# Mistaking plastic for zooplankton: Risk assessment of plastic ingestion in the Mediterranean sea

S. Fabri-Ruiz, A. Baudena, F. Moullec, F. Lombard, J.-O. Irisson, M.L.

Pedrotti

► To cite this version:

S. Fabri-Ruiz, A. Baudena, F. Moullec, F. Lombard, J.-O. Irisson, et al. Mistaking plastic for zooplankton: Risk assessment of plastic ingestion in the Mediterranean sea. Science of the Total Environment, 2023, 856 (Part 2), pp.159011. 10.1016/j.scitotenv.2022.159011. hal-03959364

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**Fabri-Ruiz, S.; Baudena, A.; Moullec, F.; Lombard, F.; Irisson, J.-O.; Pedrotti, M.L.** (2023). Mistaking plastic for zooplankton: Risk assessment of plastic ingestion in the Mediterranean sea. *Sci. Total Environ.* **856**: 159011. DOI: 10.1016/j.scitotenv.2022.159011

Published version: https://dx.doi.org/10.1016/j.scitotenv.2022.159011

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1 Mistaking plastic for zooplankton: risk assessment of plastic ingestion in the

2 Mediterranean sea

3 [Fabri-Ruiz, S¹,², Baudena, A¹]*., Moullec F³., Lombard, F.¹, Irisson, J.-O¹., Pedrotti, M.L¹.

4 ¹Sorbonne Université, CNRS, Laboratoire d'Océanographie de Villefranche, Villefranche-sur-

5 Mer, France

6 ²DECOD (Ecosystem Dynamics and Sustainability), IFREMER, INRAE, Institut Agro, Nantes,

7 France

8 ³Department of Coastal Systems, Royal Netherlands Institute for Sea Research, P.O. Box 59,

9 1790 AB Den Burg, Texel, The Netherlands

10 Corresponding authors: alberto.baudena@gmail.com, salome.fabri.ruiz@ifremer.fr

11

12 *co-first authors

13 Abstract

14 Floating plastic debris is a pervasive pollutant in seas and oceans, affecting a wide range of

15 animals. In particular, microplastics (< 5 mm in size) increase the possibility that marine

16 species consume plastic and enter the food chain. The present study investigates this

17 potential mistake between plastic debris and zooplankton by calculating the plastic debris to

18 zooplankton ratio over the whole Mediterranean Sea. To this aim, in situ data from the Tara

19 Mediterranean Expedition are combined with environmental and Lagrangian diagnostics in a

20 machine learning approach to produce spatially-explicit maps of plastic debris and
21 zooplankton abundance. We then analyse the plastic to zooplankton ratio in regions with high
22 abundances of pelagic fish. Two of the major hotspots of pelagic fish, located in the Gulf of
23 Gabès and Cilician basin, were associated with high ratio values. Finally, we compare the
24 plastic to zooplankton ratio values in the Pelagos Sanctuary, an important hotspot for marine
25 mammals, with other Geographical Sub-Areas, and find that they were among the larger of
26 the Western Mediterranean Sea. Our results indicate a high potential risk of contamination
27 of marine fauna by plastic and advocate for novel integrated modelling approaches which
28 account for potential trophic transfer within the food chain.

29

30 I. **Introduction**

31 Plastic pollution is ubiquitous in the global ocean, from the sea surface to the seafloor
32 (Woodall et al. 2014, Esposito et al., 2022), and represents a major threat to socio-economic
33 services, tourism, and, ultimately, marine ecosystems (Aretoulaki et al., 2021; Beaumont et
34 al., 2019). It is estimated that 19 to 23 million metric tons of plastic waste entered the aquatic
35 systems in 2016, with an increasing trend expected in the next few years (Borrelle et al., 2020;
36 Lau et al., 2020). The Mediterranean Sea is among the world's seas most polluted by plastic
37 (Gerigny et al., 2019), with levels of concentration similar to those found in the Great Pacific
38 Garbage Patch (Cózar et al., 2015; Pedrotti et al., 2022). At the same time, this quasi-enclosed
39 sea is a key hotspot of marine biodiversity, with more than 17 000 species recorded (Coll et
40 al., 2010), and supports an overall fishery activity of ca. 9 billion dollars every year (FAO 2020).
41 Mediterranean marine ecosystems are, therefore, highly sensitive to plastic pollution
42 (Solomando et al., 2022; Soto-Navarro et al., 2021). Understanding the magnitude of the
43 impact of this pollutant on marine life is essential for conservation and mitigation strategies
44 (Galgani et al., 2014; Kershaw et al., 2019). In particular, it is fundamental to evaluate the
45 risk of plastic transfer along marine trophic webs, including humans as well (Provencher et
46 al., 2019; Rochman et al., 2015; Savoca et al., 2021). The need to assess microplastic risk at
47 the scale of the Mediterranean sea is important considering the long distances crossed by
48 mobile organisms (ie. cetaceans, fish, ...). With the increase of multiple anthropogenic
49 pressures (including microplastics) on these species, the risks must be evaluated at an
50 appropriate spatial scale to mitigate their effect with adapted conservation measures.
51 However, this understanding is hampered by the scarcity of available data, which usually
52 cover only limited portions of the basin and differ in methodology (e.g., Mansui et al., 2020).
53 In addition, only few studies measured plastic and organism concentrations concomitantly
54 (Gérigny et al., 2022), making understanding of plastic impact difficult. In addition, the role
55 of circulation on the distribution of plastic and zooplankton organisms is poorly unknown to
56 date. An alternative method to estimate plastic concentration is the use of Lagrangian
57 models. These are based on the release of virtual plastic particles which are then advected by
58 current fields. From the virtual plastic trajectories different information can be obtained,
59 including zones of potential accumulation or passage of plastic debris (Baudena et al., 2022,
60 2019; Beaumont et al., 2019; Liubartseva et al., 2019; Mansui et al., 2020) dispersion. Only a
61 few have, however, been validated quantitatively with in situ data to date (Baudena et al.,
62 2022). Furthermore, Lagrangian models only simulate plastic dispersion, and usually do not
63 include biological activity such as the presence of zooplankton.

64 In the present study, we aim to quantify the relative presence of plastic compared to that of
65 zooplankton, to assess the potential mistake encountered by marine predators. For this
66 purpose, we used in situ observations from the Tara Mediterranean Expedition, which
constitutes the largest plastic dataset in the Mediterranean Sea to date: 122 stations covering the entire basin with homogenised and standardised sampling techniques. We considered plastic debris between 0.33–5.00 mm (usually referred to as microplastics[1][2]). Microplastics constituted an important proportion of the plastic debris at sea, and were the vast bulk (~95%) of the debris collected during the Tara Mediterranean Expedition, making plastic estimates more reliable (Pedrotti et al., 2022). Importantly, along with plastic debris, zooplankton organisms of the same size class (0.33–5.00 mm) were concomitantly sampled. We then combined them with a machine learning approach to study the environmental drivers of plastic and zooplankton concentrations, unravelling some physical processes responsible for their distribution. With this information, we obtained spatial predictions for the whole Mediterranean Sea surface layer. These quantities were used to estimate a ratio between plastic debris and zooplankton abundance. This value represents the proportion of plastic debris with respect to the zooplankton organisms and can be seen as an indicator of potential impact on marine life. A ratio of 0.1 means that, for every ten zooplankton organisms present in a given seawater parcel, one plastic debris is present as well. Previous studies only estimated this metric in correspondence with the location of the sampling stations (Cole et al., 2011; Collignon et al., 2014; Doyle et al., 2011; Gérigny et al., 2022; Gove et al., 2019 (plastic:larval Fish); Lattin et al., 2004; Moore et al., 2002; Pedrotti et al., 2016). However, for conservation purposes, information on the impact of plastic at larger spatial scales is needed: here, we provide an estimate over the entire Mediterranean Sea of both plastic and zooplankton abundances as well as their ratio.

Furthermore, we use this metric to assess the overlap with potential predators through two illustrative case studies. In the first, we only consider (i) species for which plastic ingestion has
been reported (Fossi et al., 2014; Lefebvre et al., 2019; Pennino et al., 2020); (ii) species which are known to adopt non selective feeding strategies for certain prey sizes (Garrido et al., 2008, 2007; Queiros et al., 2019), as they could not be able to distinguish between plastic debris and zooplankton; (iii) species which are widely distributed over the entire Mediterranean basin (Bray et al., 2019); (iv) plastic debris and zooplankton in the same size class (0.33–5.00 mm) which can potentially confuse predators. As potential predators, we consider small pelagic fish (such as anchovies or sardines), which can acquire food through filter feeding behaviour. They feed on organisms smaller than 5 mm in size (Le Bourg et al., 2015) and have an important ecological and socio-economic role in the Mediterranean Sea. In the second case study, we focus on cetaceans by investigating the ratio values in the Pelagos sanctuary, which is a key foraging ground for marine mammals in the northwestern Mediterranean Sea (Croll et al., 2018; Fossi et al., 2014), and comparing them with Geographical Sub-Areas (GSAs) in the Mediterranean.

103

## II. Materials and Methods

We used a machine learning approach to correlate the response (i.e., in situ plastic and zooplankton abundances data obtained from the Tara Mediterranean Expedition) and explanatory (i.e., physical and biogeochemical data and Lagrangian and Eulerian diagnostics) variables. In the following sections, we described the data and the modelling framework used as well as the methodology employed to determine the plastic to zooplankton ratio, which is then applied to two case studies. To ensure reproducibility and transparency of results, we provided an ODMAP (Overview, Data, Model, Assessment and Prediction) protocol in the Supplementary Material. ODMAP is a standard protocol for species distribution models
113 based-approach that we used in our study (Fitzpatrick et al., 2021; Zurell et al., 2020).

114

115 **2.1. Response variables: in situ plastic and zooplankton data from Tara Mediterranean**

116 **Expedition**

117

118 The Tara Mediterranean Expedition was conducted in the Mediterranean Sea between June
119 and November 2014. It sampled plastic debris and zooplankton in 122 stations across the
120 whole basin (Figure 1), representing the largest coupled plastic zooplankton database in the
121 Mediterranean Sea to date (Pedrotti et al., 2022). Plastic items were collected with a manta
122 net (height 25 cm, width 60 cm, mesh size 333 µm) towed at the sea surface and at an average
123 speed of ~2.5 knots for 60 minutes over a mean distance of ~4 km. Details of plastic processing
124 are described in Pedrotti et al., (2022), and are briefly reported here. Plastic items were
125 manually separated from zooplankton and organic tissue and scanned using the ZooScan
126 system (Gorsky et al., 2010) under dry conditions, while zooplankton organisms were scanned
127 separately under aqueous conditions. Particles and zooplankton were automatically detected
128 and their morphological attributes were extracted by post-processing with Zooprocess and
129 Plankton Identifier software. All obtained images were imported within EcoTaxa
130 (http://ecotaxa.obs-vlfr.fr, Picheral et al., 2017) and classified in different taxonomic or
131 particle categories. Microplastic (0.33–5 mm) abundances (in items per square km; N/km²)
132 were calculated from particle counts. In addition, plastic debris abundance was calculated
133 also for the following size classes: debris between 0.33–1 mm; debris between 1–5 mm; all
134 plastic debris collected (i.e., larger than 0.33 mm). The size class limits were chosen in order
135 to test the robustness of the results. Similarly, total zooplankton abundance per sample was
136 calculated from zooscan and under the same size classes defined for plastic debris abundance.
137

Diel vertical migration was not observed over the 24 hour day (Figure S1)

<figure><img src="image_3.png" /></figure>

138

139

**Figure 1 : Overview of the domain studied:** Location of the 122 Tara Mediterranean

140

Expedition stations (colored circles) and corresponding estimated plastic concentrations

141

(right-hand yellow-to-red scale bar). Green and blue dots indicate the position of the coastal

142

cities and river mouths used as potential plastic sources. The purple dashed lines separate the

143

different Geographical Sub-Areas (GSAs) investigated in this study, indicated by a number. 1:

144

Alboran Sea, 2: Algeria, 3: Balearic Islands, 4: Northern Spain and Gulf of Lion, 5: Pelagos

145

sanctuary, 6: Tyrrhenian Sea, 7: Sardinia, 8: Tunisia, 9: Southern Sicily and Malta, 10: Adriatic

146

Sea, 11: Western Ionian Sea, 12: Eastern Ionian Sea, 13: Southern Ionian Sea, 14: Aegean Sea,

147

15: Northern Levant Sea and Cyprus, 16 : Southern and Eastern Levant Sea

148

**2.2. Explanatory variables**

149

**2.2.1. Physical and biogeochemical data : salinity, temperature, nitrates, phosphates,**

150

**dissolved oxygen concentration**
151 Physical and biogeochemical data were extracted from the Copernicus Marine Environment
152 Monitoring Service (CMEMS, http://marine.copernicus.eu, product
153 MEDSEA_REANALYSIS PHYS 006 004) at 1/16° resolution. The product was supplied by the
154 Nucleus for European Modelling of the Ocean (NEMO), with a variational data assimilation
155 scheme (OceanVAR) for temperature and salinity vertical profiles and satellite Sea Level
156 Anomaly along track data. We only considered the surface layer (Simoncelli et al., 2014).
157 Physical and biogeochemical data were provided at daily and weekly resolution, respectively.
158 Climatologies were calculated by averaging over the six months of the Tara Mediterranean
159 Expedition (June to November 2014; Table S1). Spatial resolution was decreased from 1/16°
160 to 1/8° by bilinear interpolation to fit the same spatial resolution of the Lagrangian and
161 Eulerian diagnostics. The environmental explanatory variables obtained include mean values
162 for temperature, salinity, phosphate, nitrate and dissolved oxygen concentration.
163 Zooplankton concentration (mass content of zooplankton expressed as carbon in seawater,
164 g/km²; Seapodym model; Lehodey et al., 2010) provided by the
165 GLOBAL_MULTIYEAR BGC 001 033 product (CMEMS platform) was used to calculate a
166 zooplankton climatology between June and November 2014 at 0.083° spatial resolution. The
167 latter was used as a qualitative reference for the zooplankton projection (Subsec. 4.1), and
168 not as an explanatory variable.

169 **2.2.2. Lagrangian and Eulerian diagnostics**

170 Velocity field and trajectory calculation: The velocity field was obtained by combining
171 together two hydrodynamical fields, both downloaded from the CMEMS platform. The first
172 product was the MEDSEA_REANALYSIS PHYS 006 004, which provides surface currents and
173 includes the geostrophic and the Ekman components. It has a spatial resolution of 1/16° and
174 a temporal resolution of one day. The second product was the
175 MEDSEA_HINDCAST_WAV_006_012. It provides drift due to waves (Stokes drift). It has a
176 spatial resolution of 1/24° and a temporal resolution of one day. The velocity fields were
177 spatially and temporally interpolated (bilinear interpolation) and summed together, providing
178 the final velocity field (1/24°; 1 hour). Thus, the surface currents used take into account the
179 Stokes drift, which indirectly includes windage, an important component for microplastic
180 transport in marine environments (Onink et al., 2019), especially in the Mediterranean Sea
181 (Liubartseva et al., 2018). As zooplankton samplings were collected at the same depth as
182 plastic debris, we used the same velocity field for both explanatory variables. Trajectories
183 were calculated with a 4th order Runge-Kutta scheme in both space and time, with a time step
184 of 20 minutes.

185

186 **Lagrangian and Eulerian diagnostics description:** Different Lagrangian and Eulerian
187 diagnostics were used as explanatory variables. Lagrangian diagnostics were derived from
188 trajectories, whereas Eulerian diagnostics were obtained by properties at a fixed location. The
189 Eulerian diagnostics calculated were: Absolute velocity: $U=sqrt(u^2+v^2)$, with u and v being the
190 zonal and meridional component of the velocity field; Kinetic Energy = $u^2+v^2$ which, together
191 with the absolute velocity is considered as a proxy of the intensity of the currents; Vorticity:
192 denotes the presence (when positive or negative) or absence (when close to 0) of eddies;
193 **Okubo-Weiss:** When negative (positive), this metric indicates a water parcel inside (outside)
194 an eddy; Turbulent Kinetic Energy (TKE): This metric analyses the standard deviation of the
195 velocity field time series and quantifies whether a region was subjected to strong turbulence.
196 The Lagrangian diagnostics calculated were: Finite-Time Lyapunov Exponents (FTLE): FTLEs
197 quantify the rate of separation due to currents. They are used to identify barriers to transport
or regions affected by strong turbulence (Baudena et al., 2021; d'Ovidio et al., 2004);

*Lagrangian Betweenness*: this metric is used to identify regions that act as “bottlenecks” for
the circulation, i.e. in which water parcels of multiple origins pass and then go to several
different destinations (Ser-Giacomi et al., 2021); *Retention time*: this metric estimates the
amount of time a water parcel spent inside an eddy (if it was inside it) (d'Ovidio et al., 2015);

*Lagrangian divergence*: this metric calculates the Eulerian divergence along the backward
trajectory. When negative (positive), it indicates convergence (divergence) of water masses,
and can be used as a proxy of downwelling (upwelling) Hernández-Carrasco et al., (2018);

*Lagrangian Plastic Pollution Index (LPPI)*: this metric estimates the amount of plastic debris in
a water parcel based on the plastic sources (cities and rivers, blue and green dots in Fig. 1)
encountered along the water parcel's previous path. The water parcel "encounters" a plastic
source when it passes below a distance threshold from it (details in Pedrotti et al., 2022).

As for the physical and biogeochemical explanatory variables, climatologies of Lagrangian and
Eulerian diagnostics were calculated between June and November 2014 at 1/8° of spatial
resolution over the entire Mediterranean Sea. Details about the different parameters used
for each of the diagnostics are reported in Supplementary Table S2.

214 **2.3. Modelling framework: Xgboost models**

215 **2.3.1. Collinearity between explanatory variables**

216 Multicollinearity occurs when two or more explanatory variables in a multiple regression
217 model are highly correlated, which means that one can be predicted linearly from the others
218 with a high degree of accuracy. Multicollinearity can cause data redundancy in the
219 explanatory variables which can lead to model overfitting or reduction in model predictive
220 ability (Dormann et al., 2013). If multicollinearity patterns differ between fitted data in the
model and new data, large errors could be introduced in the predictions. To restrict these
possible biases, multicollinearity between explanatory variables was initially examined using
variance inflation (VIF) factors in a stepwise procedure (Dormann et al., 2013). VIF is
computed from equation [1], where R²ᵢ is obtained from a regression between variable i'th
against all other explanatory variables.

226

227

$$VIF_j = \frac{1}{1-R_j^2} [1]$$

228

In a stepwise procedure, the function calculates VIF values for all explanatory variables, removes the variable with the highest value, and repeats until all VIF variable values are below a given threshold (here 5). Finally, we computed Spearman pairwise correlation (rS) between descriptors and removed one of the two descriptors when correlation values of rS were above 0.7 (Dormann et al., 2013). Analyses were performed using the usdm (Babak, 2015) and corrplot (Wei et al., 2017) R packages. Table S3 lists the remaining variables used in Xgboost models for each category.

236

### 2.3.2. Parameterization and calibration of XGboost models

The extreme gradient boosting (XGBoost) modelling approach (Chen and Guestrin, 2016) was used to model plastic debris and zooplankton abundances for each size class. Xgboost is an efficient gradient machine learning method that combines two algorithms: first, simple and small regression trees, calculated on random subsets of data while minimising residuals; and subsequently boosting, which combines all the models into a unique solution (for statistical details see Chen and Guestrin, 2016). XGBoost models are able to fit complex functions, which could reflect the complexity of processes shaping plastic debris and zooplankton patterns.
244 Xgboost models were performed using the xgboost R package (Chen et al., 2015).

245 Cross validation procedure was used to estimate the best parameters of the xgboost model.

246 This procedure uses a single parameter k that refers to the number of groups for a given dataset to be split into. In our case, we used k=4. For each group, we take the remaining 247 groups as a training data set and use the selected group to evaluate the model. This procedure 248 was performed for different xgboost parameters and poisson distribution: number of trees (1 249 to 900), maximum depth (2, 4, 6) where the higher is the value the more complex the model 250 is, eta (0.005, 0.01, 0.02, 0.03, 0.04) which is the learning rate, min_child_w eigth (1, 5) which 251 defines the minimum sum of weights of all observations required in a child (used to control 252 over-fitting). Best parameters were selected by minimising the negative log-likelihood for 253 Poisson regression. We evaluated the model using the R² coefficient between observed and 254 predicted data. Because several distance threshold values were provided for plastic data 255 (based on the LPPI), we fitted models for each and kept the best one (Table S4). 256

257 **2.3.3. Prediction and projection**

258 The partial dependence plots (PDP) technique (Friedman, 2001) was used to achieve a 259 graphical representation of the marginal effect of a variable on the response variable. We also 260 extract variable importance from the model, which shows how a feature is important in 261 making a branch of a decision tree purer. A high percentage means important explanatory 262 variables which constrain response variables. Calculated functions in the model were then 263 used to extrapolate plastic debris and zooplankton abundances for each grid cell over the 264 entire Mediterranean Sea at a period corresponding to June-November 2014. We projected 265 mean plastic debris and zooplankton abundances and associated standard deviations. 266

266
267 **2.4. Plastic to zooplankton ratio definition**

268

269 Using zooplankton and plastic debris abundance projections over the entire Mediterranean

270 Sea, we calculated the plastic debris to zooplankton ratio (amount of plastic debris divided by

271 the amount of zooplankton) at 1/8° of spatial resolution. For each grid cell i, the ratio was

272 obtained by dividing the plastic and the zooplankton abundances (expressed in N/km²) of the

273 same size class j:

$$
274 \quad Ratio_{i,j} = \frac{Plastic\ abundance_{i,j}}{Zooplankton\ abundance_{i,j}} \quad (1)
$$

275 Equation 1 implies that, for example, if the ratio equals 0.1, for every 1000 zooplankton

276 organisms in a given water parcel 100 plastic debris of similar size is present. The ratio was

277 calculated using plastic debris and zooplankton organisms of size classes between 0.33—5
278 mm. Ratios calculated using different size classes (Subsec. 2.1) are reported in Supplementary

279 Materials. The uncertainty on the ratio was calculated with the variance formula using the

280 plastic and zooplankton associated standard deviations and Equation (1).

281 The ratio obtained using the size class of 0.33—5 mm was evaluated (i) in the Pelagos

282 Sanctuary, a hotspot for cetaceans, representing a foraging ground for different whale species

283 (Morgado et al., 2017). The ratio in the Pelagos Sanctuary was compared with the ratio

284 calculated in the Mediterranean GSAs (Fig. 1). GSAs were obtained from the FAO platform

285 (https://www.fao.org/gfcm/data/maps/gsas/en/). In order to have GSAs of similar surface

286 area, we merged together the following GSAs: Northern Alboran Sea, Southern Alboran Sea,

287 and Alboran Island, creating the Alboran Sea GSA (GSA 1); Northern Spain and Gulf of Lion

288 GSA (GSA 4); Ligurian Sea and Northern Tyrrhenian Sea and Southern and Central Tyrrhenian

289 Sea, creating the Tyrrhenian Sea GSA (GSA 6); Western and Eastern Sardinia, creating the

289 Sea, creating the Tyrrhenian Sea GSA (GSA 6); Western and Eastern Sardinia, creating the
290 Sardinia GSA (GSA 7); Northern Tunisia, Gulf of Hammamet, and Gulf of Gabès, creating the
291 Tunisia GSA (GSA 8); Southern Sicily and Malta GSA (GSA 9); Northern and Southern Adriatic
292 Sea, creating the Adriatic Sea GSA (GSA 10); Crete and Aegean Sea, creating the Aegean Sea
293 GSA (GSA 14); Northern Levant Sea and Cyprus, creating the Northern Levant Sea and Cyprus
294 GSA (GSA 15); Southern and Eastern Levant Sea GSA (GSA 16). In addition, the Corsica GSA
295 was not considered because it was included in the Pelagos Sanctuary, while the Gulf of Lion
296 and Ligurian Sea and Northern Tyrrhenian Sea were reduced to avoid overlap with the Pelagos
297 Sanctuary.

298 The ratio values in the Pelagos Sanctuary and in the GSAs were compared using a paired
299 sample Wilcoxon test with corrections applied for multiple testing; (ii) in the Mediterranean
300 areas where the total biomass of small pelagic fish is large according to simulations provided
301 by the end-to-end ecosystem model OSMOSE-MED (Moullec et al., 2019b). Results for the
302 other size classes are available in the Supplementary Material.

303

304 **2.5. The end-to-end model OSMOSE-MED**

305

306 OSMOSE-MED is an end-to-end modelling chain, including a general circulation model, a
307 regional climate model, a regional biogeochemistry model and a multispecies dynamic model
308 (OSMOSE; Moullec et al., 2019). OSMOSE is a spatially explicit individual-based model which
309 simulates the whole life cycle of several interacting fish and macro-invertebrates species from
310 eggs to adult stages. Major ecological processes of the life cycle, such as growth, predation,
311 reproduction, and mortality sources, are modelled step by step (15 days in this study)
312 (www.osmose-model.org; (Shin et al., 2004; Shin and Cury, 2001). OSMOSE-MED covers the
313 whole Mediterranean Sea and represents the Mediterranean food web from pankton to main
314 top-predators in the 2006-2013 period. Hundred marine species (fish, cephalopods and
315 crustaceans), representing ca. 95% of total declared catches in the region over the 2006-2013
316 period were explicitly modelled. For a full description of the parameterization and calibration
317 of OSMOSE-MED (see Moullec et al., 2022, 2019a, 2019b).

318 The biomass of small pelagic fish is an output of OSMOSE-MED simulations. As OSMOSE is a
319 stochastic model, ten replicated simulations were run and averaged to analyse the outputs.

320 For this study, the total biomass of 10 small pelagic species only (e.g., European anchovy,
321 European sardine, Round sardinella or European sprat) was considered. Areas of small-pelagic
322 high biomass were defined as the locations with a biomass value higher than the 90th
323 percentile of all the Mediterranean Sea biomass values. Results corresponding to the 80th and
324 95th percentiles are reported in the Supplementary Materials.

325

326

### III. **Results**

327 **3.1. Plastic and zooplankton distributions in the Mediterranean Sea and their drivers**

328 The predictive performance of the Xgboost model was assessed by calculating the correlation
329 coefficient (R²) between observed and predicted values of plastic and zooplankton abundance
330 (Table 1, S4, S5, Figure S2, S3). R² coefficients were 0.68 and 0.57 for plastic debris and
331 zooplankton abundances, respectively (Table 1). Overall, the relationships found tended to
332 overestimate low values and to underestimate large ones (Figure S2, S3). R² is known to be
333 sensitive to the extent of dependent variables (Gelman and Hill, 2006) which range between
334 2260 N/km² and 7974561 N/km² in this study. This could explain the low cross-validated R²
335 which is strengthened by the low amount of available data (122 sampling stations). High
336 zooplankton abundances were associated with negative Okubo-Weiss values (19 % of the
337 explanatory power in the model, Fig. 2c), and with positive and negative vorticity values (14%). Both these explanatory variables indicate an eddy presence, and do not reveal a clear preference for cyclones or anticyclones. The temperature influenced the total zooplankton abundance as well (16%), with lower abundances associated with higher temperatures.
338
339
340
341 Attracting fronts, identified by Finite-Time Lyapunov Exponents (FTLEs, 9%) calculated backward in time, were positively correlated with zooplankton abundance, while the relationship was of opposite sign with diverging fronts (FTLE forward in time: 8%; and divergence: 7%). Explanatory variables that most contributed to explain plastic debris abundance were the kinetic energy (50%) and TKE (10%) (Figure 2d), indicating a greater concentration of plastic debris in retentive and low turbulence regions. Nitrates (11%, a proxy for riverine outputs) indicated a positive relationship between plastic debris presence and the proximity to river mouths.
342
343
344
345
346
347
348

349 When considering the plastic and zooplankton abundances projected over the entire Mediterranean Sea (Fig. 2a,b), highest concentrations were mainly observed in the Adriatic and Aegean Seas (GSAs 10 and 14 in Fig. 1, respectively). In these regions, abundances were estimated around 20x10⁶ N/km² for zooplankton (Figure 2a) and 5x10⁶ N/km² for plastic debris (Figure 2b). The Eastern basin (GSAs 15 and 16) presented low abundances of zooplankton and plastic debris. However, the latter was abundant along Cyprus (located in GSA 15) and Lebanon (located in GSA 16) coasts and in the Gulf of Gabès (located in GSA 8).
350
351
352
353
354
355
356 The Tyrrhenian Sea (GSA 6) was also a hotspot of plastic debris abundance. In the Strait of Gibraltar (located in GSA 1), the models predicted high values of zooplankton abundances and low plastic debris concentrations.
357
358

359 The results obtained with different size classes showed very similar patterns (Figure S4, S5
and S6), and high R² coefficients were obtained as well (Table S5). Plastic standard deviation maps (Fig. S7a) show that the uncertainties corresponded to about 10% of the plastic abundance values. The same considerations were valid for the zooplankton uncertainties (Fig. S7b), showing the robustness of the models obtained.

**Table 1:** Predictive performance and best parameters estimated by 4-fold cross validation for size between 0.33 and 5 mm

<table><thead><tr><th rowspan="2">Group</th><th>Predictive performance</th><th colspan="4">Model parameters</th></tr><tr><th>R<sup>2</sup></th><th>trees</th><th>Maximum depth</th><th>Eta</th><th>Minimum child weight</th></tr></thead><tbody><tr><td>Zooplankton (&gt; 0.33 and &lt; 5 mm)</td><td>0.57</td><td>707</td><td>2</td><td>0.04</td><td>5</td></tr><tr><td>Microplastic (&gt; 0.33 and &lt; 5 mm)</td><td>0.68</td><td>851</td><td>2</td><td>0.03</td><td>1</td></tr></tbody></table>
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

368

369 **Figure 2 : Plastic and zooplankton abundances** : Spatial projection and partial dependence plot of zooplankton (panels a and c, respectively) and plastic debris abundance (panels b and d, respectively) for the size class < 5 mm. oW0 = Okubo Weiss, kinNRJ = Kinetic Energy, temp_mean = mean temperature, NO3_mean = mean nitrate concentration, vort0 = vorticity, tke = Turbulent Kinetic Energy, ftle45_F = FTLE 45 days forward in time, ftle45_B = FTLE 45 days backward in time, div0 = Eulerian Divergence, betw15 = Betweenness at 15 days, sal_mean = mean salinity, lppi0100_60 = LPPI with distance threshold of 1° and 60 days backward in time, lppi0100_0 = LPPI with distance threshold of 1° and no advection

377 **3.2. Plastic to zooplankton ratio**

378 Using zooplankton and plastic debris abundance projections, we calculated the plastic to zooplankton ratio (number of plastic debris per km² divided by the number of zooplankton
380 organisms per km², MM 2.4) over the entire Mediterranean Sea for the size class between 381 0.33–5 mm (Fig. 3a). The average plastic to zooplankton ratio was 0.029 (±0.012) at the 382 Mediterranean scale. Areas with the highest average plastic to zooplankton ratio were the 383 gulf of Gabès (located in GSA 8), the Cilician (GSA 15), the Levantine (GSA 15 and 16), and the 384 Southern Tyrrhenian Seas (GSA 6), with values of ~0.10. Intermediate values (~0.05) were 385 found in the Balearic (GSA 3) and Adriatic Seas (GSA 3) and in the Central Mediterranean (GSA 386 13). Lower values (~0.01) were in the Alboran Sea (GSA 1) and the central sector of the 387 Eastern Mediterranean Sea (GSA 16 and eastern part of GSA 15).[3][4][5]

388 When considering the results obtained with different size classes the spatial pattern did not 389 change consistently (Fig. S10). The ratio decreased to 0.004–0.1 when considering a size class 390 between 0.33–1 mm, while it increased to 0.005–0.3 when considering the size class 391 between 1 and 5 mm, due to the different abundances considered. The uncertainty on the 392 ratio (Fig. S7c) was greater in regions of larger ratio and lower elsewhere, and corresponded 393 to ~10% of the ratio value. These results show the soundness of this metric.
<figure><img src="image_8.png" /></figure>

394

395 **Figure 3 : Plastic to zooplankton ratio** : Spatial projection of the plastic to zooplankton ratio

396 for the size class between 0.33—5 mm

397 **3.3. Case studies**

398

399 **3.3.1. Wildlife exposure: plastic ingestion risk for small pelagic fish[6]**

400

401 The plastic to zooplankton ratio was applied to two case studies to analyse the probability of plastic ingestion by wildlife as described in the next subsections.

402 In the first case study, we considered the total biomass of small pelagic fish in the Mediterranean Sea for the time period 2006-2013, obtained from the Osmose model.

403 One of the largest Mediterranean hotspots of small pelagic fish, located in the Gulf of Gabès (located in GSA 8), as well as a smaller one in the Cilician basin (between Cyprus and Turkey, GSA 15), were found in correspondence with plastic to zooplankton ratios greater than 0.06
408 (Fig. 4a). Two large hotspots of small pelagic fish, in the Adriatic Sea (GSA 10) and in the
409 Southern Catalan Sea (GSA 4), were associated with moderate plastic to zooplankton ratios
410 greater than 0.03. The remaining part of the small pelagic hotspots was highly patchy and
411 mainly distributed along the coasts, with variable ratio values.

412 When using different percentiles to identify the main small pelagic fish hotspots (Fig. S9),
413 these were still located in the Adriatic Sea and in the Gulf of Gabès, confirming that these
414 hotspots were threatened by plastic pollution and providing evidence of the robustness of
415 the analyses.

416

417 **3.3.2 Wildlife exposure: plastic ingestion risk in the Pelagos Sanctuary and different**
418 **Mediterranean regions**

419

420 In the second case study, we calculated the plastic to zooplankton ratio in the Pelagos
421 Sanctuary and compared it with the ratio of 15 Mediterranean Sea GSAs (Fig.1). The Pelagos
422 Sanctuary showed ratio values which were significantly larger than those predicted in the
423 Alboran Sea (GSA 1), Algeria (GSA 2), Balearic Islands (GSA 3), Northern Spain and Gulf of Lion
424 (GSA 4) and Aegean Sea (GSA 14), while they were significantly lower than those in the
425 Tyrrhenian Sea (GSA 6), Southern Sicily and Malta (GSA 9), and the Northern Levant Sea (GSA
426 16). No significant differences were found with other GSAs (Fig. 4b).

427

428
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

429

430 **Figure 4 : Application of plastic to zooplankton ratio to two case studies :**a) Plastic to

431 zooplankton ratio for plastic debris of size between 0.33–5 mm, showed only in
correspondence with the hotspots of small pelagic fish (defined as the locations with a

biomass larger than the 90th percentile), b) boxplot of ratio values in the Pelagos Sanctuary

and in 15 Mediterranean GSAs (shown in Fig. 1, MM 4). The letters on top of each box

display pairwise wilcoxon test results.

436

437 IV. **Discussion**

438 **4.1. Zooplankton distribution in the Mediterranean Sea and its drivers**[7][8][9][10][11]

439

Zooplankton abundance was boosted by eddy presence, even if no clear preference for

cyclones or anticyclones was detected. This was in accordance with previous studies finding

large zooplankton concentration in eddies (Chambault et al., 2019; Godø et al., 2012; Riandey

et al., 2005). Temperature played an important role in zooplankton abundances which

decreased when temperature increased. This is because temperature affects the mixed layer

depth (MLD) (D'Ortenzio et al., 2005). MLD governs the availability of nutrients and light,

which are essential for phytoplankton, the primary food source for zooplankton. In regions

with higher temperatures, such as the Levantine Basin, the MLD is relatively shallow (due to

a low mixing in winter and a strong stratification in summer): this hampers nutrient supply to

the phytoplankton, and in turn inhibits zooplankton growth; conversely, in the western basin,

colder, the MLD is deeper, boosting biological activity (D'Ortenzio et al., 2005). High

zooplankton abundance in coastal regions can also be attributed to phytoplankton blooms

occurring near highly populated coasts, subject to terrigenous and river inputs in nutrients

(Siokou-Frangou et al., 2010).

Frontal converging regions (identified through backward FTLE) were positively correlated with
zooplankton abundance. This is in coherence with previous studies highlighting the importance of these features for mid trophic levels and, in general, the whole trophic chain (Baudena et al., 2021; Chambault et al., 2017; d'Ovidio et al., 2010; Della Penna et al., 2015).

The negative correlation found with diverging zones (identified through forward FTLE and divergence) suggest that these structures, even if fostering primary production, hamper zooplankton concentration, possibly by spreading it away.

Notably, the pattern of zooplankton abundance over the whole Mediterranean was in agreement with the average zooplankton mass in June–November 2014 obtained by the Seapodym model (Lehodey et al., 2010; Fig. S11): the western Mediterranean showed larger values than the eastern Mediterranean; the largest values were predicted in the northern Adriatic Sea and in the Algerian and Alboran basins both by our projection and by the Seapodym model. Differences could be explained by the fact that we used zooplankton abundance (in N/km²) while Seapodym provided zooplankton mass, and by the different methodological approaches.

## 4.2. Plastic debris distribution in the Mediterranean Sea and its drivers

Plastic debris concentration obtained from the projections was lower in regions characterised by strong currents and turbulence. This was confirmed by the decreasing relationship found between plastic debris and kinetic energy (50 % of the variance), TKE, and FTLE calculated both forward and backward in time (Fig. 2d). The positive relationship found with nitrates, a proxy for land-based pollution (e.g. river mouths or cities), indicated that plastic debris abundance was linked with coastal sources. This suggests that plastic debris is released mainly from coastal sources, in coherence with previous studies (Baudena et al., 2022; Liubartseva
479 et al., 2018), where less energetic features are found. When plastic debris moves offshore,
480 turbulent activity and currents mix it, lowering plastic abundance. This is corroborated by the
481 slightly negative relationship with FTLE calculated both forward and backward in time: plastic
482 debris may be attracted by frontal converging regions but then spread away.

483 The projection of plastic concentration does not show regions of strong plastic accumulation.

484 This is in coherence with previous studies (Baudena et al., 2022; Liubartseva et al., 2018),
485 which attributed this lack to the strong spatio-temporal variability of Mediterranean currents.

486 The plastic concentration pattern we obtained was similar to the one reported by (Liubartseva
487 et al., 2018) despite the different methodological approach (a Lagrangian tracking model).

488 Higher concentrations of plastic debris were found in the northern Adriatic and (GSA 10) in
489 the Balearic and northern Spain Sea (GSAs 3 and 4) and in the Levantine Basin (GSAs 15 and
490 16). Lower concentrations were found in the central part of the Eastern Mediterranean (GSAs
491 14–16), in the Bomba Gulf (Southern part of GSA 13), and in the Alboran Sea (GSA 1). A larger
492 concentration was predicted in the Cilician basin (GSA 15) and in a portion of the Gulf of Lion
493 (GSA 4) by Liubartseva et al. (2018) model, likely driven by the nearby presence of coastal
494 plastic sources. This result provides evidence of the soundness of the pattern we obtained.

495 Our model predicted a total of 4·10¹¹ plastic debris floating at the surface of the
496 Mediterranean Sea. This value was in agreement with the results from (Pedrotti et al., 2022);
497 6·10¹¹, confidence interval 4–14·10¹¹). The difference may be explained by the fact that our
498 model predicted the plastic debris concentration over the entire basin and not only at the
499 location of the Tara Mediterranean Expedition stations.

500 In general, plastic models had better estimations than zooplankton models. This may be due
501 to the relatively easier description of plastic dynamics rather than zooplankton abundance,
502 which is driven by complex biological and ecological processes including growth, predation,
503 competition or behaviour including vertical migrations (Quéré et al., 2005).

504

505 **4.3. Plastic to zooplankton ratio and wildlife exposure**

506

507 The plastic and zooplankton projections allowed us to calculate the plastic to zooplankton ratio at the Mediterranean Sea scale. This ratio ranged approximately between 0.005 (i.e., 5 plastic debris every 1000 zooplankton organisms) and 0.100 (i.e., 100 plastic debris every 1000 zooplankton organisms) throughout the whole basin.

511 Our results on the risk of plastic ingestion for small pelagic fish suggested that they could ingest large amounts of debris. Indeed, the ratio of plastic to zooplankton was moderate to high in the two larger pelagic fish hotspots, located in the Gulf of Gabès (GSA 8) and in the Adriatic Sea (GSA 10). This is of particular concern since both regions are heavily exploited by fishing industries (Colloca et al., 2017). While we could not explicitly observe an ingestion of plastic debris by small pelagic fish in situ, previous studies reported microplastic presence in the stomach content of at least 87 fish species in the Mediterranean Sea (Habib et al., 2021), many of them being commercially important (Jâms et al., 2020; Renzi et al., 2019).

519 The Pelagos Sanctuary showed plastic to zooplankton ratio values (0.031±0.014) significantly larger than all the GSAs in the Western Mediterranean, with the exclusion of the Tyrrhenian Sea, and which were comparable to several GSAs across the basin. This value was similar to the average plastic to zooplankton ratio obtained in the northwestern Mediterranean by Pedrotti et al., (2016) (2016; 0.03 ±1.40) These results highlight the potential ingestion of plastic debris by whales in this region. Fossi et al., (2012) recorded phthalates (a main constituent of plastic) in fin whale *Balaenoptera physalus* blubber, showing that they could consume plastic debris directly or indirectly from water and plankton. Hence, these mammals,
527 among the largest filter feeders in the world and adapted to absorbing large quantities of prey
528 in a single mouthful, are threatened in one of their main feeding areas which is heavily
529 contaminated.

530 We stress that the latter cases are just two examples of how the plastic to zooplankton ratio
531 can be used to estimate the threat represented by plastic debris on marine biota and,
532 ultimately, on human health (Bhuyan et al., 2022). Several other applications are possible,
533 including the study of marine protected areas (Soto-Navarro et al., 2021), fishery grounds
534 (Colloca et al., 2017), or specific regional analyses, also unravelling the origin of the plastic
535 debris (Liubartseva et al., 2019).

536

537 **4.4. Limits**

538

539 The present study is not a mechanistic understanding of plastic debris ingestion by small
540 pelagic fish. Zooplankton communities and their quality as food supply for predators were not
541 assessed. Feuilloley et al. (2022), using a long time series of zooplankton observations in the
542 northwestern Mediterranean, showed that changes in zooplankton composition, size, and
543 density may impact higher trophic levels, such as the fitness of small pelagic fish. Results
544 should be considered cautiously as both plastic and zooplankton were averaged over 6
545 months in the same year (June–November 2014). We only analysed surface data, while
546 zooplankton organisms live in the whole water column and several species have a diel vertical
547 migration.

548 It was not possible to identify a value of plastic to zooplankton ratio over which the risk of
549 plastic ingestion could be considered high, as no studies have investigated this question to
550 date. A possible choice would be the percentile of the ratio distribution over the
551 Mediterranean Sea. However, as the plastic pollution is expected to increase in the future
552 (Borrelle et al., 2020; Lau et al., 2020), so will the ratio. Further studies are needed on this
553 issue.

554 Multiple caveats are associated with the use of statistical approaches in a dynamic
555 environment: i) they did not allow us to establish a causal relationship between
556 plastic/zooplankton abundances and environment; ii) they have limited capacities to
557 extrapolate in space and time (Guisan and Thuiller, 2005; Ralston and Moore, 2020; Yates et
558 al., 2018). However, these approaches have proved to be useful, valuable, and cost-effective
559 tools to quantify plastic/zooplankton distribution, especially in data-poor areas (Fabri-Ruiz et
560 al., 2019; Guillaumot et al., 2019).

561

562

563

564

V. **Conclusions**

565

566 All in all, our findings showed that plastic debris is widespread in the Mediterranean Sea, with
567 a number of debris in a similar order of magnitude than zooplankton organisms across the
568 entire basin. This highlights the potential stress induced by this invasive element on the
569 marine ecosystems, and the necessity of further research efforts on these questions. Further
570 samplings of plastic debris and zooplankton organisms are needed, with larger spatio-
571 temporal resolutions and water column observations. In addition, the understanding of the
572 interaction between top predators, fish, and lower trophic levels in the presence of plastic
573 will be crucial to correctly assess the threat encountered and to design mitigation strategies.
574

575 Acknowledgments and funding

576

577 We thank the commitment of the following institutions: CNRS, Sorbonne University, LOV. The

578 Tara Ocean Foundation and its founders and sponsors: agnès b.®, Etienne Bourgois, the Veolia

579 Environment Foundation, Lorient Agglomeration, Serge Ferrari, the Foundation Prince Albert

580 II of Monaco, IDEC, the “Tara” schooner, crews and teams. We thank MERCATOR-CORIOLIS

581 and ACRI-ST for providing daily satellite data during the expedition. We are also grateful to

582 the French Ministry of Foreign Affairs for supporting the expedition and to the countries that

583 graciously granted sampling permission. The authors are grateful to Enrico Ser-Giacomi for

584 his helpful advice on the design of the research. This study was financed by the French

585 Ministry for Ecological Transition (MTES) who supported a post-doctoral fellowship for

586 Salome Fabri-Ruiz and EU H2020 LABPLAS project under the grant agreement (ID:

587 101003954), DOI: 10.3030/101003954.

588

589 Data availability

590

591 All the data necessary to produce the figures of the Main Text, i.e. the plastic and zooplankton

592 abundances calculated over the whole Mediterranean Sea, the plastic to zooplankton ratio,

593 the partition of the different GSAs used, as well as the climatology of the small pelagic fish

594 biomass are available at https://doi.org/10.5281/zenodo.7076175. The in situ plastic

595 concentrations are available at https://doi.org/10.5281/zenodo.5538237. The velocity field

596 used to calculate the Lagrangian diagnostics are available on the E.U. Copernicus Marine
597 Environment Service Information website (CMEMS, http://marine.copernicus.eu/).

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921

922

923

924

925

926

**Supplementary Materials**
927

928 **ODMAP protocol:**

929 Confounding plastic and plankton: risk assessment of plastic ingestion in the Mediterranean

930 sea

931 – ODMAP Protocol –

932 Salomé Fabri-Ruiz, Alberto Baudena, Fabien Moullec, Fabien Lombard, Jean-Olivier Irisson,

933 Maria-Luiza Pedrotti

934 2022-08-02

935

936 **Overview**

937 **Authorship**

938 Contact : salome.fabri.ruiz@ifremer.fr

939 <Study link>

940 **Model objective**

941 Model objective: Inference and explanation

942 **Focal Taxon**

943 Focal Taxon: Microplastic and zooplankton

944 **Location**

945 Location: Mediterranean Sea

946 **Scale of Analysis**

947 Spatial extent: -3.7, 34.7, 30.9, 45 (xmin, xmax, ymin, ymax)

948 Spatial resolution: ≈13-km

949 Temporal extent: June to November 2014

950 Temporal resolution: 6 months

951 Boundary: rectangle

952 **Biodiversity data**

953 Observation type: field survey

954 Response data type: counts
<table>
  <tr>
    <td>955</td>
    <td>Explanatory variables</td>
  </tr>
  <tr>
    <td>956</td>
    <td>Explanatory variable types: Abiotic explanatory variables and Lagrangian diagnostics</td>
  </tr>
  <tr>
    <td>957</td>
    <td>Hypotheses</td>
  </tr>
  <tr>
    <td>958</td>
    <td>Hypotheses: Microplastic abundances are driven by water masses characteristics.</td>
  </tr>
  <tr>
    <td>959</td>
    <td>Zooplankton abundances are driven by abiotic explanatory variables and water masses</td>
  </tr>
  <tr>
    <td>960</td>
    <td>characteristics</td>
  </tr>
  <tr>
    <td>961</td>
    <td>Assumptions</td>
  </tr>
  <tr>
    <td>962</td>
    <td>Model assumptions: Species/Microplastic-environment equilibrium</td>
  </tr>
  <tr>
    <td>963</td>
    <td>Algorithms</td>
  </tr>
  <tr>
    <td>964</td>
    <td>Modelling techniques: Xgboost</td>
  </tr>
  <tr>
    <td>965</td>
    <td>Model complexity: As we want to explain abundances patterns, we used only one algorithm.</td>
  </tr>
  <tr>
    <td>966</td>
    <td>Model parametrization was made using cross-validation procedure and minimise log-likelihood for Poisson regression</td>
  </tr>
  <tr>
    <td>967</td>
    <td>likelihood for Poisson regression</td>
  </tr>
  <tr>
    <td>968</td>
    <td>Model averaging: No</td>
  </tr>
  <tr>
    <td>969</td>
    <td>Workflow</td>
  </tr>
  <tr>
    <td>970</td>
    <td>Model workflow: add each steps here</td>
  </tr>
  <tr>
    <td>971</td>
    <td>Software</td>
  </tr>
  <tr>
    <td>972</td>
    <td>Software: R software and packages used : xgboost, ModelMetrics,usdm,corrplot, tidyverse,</td>
  </tr>
  <tr>
    <td>973</td>
    <td>ggplot2</td>
  </tr>
  <tr>
    <td>974</td>
    <td>&lt;Code availability&gt;</td>
  </tr>
  <tr>
    <td>975</td>
    <td>&lt;Data availability&gt;</td>
  </tr>
  <tr>
    <td>976</td>
    <td>Data</td>
  </tr>
  <tr>
    <td>977</td>
    <td>Biodiversity data</td>
  </tr>
  <tr>
    <td>978</td>
    <td>Taxon names: Zooplankton and plastic (between 0.33 and 5 mm, between 0.33 and 1 mm,</td>
  </tr>
  <tr>
    <td>979</td>
    <td>between 1 and 5 mm, and all sizes i.e. larger than 0.33 mm)</td>
  </tr>
  <tr>
    <td>980</td>
    <td>&lt;Taxonomic reference system&gt;</td>
  </tr>
  <tr>
    <td>981</td>
    <td>&lt;Ecological level&gt;</td>
  </tr>
  <tr>
    <td>982</td>
    <td>&lt;Data sources&gt;</td>
  </tr>
  <tr>
    <td>983</td>
    <td>Sampling design: Details about sampling plan of Tara expedition, temporal design : from</td>
  </tr>
  <tr>
    <td>984</td>
    <td>June to November 2014, No nestedness</td>
  </tr>
  <tr>
    <td>985</td>
    <td>Sample size: Counts</td>
  </tr>
</table>
986 Absence data: No absences data

987 Background data: No background data

988 Explanatory variables :

989 Table S1 : Physical and biogeochemical data extracted from Copernicus

990

<table><thead><tr><th>Variable</th><th>Acronyms</th><th>Units</th><th>Initial spatial resolution</th><th>Spatial resolution used</th><th>Time period</th><th>Time resolution</th></tr></thead><tbody><tr><td>Mean temperature</td><td>temp_mean</td><td>°C</td><td>0.0625° x 0.0625°</td><td>0.125°x 0.125°</td><td>06/2014 to 11/2014</td><td>Daily then 6 months averaged</td></tr><tr><td>Mean Salinity</td><td>sal_mean</td><td>Psu</td><td>0.0625° x 0.0625°</td><td>0.125°x 0.125°</td><td>06/2014 to 11/2014</td><td>Weekly then 6 months averaged</td></tr><tr><td>Mean Dissolved oxygen (O<sub>2</sub>)</td><td>O2_mean</td><td>mmol.m<sup>-3</sup></td><td>0.0625° x 0.0625°</td><td>0.125°x 0.125°</td><td>06/2014 to 11/2014</td><td>Weekly then 6 months averaged</td></tr><tr><td>Mean Nitrate (NO<sub>3</sub>)</td><td>NO3_mean</td><td>mmol.m<sup>-3</sup></td><td>0.0625° x 0.0625°</td><td>0.125°x 0.125°</td><td>06/2014 to 11/2014</td><td>Weekly then 6 months averaged</td></tr><tr><td>Mean Phosphate (PO<sub>4</sub>)</td><td>PO4_mean</td><td>mmol.m<sup>-3</sup></td><td>0.0625° x 0.0625°</td><td>0.125°x 0.125°</td><td>06/2014 to 11/2014</td><td>Weekly then 6 months averaged</td></tr></tbody></table>

991

992

<table><caption>Table S2 : Summary of Lagrangian and Eulerian diagnostic parameters</caption><thead><tr><th>Lagrangian variables</th><th>Acronyms</th><th>Units</th><th>Spatial Resolution</th><th>Time period</th><th>Time resolution</th><th>Advection time</th><th>Distance threshold (only for Ippi)</th></tr></thead><tbody><tr><td>Finite Time Lyapunov Exponents</td><td>ftle</td><td>days<sup>-1</sup></td><td>0.125°x0.125°</td><td>06/2014 to 11/2014</td><td></td><td>15, 30, 45</td><td>-</td></tr><tr><td>Betweenness</td><td>betw</td><td>adim.</td><td>0.125°x0.125°</td><td>06/2014 to 11/2014</td><td></td><td>15, 30, 45</td><td>-</td></tr><tr><td>Retention time</td><td>ret.time</td><td>days</td><td>0.125°x0.125°</td><td>06/2014 to 11/2014</td><td></td><td>-</td><td>-</td></tr><tr><td>Okubo Weiss</td><td>ow</td><td>s<sup>-2</sup></td><td>0.125°x0.125°</td><td>06/2014 to 11/2014</td><td></td><td>0</td><td>-</td></tr><tr><td>Vorticity</td><td>vort</td><td>s<sup>-1</sup></td><td>0.125°x0.125°</td><td>06/2014 to 11/2014</td><td></td><td>0</td><td>-</td></tr><tr><td>Divergence</td><td>div</td><td>s<sup>-1</sup></td><td>0.125°x0.125°</td><td>06/2014 to 11/2014</td><td></td><td>0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60</td><td>-</td></tr><tr><td>Absolute velocity</td><td>vel.abs</td><td>m/s</td><td>0.125°x0.125°</td><td>06/2014 to 11/2014</td><td></td><td>-</td><td>-</td></tr><tr><td>Kinetic energy</td><td>kinNRJ</td><td>m<sup>2</sup>/s<sup>2</sup></td><td>0.125°x0.125°</td><td>06/2014 to 11/2014</td><td></td><td>-</td><td>-</td></tr><tr><td>Turbulent Kinetic Energy</td><td>tke</td><td>m<sup>2</sup>/s<sup>2</sup></td><td>0.125°x0.125°</td><td>06/2014 to 11/2014</td><td></td><td>-</td><td>-</td></tr><tr><td>Lagrangian Plastic</td><td>Ippi</td><td>adim.</td><td>0.125°x0.125°</td><td>06/2014 to 11/2014</td><td></td><td>0, 5, 10, 15, 0.5°, 0.75°, 1°</td><td></td></tr></tbody></table>
<table><tr><td>Pollution Index</td><td>to 11/2014</td><td>20, 25, 30, 35, 40, 45, 50, 55, 60</td><td>1.5°</td></tr></table>

993

<table><caption>**Table S3** : Explanatory variables used in Xgboost models for each group</caption><thead><tr><th>Group</th><th>Zooplankton</th><th>Plastic</th><th>Plastic</th></tr></thead><tbody><tr><td>Size</td><td>between 0.33 and 1 mm, between 1 and 5 mm, between 0.33 and 5 mm, all sizes</td><td>between 0.33 and 1 mm, between 1 and 5 mm, between 0.33 and 5 mm</td><td>All sizes</td></tr><tr><td>Distance threshold for lppi calculation</td><td>-</td><td>0.5°, 0.75°, 1°, 1.5°</td><td>0.5°, 0.75°, 1°, 1.5°</td></tr><tr><td>Variables</td><td>temp_mean</td><td>NO3_mean</td><td>PO4_mean</td></tr><tr><td></td><td>sal_mean</td><td>ftle45_B</td><td>NO3_mean</td></tr><tr><td></td><td>NO3_mean</td><td>ftle45_F</td><td>ftle45_B</td></tr><tr><td></td><td>ftle45_B</td><td>betw15</td><td>ftle45_F</td></tr><tr><td></td><td>ftle45_F</td><td>tke</td><td>betw15</td></tr><tr><td></td><td>tke</td><td>ow0</td><td>tke</td></tr><tr><td></td><td>ow0</td><td>vort0</td><td>ow0</td></tr><tr><td></td><td>vort0</td><td>div0</td><td>vort0</td></tr><tr><td></td><td>div0</td><td>kinNRJ</td><td>div0</td></tr><tr><td></td><td>kinNRJ</td><td>lppi with advection time = 0 days</td><td>kinNRJ</td></tr><tr><td></td><td></td><td>lppi with advection time = 60 days</td><td>lppi with advection time = 0</td></tr><tr><td></td><td></td><td>lppi with advection time = 60 days</td><td>lppi with advection time = 60 days</td></tr></tbody></table>

994

995

Data sources: http://marine.copernicus.eu/services-portfolio/access-to-

996

products/?option=com_csw&view=details&product_id=MEDSEA_REANALYSIS PHYS 006

997

004

998

Spatial extent: -3.7, 34.7, 30.9, 45 (xmin, xmax, ymin, ymax)
999 Spatial resolution: ~13km

1000 Coordinate reference system: WGS84

1001 Temporal extent: From June to November 2016

1002 Transfer data

1003 Spatial extent: -3.7, 34.7, 30.9, 45 (xmin, xmax, ymin, ymax)

1004 Spatial resolution: ~13km

1005 Temporal extent: From June to November 2016

1006 Model

1007 Multicollinearity

1008 Multicollinearity: Variance inflation factors in a stepwise procedure and Spearman pair-wise

1009 correlation test

1010 Model settings

1011 Table S4: Predictive performance for each size class and each distance threshold for plastic

<table><thead><tr><td>Group</td><td>Distance threshold for lppi calculation</td><td>Size</td><td>R<sup>2</sup></td><td>Model selected</td></tr></thead><tbody><tr><td>Plastic</td><td>0.5°</td><td>Between 1 and 5 mm</td><td>0.68</td><td></td></tr><tr><td>Plastic</td><td>0.75°</td><td>Between 1 and 5 mm</td><td>0.68</td><td></td></tr><tr><td>Plastic</td><td>1°</td><td>Between 1 and 5 mm</td><td>0.5</td><td></td></tr><tr><td>Plastic</td><td>1.5°</td><td>Between 1 and 5 mm</td><td>0.7</td><td>x</td></tr><tr><td>Plastic</td><td>0.5°</td><td>Between 0.33 and 1 mm</td><td>0.7</td><td></td></tr><tr><td>Plastic</td><td>0.75°</td><td>Between 0.33 and 1 mm</td><td>0.74</td><td>x</td></tr><tr><td>Plastic</td><td>1°</td><td>Between 0.33 and 1 mm</td><td>0.73</td><td></td></tr><tr><td>Plastic</td><td>1.5°</td><td>Between 0.33 and 1 mm</td><td>0.73</td><td></td></tr><tr><td>Plastic</td><td>0.5°</td><td>All size</td><td>0.78</td><td>x</td></tr><tr><td>Plastic</td><td>0.75°</td><td>All size</td><td>0.6</td><td></td></tr></tbody></table>
<table><tr><td>Plastic</td><td>1°</td><td>All size</td><td>0.5</td></tr><tr><td>Plastic</td><td>1.5°</td><td>All size</td><td>0.46</td></tr></table>

1012

1013

1014

1015

1016

1017

1018

1019

<caption>Table S5: Best parameters estimated from calibration model for each categories</caption>

1020

<table><thead><tr><th rowspan="2">Group</th><th rowspan="2">Size</th><th rowspan="2">R<sup>2</sup></th><th colspan="4">Model parameters</th></tr><tr><th>Trees</th><th>Maximum depth</th><th>Eta</th><th>Minimum child weight</th></tr></thead><tbody><tr><td>Zooplankton</td><td>Between 0.33 and 1 mm</td><td>0.66</td><td>719</td><td>2</td><td>0.4</td><td>1</td></tr><tr><td>Zooplankton</td><td>Between 1 and 5 mm</td><td>0.77</td><td>702</td><td>2</td><td>0.4</td><td>5</td></tr><tr><td>Zooplankton</td><td>All sizes (i.e. > 0.33 mm)</td><td>0.62</td><td>717</td><td>2</td><td>0.4</td><td>1</td></tr><tr><td>Plastic</td><td>Between 0.33 and 1 mm</td><td>0.73</td><td>625</td><td>2</td><td>0.4</td><td>5</td></tr><tr><td>Plastic</td><td>Between 1 and 5 mm</td><td>0.7</td><td>627</td><td>2</td><td>0.4</td><td>1</td></tr><tr><td>Plastic</td><td>All sizes (i.e. > 0.33 mm)</td><td>0.78</td><td>661</td><td>2</td><td>0.4</td><td>5</td></tr></tbody></table>

1021

1022

1023 Model estimates

1024 Coefficients: mean and standard deviation

1025 Analysis and Correction of non-independence

1026 Spatial autocorrelation: No
1027 **Assessment**

1028 **Performance statistics**

1029 Performance on training data: log-likelihood for Poisson regression.

1030 Performance to assess difference between observed and predicted abundances : R² and

1031 pearson correlation coefficient.

1032 **Plausibility check**

1033 Response shapes: partial dependence plots

1034

1035

1036

1037

1038

1039 **Supplementary Figures**

<figure><img src="image_11.png" /></figure>

1040

1041

**Figure S1:** Zooplankton concentration (y-axis) as a function of the sampling hour (x-axis). R²=0.03, t-test not significant.

1042
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

1043

1044

1045

1046

1047

**Figure s2:** Regression between predicted and observed abundance (blue line) for plastic a) between 0.33 and 5 mm, b) between 0.33 and 1 mm, c) between 1 and 5 mm and d) all sizes (i.e. > 0.33 mm). Red line is a line of equation 1:1, if points are on this line predicted values=observation. If points are above, predicted values are > observation and if below predicted values < observations

<figure><img src="image_16.png" /></figure>

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

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

1048

1049

1050

1051

1052

**Figure S3:** Regression between predicted and observed abundance for zooplankton a) between 0.33 and 5 mm, b) between 0.33 and 1 mm, c) between 1 and 5 mm and d) all sizes (i.e. > 0.33 mm). Red line is a line of equation 1:1, if points are on this line predicted values=observation. If points are above, predicted values are > observation and if below predicted values < observations
<figure><img src="image_20.png" /><figcaption>Figure S4: Spatial projection and partial dependence plot of zooplankton (panels a and c respectively) and plastic debris Between 0.33 and 1 mm (b and d respectively). oW0 = Okubo Weiss, kinNRJ = Kinetic Energy, temp_mean = mean temperature, NO3_mean = mean nitrate concentration, vort0 = vorticity, tke = Turbulent Kinetic Energy, ftle45_F = FTLE 45 days forward in time, ftle45_B = FTLE 45 days backward in time, div0 = Eulerian Divergence, betw15 = Betweenness at 15 days, sal_mean = mean salinity, lppi0075_60 = LPPI with distance threshold of 0.75° and 60 days backward in time, lppi0075_0 = LPPI with distance threshold of 0.75° and no advection</figcaption></figure>

1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
<figure><img src="image_21.png" /></figure>

Figure S5: Spatial projection and partial dependence plot of zooplankton (a and c respectively) and plastic between 1 and 5 mm (b and d respectively). Ippi0150_60 = LPPI with distance threshold of 1.5° and 60 days backward in time, Ippi0150_0 = LPPI with distance threshold of 1.5° and no advection. The meaning of the other abbreviations in the partial dependence plots is reported in Fig. 2, Fig.S4, and in Table S1.
<figure><img src="image_22.png" /></figure>

Figure S6: Spatial projection and partial dependence plot of zooplankton (a and c respectively) and plastic all size (b and d respectively). lppi0150_60 = LPPI with distance threshold of 1.5° and 60 days backward in time, lppi0150_0 = LPPI with distance threshold of 1.5° and no advection. The meaning of the other abbreviations in the partial dependence plots is reported in Fig. 2, Fig.S4, and in Table S1.

1077

1078

1079

1080

1081

1082

1083

1084

1085
<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

1086

1087 Figure S7: Standard deviation of the projection of a) zooplankton b) plastic debris

1088 concentration; c) plastic to zooplankton ratio (obtained from the error propagation using a

1089 and b panel values) for the size class between 0.33 and 5 mm.

<figure><img src="image_26.png" /></figure>

1090

1091 Figure S8: Small pelagic biomass in the Mediterranean Sea, calculated from the Osmose model

1092 (Materials and Methods)
<figure><img src="image_27.png" /></figure>

1093

1094 Figure S9: Ratio zooplankton:plastic debris of size between 0.33 and 5 mm, showed only in the

1095 regions where the small pelagic biomass is larger than the 80th percentile (left panel) and 95th

1096 percentile (right panel) of all the small pelagic biomass values reported in Fig. S9

1097

1098

1099

<figure><img src="image_28.png" /></figure>

1100

1101

1102 Figure S10: Ratio plastic:plankton for size a) between 0.33 and 1 mm and b) between 1 and 5 mm.

1103
<figure><img src="image_29.png" /></figure>

Figure S11: Climatology of zooplankton concentration (expressed as mass content of zooplankton in g/m²) obtained from GLOBAL_MULTIYEAR_BGC_001_033 product (CMEMS platform) between June and November 2014.

1104

1105

1106

1107

1108

1109