# Long-term changes in depth distribution and phenology of four North Atlantic plankton species

Jérôme Castant, Vincent Vantrepotte, Hélène Thibault, Grégory Beaugrand

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Jérôme Castant, Vincent Vantrepotte, Hélène Thibault, Grégory Beaugrand. Long-term changes in depth distribution and phenology of four North Atlantic plankton species. Marine Ecology Progress Series, 2025, 771, pp.33-53. 10.3354/meps14945. hal-05397833

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Distributed under a Creative Commons Attribution 4.0 International License
Long-term changes in depth
distribution and phenology of four
North Atlantic plankton species

Jérôme Castant<fnref n="1" />*, Vincent Vantrepotte<fnref n="1" />, Hélène Thibault<fnref n="2" />
and Grégory Beaugrand<fnref n="1" />

Published in: Marine Ecological Progress Series (2025)

DOI: https://doi.org/10.3354/meps14945

Abstract

Plankton are highly sensitive to climate change and understanding their shifts in
behaviour, physiology, phenology, and biogeography is important for anticipating
changes in ecosystem services. This study uses a recently developed ecological niche
model to reanalyze data from the Continuous Plankton Recorder (CPR) survey
using eight environmental variables to build for the first time a CPR-based 4D
dataset (Longitude × Latitude × Depth × Day) of plankton abundance. First, we
analyze spatio-temporal changes in the abundance of four plankton species daily
from 1958 to 2022: the two diatoms *Ephemera planamembranacea* and *Odontella sinensis* and the two calanoid copepods *Calanus finmarchicus* and *C. helgolandicus*.
We validate our new CPR-based 4D dataset with existing *in-situ* data and find
that modeled spatio-temporal changes in abundance reproduce observations well.
Then, we show pronounced phenological and biogeographical shifts for the four
species. Long-term latitudinal shifts in *C. finmarchicus* and *E. planamembranacea*
are observed and correlate with the Atlantic subarctic gyre state, the North Atlantic
Oscillation, and the Atlantic Multidecadal Oscillation. Our results also reveal a
prominent positive influence of temperature on long-term changes in the abundance
of *E. planamembranacea*, *O. sinensis* and *C. helgolandicus*, whereas they identify a
negative influence of temperature on *C. finmarchicus*. Interestingly, our modeled
data suggest a rise in the abundance of *C. finmarchicus* at greater depths during
peak seasons, albeit this depth adjustment in response to changing conditions is
unlikely to overcome the overall surface decline of the species.

**Keywords:** plankton, ecological niche modeling, climate change, phenology, range shifts, depth adjustment

<footnote n="1">¹Laboratoire d'Océanologie et de Géosciences (LOG), UMR 8187, Université du Littoral Côte d'Opale, Université Lille, Centre National de la Recherche Scientifique (CNRS), Wimereux 62930, France</footnote>

<footnote n="2">²Institut Méditerranéen d'Océanologie (MIO), Aix Marseille Université, Université de Toulon, CNRS, Institut de Recherche pour le Développement (IRD), Marseille 13288, France</footnote>

<footnote>*Corresponding author: jerome.castant@univ-littoral.fr</footnote>
# 1 Introduction

Plankton are a fundamental component of marine pelagic ecosystems, influencing their trophodynamics and productivity (Passow et al., 2012; Lima-Mendez et al., 2015). Through photosynthesis, phytoplankton currently account for almost half of global net primary production, promoting energy transfer to higher trophic levels and supporting marine food webs (Falkowski et al., 2004; Buitenhuis et al., 2013; Kulk et al., 2020). Zooplankton are a key component transferring this energy to higher trophic levels, including fish, seabirds and marine mammals (Luczak et al., 2011; Edwards et al., 2013; Behrenfeld, 2014; Vargas et al., 2022). Variations in plankton abundance and composition may thereby alter the entire ecosystem (Luczak et al., 2011; Edwards et al., 2013). Plankton are also important indicators of ecosystem structure and function (Beaugrand, 2015; Otero et al., 2020). They are particularly sensitive to climate change, exhibiting behavioural, physiological, phenological, and biogeographical shifts in response to subtle environmental fluctuations (Beaugrand et al., 2002; Edwards et al., 2004; Beaugrand et al., 2018). In addition, they are generally not commercially exploited and have relatively short life cycles.

Compelling evidence have revealed rapid alterations in plankton phenology, abundance, and biogeography. Both phytoplankton and zooplankton are exhibiting an earlier phenology on a global scale (Poloczanska et al., 2016; Thackeray et al., 2016; Friedland et al., 2018) and some works have identified rapid biogeographical shifts in zooplankton at a basin scale (Beaugrand et al., 2002). These studies were primarily based on surface observations, however, and it has been shown that vertical adjustment of marine species may strongly affect their responses to environmental changes (Perry et al., 2005; Brewin et al., 2022; Viljoen et al., 2024). In a 3-dimensional (3D) structured seascape, a comprehensive understanding of the influence of climate change on plankton thus requires a detailed knowledge of their vertical distribution (Reid et al., 2001). However, large-scale plankton monitoring programmes are scarce (Richardson et al., 2008) and none of them include information on depth distribution. For example, the Continuous Plankton Recorder (CPR) survey, operational since 1946 in the North Atlantic and its adjacent seas, provides valuable long-term data on plankton but samples species at a constant depth of 7-10 m (Warner et al., 1994; Reid et al., 2003).

In this study, we applied a recently developed Ecological Niche Model (ENM) called the Environmental String Model (ESM, Beaugrand, 2024) to tentatively propose for the first time a comprehensive high spatio-temporal resolution 4D dataset (Longitude × Latitude × Depth × Day) of the distribution of four plankton species for the period 1958-2022 using data from the CPR survey. We chose four plankton species characterized by different degree of eurygraphy (i.e., size of geographic distribution) and distinct
biogeography: (i) two phytoplankton diatom species *Ephemera planamembranacea* (oceanic cold-water species), *Odontella sinensis* (neritic coastal temperate species) and (ii) two calanoid copepods *Calanus finmarchicus* (cold-water species) and *C. helgolandicus* (warm-water pseudo-oceanic species). Such a study is challenging, however, because it assumes that the estimation of the ecological niche based on a long-term monitoring at a constant depth can be used to assess changes in depth distribution using 4D environmental datasets. We therefore first test extensively our CPR-based modeled 4D dataset of plankton abundance and then uses it to assess changes in the vertical structuring of pelagic seascapes in the context of climate change. Finally, we analyze spatial and temporal changes in species abundance and show coherent phenological, biogeographical, latitudinal and depth responses of the four species in agreement with expected alterations in the context of climatic variability and global climate change.

# 2 Materials and Methods

## 2.1 Summary

In this study, our first aim is to build a comprehensive 4D (Latitude × Longitude × Depth × Day) gridded dataset depicting the distribution of four planktonic species (*Ephemera planamembranacea*, *Odontella sinensis*, *Calanus finmarchicus* and *C. helgolandicus*) in depths ranging from 0 to 250 meters by increment of 1 m within the North Atlantic, spanning the period from 1958 to 2022. The dataset is generated at a spatial resolution of 0.25° latitude by 0.25° longitude. Initially, environmental data were downloaded and resampled on a 0.25° latitude × 0.25° longitude grid. Data only available at a monthly scale were extrapolated to daily intervals through linear interpolation at each depth level (Section 2.2). Environmental parameter values were assigned to each CPR sample across the study area (Section 2.3).

The Environmental String Model (ESM, Beaugrand, 2024) was applied to model the abundance of the target planktonic species at various depth layers ranging from 0 to 250 m (Section 2.4). To refine the outputs, a bathymetric linear filter was firstly applied for phytoplankton abundance (*Ephemera planamembranacea* and *Odontella sinensis*), and a smoothing process using a linear function incorporating data from the preceding 20 days was applied for zooplankton abundances (*Calanus finmarchicus* and *C. helgolandicus*; Section 2.5). Then, a first-order asymmetrical moving average was applied to integrate the upper 10 meters of depth for each species. A sketch diagram of the methodology is presented in Fig. S1.

Uncertainty analysis was performed, and our reanalyzed CPR-based 4D dataset was
tested using independent *in-situ* data from Georges Bank and Skagerrak (Section 2.6). To assess long-term changes in species distribution, phenological indices were computed and a standardized Principal Component Analysis (PCA) was carried out on annual mean SST and compared with mean species abundance and anomalies. A special attention was devoted to long-term changes in depth distribution for *Calanus finmarchicus* in the North Sea using monthly anomalies.

## 2.2 Environmental variables

We used a set of 8 environmental variables: bathymetry data, potential temperature (Toa), mixed layer depth (MLD), photosynthetically active radiation (PAR), nitrate, phosphate, silicate, and chlorophyll a (chl *a*).

**Bathymetry Data:** Bathymetry data were obtained from the GEBCO2021 Grid, downloaded from the General Bathymetric Chart of the Oceans (GEBCO) website (https://www.gebco.net/data_and_products/gridded_bathymetry_data/#global, GEBCO, 2021). Toa and MLD were compiled from the Global Ocean Reanalysis System version 5 (ORAS5; Zuo et al., 2019); these variables were downloaded monthly (1958-2022) for depths ranging from 0 to 250 meters. The data have a spatial resolution of 0.25° and were linearly interpolated to daily intervals. ORAS5 utilizes the NEMO ocean model (Version 3.4.1) and surface forcing from the ERA-Interim reanalysis, as well as merged *in-situ* and satellite data (Good et al., 2013). Data of daily mean PAR since 1958 were retrieved at a resolution of 0.25° (Castant et al., 2024). In this data product, there is no missing data. The dataset was built by means of the ESM, which was applied on various environmental inputs, mainly originating from the ERA5 database (Hersbach et al., 2016), to estimate PAR0+ (PAR just above the surface, inputs were solar radiation, cloud cover, and liquid water in cloud cover) and KdPAR (Light attenuation coefficient, inputs were SST, bathymetry, distance to coast, and Stokes drift). PAR was then computed along depth using the Beer-Lambert law for the exponential extinction of light in water (Ingle et al., 1988).

Macronutrients (nitrate, phosphate and silicate) and chl *a* data were downloaded monthly (1958-2022) for depths ranging from 0 to 250 m from the Coupled Model Intercomparison Project Phase 6 (CMIP6; Eyring et al., 2016) at a resolution of 1°. The data were spatially interpolated on a 0.25° latitude × 0.25° longitude grid and linearly interpolated to daily intervals. The simulations used were from IPSL-CM6A-LR, covering the period 1850-2014 (historical simulation) and 2015-2022 (future projections, modeled with the Shared Socioeconomic Pathways 585, corresponding to high radiative forcing by 2100; O'Neill et al., 2017; Boucher et al., 2018). Assuming a log-normal distribution, chl *a* was log-transformed to base 10 prior to computation to better consider it in the ESM
(O'Reilly et al., 2019; Brotas et al., 2023).

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Depths available, shared and selected within the environmental data were: 0, 3, 7, 10,
14, 19, 23, 31, 36, 41, 47, 54, 61, 69, 78, 97, 108, 120, 133, 147, 163, 181, 200, 221, and
250 m.

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## 2.3 Biological datasets

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Plankton abundance data originated from the Continuous Plankton Recorder (CPR) survey, a long-term programme that has been dedicated to the systematic monitoring of plankton populations within the North Atlantic Ocean and its surrounding seas since 1946. The methodological approach involves the deployment of a high-speed plankton recorder, towed behind merchant vessels at a depth ranging from approximately 7 to 10 m (Warner et al., 1994; Batten et al., 2003). Each recorded abundance value corresponds to the quantification of planktonic species per CPR sample, which represents an approximate filtration volume of 3 m³ of seawater with an average mesh size of 270 µm (Jonas et al., 2004). The CPR provides information on plankton species abundance at an hourly scale, but the dataset used in this study was constrained to daily resolution due to the absence of hourly information in most environmental datasets.

In this paper, we used the daily abundance data collected since 1958 for four species chosen based on their different degree of eurygraphy and distinct biogeography (see Table S1): *Calanus finmarchicus* and *C. helgolandicus* (copepods, life stage CVI), alongside *Odontella sinensis* and *Ephemera planamembranacea* (diatoms). These species were selected based on their documented spatial distributions and their prior inclusion in research endeavours, ensuring the availability of comparative observation values. Although the CPR samples were taken at a depth of approximately 7-10 m, we initialized the model data at the sea surface (i.e., 0 m). This step was performed because the species sampled at 7-10 m could originate from upper depths due to sinking or migrating behaviour, meaning their ecological niche might extend to the surface. To account for the CPR depth of acquisition while still representing these ecological dynamics, we further apply a moving average (Section 2.5).

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## 2.4 Environmental String Model

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Our modeling approach is based on the ESM (Beaugrand, 2024). This is a recent ENM that uses the concept of environmental strings (ESs) (Fig. S2) to elucidate species environment relationships and assess species abundance following 3 steps.

Firstly, the model represents the environmental conditions as ESs in a 2D Euclidean space. Here, an ES is defined as a combination of environmental variables that is as-
sociated with the value of a dependent (response) variable (here species abundance).
This representation of environmental parameters preserves local relationships among
variables and facilitates the condensation of the multidimensional niche space, as per
Hutchinson's niche theory (Hutchinson, 1957), into 2 dimensions. Each ES comprises
nodes corresponding to the number of environmental dimensions considered. Hypothetical
environmental dimensions are depicted in Fig. S2, with certain strings denoting known
levels of species abundance, termed abundance-known ES. In this example, the blue and
yellow strings indicate an abundance level of 0, while the red, green, and black strings
represent abundance levels of 50, 60, and 5, respectively. Conversely, some strings lack
abundance estimations and are referred to as Target ESs (TES, white string in Fig. S2).

Secondly, the algorithm selects ESs that will be used for the estimation of the abundance associated with a TES. A threshold distance *d* is selected to delimit the shaded region, which represents the Target Zone (TZ); within which each node of an ES must be located such as it contributes to the assessment of the abundance of the TES. To quantify this relationship, the average distance between the ESs and the TES is computed across all ecological dimensions. In the example shown in Fig. S2, only ESs within the TZ (e.g., the green and red ESs) are used for abundance estimation, while others, such as the blue, yellow, and black ESs, are excluded.

Finally, the abundance associated with a TES is assessed by means of a weighted average performed on the selected ESs. The average distance of each ES within the TZ is used to weight its influence in estimating the abundance of the TES. A maximum weight *w* is also set. The maximum weight is used when the distance between an ES and the TES is 0. By contrast, the weight is 1 when an ES has a maximum distance with the TES. Therefore, the weight varies between 1 and *w*. As the number of strings increases, the abundance of the TES can be reliably estimated from local ESs. Complete mathematical description of the algorithm is available in Beaugrand (2024).

The surface values of 8 environmental parameters were linked to each CPR sample
before applying the ESM. Different combinations of environmental factors were used to
model phytoplankton or zooplankton, based on ecological mechanisms governing plankton
distribution in the water column. For *Odontella sinensis* and *Ephemera planamembranacea*,
MLD (calculated as MLD minus the depth of estimation, i.e., MLD-0 at the surface) and
PAR were included to represent seasonal and in-depth light limitation (Reynolds, 2006;
Longobardi et al., 2022); Toa was selected to capture species' thermal niche (Beaugrand
et al., 2008); and Nitrates (NO₃), Phosphates (PO₄) and Silicates (Si) were used to reflect
potential nutrient limitation (Hillebrand et al., 2013; Hopkins et al., 2021). To improve
depth-related estimates, additional ESs with PAR = 0 and additional ESs in which the
depth to estimate abundance was greater than the MLD (i.e., MLD - Depth <0, with
the assumption that MLD limits biological production, Sverdrup, 1953), were associated
with a phytoplankton abundance of 0 and included in the original CPR surface training
set. For *Calanus finmarchicus* and *Calanus helgolandicus*, Toa and PAR were selected
based on similar ecological principles as for phytoplankton, while chl *a* served as a proxy
for prey availability (Druon et al., 2019). Bathymetry was used to represent species'
biogeographical limits (Spoel, 1994).

In the ESM, *d* = 0.05 and *w* = 10² were set as input parameters. A *d* value of 0.05 represents a threshold that minimizes the risk of significant estimate absences. A value below 0.05 could result in the ESM failing to find enough ESs to provide an estimate, while a value above 0.2 could lead to very strong smoothing (Beaugrand, 2024; Castant et al., 2024). The parameter *w* was set to 10², which ensures an adequate degree of temporal smoothing. Given the daily resolution, a higher *w* value could have led to excessive variations over close areas.

2.5 Post-processing filters

While bathymetry was included directly for zooplankton in the ESM, it was applied post hoc for phytoplankton because their distributions are more constrained by this parameter (Barnard et al., 2004).

After the application of the ESM, we used a trapezoidal function to consider bathymetry.
The function was categorized into distinct classes: (1) 0-50 m (coastal), (2) 50-100 m
(shallow neritic), (3) 100-200 m (neritic), (4) 200-2000 m (shelf-edge), (5) 2000-4000 m
(shallow oceanic), and (vi) >4000 m (oceanic). We determined the preferred depth ranges
for each species from the visual inspection of the CPR plankton atlas (Barnard et al., 2004;
Beaugrand, 2004). A presence value of 1 was assigned for bathymetry where a species
occurs. Values were then linearly interpolated from 1 to 0 (bathymetry where a species is
rarely observed) at all depths. For *Odontella sinensis*, we identified the bathymetric limits
at a maximum depth of 200 m; values were then linearly interpolated from 200 (value of 1)
to 2000 m (value of 0). For *Ephemera planamembranacea*, we identified the bathymetric
limits at a minimum depth of 2000 m; values were subsequently linearly interpolated from
2000 m (value of 1) to 200 m (value of 0; see Fig. S3).

Temporal smoothing was applied to zooplankton species to account for their longer
development time compared to phytoplankton species (Kumar et al., 1991; Obayashi
et al., 2002). Specifically, an asymmetrical weighted averaging technique incorporating a
linear function placing greater emphasis on more recent observations was applied:
$$A(t) = \frac{\sum_{k=0}^{20} \left(1 - \frac{k}{20}\right) A(t-k)}{\sum_{k=0}^{20} \left(1 - \frac{k}{20}\right)} \quad (1)$$

where A(t) is the weighted average abundance on day t and A(t-k) is the estimated abundance on day t-k, with k ranging from 0 to 20. This technique encompasses the preceding 20 d of species estimations, using linearly decreasing weights from 1 to 0 when k increases (Campbell et al., 2021; Kiørboe, 2024).

Finally, to account for the acquisition depth of the CPR (~10 m) and to integrate upper depth data (from surface to depth of acquisition), an asymmetrical moving average was applied. This asymmetrical moving average calculated the output value as the mean of the 10 m immediately above it (e.g., for a current depth of 20 m, the mean abundances from 10 to 20 m), ensuring that the model accurately reflects the depth range sampled by the CPR and includes plankton originating from shallower depths.

At the end of the procedure, we obtained a final product of species abundance (1958-2022) of 161 latitudes × 561 longitudes × 33 depths × 23741 d (365 d × 65 yr).

## 2.6 Analysis of our reanalyzed CPR-based 4D dataset

### 2.6.1 Model evaluation and comparison with *in-situ* data

The modeled abundances were mapped for the 4 species by averaging all days between 1958 and 2020 within the 0-10 m layer (Fig. 1i-l), then averaged using only the exact dates and locations of the CPR samples (Fig. 1e-h). Both maps were compared with CPR data to assess spatial distribution correspondence with *in-situ* CPR data (Fig. 1a-d), and correlations were calculated between observed and corresponding modeled data.

The uncertainty on the abundance estimation in depth was evaluated using the indicators described in Beaugrand (2024). Four indicators were used: (1) the total number of ES used to provide an estimate for TES, (2) the weighted Standard Deviation (SDw), (3) the weighted Coefficient of Variation (CVw) and (4) the average multivariate distance between ES and TES. The results for CVw and those for the other indicators are illustrated in this study (see Fig. 2 and Fig. S4, respectively). More details about these indicators can be found in Beaugrand (2024).

We also tested our reanalyzed CPR-based 4D dataset using independent *in-situ* data, focusing on temporal patterns, because despite differences in mesh size, the CPR has
been shown to capture similar seasonal trends in abundance when compared to other
sampling methods (Helaouët et al., 2016). Monthly mean data from the U.S. Global
Ocean Ecosystem Dynamics (GLOBEC) Georges Bank programme (data provided by the
Laboratoire d'Océanologie et de Géosciences from Helaouët et al., 2016 study) were used
to compare patterns of *Calanus finmarchicus* observation at depths ranging from 0 to 250
meters with our model outputs patterns (Fig. 3, January 1995 to February 2000; Helaouët
et al., 2016). Data at Georges Bank (Fig. S5) were collected using a Multiple Opening
and Closing Net with Environmental Sensing System (MOCNESS-1) equipped with a
335 µm mesh. Supplementary data for *Calanus finmarchicus* collected in a similar area
(40°N to 47.5°N and 70°W to 56°W) by Dr. D. D. Sameoto utilizing various sampling
devices (the BIONESS sampler, ring nets, and an Icelandic high-speed sampler) were
downloaded from the BioChem: Sameoto zooplankton collection and monthly mean (Fig.
S6, 1967-2006, 0-250 m, Sameoto et al., 2013).

Monthly data (1994-2019) for *Calanus finmarchicus* and *Calanus helgolandicus* in the Skagerrak zone were then digitized from Falkenhaug et al. (2022). The data were collected at the Arendal sampling site, located approximately 1 nautical mile offshore from southern Norway (Fig. S5), using vertical hauls from 50 m to the surface with a standard WP2 net (180 µm mesh size). Correlations were calculated between *in-situ* data and modeled data (Fig. 4). For the English Channel, data on *Odontella sinensis* were compared to the existing literature (Gómez et al., 2010).

## 2.6.2 Long-term changes in phenology and biogeography

### Daily changes in plankton abundance with depth (1965 and 2022)

To understand the impact of climate variability and global climate change on the four
North Atlantic plankton species, we analyzed temporal and depth changes in plankton
distribution within key habitat zones (i.e., North Sea for *C. finmarchicus* and *C. hel-
golandicus*, English Channel for *Odontella sinensis*, and North West Atlantic for *Ephemera
planamembranacea*, Fig. S5). We focused our attention on the years 1965 (relatively
cold, average SST of 9.42°C in the North Atlantic Ocean and its adjacent seas) and 2022
(relatively warm, average SST of 9.95°C in the North Atlantic Ocean and its adjacent
seas), presenting the abundance of species as a function of depth and days, with the
euphotic depth and MLD superimposed (Fig. 5).

### Long-term daily changes in plankton surface abundance (1958-2022)

The 30-day moving average of daily surface abundance of *Calanus helgolandicus* and
*C. finmarchicus* (both in the North Sea), *Ephemera planamembranacea* (North West
Atlantic) and *Odontella sinensis* (English Channel) were analyzed for 1958-2022 (Fig. 6). Daily anomalies were assessed and used to calculate Kendall's τ coefficient, to estimate the long-term trends in species abundance since 1958 (Grossi et al., 2024). In parallel, a standardized Principal Component Analysis (PCA) of annual mean SST data on a 1° spatial scale was performed (Fig. S7). The first principal component, representing the dominant pattern of SST variability, was compared with surface plankton abundance anomalies to investigate potential influences. Finally computed four phenological indices based on the daily abundances of each species: (1) the day when the maximum annual abundance (MAA) is reached, (2) the initiation and (3) termination of the seasonal reproductive period (SRP), respectively the first and the last days of the seasonal species cycle where the abundance is ≥50% of MAA, and (4) seasonal duration, calculated as the number of consecutive days where abundance is ≥50% of MAA (Kléparski et al., 2023; Fig. 6).

## Long-term latitudinal changes in plankton surface abundance in the North Atlantic (1958-2022)

To explore the influence of changes in both hydro-climatic conditions and oceanic circulation as well as climate warming, latitudinal changes in the annual mean abundance of *Ephemera planamembranacea* and *Calanus finmarchicus* were compared with the first 2 principal components of SST (*PC1*<sub>sst</sub> and *PC2*<sub>sst</sub>), the North Atlantic Oscillation (NAO), the Atlantic Multi-decadal Oscillation (AMO) and the North Atlantic Subpolar Gyre index (Gyre<sub>index</sub>) (Fig. 7; Table 1). The Gyre<sub>index</sub> was evaluated using an Empirical Orthogonal Function (EOF) of the Sea Surface Height (SSH) across the North Atlantic subpolar and subtropical gyres (30°to 65°N and 80°to 0°E) between 1994 and 2020 (Hátún et al., 2018; Koul et al., 2019; Kléparski et al., 2024).

To isolate the dominant modes of interannual variability and reduce spatio-temporal noise in our biological time series, a Singular Value Decomposition (SVD, Kutz et al., 2019) was applied to the *E. planamembranacea* and *C. finmarchicus* matrices. Only the first 2 components (i.e., the top 2 singular values and their corresponding singular vectors) were retained for each matrix. This approach approximates the original species abundance matrices and computes new smoothed, dimensionally reduced matrices for better comparisons with climate indices. Linear correlations were calculated between annual surface abundance of *C. finmarchicus* and *E. planamembranacea* with the NAO, AMO, and *PC1*<sub>sst</sub> and *PC2*<sub>sst</sub>. When correlations were calculated in time, the probability of significance of the correlation coefficient was adjusted to account for temporal autocorrelation (Pyper et al., 1998).
Long-term changes in the abundance of *Calanus finmarchicus* in the North Sea as a function of depth

Finally, we investigated long-term changes in *Calanus finmarchicus* abundance in the North Sea across different depths from 1958 to 2022 (Fig. 8). This analysis included monthly anomalies of *C. finmarchicus* abundance at various depths. Kendall τ coefficient was computed on the monthly anomalies since 1958 at each depth (Holland et al., 2023, 2024). Additionally, we also computed Kendall τ coefficient by specific months to discern depth-specific trends and seasonal variations in *C. finmarchicus* populations.

# 3 Results

## 3.1 Model evaluation and comparison with in-situ data

To validate our approach, we first compared modeled and observed CPR abundances of the four species for the sampling period 1958-2020 (Fig. 1). Overall, there was a good correspondence between observed and modeled species abundance, albeit modeled abundances were generally lower than observed ones (~1.3 times smaller). Spatial abundance patterns of *C. finmarchicus* were reproduced well by our approach (r = 0.71, p < 0.05, n = 18517; Fig. 1a, e, i). Observed and modeled distributions of *C. helgolandicus* were found in the eastern North Atlantic, particularly along the European continental shelf. Modeled data overestimated species abundance over Georges Bank, however (Fig. 1b, f, j). Observed and modeled data were significantly positively correlated (r = 0.68, p < 0.05, n = 18517; Fig. 1b *versus* 1f).

Although sparsely distributed, *E. planamembranacea* mainly occurred in the Subarctic Gyre (Fig. 1c). Modeled distribution reproduced well the spatial distribution of the species (Fig. 1g, k). Modeled abundance was also high north of Iceland, but this region lacks comparable CPR samples. The correlation between observed and modeled data was also significant (r = 0.66, p < 0.05, n = 18517; Fig. 1c *versus* 1g). Finally, spatial abundance patterns of *O. sinensis* (mainly found in the shallow regions of the North Sea and the English Channel) were also well correlated with modeled data (r = 0.64, p < 0.05, n = 18517; Fig. 1d, h, l).

To assess model uncertainty along the vertical component, we examined the weighted Coefficient of Variation (CVw) as a function of the weighted mean (Xw) for each species' abundance across six depth intervals (Fig. 2). In the upper layers (0-20 m, Fig. 2a, b, c), *E. planamembranacea* and *O. sinensis* displayed relatively high CVw (>100%) when abundances were low to moderate (log₁₀(x+1) scale: 0 to 1). Expectedly (CVw = SDw/Xw), CVw decreased sharply once both phytoplankton abundances rose. *C*.
<figure><img src="image_2.png" /><figcaption>Fig. 1: Mean spatial distribution of observed (a-d) and modeled (Mean of 0-10 m, e-l) abundance of four plankton species in the North Atlantic (latitudes 40°N-80°N, longitudes 100°W-40°E) *C. finmarchicus* (a, e, i), *C. helgolandicus* (b, f, j), *E. planamembranacea* (c, g, k), and *O. sinensis* (d, h, l). Observed data are shown in a-d, while the corresponding modeled data are presented in e-h. Average gridded modeled values are depicted for the period 1958-2020 in i-l. Red rectangles highlight regional zones used in the study for further analyses (Georges Bank, North West Atlantic, English Channel and North Sea). The red cross represents the Skagerrak location.</figcaption></figure>
<figure><img src="image_3.png" /></figure>

Fig. 2: Weighted Coefficient of Variation (CVw, %) derived from the Environmental String Model (ESM) outputs, with CVw averaged at each abundance value (Log₁₀(x+1)) from 0 to 5, in increments of 0.01, for *Calanus finmarchicus* (blue line), *Calanus helgolandicus* (orange line), *Ephemera planamembranacea* (yellow line), and *Odontella sinensis* (purple line). Panels (a)-(f) show results at 0 m, 10 m, 20 m, 40 m, 50 m, and 60 m depth, respectively. The y-axis is presented on a Log₁₀ scale.

*finmarchicus* and *C. helgolandicus* exhibited lower CVw values across the entire abundance range in the surface layers compared to phytoplankton species, with CVw also decreasing when abundances were highest. Deeper in the water column (40-60 m, Fig. 2d, e, f), all species showed narrower abundance ranges. At those depths, phytoplankton CVw dropped to near zero likely due to PAR limitationwhile zooplankton CVw remained higher and stable.

We compared the monthly climatological patterns of CPR observations, CPR-based model outputs, and GLOBEC *in-situ* observations of *C. finmarchicus* abundance for the period 1995-2000 (Fig. 3). Observed and modeled CPR-based monthly changes in surface abundance were significantly and positively correlated (r = 0.9, p < 0.05, n = 12; Fig. 3a). Modeled *C. finmarchicus* exhibited high abundance from April to May while observed data remained high until June. *Calanus finmarchicus* exhibited highest modeled abundance patterns near the surface (0-30 meters) from March to June, with abundances
diminishing with depth. Abundances remained low from July to November (Fig. 3b). Despite a higher magnitude (approximately 3-fold), patterns in GLOBEC observations indicated surface-concentrated abundance during spring, with a noticeable peak near the surface, showing a similar pattern to the ESM-CPR based modeled data (Fig. 3c). Although some discrepancies were found from September to November (i.e., much higher abundance observed in the GLOBEC data), a significant positive correlation was found between the two datasets (r = 0.64 p < 0.05, n = 2202, Fig. 3b *versus* Fig. 3c).

When exploring the correlations at each depth (Fig. 3d), the substantial significant positive correlations found at various depth further highlight the model's ability to capture the vertical distribution of *C. finmarchicus*. Some discrepancies remained, however, especially below 200 m due to the absence of *in-situ* observations after July, as well as in the surface layers due to a peak in abundance occurring in autumn in the *in-situ* observations, though this peak was not observed in the *C. finmarchicus* monthly climatology (1967-2006) from the Sameoto collection in a similar area (Fig. S6).

We then compared monthly climatologies of the CPR-based modeled and observed abundances for *Calanus finmarchicus* and *C. helgolandicus* in the Skagerrak region (1994-2019, 0-50 m depth, Fig. 4). *Calanus finmarchicus* was most abundant from April to May, with peak abundance near the surface from February to June. *C. helgolandicus* had peak abundances from June to October, with surface concentrations highest in July and September. Both species exhibited higher abundances near the surface and a general decrease with depth, with the observed peak occurring approximately one month earlier than the mean modeled peak, likely reflecting differences in sampling method and spatial resolution. Despite this lag, there was good correspondence between observed and modeled abundances, especially when comparing the observations with the maximum modeled abundance (r = 0.81, p < 0.05, n = 12 for *C. finmarchicus*, r = 0.71, p < 0.05, n = 12 for *C. helgolandicus*) compared with mean modeled abundance (r = 0.52, p < 0.05, n = 12 for *C. finmarchicus*, r = 0.74, p < 0.05, n = 12 for *C. helgolandicus*).

## 3.2 Long-term changes in abundance

In this section, we examined long-term changes in plankton abundance in different regions of the North Atlantic Ocean and its adjacent seas (Georges Bank, North West Atlantic, English Channel and North Sea; Fig. 1; Fig. S5), focusing on modeled changes (i) in depth distribution *versus* months for 1965 (cold year) and 2022 (warm year) (Fig. 5), (ii) long-term phenological shifts (Fig. 6), (iii) long-term latitudinal shifts (Fig. 7) and (iv) long-term changes in depth distribution (Fig. 8).
<figure><img src="image_4.png" /><figcaption>Fig. 3: Comparisons of observed and modeled patterns of monthly standardized abundance in depth of *Calanus finmarchicus* over Georges Bank (1995-2000). (a) Monthly changes in surface (0-10 m) modeled abundance (red line) in relation with GLOBEC *in-situ* (blue line) and CPR observations (black line). (b) Modeled monthly changes in the depth abundance of *C. finmarchicus* from our ESM-CPR framework (0-250 m). (c) Monthly changes in observed GLOBEC-based depth abundance of *C. finmarchicus* (0-250 m). (d) Correlation coefficients between depth-specific GLOBEC-based and ESM-based monthly abundance. Red bars indicate statistically significant correlations (p < 0.05), while blue bars indicate non-significant correlations.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 4: Monthly changes in the abundance of *Calanus finmarchicus* and *C. helgolandicus* in Skagerrak (1994-2019, 0-50 m). Monthly changes in the mean (red line) and maximum (red dotted line) standardized modeled abundance in relation with *in-situ* observed standardized abundance (black line) of *C. finmarchicus* (a) and *C. helgolandicus* (c). Monthly changes in modeled depth abundance of *C. finmarchicus* (b) and *C. helgolandicus* (d).</figcaption></figure>

i) Daily changes in plankton abundance with depth (1965 and 2022)

The first analysis examined seasonal changes in depth distribution for 1965 and 2022 (Fig. 5). In 1965, the highest abundance of *C. finmarchicus* in the North Sea occurred at subsurface from April to October, with a peak in May. Minimal abundance was found from November to March (Fig. 5a). Despite similar surface peak abundance in May between the two years, a strong decrease in abundance was found at all depths in 2022 compared to 1965. In 2022, the phenological shift appeared at all depths with lower species abundance extending from June to December (Fig. 5e, I), with a depletion in the surface layers (minimal abundance found in August at 20 m) compared to 1965 (in August at 40 m).
<figure><img src="image_6.png" /></figure>

Fig. 5: Comparison of mean daily modeled abundance of *C. finmarchicus* (North Sea, a, e), *C. helgolandicus* (North Sea, b, f), *E. planamembranacea* (North West Atlantic, c, g) and *Odontella sinensis* (English Channel, d, h) for 1965 (a-d) and 2022 (e-h). Panels (i-l) illustrate the differences in mean daily abundance between 1965 and 2022 for each species. White dotted line represents the daily average Mixed Layer Depth (MLD), white line represents euphotic depth limits where Photosynthetically Active Radiation (PAR) reaches 1% of its surface value, and the red line represents PAR thresholds of 0.1 W.m⁻².

In 1965, the highest abundance of *C. helgolandicus* in the North Sea was found in the first 20 meters from July to October. The species began to appear at depth from July and gradually moved towards the surface by October, maintaining a consistent abundance at depth throughout this period (Fig. 5b). In 2022, a change in depth distribution associated with an increase in abundance towards the surface was modeled (Fig. 5f, j). A strong decline was observed in May-June below 40 m, contrasting with the deeper distribution of *C. finmarchicus* during the same period.

Phytoplanktonic modeled abundances highlighted a summer peak of *E. planamem-***
branacea (Fig. 5c, g) and an autumn-winter peak of *O. sinensis* (Fig. 5d, h) in the
North West Atlantic. The abundance of *E. planamembranacea* in the North West Atlantic
increased and exhibited an earlier phenology associated with a deepening between 1965
and 2022 (Fig. 5k). An expansion of the surface phenology (i.e., earlier onset and later
end of surface presence) also occurred between the two years for *O. sinensis* in the English
Channel, associated to in-depth changes with a decrease in abundance from late August
to October and an increase from October to November (Fig. 5l).

ii) Long-term daily changes in plankton surface abundance (1958-2022)

The second analysis examined the long-term (1958-2022) phenological shifts shown in Fig. 5 in the four species, focusing on the surface layer (Fig. 6). A moving average was used to reveal these clear shifts (see Section 2.6.2). Long-term phenological changes in both Calanus species were opposite (Fig. 6a, e). The calanoid *C. finmarchicus* showed a phenological contraction, due to an earlier termination of its Seasonal Reproductive Period (SRP) resulting in a decrease in its seasonal duration after the late 1990s (Fig. 6c) associated to a strong decrease in its surface abundance (Fig. 6b). Conversely, *C. helgolandicus* exhibited a phenological expansion with an increase in both daily abundances (Fig. 6f) and seasonal duration (Fig. 6g) for the same period. These trends for both copepod daily abundances were highlighted by the Kendall τ coefficient, with clear inverted τ coefficients (negative for *C. finmarchicus* and positive for *C. helgolandicus*) throughout the year (Fig. 6d, h). The first principal component of SST suggested an inverse correlation between SST anomalies and abundance for *C. finmarchicus* and a positive correlation for *C. helgolandicus* (Fig. 6b, f). Phenological changes were also noted in phytoplankton species, with *Ephemera planamembranacea* showing earlier SRP initiation and earlier Maximum Annual Abundance (MAA) at the beginning of summer, associated to shorter seasonal duration (Fig. 6i, k). The daily anomalies and the associated Kendall τ coefficient also identified these phenological shifts (Fig. 6j, l), whose timing corresponds to a pronounced increase in SST after the 90s. Modeling of *Odontella sinensis* indicated an autumn phenology and increased maximum surface abundance after the late 1990s (Fig. 6m, n, p), but no clear changes in the seasonal duration were observed (Fig. 6o). These predictions aligned well with the observations of Gomez and Souissi (2010). Long-term changes in daily abundance of *O. sinensis* during peak bloom (October-December) paralleled changes in the first principal component of SST (Fig. 6n).

iii) Long-term latitudinal changes in plankton surface abundance in the North Atlantic (1958-2022)

The third analysis focused on long-term latitudinal shifts in surface abundance of *C.*
<figure><img src="image_7.png" /><figcaption>Fig. 6: Long-term (1958-2022) monthly changes in modeled surface abundance (a, e, i, m), associated daily anomalies with the first principal component of sea surface temperature (SST, black line, see Fig. S7) superimposed (b, f, j, n), Seasonal Reproductive Period (SRP), determined by the initiation and termination of the SRP, with anomaly-based colouring and black points marking the Maximum Annual Abundance (MAA, c, g, k, o), and significant (p < 0.05) Kendall τ coefficients based on daily anomalies (d, h, l, p) for *C. finmarchicus* (North Sea, a-d), *C. helgolandicus* (North Sea, e-h), *E. planamembranacea* (North West Atlantic, i-l), and *O. sinensis* (English Channel, m-p). The first principal component of SST is superimposed to abundance anomalies (black line, see Fig. S7).</figcaption></figure>

*finmarchicus* and *E. planamembranacea* over a latitudinal transect at 30°W. A smoothing of the abundance was carried out by Singular Value Decomposition (SVD) using the first two SVD modes to highlight patterns of variability and changes in abundance (See Section 2.6.2). Modeled long-term latitudinal changes in surface abundance exhibited distinct latitudinal patterns and year-to-year variability (Fig. 7). Higher abundances were predominantly observed between latitudes 50°N and 65°N, which reflects the spatial distribution of the species (Fig. 1). There was a marked increase in abundance from the 1960s to the early 1990s, followed by a decline in abundance starting in the late 1990s (Fig. 7a-c). Mean yearly abundances were compared with the second principal component of SST (Fig. 7c), the NAO and the AMO (Fig. 7d, Table 1).

Long-term changes in the first principal component of SST were not significantly correlated with the abundance patterns of *C. finmarchicus* and *E. planamembranacea* (Table 1). In contrast, the second principal component of SST was significantly and negatively correlated with the abundance of *C. finmarchicus* (r = -0.43, p < 0.05, n = 65), highlighting the
<figure><img src="image_8.png" /><figcaption>Fig. 7: Reconstructed annual surface abundance of (a) *C. finmarchicus* and (b) *E. planamembranacea* over a latitudinal transect using two first Singular Value Decomposition (SVD) modes (1958-2022, latitudes 40°N-65°N, longitude = 30°W); (c) mean standardized reconstructed annual abundance along this transect for *C. finmarchicus* (red line) and *E. planamembranacea* (blue line), compared with the inverted second principal component of Sea Surface Temperature (SST, black line, see Fig. S7) and (d) the relationship between the North Atlantic Oscillation (NAO, purple line), and the Atlantic Multidecadal Oscillation (AMO, green line).</figcaption></figure>
<table><caption>Table 1: Correlations with a p significance threshold of 0.05 (*) between annual surface abundance of *C. finmarchicus* and *E. planamembranacea* (1958-2022, latitudes 40°N-65°N, longitude 30°W) with the North Atlantic Oscillation (NAO), the Atlantic Multidecadal Oscillation (AMO), and the two Principal Components (PC) of Sea Surface Temperature (SST). Correlations with a one-year lag (t+1) for *Calanus finmarchicus* and *E. planamembranacea* are also presented</caption><thead><tr><th></th><th>PC1SST</th><th>PC2SST</th><th>NAO</th><th>AMO</th><th>Gyre<sub>index</sub><br>(1994-2020)</th></tr></thead><tbody><tr><td><em>C. finmarchicus</em></td><td>-0.38</td><td>-0.43 *</td><td>0.18</td><td>-0.40</td><td>-0.77 *</td></tr><tr><td><em>C. finmarchicus</em><sub>(t+1)</sub></td><td>-0.46</td><td>-0.82 *</td><td>0.62*</td><td>-0.57 *</td><td>-0.60 *</td></tr><tr><td><em>E. planamembranacea</em></td><td>-0.53</td><td>-0.21</td><td>0.14</td><td>-0.46</td><td>-0.46 *</td></tr><tr><td><em>E. planamembranacea</em><sub>(t+1)</sub></td><td>-0.58</td><td>-0.46 *</td><td>0.46*</td><td>-0.59 *</td><td>-0.29</td></tr><tr><td>PC1SST</td><td></td><td></td><td>0.06</td><td>0.84*</td><td>0.41</td></tr><tr><td>PC2SST</td><td></td><td></td><td>-0.60 *</td><td>0.24</td><td>0.51</td></tr><tr><td>NAO</td><td></td><td></td><td></td><td>-0.30 *</td><td>-0.31</td></tr><tr><td>AMO</td><td></td><td></td><td></td><td></td><td>0.23</td></tr></tbody></table>

influence of short-term, regionally specific SST variability in regulating the abundance of this species. This correlation was not significant for *E. planamembranacea* (r = -0.21, p > 0.05, n = 65) (Table 1). Interestingly, with a one-year lag (i.e., SST changes occurred one year before changes in plankton), the second principal component of SST showed a much stronger negative correlation with the abundances of both species: *C. finmarchicus* (r = -0.82, p < 0.05, n = 65) and *E. planamembranacea* (r = -0.46, p < 0.05, n = 65) (Fig. 7c).

Regarding climate indices, the AMO was highly correlated with the first principal component of SST (r = 0.84, p < 0.05, n = 65), whereas the NAO was inversely correlated with the second principal component of SST (r = -0.60, p < 0.05, n = 65) (Table 1). These relationships were also evident when comparing these indices directly with species abundances (Fig. 7d). *C. finmarchicus* abundances with a one-year lag presented significant correlations with the NAO (r = 0.62, p < 0.05, n = 65) and the AMO (r = -0.57, p < 0.05, n = 65) as for *E. planamembranacea* abundances with a one-year lag, which presented significant correlations with the NAO (r = 0.46, p < 0.05, n = 65) and the AMO (r = -0.59, p < 0.05, n = 65). Furthermore, the gyre index, when compared with species abundances between 1994 and 2020, showed significant negative correlations without any lag for *C. finmarchicus* (r = -0.77, p < 0.05, n = 65) and *E. planamembranacea* (r = -0.46, p < 0.05, n = 65).

iv) Long-term changes in the abundance of *Calanus finmarchicus* in the North Sea as a function of depth

Finally, building on the depth changes introduced in Fig. 5, the fourth analysis
<figure><img src="image_9.png" /><figcaption>Fig. 8: (a) North Sea monthly anomalies of *C. finmarchicus* abundance at different depths from 1958 to 2022, (b) significant (p < 0.05) Kendall τ coefficient computed on monthly anomalies for each depth and (c) significant (p < 0.05) Kendall τ coefficient computed for each month and depth.</figcaption></figure>

explored long-term trends in *C. finmarchicus* in the North Sea (Fig. 8). Although substantial year-to-year variability in monthly anomalies occurred during 1958-2022 (Fig. 8a), negative monthly anomalies were more prominent after the end of the 1990s and conversely. Interestingly, Kendall τ coefficient calculated for the entire period (1958-2022) at each depth (0-80 m) revealed a partial compensation of the strong negative surface trend below 30 m (Fig. 8b). At a monthly scale, our reanalyzed CPR-based dataset suggests a compensation of the negative surface trend below 30 m in spring (Fig. 8c). In summer, the decreasing pattern extended to deeper depths. Once again, the maximum negative Kendall τ coefficients were more pronounced compared with the positive ones.

# 4 Discussion

Ecosystems are complex systems with a high level of dimensionality, which means that a high number of environmental variables influence their dynamic equilibrium and their inherent variability (Parrott, 2010; Kléparski et al., 2022). In addition, pelagic ecosystems are structured according to the horizontal (i.e., meridional and zonal), vertical and temporal (from daily to multidecadal) dimensions. Although the horizontal and temporal dimensions are well covered by programmes such as the CPR survey, the vertical dimension is frequently forgotten despite its potentially high importance in terms of
climate mitigation by pelagic species (Perry et al., 2005).

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Here, we propose a framework to assess the abundance of plankton along the vertical dimension through the concept of the ecological niche sensu Hutchinson (1957). We used information on the horizontal and temporal dimensions to characterize the niche of four plankton species and subsequently assess their vertical abundance based on a large number of environmental variables. In addition, our framework is also applied to assess abundance at a daily scale, reanalyzing the CPR dataset for the four species. Therefore, for the first time, we propose a full dataset with information on the abundance of four CPR-monitored plankton species at a daily temporal resolution from 1958 to 2022 along the horizontal and vertical (0-250 m) dimensions. The approach used here can be extended to all species relatively well sampled by the CPR survey (Barnard et al., 2004; Beaugrand, 2004). These datasets are intended to be regularly updated and are available upon request to the authors prior to their deposition in a public repository.

Our dataset should be used with caution, however, because it is based on a number of assumptions and has some potential limitations. Firstly, we assumed that the ecological (dimensionless) information along the horizontal and time dimension can be used to reconstruct abundance along the vertical dimension. Studies have already explored how surface chlorophyll-a concentrations infer the distribution and composition of phytoplankton communities within the euphotic zone (Morel et al., 1989; Uitz et al., 2006), making it reasonable that surface patterns may to some extent reflect subsurface distributions. Although we expect environmental parameters that affect the abundance to behave in the same way along the vertical dimension, we did not use information on pressure and we thereby assumed that for the 0-250 m depth, there was no influence of pressure on the four species. This assumption is supported by inconsistent pressure response observations, although we acknowledge the need for further testing any potential pressure effects on plankton (Rice, 1962; Lincoln, 1971; Zarubin et al., 2016).

Secondly, we chose to calibrate our model with environmental data at the surface level despite the fact that actual sampling of the CPR occurs at ~7-10 meters. This choice was made because we assumed that plankton collected at that depth is highly dependent upon plankton abundance above ~7-10 m. It is clear, however, that our estimates can be influenced by this choice because it affects strongly parameters such as PAR. Third, as it is always the case in modeling when thresholds are used (Vallina et al., 2014), differences in abundance estimates may arise from the choice of our thresholds. For example, using a linear model that considers the preceding 20 days of zooplankton species does not accurately reflect the diverse life cycles of different zooplankton species, which can vary greatly and are strongly influenced by temperature (Campbell et al., 2001; Kiørboe, 2024).
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A more detailed approach, incorporating species-specific and temperature-dependent variations, would likely produce more accurate results, but we considered this 20-day lag as a good average of the life dynamics of the two Calanus investigated in this study.

Limitations also originate from the CPR programme itself. The CPR survey often underestimates abundance (Owens et al., 2013). Although it samples well copepods and zooplankton of similar size spectrum, the CPR survey underestimates smaller and larger organisms such as tintinnids and fish larvae. Furthermore, it is not well adapted to delicate gelatinous plankton (Batten et al., 2003; Jonas et al., 2004) and provides only semi-quantitative phytoplankton data, leading to potential undersampling of smaller phytoplankton due to the average mesh size of 270 µm (Jonas et al., 2004; Edwards et al., 2006; Richardson et al., 2006). Should our approach be used on such plankton organisms, caution is advised. The CPR data has been traditionally analyzed on a monthly basis, even though it provides information down to the hourly scale (Beaugrand et al., 2001), though the hourly information was not available in the provided dataset. This preference for monthly analysis arises from the nature of the sampling process; one tow is sampled monthly but because several tows can cross in some areas (e.g., North Sea), it is possible to have more than one sample per month. Information on the day of sampling is not uniformly distributed through the month. CPRs are dispatched to harbours at the beginning of the month with a request for towing as soon as feasible (David Johns, Sir Alister Hardy Foundation for Ocean Science, Plymouth, personal communication). Consequently, more samples are collected around the middle of the month (Fig. S8). Moreover, the reliance on daily mean abundances may have influenced our estimates depending on the global temporal coverage throughout the day, which may restrict our interpretations for species characterized by pronounced Diel Vertical Migration or deeper distributions (Hays, 1996; Brierley, 2014). Despite these limitations, the vast majority of plankton sampled by the CPR survey effectively reflects significant changes in abundance, distribution, and composition (Edwards et al., 2006).

The model we used is a recently developed ENM (Beaugrand, 2024; Castant et al., 2024). The ESM is particularly well-suited when a large number of environmental variables are used because it maintains the local relationships among environmental dimensions, unlike models that rely on global correlation or regression coefficients (Beaugrand, 2024). Furthermore, the ESM addresses the issue of model transferability by providing estimates only when corresponding environmental strings exist in the new area. It averages local abundance values weighted by the total distance between environmental strings and the target environmental string. We chose a relatively low maximum weight w within the ESM (see Section 2.4) to deal with the CPR data to balance noise reduction and the risk of numerical artifacts that may arise from daily-scale computations (Beaugrand, 2024;
Castant et al., 2024).

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The ESM also provides indicators to assess uncertainty and evaluate the robustness of estimated abundance (Beaugrand, 2024). CVw computed for the four species suggests that the model yields more accurate estimations for zooplankton than for phytoplankton (Fig. 2). The CVw remained high at lower abundance levels but declined rapidly as abundance increased, which suggests higher uncertainty in predicting abundance under environmental conditions near the species' tolerance limits and demonstrates greater confidence in forecasting high abundance scenarios under more suitable conditions. The stable level of uncertainty observed throughout the water column for zooplankton species is likely due to less in-depth variability in resource availability.

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Although it was difficult to find data to test our reanalyzed CPR-based 4D dataset, we compared it with: (i) independent in-depth *in-situ C. finmarchicus* data from the GLOBEC programme and from the Sameoto zooplankton collection in Georges Bank, (ii) independent mean over depth *in-situ C. finmarchicus* and *C. helgolandicus* data from the Arendal station at Skagerrak, and (iii) the existing literature on *O. sinensis* in the English Channel. Despite differences in the abundances of *C. finmarchicus* sampled from GLOBEC and those estimated from the ESM, the comparison between the two datasets exhibited similar trends with a strong correlation in depth over monthly climatologies, which was also observed with our ESM-based estimations compared to Sameoto *C. finmarchicus* collection (Fig. 3, Fig. S6). The comparison with Arendal station highlighted similar patterns between observations and our estimations for both calanoids (Falkenhaug et al., 2022; Fig. 4), despite the challenge of comparing a single plankton sampling station with larger spatial estimates as it is unlikely to represent the entire community and cannot be directly combined into a single time series (McQuatters-Gollop et al., 2015). For *O. sinensis*, our estimates aligned with observed patterns from Gomez and Souissi (2010), who also reported higher abundances at depth. Together, these three comparisons increased our confidence in the ability of the reanalyzed CPR-based 4D dataset to help explore planktonic species distributions in all dimensions.

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Investigating species phenology and distributional range as well as their alterations through time from the CPR survey is challenging. Despite being the world's largest marine plankton monitoring programme, the survey only collects ~5000 samples every year, which remains limited at the scale of the North Atlantic Ocean and its adjacent seas. Our reanalysis of the CPR dataset enables a better investigation of phenological and biogeographical shifts (i) because species abundances are re-estimated at a relatively high spatial and temporal resolution and (ii) because the reanalyzed dataset includes information on depth (0-250 m). Horizontal, vertical, and temporal dimensions are closely
imbricated and structurally interlinked as changes in the environmental regime (including climate) propagate across these dimensions. Although many studies have focused on some of these key dimensions, they have rarely considered them jointly. Our reanalyzed dataset reconstructed well the phenology and the distributional range of species of the four plankton species (Fig. 1), as well as their shifts through time (Fig. 6) and depth (Fig. 5, 8).

We found latitudinal changes in *C. finmarchicus* and *E. planamembranacea* in the North West Atlantic to correlate well with the state of the Atlantic subarctic gyre (Table 1). Interestingly, strong correlations were found between long-term latitudinal changes in species abundances and PC2SST, the state of NAO and the AMO with a one-year lag (i.e., changes occurred one year before changes in plankton). This implies that large-scale atmospheric forcing, which have distinct impacts on regional and local hydrodynamics and play an important role in the interannual dynamics of plankton species (Drinkwater et al., 2003; Stenseth et al., 2003; Goberville et al., 2014), may also significantly drive the next year's variability in plankton species dynamics. Our analyses also detected well the influence of the Atlantic cold blob on the abundance of *C. finmarchicus* and *E. planamembranacea* (Fig. 7). This event, associated with a strong winter cooling over the Labrador-Irminger Seas during the winters between 2013 and 2015, resulted in exceptional deep convection (Piron et al., 2017; Yashayaev et al., 2017; Sanders et al., 2022). This phenomenon, evident in our SST anomalies, should have boosted production in several ecosystem components between 2014 and 2016 (Hátún et al., 2017), as retrieved in the abundance of *C. finmarchicus* and *E. planamembranacea* along the latitudinal transect.

Strong alteration in phenology have been revealed by our analyses (Fig. 6). In the North Sea, *C. helgolandicus* and *C. finmarchicus* exhibited contrasting shifts in abundances and phenology. This discrepancy is likely attributable to the differing thermal tolerances of the two species. *C. finmarchicus*, a cold-water species, is highly decreasing, especially after the end of summer, reducing considerably its Seasonal Reproductive Period (SRP). This led *C. helgolandicus*, which can tolerate warmer temperatures, to expand into surface areas formerly dominated by *C. finmarchicus* and to extend its SRP (Bonnet et al., 2005; Helaouët et al., 2007; Holland et al., 2024). In depth, *C. finmarchicus* is predominantly located above the thermocline, whereas *C. helgolandicus* is found below it. This separation is consistent with previous observations in the North Sea (Williams, 1985; Jónasdóttir et al., 2011). The decline of *C. finmarchicus* potentially allows *C. helgolandicus* to have an earlier surface blooming season in the North Sea by migrating above the thermocline, while still maintaining high abundance at greater depths.

Changes in the phenology of *E. planamembranacea* have also been observed, with earlier timing of the bloom being noted in the North West Atlantic. As for zooplankton
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species, temperature is also a key component for phytoplankton species (Chivers et al., 2020) and higher temperatures are often thought to induce these earlier blooms by causing thermal stratification, resulting in high light intensities in a shallow mixed layer (Sverdrup, 1953; Sommer et al., 2012; Hieronymus et al., 2024). However, in the North West Atlantic, the primary driver for earlier phytoplankton blooms could be enhanced light availability (Yamaguchi et al., 2022). In our model, these parameters (i.e., Temperature, MLD and PAR) greatly affect the depth distribution of phytoplankton species, especially with the additional strings of abundance set to 0 when PAR = 0 and when the depth to estimate abundance was greater than MLD (see Section 2.4). This explains why a summer species like *E. planamembranacea*, which thrives in high PAR conditions, shows a rapid decrease in abundance with depth compared with *O. sinensis*, which is more prevalent in autumn and winter. Finally, phenological expansions were observed for *O. sinensis* in the English Channel. These expansions may have been favoured by the anomalous meteorological conditions in the warmer season in recent years, especially after mixing events that introduced nutrient inputs (Gómez et al., 2010).

Depth adjustment in time of planktonic species is challenging to study, primarily due to the lack of long-term *in-situ* or modeled data focusing on deeper water layers. To explore this aspect, our research focused on *C. finmarchicus* in the North Sea. At greater depths, there is evidence of increased abundance, especially during spring, which is the peak season for *C. finmarchicus* (Fig. 8). This pattern indicates that the species may adjust to deeper waters where environmental conditions are more suitable as a response to changing surface conditions. Recent research has suggested that *C. finmarchicus* migrates to higher latitudes where temperatures are cooler (Chust et al., 2014). Our study suggests that a similar adjustment may be occurring vertically, with *C. finmarchicus* moving to deeper water layers as surface conditions become less suitable, as suggested by Jónasdóttir & Koski (2011). This vertical shift may be another strategy through which the species might cope with changing environmental conditions. The positive trends observed are much less pronounced compared with the significant negative trends at the surface. This disparity suggests that, while depth adjustment might offer some relief, it is not sufficient to counteract the overall decline of *C. finmarchicus*, as suggested by Helaouet et al. (2016). However, *in-situ* observations are needed to confirm our conclusions and to clarify how changing vertical dynamics influence behaviours such as Diel Vertical Migration and diapause, and what implications these changes may have for marine trophic interactions.
## 5 Conclusion

For the first time, we build a comprehensive dataset on the daily abundance of four plankton species from 1958 to 2022 across both horizontal and vertical dimensions. Our study demonstrates that understanding the complex interconnections between various dimensions in the distribution of planktonic species is crucial for grasping changes in species abundance. The methods we developed have the potential to be extended to many plankton species monitored by the CPR survey, offering pivotal insights into tracking shifts in planktonic species distributions across time and depth, exploring marine ecosystem resilience, and shaping effective global conservation strategies.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

The authors gratefully acknowledge the Centre National d'Etudes Spatiales (CNES) and l'Université du Littoral Côte d'Opale (ULCO) for co-funding the doctoral scholarship of J. Castant. We also thank the CPER programme IDEAL and the graduate school IFSEA that benefits from grant ANR-21-EXES-0011 from the French National Research Agency, under the Investments for the Future Programme. This work was also funded by the TOSCA/CNES program. Finally, some experiments presented in this paper were carried out using the CALCULCO computing platform, supported by SCOSI/ULCO (Service COmmun du Système d'Information de l'Université du Littoral Côte d'Opale).

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<figure><img src="image_10.png" /><figcaption>Fig. S1: Conceptual scheme summarizing the methodology.</figcaption></figure>
<figure><img src="image_11.png" /></figure>

Fig. S2: Schematics showing how the Environmental String Model (ESM) works with the abundance-known Environmental String (yellow, blue, green, red and black strings, ES) and the abundance-unknown ES or Target Environmental String (white string, TES). The grey zone represents the Target Zone (TZ) which defines the environmental range that the nodes of each ES must fall within (here, the green and red strings) in order to be used to estimate the abundance at the TES. Adapted from Beaugrand (2024).

<figure><img src="image_12.png" /></figure>

Fig. S3: Trapezoidal functions used as bathymetric filter for the diatoms (a) *Ephemera planamembranacea* and (b) *Odontella sinensis*.
<figure><img src="image_13.png" /><figcaption>Fig. S4: (a-c) Weighted Standard Deviation, (d-f) Average multivariate distance and (g-h) total number of Environmental Strings (ES) indicators derived from the Environmental String Model (ESM) outputs averaged at each abundance value (Log10(x+1)) from 0 to 5, in increments of 0.01, for *Calanus finmarchicus* (blue line), *Calanus helgolandicus* (orange line), *Ephemera planamembranacea* (yellow line), and *Odontella sinensis* (purple line) at 0 m (a, d, g), 20 m (b, e, h) and 50 m depth (c, f, i).</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>Fig. S5: Zones (red squares) and location (blue cross) selected for this study: Georges Bank (GB), the North West Atlantic (NWA), The North Sea (NS) and Skagerrak.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Fig. S6: Comparisons of observed (blue line) and modeled (red line) patterns of monthly standardized abundance in depth of *Calanus finmarchicus* (40°N to 47.5°N and 70°W to 56°W, 1967-2006, 0-250 m, Sameoto et al., 2013).</figcaption></figure>
<figure><img src="image_16.png" /></figure>

Fig. S7: Principal Component Analysis (PCA) of long-term changes in SST in the North Atlantic Ocean. (a) First eigenvector (maps) and associated Principal Component (PC) (in black). (b) Second eigenvector and associated PC (in black).

<figure><img src="image_17.png" /></figure>

Fig. S8: Mean number of Continuous Plankton Recorder (CPR) samples per day (1958-2020)
1079 Table

<table><caption>Table S1: Comparative summary of biological and ecological traits of selected species in the areas considered in our study.</caption><thead><tr><th>Species</th><th>Ephemera planamembranacea</th><th>Odontella sinensis</th><th>Calanus finmarchicus</th><th>Calanus helgolandicus</th></tr></thead><tbody><tr><td>Taxonomic Group</td><td>Diatom</td><td>Diatom</td><td>Copepod</td><td>Copepod</td></tr><tr><td>Reproduction</td><td>Limited reproductive data</td><td>Rapid cell division under nutrient-rich conditions; capable of up to 2 divisions per day</td><td>Egg production at surface after diapause during peak bloom, with intensity depending on food availability</td><td>Egg production affected by seasonality, chlorophyll-a and latitude, all partially modulated by body size</td></tr><tr><td>Environmental Tolerance</td><td>Thrives in high-nutrient, shallow mixed-layer and low-Si/N stratified waters</td><td>Tolerant to a wide range of environmental conditions, including light and nutrients</td><td>Sub-arctic waters; sensitive to temperature and food availability</td><td>Warm-temperate waters; tolerant to a wide range of seasonal variations</td></tr><tr><td>Bloom Timing</td><td>Early summer (e.g., June)</td><td>Autumn to winter (e.g., Nov/Dec)</td><td>Spring (e.g., April-June)</td><td>Late spring to autumn (e.g., May-October)</td></tr><tr><td>Distribution Zone</td><td>Sub-Arctic &amp; North West Atlantic; some observations in NE Atlantic temperate waters</td><td>Temperate coastal regions (e.g., English Channel, North Sea)</td><td>Sub-arctic to North Atlantic; common in Labrador Sea, Norwegian Sea as well as in the North Sea</td><td>Temperate NE Atlantic; common in Celtic Sea, English Channel, southern North Sea</td></tr><tr><td>References</td><td>Semina, 1997; Yallop, 2001; Fragoso et al., 2016</td><td>Yallop, 2001; Gollasch, 2009; Gómez et al., 2010</td><td>Planque et al., 2000; Melle et al., 2014; Eysteinsson et al., 2018; Falkenhaug et al., 2022</td><td>Beaugrand et al., 2002; Bonnet et al., 2005; Falkenhaug et al., 2022</td></tr></tbody></table>

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