tr><tr><td>objectConvexhullfillratio</td><td>ratio object_area/convexhullarea</td></tr><tr><td>objectConvexexperimeter</td><td>Perimeter of the convex hull of the object</td></tr><tr><td>object_n_number_ofastarts</td><td>Number of horizontal strings of consecutive foreground pixels in the object</td></tr><tr><td>object_n_chained pixels</td><td>Number of chained pixels in the object</td></tr><tr><td>object_n Convex_hull_points</td><td>Number of summits of the object's convex hull polygon</td></tr><tr><td>object_n_number_of_holes</td><td>Number of holes (closed white pixel area) in the object</td></tr><tr><td>object_transparence</td><td>Ratio object_sumgrey/object_area</td></tr><tr><td>object_roughness</td><td>Measure of small-scale variations of amplitude in the object's grey levels</td></tr><tr><td>objectRectangularity</td><td>Ratio of the object's area to the area of its best bounding rectangle</td></tr><tr><td>objectSkewness</td><td>Skewness of the object's grey-level distribution</td></tr><tr><td>objectKurtosis</td><td>Kurtosis of the object's grey-level distribution</td></tr><tr><td>objectFractal_box</td><td>Fractal dimension of the object's perimeter</td></tr><tr><td>objectValhist25</td><td>Grey level value at quantile 0.25 of the normalized cumulative histogram of the object's grey level values</td></tr><tr><td>objectValhist50</td><td>Grey level value at quantile 0.5 of the normalized cumulative histogram of the object's grey level values</td></tr><tr><td>objectValhist75</td><td>Grey level value at quantile 0.75 of the normalized cumulative histogram of the object's grey level values</td></tr><tr><td>objectValhist25</td><td>Sum of grey levels at the 0.25 quantile of the normalized cumulative histogram of the object's grey level values</td></tr><tr><td>objectValhist50</td><td>Sum of grey levels at the 0.5 quantile of the normalized cumulative histogram of the object's grey level values</td></tr><tr><td>objectValhist75</td><td>Sum of grey levels at the 0.75 quantile of the normalized cumulative histogram of the object's grey level values</td></tr><tr><td>objectNobj25</td><td>Number of objects after thresholding at grey level object_valhist25</td></tr><tr><td>objectNobj50</td><td>Number of objects after thresholding at grey level object_valhist50</td></tr><tr><td>objectNobj75</td><td>Number of objects after thresholding at grey level object_valhist75</td></tr><tr><td>objectSymmetry</td><td>Index of horizontal symmetry</td></tr><tr><td>objectSymmetry</td><td>Index of vertical symmetry</td></tr><tr><td>objectThick_r</td><td>Maximum object thickness/mean object thickness</td></tr><tr><td>objectCdist</td><td>Distance between the mass and the grey-level object's centroids</td></tr><tr><td>objectBord</td><td>Tag for an object touching the frame edge</td></tr><tr><td>sample_id</td><td>Name of the sample from which the object originates</td></tr><tr><td>sampleShip</td><td>Name of the ship used to collect the samples</td></tr><tr><td>sampleCampaign</td><td>Name of the cruise during which the samples were collected</td></tr><tr><td>sampleStation</td><td>Name of the station where the samples were collected</td></tr><tr><td>sampleDepth</td><td>Bottom depth at station</td></tr><tr><td>sampleDevice</td><td>Net used to collect the sample</td></tr><tr><td>sampleFishingvolume</td><td>Seawater volume sampled</td></tr><tr><td>sampleSub_part</td><td>Subsampling elevation factor</td></tr><tr><td>process_id</td><td>Name of software/software version used to analyse the digitized sample images</td></tr><tr><td>processResolutionYN cameraMicron_par pixel</td><td>Pixel size</td></tr></tbody></table>
<table><caption>Table A2. ZooScan dataset column headers – definitions of data and metadata fields.</caption><thead><tr><td>Column name</td><td>Definition</td></tr></thead><tbody><tr><td>object_id</td><td>Name of object and associated image</td></tr><tr><td>objid</td><td>Unique Ecotaxa internal object identifier</td></tr><tr><td>object_lat</td><td>Latitude of sampling</td></tr><tr><td>object_lon</td><td>Longitude of sampling</td></tr><tr><td>object_date</td><td>Date of sampling</td></tr><tr><td>object_time</td><td>Time of sampling</td></tr><tr><td>object_depth_min</td><td>Minimum sampling depth</td></tr><tr><td>object_depth_max</td><td>Maximum sampling depth</td></tr><tr><td>object_taxon</td><td>Taxonomic name</td></tr><tr><td>object_lineage</td><td>Full taxonomic lineage corresponding to the taxon</td></tr><tr><td>classif_id</td><td>Unique Ecotaxa internal taxon identifier</td></tr><tr><td>object_area</td><td>Object's surface</td></tr><tr><td>object_mean</td><td>Mean object grey level</td></tr><tr><td>object_stddev</td><td>Object grey-level standard deviation</td></tr><tr><td>object_mode</td><td>Modal object grey level</td></tr><tr><td>object_min</td><td>Minimum object grey level</td></tr><tr><td>object_max</td><td>Maximum object grey level</td></tr><tr><td>object_perim</td><td>Object's perimeter</td></tr><tr><td>object_major</td><td>Length of major axis of best-fitting ellipse</td></tr><tr><td>object_minor</td><td>Length of minor axis of best-fitting ellipse</td></tr><tr><td>object_circ</td><td>Circularity: 4 × pi(object_area/object_perim.<sup>2</sup>)</td></tr><tr><td>object_feret</td><td>Maximum Feret diameter</td></tr><tr><td>object_intden</td><td>Object grey-level integrated density: object_mean × object_area</td></tr><tr><td>object_major</td><td>Median object grey level</td></tr><tr><td>object_skew</td><td>Skewness of the object's grey-level distribution</td></tr><tr><td>object_kurt</td><td>Kurtosis of the object's grey-level distribution</td></tr><tr><td>object_%area</td><td>Proportion of the image corresponding to the object</td></tr><tr><td>object_area_exc</td><td>Object surface excluding white pixels</td></tr><tr><td>object_fractal</td><td>Fractal dimension of the object's perimeter</td></tr><tr><td>object_skelarea</td><td>Surface of the one-pixel-wide skeleton of the object</td></tr><tr><td>object_slope</td><td>Slope of the cumulated histogram of the object's grey levels</td></tr><tr><td>object_histcum1</td><td>Grey level value at the 0.25 quantile of the histogram of the object's grey levels</td></tr><tr><td>object_histcum2</td><td>Grey level at the 0.5 quantile of the histogram of the object's grey levels</td></tr><tr><td>object_histcum3</td><td>Grey level at the 0.75 quantile of the histogram of the object's grey levels</td></tr><tr><td>object_nb1</td><td>Number of objects after thresholding at object_histcum1 grey level</td></tr><tr><td>object_nb2</td><td>Number of objects after thresholding at the grey level object_histcum2</td></tr><tr><td>object_symetrieh</td><td>Index of horizontal symmetry</td></tr><tr><td>object_symetriev</td><td>Index of vertical symmetry</td></tr><tr><td>object_symetriehc</td><td>Index of horizontal symmetry after thresholding at object_histcum1 grey level</td></tr><tr><td>object_symetrievc</td><td>Index of vertical symmetry after thresholding at object_histcum1 grey level</td></tr><tr><td>objectoj convperim</td><td>Perimeter of the convex hull of the object</td></tr><tr><td>object convarea</td><td>Area of the convex hull of the object</td></tr><tr><td>object_fcons</td><td>Object's contrast</td></tr><tr><td>object_thickr</td><td>Maximum object thickness/mean object thickness</td></tr><tr><td>object_esd</td><td>Object's equivalent spherical diameter: 2 × (object_area/pi)<sup>1/2</sup></td></tr><tr><td>object elongation</td><td>Elongation index: major/minor</td></tr><tr><td>object_range</td><td>Range of greys: max − min</td></tr><tr><td>object_meanpos</td><td>Relative position of the mean grey: (max − min)/range</td></tr><tr><td>object_centroids</td><td>Distance between the mass and the grey-level object's centroids</td></tr><tr><td>object_cv</td><td>Coefficient of variation of greys: 100 × (stddev/mean)</td></tr><tr><td>object SR</td><td>Index of variation of greys: 100 × (stddev/range)</td></tr><tr><td>object_perimareaexc</td><td>Index of the relative complexity of the perimeter: object_perim/object_area_exc</td></tr><tr><td>object_feretareaexc</td><td>Another elongation index: object_feret/object_area_exc</td></tr><tr><td>object_perimferet</td><td>Index of the relative complexity of the perimeter: object_perim/object_feret</td></tr><tr><td>object perimmajor</td><td>Index of the relative complexity of the perimeter: object_perim/object_major</td></tr><tr><td>object_circex</td><td>Circularity of the object excluding white pixels: 4 × pi(object_area_exc/object_perim.<sup>2</sup>)</td></tr><tr><td>object_cdcexc</td><td>Distance between the mass and the grey-level object's centroids calculated with object_area_exc</td></tr><tr><td>sample_id</td><td>Name of the sample from which the object originates</td></tr><tr><td>sample ship</td><td>Name of the ship used to collect the samples</td></tr><tr><td>sample_program</td><td>Name of the cruise from which the samples were collected</td></tr><tr><td>sampleStationid</td><td>Name of the station where the samples were collected</td></tr><tr><td>sample_bottomdepth</td><td>Bottom depth at station</td></tr><tr><td>sample_net_type</td><td>Net used to collect the sample</td></tr><tr><td>sample Tot Vol</td><td>Seawater volume sampled</td></tr><tr><td>sample_comment</td><td>Comments associated with sampling/sample treatment</td></tr><tr><td>process_id</td><td>Name of software/software version used to analyse digitized sample images</td></tr><tr><td>processparticlePixel_size_mm</td><td>Pixel size</td></tr><tr><td>acq_id</td><td>Name of subsample (if any)</td></tr><tr><td>acq_min Mesh</td><td>Minimum sieve size of subsample</td></tr><tr><td>acq_max Mesh</td><td>Maximum sieve size of subsample</td></tr><tr><td>acq_sub_part</td><td>Subsampling elevation factor</td></tr></tbody></table>
**Author contributions.** NG scanned and validated most of the ZooScan dataset, assembled the datasets, and led the drafting. PB collected and managed the samples from 2004 on and participated in the manual validation of identifications. ED scanned a substantial fraction of the ZooScan samples and participated in the initial sorting of vignettes. MMD participated in the collection of samples and was involved in the development of the ZooCAM. MD was chief scientist on the PELGAS surveys and participated in the drafting. CD supervised NG's work and participated in the drafting. BF developed, improved and maintained the ZooCAM software. LJ curated a substantial fraction of the manual validations of identifications for the ZooScan dataset. MH participated in the collection of samples, led the DEFIPEL project and participated in the drafting. SLM participated in the collection of samples and managed the ZooCAM. AN curated a substantial fraction of the manual validations of identifications for the ZooScan and the ZooCAM datasets. PP supervised NG's work and participated in the drafting. PhP participated in the collection of samples and participated in the drafting. JR supervised the development and improvement of the ZooCAM. MT developed and improved the ZooCAM and participated in the collection of samples. JBR supervised NG's work, participated in the collection of samples, curated a substantial fraction of the manual validations of identifications for the ZooCAM dataset, and led the drafting.

**Competing interests.** The contact author has declared that none of the authors has any competing interests.

**Disclaimer.** Data are published without any warranty, express or implied. The user assumes all risk arising from his/her use of data. Data are intended to be research-quality, but it is possible that the data themselves contain errors. It is the sole responsibility of the user to assess if the data are appropriate for his/her use, and to interpret the data accordingly. Authors welcome users to ask questions and report problems.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

**Acknowledgements.** Nina Grandremy acknowledges the funding of her PhD by Region Pays de la Loire, France and Ifremer. The authors wish to thank Jean-Yves Coail, Gérard Guyader and Patrick Berriet (Ifremer – Département Ressources physiques et Ecosystèmes de fond de Mer (REM), Unité Recherches et Développements Technologiques (RDT), and Service Ingénierie et Instrumentation Marine (SIIM)) for their contribution to the hardware assembly of the ZooCAM. The authors acknowledge the work of Elio Raphalen for scanning year 2005 samples. The authors thank the European Marine Biological Resource Centre (EMBRC) platform PIQs (Quantitative Imaging Platform of Villefranche-sur-Mer) for image analysis. This work was supported by EMBRC France, whose French state funds are managed by the French National Re-

search Agency within the Investments of the Future program under reference ANR-10-INBS-02. Finally, the authors wish also to thank the many other students, technicians and scientists who participated in the sampling and sample imaging onboard, and the successive crews of the R/V *Thalassa* involved in the PELGAS surveys from 2004 to 2019.

**Financial support.** This research has been supported by France Filière Pêche (Enjeux d'avenir) through the project DEFIPEL (Développement d'une approche intégrée pour la filière petits pélagiques française).

**Review statement.** This paper was edited by François G. Schmitt and reviewed by two anonymous referees.

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