# Benthic food resources and the condition of benthodemersal fish: spatial trends and relationships in the northern shelf of the Bay of Biscay.

Thomas Outrequin, Hubert Du Pontavice, Jérôme Guitton, Pauline Boët, Jacques Grall, Pascal Laffargue, Hervé Le Bris

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## To cite this version:

Thomas Outrequin, Hubert Du Pontavice, Jérôme Guitton, Pauline Boët, Jacques Grall, et al. Ben-thic food resources and the condition of benthodemersal fish: spatial trends and relationships in the northern shelf of the Bay of Biscay. Estuarine, Coastal and Shelf Science, 2025, 327, pp.109593. 10.1016/j.ecss.2025.109593. hal-05343565

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Benthic food resources and the condition of benthodemersal fish: spatial trends and relationships in the northern shelf of the Bay of Biscay.

Thomas Outrequin, Hubert du Pontavice, Jérôme Guitton, Pauline Boët, Jacques
Grall, Pascal Laffargue, Hervé Le Bris

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

PII: S0272-7714(25)00471-8

DOI: https://doi.org/10.1016/j.ecss.2025.109593

Reference: YECSS 109593

To appear in: *Estuarine, Coastal and Shelf Science*

Received Date: 14 February 2025

Revised Date: 29 October 2025

Accepted Date: 29 October 2025

Please cite this article as: Outrequin, T., du Pontavice, H., Guitton, J., Boët, P., Grall, J., Laffargue, P., Le Bris, H., Benthic food resources and the condition of benthodemersal fish: spatial trends and relationships in the northern shelf of the Bay of Biscay., *Estuarine, Coastal and Shelf Science*, https://doi.org/10.1016/j.ecss.2025.109593.

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1 **Title:** Benthic food resources and the condition of benthodemersal fish: spatial trends and relationships
2 in the northern shelf of the Bay of Biscay.

3

4 **Authors:** Thomas Outrequin¹, Hubert du Pontavice ², Jérôme Guitton¹, Pauline Boët¹, Jacques Grall³,
5 Pascal Laffargue¹, Hervé Le Bris¹

6

7 **Affiliation:**

8 1. DECOD (Ecosystem Dynamics and Sustainability), INRAE, Institut Agro, IFREMER, Rennes, Nantes,
9 France

10 2. IFREMER, HMMN, Laboratoire Ressources Halieutiques, F-14520 Port en Bessin, France

11 3. OSU UAR 313 IUEM, Univ Brest, CNRS, IRD, 29280 Plouzané, France

12 The authors made the following contributions. Thomas Outrequin: Conceptualization, Writing - Original

13 Draft, Writing - Review and Editing; Hubert du Pontavice: Conceptualization and Investigation; Jérôme

14 Guitton: Methodology and Resources; Pauline Boët: Investigation and Resources; Jacques Grall:

15 Writing - Review and Editing, Supervision; Pascal Laffargue: Writing - Review and Editing, Supervision;

16 Hervé Le Bris: Writing - Review and Editing, Supervision.

17 Correspondence concerning this article should be addressed to Thomas Outrequin.

18 Postal address: 65 rue de Saint-Brieuc, Rennes, 35000, France

19 E-mail: thomas.outrequin@institut-agro.fr
20

**Abstract:**

Located at the interface between the continent and the open ocean, continental shelves exhibit strong gradients in nutrients, organic matter, and primary production. Within these ecosystems, benthic invertebrates play a key role in transferring energy and matter from primary producers to benthodemersal fish. Consequently, macrobenthic biomass can serve as an indicator of food resource availability for these species. This study focused on the northern part of the Bay of Biscay continental shelf to examine this relationship. Given the limited availability of field data, both empirical and biogeochemical models were employed to estimate macrobenthic biomass indices and to generate spatial distribution maps. Modeled biomass indices showed strong correlations with the few available field observations, although discrepancies were evident in deeper, poorly sampled areas. A pronounced coastal-offshore gradient was observed, with macrobenthic biomass decreasing towards offshore waters and locally elevated values occurring in deeper muddy sediments. Among the eight benthodemersal fish species examined, the four identified as predominantly benthic-feeders exhibited positive relationships between body condition and macrobenthic biomass indices, particularly among smaller individuals. These findings suggest that modeled macrobenthic biomass indices and maps can serve as effective proxies for benthic resources gradients and provide insights into spatial variability in energy transfer within the food web. Nonetheless, improved field sampling, especially in deep circalittoral zones, remains essential to refine spatial trends, enhance our understanding of benthic food-web processes, and support the sustainable management of benthodemersal fisheries.
39 **Highlights:**

40 - Estimates of macrobenthic biomass exhibited coastal-to-offshore trend on the shelf.

41 - The sparse field data macrobenthic biomass correlated with data from models.

42 - In deeper areas, muddy sediments contained the greatest macrobenthic biomass.

43 - Body condition of benthivorous fish improved with increased macrobenthic biomass.

44 - Diet literature suggests biomass maps reflect food resources beyond macrofauna.

45 **Key words:** Macrobenthic biomass, fish, body condition, continental shelf, food resource, Bay of

46 Biscay

47
48 **Introduction**

49
The northwest Atlantic shelf is a vast area whose location between the continent and the ocean creates
50
strong environmental gradients that influence ecological processes such as matter production and
51
transfer (Hall, 2002). Within this ecosystem, benthic invertebrates play a preeminent role as a link
52
between primary producers and top predators (Chen et al., 2021), particularly benthodemersal fish.
53
From the coast to the shelf break, strong declining gradients are typically observed in levels of nutrients,
54
organic matter, and primary production, and it is generally assumed that the biomass and/or production
55
of benthic invertebrates follow the same trend (Hall, 2002; Wei et al., 2010). Given the role of these
56
invertebrates as prey for benthodemersal fish (Amezcua et al., 2003; Armstrong, 1982; Day et al.,
57
2019), it is likely that such spatial trends in food resources will have repercussions for fish populations
58
across the continental shelf.

59
The northern part of the continental shelf of the Bay of Biscay offers an ideal location to study spatial
trends in these prey resources--particularly in the macrobenthic component--and the consequences for
60
the benthodemersal fish that depend on them for food (e.g., Lagardère, 1987; Le Loc'h and Hily, 2005).
61
In this area, there is a strong terrigenous influence from the numerous small coastal rivers that flow onto
62
the coastal fringe as well as two larger rivers, the Vilaine and the Loire (Borja et al., 2019). Given this, it
63
is logical to expect a coastal-to-offshore gradient in the production or biomass of benthic invertebrates.
64
However, to confirm this kind of trend, it would be necessary to have data covering the entire study area,
65
as was done in the North Sea by Reiss et al. (2010). Unfortunately, the available data on macrobenthic
66
biomass in the Bay of Biscay are scattered in time and mainly restricted to coastal areas or to specific
67
shelf habitats (e.g., mid-shelf fishing grounds) (Kopp et al., 2015; Le Loc'h et al., 2008; Rigolet et al.,
68
2014; Robert, 2017). In such a case, though, it may be possible to estimate biomass using established
69
models, such as the graph model developed by Chassé and Glémarec (1976) based on depth and
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sedimentary substrates. More recently, mechanistic models like ERSEM (Kay et al., 2018) have also
71
shown promise for predicting the spatial variability of macrobenthic biomass (e.g., Timmermann et al.,
72
2012).
73

74
Higher-quality habitats, such as those offering more food resources, are generally associated with
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improved body condition among predators (Engelhard et al., 2013; Liao et al., 1995). Therefore, it may
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be that patterns of abundance or scarcity of macrofaunal food resources across the shelf could translate
77 into spatial variability in the body condition of benthodemersal fish predators. Within the Bay of Biscay, 78 benthodemersal fish communities have been well described (Souissi et al., 2001), and the diets of most 79 species are known (e.g., Rault et al., 2017; Day et al., 2020). With this information, it is possible to 80 characterize the extent to which each species might be dependent on macrobenthic invertebrates.

81 Moreover, thanks to the EVHOE sampling surveys (Laffargue et al., 2020), biometric data are available 82 for multiple benthodemersal fish species across several years. By analyzing patterns in macrobenthic 83 biomass and the body condition of benthodemersal fish—and taking into account the diet plasticity of 84 these fish with respect to benthic macrofauna—it might be possible to detect the influence of spatial 85 trends in benthic food resources on higher trophic levels. Such information can then be used to shed 86 light on the broader trophic functioning of continental shelves.

87 The work presented here aims to (1) describe spatial variability in macrobenthic biomass at the shelf 88 scale in the Bay of Biscay based on data from models, (2) compare these data to the scarce field data 89 available, and (3) assess whether macrofaunal biomass data generated by such models exhibit a 90 relationship with the body condition of benthodemersal fish, and might thus be representative of the 91 underlying availability of macrobenthic food resources.
2. Materials and Methods

2.1. Macrobenthic biomass indices

2.1.1. From an empirical graph model (Chassé and Glémarec, 1976)

<figure><img src="image_4.png" /><figcaption>Figure 1: Graph model from Chassé and Glémarec (1976). Y-axis corresponds to logged bathymetric depth, and x-axis to granulometry divided into broad substrate categories.</figcaption></figure>

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The first model used to generate macrobenthic biomass indices was based on a graph of biomass isopleths created from grab samples in the Bay of Biscay (Chassé and Glémarec, 1976) (Figure 1). Depth data came from depth maps (GEBCO Bathymetric Compilation Group, 2023). This model is subsequently referred to as the “graph” model.

Biomass values were digitized from this graph (Figure 1) for each substrate category (silted heterogeneous sand (SHV), heterogeneous gravel (GH), clean/muddy gravel (GP/GV), coarse sand (SG), fine sand (SF), muddy fine sand (FV), sandy mud (VS), and pure mud (VV)), and for each bathymetric stratum (with a step of 10 m between 0 and 100 m depth, then from 100 to 200 m). Once rasterized, each cell was assigned a biomass value (in g.m⁻² dry weight (DW)) corresponding to the lower-bound isopleth. The maximal cell value was 32 g.m⁻² DW, associated with shallow muddy fine
sand (FV). Finally, macrobenthic biomass values were assigned to each sub-section (substrate category and bathymetric stratum), corresponding to the averaged biomass value over the proportion of cells.

This process provided an average value of macrobenthic biomass for each substrate category and bathymetric stratum. The average macrobenthic biomass values were then extrapolated onto substrate maps from 1976 and 2023. The resulting macrobenthic biomass indices derived from these maps are hereafter referred to as Biomass<sub>1976</sub> and Biomass<sub>2023</sub>, respectively.

The first substrate map used was the 'Atlas du littoral français: Atlas des fonds meubles du plateau continental du Golfe de Gascogne [Atlas of the French coastline: Atlas of the soft bottoms of the continental shelf of the Bay of Biscay]' obtained from the same study as the graph model described above (Chassé and Glémarec, 1976). The map of biomass indices resulting from this first substrate map is hereafter referred to as the 'Biomass<sub>1976</sub> map'. The second substrate map used is more recent than the first (Vasquez et al., 2023) and is based on a more-extensive dataset describing the nature of the seabed. We adapted the substrate categories in this map to correspond to those of the biomass graph model based on a comparative analysis of the two substrate maps (results are in Supplementary Materials 1). The second map of biomass indices is subsequently referred to as the 'Biomass<sub>2023</sub> map'.

Some approximations were necessary for certain areas (5% of the shelf surface for Biomass<sub>1976</sub> and Biomass<sub>2023</sub> maps) where the substrate x bathymetry intersection was not represented in the graph model, and biomass data were not available. These areas were approximated by another substrate with similar levels of macrobenthic biomass and similar trends along the depth gradient. For the Atlas substrate maps, biomass values for muddy/clean gravel (GP/GV) were approximated by biomass from coarse sand (SG) below 80 meters depth, and biomass values from muddy heterogeneous sand (SHV) were approximated by biomass from muddy fine sand (FV) below 50 meters depth. For the more-recent substrate maps, biomass values of gravel and coarse sand from the graph model were averaged (GP/GV and SG) for the "coarse substrate" category. Descriptions of the substrate categories of the graph model and the two substrate maps are available in Supplementary Materials 2.

### 2.1.2. From a biogeochemical model (POLCOMS-ERSEM)

The second set of modeled data used to generate macrobenthic biomass indices came from a mechanistic biogeochemical model. The data originated from the outputs of the POLCOMS-ERSEM model, a regional high-resolution coupled hydrodynamic-ecosystem model for low trophic levels, driven
138 by a reanalysis of data from the period 2000–2015 (Kay et al., 2018). POLCOMS-ERSEM links a general 139 ocean circulation model, POLCOMS (the Proudman Oceanographic Laboratory Coastal Ocean 140 Modelling System) and a marine ecosystem model, ERSEM (European Regional Seas Ecosystem 141 Model; Butenschön et al., 2016). It covers the European shelf seas, with a horizontal resolution of 0.1° 142 (ca 11 km). As we are interested in the biological part of the model, we will refer to it subsequently as 143 the ERSEM model. In ERSEM, the benthic model is developed on the basis of muddy sediments. In this 144 model, three groups represent the bulk of macrobenthic biomass, and they were initially calibrated using 145 data estimated by Bryant *et al.* (1995) based on macrobenthic biomass values in the North Sea. We 146 selected deposit- and suspension-feeding groups as the macrobenthic components of interest (the only 147 other group, 'meiobenthos', was not considered).

148 Biomass values were summed from all suspension and deposit feeders in ERSEM to create a 149 macrobenthic biomass index. Macrobenthic biomass values were averaged over the last 10 years 150 (2006–2015) to minimize potential yearly effects. The macrobenthic biomass index from ERSEM was 151 expressed in gC/m² and the map of these values is referred to here as the “BiomassERSEM map”. 152

## 2. 2. Biological field data

### 2.2.1. Biomass of macrobenthic invertebrates

154 Macrobenthic biomass indices from the three biomass maps described above were compared to field 155 data collected in the circalittoral area (i.e., less than 15 m depth) of the northern part of the shelf of the 156 Bay of Biscay. Table 1 provides a summary of the various surveys from which data on macrobenthic 157 biomass were obtained; the spatial distribution of sampled stations (from 2 to 5 grabs per station) is 158 presented in Figure 2. As seen in this figure, these data are very sparse over both time and space, and 159 no single survey covers the full extent of the continental shelf.

160 Table 1: Summary of different macrobenthic surveys in which biomass data were collected, with the 161 gear used, the time of the year, the year sampled, the average bathymetry (and its range), and the 162 number of stations for each.

<table><thead><tr><td>Survey</td><td>Sampling gear</td><td>Time</td><td>Bathymetry</td><td>N</td><td>Reference</td></tr></thead><tbody><tr><td>RetroB</td><td>Van Veen grab</td><td>March (2008)</td><td>20.1 (15-25.6)</td><td>17</td><td>(Le Bris, 2008)</td></tr><tr><td>Nurse2008</td><td>Van Veen grab</td><td>August - September (2008)</td><td>21.8 (16.3-35.3)</td><td>20</td><td>(Brind'Amour, 2008)</td></tr><tr><td>PLOOPS 1</td><td>Van Veen grab</td><td>July (2009)</td><td>27.6 (23-35)</td><td>18</td><td>(Dubois, 2010a)</td></tr><tr><td>PLOOPS 2</td><td>Van Veen grab</td><td>February - November (2010)</td><td>25.5 (21.3-32.8)</td><td>24</td><td>(Dubois, 2010b)</td></tr><tr><td>Isobaie 2</td><td>Van Veen grab</td><td>June (2010)</td><td>77.3 (18.9-154)</td><td>11</td><td>(Le Bris, 2010)</td></tr><tr><td>FEBBE 1</td><td>Day grab</td><td>May - August (2013)</td><td>101.3 (78-113)</td><td>18</td><td>(Laffargue, 2013a)</td></tr></tbody></table>
<table><tr><td>FEBBE 2</td><td>Day grab</td><td>August (2013)</td><td>101.9 (91-113)</td><td>23</td><td>(Laffargue, 2013b)</td></tr><tr><td>APPEAL1</td><td>Hamon grab</td><td>May (2018)</td><td>60.7 (53-74)</td><td>15</td><td>(Le Loc'h and Grall, 2018a)</td></tr><tr><td>APPEAL 2</td><td>Hamon grab</td><td>August (2018)</td><td>59.2 (52-73)</td><td>15</td><td>(Le Loc'h and Grall, 2018b)</td></tr><tr><td>APPEAL 4</td><td>Hamon grab</td><td>September (2019)</td><td>109.5 (98.4-125.1)</td><td>12</td><td>(Le Loc'h and Grall, 2019)</td></tr></table>

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RetroB and Nurse2008 were two surveys performed in a coastal bay (Bay of Vilaine) (Brind'Amour, 2008; Le Bris, 2008), in a shallow circalittoral area under the terrigenous influence. PLOOPS 1 and 2 surveyed a coastal area in the Bay of Concarneau (Dubois, 2010a; Dubois, 2010b), which, although it also features a shallow circalittoral fringe, has less terrigenous influence compared to the Bay of Vilaine. APPEAL 1 and 2 focused on the deeper limit of the shallow circalittoral fringe, to the south of the island of Groix (Le Loc'h and Grall, 2018a; Le Loc'h and Grall, 2018b). APPEAL 4 and FEBBE 1 and 2 surveyed a deep circalittoral muddy bank, the so-called "Grande Vasière" (Laffargue, 2013a; Laffargue, 2013b; Le Loc'h and Grall, 2019). Finally, Isobaie 2 covered an coastal-to-offshore transect from the Loire estuary and Bay of Vilaine towards the shelf break (Le Bris, 2010).

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

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<caption>Figure 2: Locations of macrobenthic field observation stations on the northern shelf of the Bay of Biscay. Different surveys are represented with different colors. The light gray shaded area is the mid-shelf mud</caption>
176 belt known as the "Grande Vasière". The dashed line in the figure delineates the spatial extent of the
177 modeled macrobenthic biomass maps.

178 Each survey counted taxa down to the lowest taxonomic level, and biomass was measured for most of
179 the taxa, sampled stations, and studies. When individual mass was not available, it was approximated
180 using values from the closest phylogenetic relative from the same station or from a neighboring station
181 of the same survey. Biomass values in wet weight were converted into dry weight or carbon, depending
182 on the model used for comparison, using Brey's conversion factors (www.thomas-
183 brey.de/science/virtualhandbook). For each station, observed macrobenthic biomass was the average
184 of the values obtained from two to five replicates.

185 **2.2.2. Data on individual benthodemersal fish**

186 Individual biometric data (total length and mass) from autumnal EVHOE surveys (between 2008 and
187 2022, onboard Thalassa R/V) were used to assess the body condition of a limited selection of benthod-
188 demersal fish species. Body condition is ultimately a reflection of a fish's habitat quality, and
189 consequently also of food resource availability (Cavraro et al., 2019; Karlson et al., 2018; Liao et al.,
190 1995; Schloesser and Fabrizio, 2019). We examined most of the benthivorous species for which
191 sufficient data were available across the continental shelf (Supplementary Materials Figure SM4 and
192 SM5). Four demersal species were considered: whiting (*Merlangius merlangus*), striped red mullet
193 (*Mullus surmuletus*), poor cod (*Trisopterus minutus*), and pout (*Trisopterus luscus*). Four benthic taxa
194 were also considered: scaldfish (*Arnoglossus* spp.), small-spotted catshark (*Scyliorhinus canicula*),
195 common sole (*Solea solea*), and thickback sole (*Microchirus variegatus*). *Arnoglossus* spp. includes
196 both *A. laterna* and *A. imperialis*, which are assumed to have a similar diet (Castro et al., 2013), and the
197 combination of the two made it possible to cover a wide coastal-to-offshore gradient.

198 **2.3. Data analysis**

199 **2.3.1. Comparison of macrobenthic biomass indices from models**

200 Once we had generated the maps of the three macrobenthic biomass indices, we compared the spatial
201 patterns presented. Each map was standardized and gridded with the 0.1° x 0.1° ERSEM grid, and each
202 grid cell was assigned an average biomass index value (weighted by the surface area of each biomass
203 value within the grid cell). BiomassERSEM, Biomass1976, and Biomass2023 were then compared using
204 Spearman correlations, as our primary interest was in the relative spatial patterns. Specifically, for a
given cell, a buffer area of 5 cells was considered (resulting in a 25-cell window) and Spearman correlations were computed for the values of each biomass map in this window, allowing us to highlight potential differences in spatial trends between the different maps. Negative values meant that the two maps in question depicted opposite trends in macrobenthic biomass. All analyses were conducted using R 4.3.1 (R core team, 2023); maps were processed using the raster and sf packages (Hijmans, 2023; Pebesma, 2018).

## 2.3.2. Comparison between field data on macrobenthic biomass and biomass indices from models

The macrobenthic field biomass values were compared with the modeled values in a relative way, given that significant absolute biases were observed between field values and modeled values (Figure SM7). To compare the three maps generated to values of macrobenthic biomass from field datasets, the nearest macrobenthic biomass values from each of the three maps were assigned to each field station sampled, and these values were averaged within a 500-m buffer surrounding the station. Values from biomass index maps and field values were log-transformed and normalized so that the final data ranged between 0 and 1. Then, Spearman correlations were calculated between macrobenthic biomass values from field data and indices from models.

## 2.3.3. Relationship between body condition and macrobenthic biomass indices

Fish body condition was assessed using the body condition factor developed by Le Cren (1951), which represents 'the deviation of an individual from the average weight for length' in a given population. Le Cren's condition factor, $K_n$, compensates for allometric changes, enabling comparisons of body condition between small and large individuals. Indeed, the response of body condition to macrobenthic resource availability may vary in a size-dependent manner due to trophic shifts and diet plasticity.

For each species, log length-mass relationships (LMR) were fitted using individual biometric data. Observations with a Cook's distance greater than 4/n (n = number of observations) were considered outliers and removed (Altman and Krzywinski, 2016). For each species, homogeneity of variance and the normal distribution of residuals were checked visually.
K<sub>n</sub> is the observed individual mass divided by the predicted mass according to each species' LMR. To take into account ontogenic variations, individuals were split into two size groups, to reflect the trophic size thresholds reported in the literature for six of the fish species (Table 2).

Finally, we built models to assess the relationship between the body condition factor and the macrobenthic biomass indices. Although the relationships between body condition and prey biomass may be inherently non-linear, attempts to fit more complex non-linear models were limited by the very sparse and uneven distribution of sampling stations along the macrobenthic biomass gradients. Consequently, we retained mixed-effect linear models as the most robust and interpretable approach.
One model was computed for each of the eight fish taxa and the three macrobenthic biomass index maps (Biomass<sub>1976</sub>, Biomass<sub>2023</sub>, and Biomass<sub>ERSEM</sub>), and fitted using the lme4 R package (Bates et al., 2015). Models were computed with station as a random effect to account for spatial and temporal autocorrelation (Zuur et al., 2009). Models included a size category fixed-effect (i.e., Small or Large), with the exception of the models for *Arnoglossus* spp. and *Microchirus variegatus*. Each station was assigned the average value of macrobenthic biomass within a 5000-m buffer around its location. The sensitivity of results to this buffer was explored, and no major differences were observed when comparing results from 1-km to 50-km buffers. Macrobenthic biomass indices were scaled to allow the comparison of their respective effects on K<sub>n</sub>. Chi-squared tests were used to evaluate these results against the null hypothesis (i.e., a model without a biomass effect). During model evaluation, mixed-effect linear models were refitted using maximum likelihood, and only significant models (p-value < 0.05) were kept. Confidence intervals of macrobenthic biomass indices effects on K<sub>n</sub> are available in SM9.
Additionally, the variance explained by selected mixed-effect linear models was computed using conditional R-squared and marginal R-squared values; conditional R-squared included random and fixed effects of the model, while marginal R-squared corresponded to the variance explained only by the fixed effect (Zuur et al., 2009). Residuals were visually examined, and Moran's I test was performed to check for spatial autocorrelation.

Since the spatial extent of the biomass maps varied, the amount of data per species and size class available for the mixed-effect linear models also differed. The quantity of data used for each mixed-effect linear model and each macrobenthic biomass model is provided in Table SM6.

Table 2: Size thresholds used to account for ontogenic shifts in different fish species.
<table><thead><tr><td>Species</td><td>Size shift</td><td>Trophic shift</td><td>Reference</td></tr></thead><tbody><tr><td>Arnoglossus spp.</td><td>-</td><td></td><td>(Schückel et al., 2011)</td></tr><tr><td>Merlangius merlangus</td><td>20</td><td>Shift from benthic invertebrates to fish.</td><td>(Hislop et al., 1991)</td></tr><tr><td>Microchirus variegatus</td><td>-</td><td></td><td>(Amezcua et al., 2003)</td></tr><tr><td>Mullus surmuletus</td><td>18</td><td>Shift from benthic invertebrates to shrimp, ophiurids, and polychaetes.</td><td>(Bautista-Vega et al., 2008)</td></tr><tr><td>Scyliorhinus canicula</td><td>22</td><td>Shift from small invertebrates to larger decapods, cephalopods, and fish.</td><td>(Šantić et al., 2012)</td></tr><tr><td>Solea solea</td><td>30</td><td>Shift toward epibenthic decapods and higher trophic level.</td><td>(Fanelli et al., 2022)</td></tr><tr><td>Trisopterus luscus</td><td>25</td><td>Shift from benthic invertebrates towards shrimp and fish.</td><td>(Armstrong, 1982)</td></tr><tr><td>Trisopterus minutus</td><td>17</td><td>Shift from small crustaceans to larger decapods and fish.</td><td>(Armstrong, 1982)</td></tr></tbody></table>
261 **3. Results**

262 **3.1. Maps of macrobenthic biomass indices**

263

<figure><img src="image_6.png" /><figcaption>Figure 3: Maps of macrobenthic biomass indices obtained from the graph and ERSEM models. White areas in the Biomass<sub>1976</sub> and Biomass<sub>2023</sub> maps correspond to hard bottom substrate, where soft bottom macrobenthic biomass could not be assessed. Gray lines represent the 100- and 200-meter isobaths.</figcaption></figure>

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The Biomass<sub>1976</sub> and Biomass<sub>2023</sub> maps highlighted high values of macrobenthic biomass along the coast, in concentrated areas characterized by shallow patches of mud, sandy mud, and muddy sand
substrate. Instead, index values from the Biomass<sub>ERSEM</sub> map were higher along a coastal fringe ranging from the Pertuis-Charentais to the peninsula of Quiberon (Figure 3).

The Biomass<sub>ERSEM</sub> map depicts a decreasing gradient in biomass towards the shelf break, inversely related to water depth, while the Biomass<sub>1976</sub> and Biomass<sub>2023</sub> maps highlight patches of substrate at moderate depths that support higher macrobenthic biomass. Overall, the Biomass<sub>1976</sub> and Biomass<sub>2023</sub> maps were highly correlated (Spearman correlation = 83.09%), while the Biomass<sub>ERSEM</sub> map exhibited a weaker correlation with the two others (49.82% and 62.84%, respectively). The most substantial differences were noted between the Biomass<sub>1976</sub> and Biomass<sub>ERSEM</sub> maps; patches corresponding to certain habitats (e.g., the large mud belt in the central part of the shelf, or at the edge of the continental shelf) exhibited opposing trends in macrobenthic biomass between these two maps (Figure 4).

<figure><img src="image_7.png" /></figure>
Figure 4: Maps of local Spearman correlations, with blue representing negative correlations and red positive correlations. Gray lines represent the 100- and 200-meter isobaths.

284

285

## 3.2. Biomass indices from models versus field biomass data

Despite differences in absolute trends, positive Spearman correlations were significantly (p-value < 0.05) detected between the three indices of macrobenthic biomass and field data on macrobenthic biomass (62.4%, 65.1%, and 79.9% for Biomass<sub>1976</sub>, Biomass<sub>2023</sub>, and Biomass<sub>ERSEM</sub>, respectively; Figure 5). Based on the ERSEM model, the highest level of biomass was predicted for stations located at 15–20 m depth. Instead, the graph model (Biomass<sub>1976</sub> and Biomass<sub>2023</sub>) estimated the highest levels of biomass at a depth of 20-50 m, and similar, lower, levels between stations at 15–20 m and 50–75 m. Most of the stations that were estimated to have high biomass levels (from Biomass<sub>1976</sub> and Biomass<sub>2023</sub>) were sampled as part of the PLOOPS surveys (Figure SM8). The bathymetric gradient previously noted in the Biomass<sub>ERSEM</sub> index is also evident in Figure 5, as stations tended to aggregate along the y-axis according to their bathymetry. This pattern was less marked for the Biomass<sub>1976</sub> and Biomass<sub>2023</sub> indices, which generated similar estimates of biomass at depths of 50–75 m and 75–100 m.

297
<figure><img src="image_8.png" /><figcaption>**Figure 5: Modeled macrobenthic biomass indices versus field data on macrobenthic biomass. Stations are colored according to their bathymetric range, with red shades for shallower depths and blue for deeper ones. The red dashed line corresponds to the isoline.**</figcaption></figure>

**3.3. Relationship between body condition and macrobenthic biomass indices**

Of the numerous mixed-effect linear models we constructed to test the effect of the macrobenthic biomass indices as predictors of body condition in various fish species, 15 detected significant effects, i.e., a macrobenthic biomass index explained significantly more variance than the null model (Table 3). No such models were found for *Trisopterus minutus* and *Scyliorhinus canicula*, and only one significant model was obtained for *Microchirus variegatus* and for *Merlangius merlangus*. Instead, significant
positive relationships were found between the three macrobenthic biomass indices and body condition factors for the species *Arnoglossus* spp., *Mullus surmuletus*, *Solea solea*, and *Trisopterus luscus* (Figure 6). The level of variance explained remained globally low, with the maximum observed for the relationship between body condition in *Mullus surmuletus* and the Biomass<sub>1976</sub> index (14.7%) (Table 3). On average, *Mullus surmuletus* and *Arnoglossus* spp. were the two taxa with the highest amount of variance explained by macrobenthic biomass indices.

Table 3: Summary of mixed-effect linear models that identified a significant relationship between macrobenthic biomass indices and body condition factors of fish. Chi2 and p-value correspond to the test of the model against a null model including only an intercept and the random effect of station. R2m is the marginal R-squared value and R2c is the conditional R-squared value.

<table><thead><tr><th>Macrobenthic index</th><th>Species</th><th>R2m</th><th>R2c</th><th>Chi2</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="4">Biomass<sub>1976</sub></td><td>Arnoglossus spp.</td><td>0.143</td><td>0.419</td><td>5.4</td><td>2.01e-02</td></tr><tr><td>Mullus surmuletus</td><td>0.147</td><td>0.327</td><td>14.94</td><td>1.87e-03</td></tr><tr><td>Solea solea</td><td>0.038</td><td>0.197</td><td>18.46</td><td>3.53e-04</td></tr><tr><td>Trisopterus luscus</td><td>0.066</td><td>0.183</td><td>8.11</td><td>4.38e-02</td></tr><tr><td rowspan="4">Biomass<sub>2023</sub></td><td>Arnoglossus spp.</td><td>0.073</td><td>0.383</td><td>4.01</td><td>4.52e-02</td></tr><tr><td>Merlangius merlangus</td><td>0.014</td><td>0.183</td><td>9.22</td><td>2.65e-02</td></tr><tr><td>Mullus surmuletus</td><td>0.085</td><td>0.313</td><td>15.54</td><td>1.41e-03</td></tr><tr><td>Solea solea</td><td>0.034</td><td>0.18</td><td>19.84</td><td>1.83e-04</td></tr><tr><td rowspan="4">Biomass<sub>ERSEM</sub></td><td>Trisopterus luscus</td><td>0.072</td><td>0.229</td><td>20.18</td><td>1.56e-04</td></tr><tr><td>Arnoglossus spp.</td><td>0.126</td><td>0.43</td><td>5.87</td><td>1.54e-02</td></tr><tr><td>Microchirus variegatus</td><td>0.131</td><td>0.464</td><td>5.33</td><td>2.10e-02</td></tr><tr><td>Mullus surmuletus</td><td>0.1</td><td>0.301</td><td>23.67</td><td>2.93e-05</td></tr><tr><td></td><td>Solea solea</td><td>0.038</td><td>0.184</td><td>20.22</td><td>1.53e-04</td></tr><tr><td></td><td>Trisopterus luscus</td><td>0.085</td><td>0.238</td><td>22.64</td><td>4.80e-05</td></tr></tbody></table>

In all taxa in which a significant relationship was detected between body condition and macrobenthic biomass, the relationship was positive; the only exception to this was found in *M. merlangus*. Additionally, these relationships were stronger (steeper slope) for smaller-sized individuals (Figure 6 and SM9), although this pattern was identified for large individuals of *Mullus surmuletus* as well.
<figure><img src="image_9.png" /><figcaption>Figure 6: Significant linear mixed-effect models selected. Data used to fit models are shown here; orange dots correspond to small individuals and blue triangles to larger ones.</figcaption></figure>

The only species for which $K_n$ exhibited a steep negative relationship with macrobenthic biomass was

*Merlangius merlangus* (Figure 6).

324

325

326

327

328
329 **4. Discussion**

330 The northern part of the shelf of the Bay of Biscay is a particularly productive ecosystem, supporting
331 both pelagic and benthic trophic pathways (Cresson et al., 2020) that provide essential resources for
332 fish species. One such resource is the macrobenthos; these organisms are a key food source for benthо-
333 demersal fish, which tend to be opportunists that will eat whatever benthic prey is available (Day et al.,
334 2019; Rault et al., 2017). However, spatial variability in macrobenthic biomass on this shelf remains
335 largely unexplored. To date, quantitative data on macrobenthic biomass in the Bay of Biscay are limited
336 to very coastal areas (e.g., Dutertre et al., 2013) or specific habitats (e.g., muddy areas like the Grande
337 Vasière; (Le Loc'h et al., 2008). The benthic characteristics of these areas have been extrapolated to
338 the entire shelf through ecosystem modeling (Lassalle et al., 2011). The present study aims to fill this
339 gap by analyzing the spatial distribution of macrobenthic biomass over the whole northern shelf of the
340 Bay of Biscay and evaluating the relationship between variations in this biomass and the fish that feed
341 (to a greater or lesser degree) on the macrobenthos. The lack of field data on macrofaunal biomass in
342 much of the study area prompted us to explore and use existing models, of which two were particularly
343 relevant to our objective and the marine region we were studying.

344 The "graph model" from Chassé and Glémarec (1976) is based on depth and sediment typology, with
345 the assumption that hydro-sedimentary characteristics drive ecological processes and features of
346 macrobenthic communities (Glémarec, 1969; McArthur et al., 2010; Reiss et al., 2015). The graph model
347 was defined on the basis of a limited number of sediment classes, reflecting the substrate distribution
348 presented in the "Atlas des fonds meubles du plateau continental Nord Gascogne" [Atlas of the French
349 coastline: Atlas of the soft bottoms of the continental shelf of the Bay of Biscay] (Chassé and Glémarec,
350 1976). This modeling strategy is comparable to the habitat suitability modeling framework adopted by
351 Degraer et al. (2008), since the map involved an initial biophysical dataset and spatial interpolation from
352 this dataset with physical data maps. Nevertheless, the graph model developed by Chassé and
353 Glémarec (1976) was based on data collected during the 1970s. Since then, macrobenthic communities
354 have undergone changes as a consequence of various climate and anthropogenic drivers, including
355 trawling (Hily et al., 2008). This could be indicative of a change in the functioning of communities, thereby
356 raising questions regarding the relevance of such a model in contemporary contexts. Important
357 ecological processes fueling benthic communities may have undergone modification, including the
358 diminution of the surface area influenced by the Loire and its associated inputs (Martinez Almoyna et
al., 2025). Nevertheless, major sedimentary habitats considered in the model are still present today and display spatial concordance. Moreover, the large-scale spatial patterns of macrobenthic communities persisted 30 years following the model establishment (Le Loc'h et al., 2008). Consequently, although changes have occurred within communities and their underlying processes, the model's relative spatial trends at plateau scale remain robust.

In the ERSEM model, which is part of the POLCOMS-ERSEM biophysical model, macrobenthic biomass of suspension and deposit feeders is the direct result of flows from ecological processes (primary production, organic matter sedimentation) that supply the seafloor. Benthopelagic coupling models like the ERSEM model have already demonstrated their ability to accurately represent macrobenthic biomass at a small scale (Ehrnsten et al., 2019). This model also incorporates other processes, such as organic matter ingestion or bioturbation (Butenschön et al., 2016)—but not the remobilization of buried organic matter (Butenschön et al., 2016)—that could boost macrobenthic biomass (Qin et al., 2010) and benefit the benthos during periods of low input (Zhang et al., 2019). However, the ERSEM model is only based on muddy sediments and, unlike the graph model, does not account for spatial variability in substrate types, despite the critical role these characteristics play in the modeled processes and in determining benthic invertebrate biomass. Indeed, the nature of a substrate reflects numerous features and processes (e.g., permeability, deposition, resuspension, organic content, among others) (Chen et al., 2018; Idier et al., 2010; Pace et al., 2021) that can influence macrobenthic characteristics and biomass (Ehrnsten et al., 2019; Pearson and Rosenberg, 1978).

The graph and ERSEM models reproduced biomass gradients that were largely similar to each other, particularly in the overall decrease from the coast to the shelf break and the presence of heterogeneous coastal zones with high biomass. This gradient is indicative of a significant terrigenous influence, one whose intensity increases with proximity to river outlets (Loire and Vilaine estuaries), particularly in the southeastern region of the study area. However, there were substantial differences between the two types of models. The two maps derived from the graph model showed discontinuities in this biomass gradient, with distinct patches of high biomass values, particularly in the muddiest zone (the Grande Vasière). With the graph model, the use of the most recent substrate map only marginally changed the distribution of biomass; this was unsurprising given that these maps largely reproduce substrate data from previous versions and retain high local uncertainties due to their extremely heterogeneous spatial sampling coverage and limited sedimentary field data (Kaskela et al., 2019; Pierrejean et al., 2022).
Instead, the biomass distribution provided by the ERSEM model was much less patchy, illustrating the more continuous nature of the spatial distribution of the variables used to estimate biomass, especially because this model does not take into account sedimentary substrate features.

Based on an exhaustive census of surveys and research projects carried out over the last decade, we gathered all available field data on spatial variability in macrobenthic biomass in the study area. A preliminary analysis of the data highlighted not only spatial gaps (especially between 120 and 200 m) but also heterogeneity in the sampling gear used (3 types) and in the years (10 years from 2008 to 2019) and months/seasons (from March to November) in which samples were collected. Keeping in mind these limitations, when these data were standardized and examined at a broad scale, they seemed to describe a depth gradient roughly comparable to those we estimated from models (Spearman correlations ranging from 60% to 80%). Even the limited amount of field data we were able to find is useful in understanding the strengths or deficiencies of the models. For example, when comparing coastal areas, the ERSEM model seemed to better reflect the reality of the observed biomass difference, which was lower in the Bay of Concarneau (PLOOPS surveys) than in the Bay of Vilaine (RetroB and Nurse2008 surveys). This difference was reversed in the graph model, which seemed to underestimate macrobenthic biomass in pure muddy substrates such as those found in the Bay of Vilaine (Le Bris and Glémarec, 1995). This may reflect the fact that terrigenous influences, which are known to increase benthic biomass (Deubel et al., 2003; Escobar-Briones and Soto, 1997; Zuschin and Stachowitsch, 2009), were not taken into account in the graph model, which only considered depth and sediment type. This point was discussed by Dutertre et al. (2013), who explained that in the shallow part of the Bay of Biscay, although 26% of macrobenthic diversity patterns could be explained by substrate, hydrodynamics, and depth, the inclusion of parameters quantifying riverine influence (Vilaine and Loire) made it possible to explain an additional 16%.

For both types of models, there is a notable limitation in their assessment at the continental shelf edge. The absence of an hypothesized peak in macrobenthic biomass at the shelf edge seems to indicate that the two models ignore the hydrological context of the shelf break, where biological production is supposed to be stimulated by a shelf edge upwelling (Simpson and Sharples, 2012; Vandromme et al., 2014). Such a peak could be expected, and would have been consistent with estimates made by Le Loc'h et al. (2008) at three sampling stations in the shelf edge area, which they compared to the highly productive mud zone in the middle of the continental shelf. Likewise, benthic biomass enhancement
driven by shelf-edge upwelling aligns with inferences from epibenthic megainvertebrate communities. Although field data in this area are limited, the deepest observations suggest the possible presence of a peak and highlight the need for further investigation of the benthic compartment at the deep shelf edge.

By analyzing the relationship between fish body condition and macrobenthic biomass indices, our aim was to explore the impact of the distribution of macrobenthic food resources on the shelf as indicated indirectly by the macrobenthic component. Condition indices result from a combination of biotic (e.g., density dependence) and abiotic (e.g., temperature) factors (Gernez et al., 2025). The inclusion of such covariates in our analyses, with the aim of removing their effects and isolating the specific influence of biomass did not reveal strong relationships between condition indices and the covariates considered. It suggested that the effect of biomass observed remains substantial and accounts for a great proportion of the observed variability. In our study, the relationship between macrobenthic biomass indices and body condition factor ($K_n$) was significant and positive for four of the eight benthо-demersal fish species tested. These relationships were similar for all models of macrobenthic biomass and for most of the fish species examined, which emphasizes the importance of the coastal-to-offshore gradient in the spatial distribution of macrobenthic food resources. It also suggests bottom-up processes in which food availability may be a determining factor, as seen in other contexts (Brosset et al., 2015; Laurel et al., 2001; Porath and Peters, 1997).

In many cases, the presence or absence of significant relationships can be explained by the trophic or life-history characteristics of the species analyzed. Among the different species studied, whiting (*Merlangius merlangus*), poor cod (*Trisopterus minutus*), and small-spotted catshark (*Scyliorhinus canicula*) exhibited a negative or non-existent relationship between body condition and biomass indices. For whiting, this could be explained by the fact that the diet of small individuals (less than 22 cm) is both pelagic and benthic, while that of larger individuals is also based on pelagic and demersal fishes (Day et al., 2019). This feeding characteristic is shared by the poor cod, which has a very generalist diet featuring macrobenthic, suprabenthic, and pelagic prey, including fish at larger sizes (Armstrong, 1982). The small-spotted catshark is also thought to feed on pelagic resources such as euphausiids (Rodríguez-Cabello et al., 2007), which are often found near the continental slope of the Bay of Biscay (Peña et al., 2019), where the fish in our study were mainly found. For these species, the absence of
448 significant relationships with macrobenthic biomass may reflect dietary compensation with pelagic prey
449 when benthic food resources are less available.

450 Compared to the other flatfish studied, the common sole (*Solea solea*) did not exhibit relationship
451 between body condition and macrobenthic biomass indices, even though it is mostly benthivorous
452 (Lagardère, 1987; Rault et al., 2017). Only a Size category effect was observed, with bigger individuals
453 showing higher bondy condition. Small sole individuals distribution is more coastal than that of large
454 individuals, which are mainly found further offshore (Figure SM5). It is likely that both size category are
455 not sufficiently widely distributed. The relatively high body condition for larger common sole in an area
456 of poorer macrobenthic biomass may be related to the fact that these individuals have accumulated
457 reserves in more coastal areas during the summer period (Rijnsdorp, 1990), before migrating offshore
458 in the autumn (where they were sampled) to reproduce a few months later (Le Bec, 1983). This may not
459 be the case for the other fish that did show a relationship between their body condition and macrobenthic
460 biomass, such as *Arnoglossus* spp. and *M. surmuletus*, which are known to reproduce later than the
461 common sole (Gibson and Ezzi, 1980; N'Da and Déniel, 1993).

462 In the case of pout (*Trisopterus luscus*), only the smaller individuals demonstrated a strong positive
463 relationship between body condition and macrobenthic biomass. The small individuals of this species
464 are known to feed mainly on macrobenthic and suprabenthic invertebrates (Castro et al., 2013), while
465 the diet of larger individuals includes fish (Armstrong, 1982). Scaldfishes (*Arnoglossus* spp.) and the
466 two age classes of striped red mullet (*M. surmuletus*) also exhibited strong relationships between body
467 condition and macrobenthic biomass indices. Scaldfishes feed mainly on suprabenthic invertebrates
468 (Castro et al., 2013), but also on macrofauna and even benthic fish (Déniel, 1981). Striped red mullet
469 feed on epibenthic crustaceans (e.g., Galatheidae and Portunidae) but mainly rely on more infaunal
470 prey such as bivalves or polychaetes (Pavičić et al., 2018; Serrano et al., 2003). Unlike these other
471 species, thickback sole (*M. variegatus*) demonstrated a relationship only between its body condition and
472 the macrobenthic biomass index from the ERSEM map. Its diet relies heavily on benthic
473 macroinvertebrates (Amezcua et al., 2003; King and Fives, 1990) and it is difficult to explain why the
474 relationship was not established using the data from the other two maps.

475 With the diets of these species in mind, it seems justifiable to assume that the macrobenthic biomass
476 maps also represent a wider range of benthic prey, such as suprabenthic prey which are less effectively
sampled by grabs (Sorbe, 1981). The various relationships observed between fish body condition and biomass maps illustrate both differences among fish species in their dependence on benthic resources as well as variability in the availability of such resources. Certain areas (e.g., coastal habitats or mudflats like the Grande Vasière) offer better feeding conditions for benthic-feeding fishes. In such areas, competition between benthic feeders is reduced through processes such as individual specialization (Sánchez-Hernández et al., 2021). Areas with scarcer benthic resources, such as deeper zones (e.g., in the vicinity of the shelf edge), may instead experience heightened intra- and interspecies competition (Holbrook and Schmitt, 1989). This could drive adaptive strategies such as shifts to (i) prey with lower energy content, with consequences for fish growth and body condition (Cormon et al., 2016; Hiddink et al., 2016; Kamimura et al., 2021) and/or (ii) prey at higher trophic levels as suggested by the isotope study of Schaal et al. (2016). This highlights the trophic plasticity of fish with respect to conditions of resource availability or competition (Pelage et al., 2022). Our study supports the idea that demersal fishes generally depend on trophic resources at spatial scales that are smaller than that of the continental shelf, a hypothesis that has already been corroborated by different approaches (e.g., Chouvelon et al., 2012). Our body condition results are also consistent with the trends reported by Schaal et al. (2016) regarding the trophic levels of demersal fishes along the coastal-to-offshore isotopic gradient, which were characterized by a trophic shift that those authors hypothesized to be due to lower prey abundance offshore.

495
496 **5. Conclusion**

497 By providing visual representations of the spatial distribution of benthic resource availability, the
498 macrobenthic biomass maps analyzed here could be valuable tools in the study of spatial variability in
499 trophic networks, benthopelagic coupling, and matter fluxes on the continental shelf. The two models
500 we used to estimate macrobenthic biomass had different strengths and limitations based on the different
501 modeling assumptions employed. Nevertheless, both models, which are based on different ecological
502 hypotheses, revealed similar coastal-to-offshore distribution trends, with notable differences in the mid-
503 shelf and near-coastal areas. Unfortunately, few of Europe's continental shelves (e.g., in the North Sea;
504 Reiss et al., 2010) have been sampled thoroughly enough to effectively reveal benthic biomass
505 gradients on a large spatial scale. These data are crucial for understanding certain processes affecting
506 marine populations or, more recently, for the development of ecosystem models (e.g., Lassalle et al.,
507 Corrales et al., 2022; Lopez de Gamiz-Zearra et al., 2023), especially in deeper areas where
508 macrobenthic samples are limited (e.g., beyond the Grande Vasière in the Bay of Biscay).

509 To gain a deeper understanding of fish population dynamics and their current status, our study
510 underscores the need to account for spatial variability in macrobenthic resources on which these species
511 depend. The observed coastal-to-offshore gradient in macrobenthic biomass, and its association with
512 body condition in certain benthodemersal fish, raises important questions about the long-term viability
513 of deeper populations. This issue is particularly relevant in the context of climate change: for instance,
514 depth-related shifts have been clearly documented in benthodemersal species on the continental
515 shelves of the Northeast Atlantic (e.g., Dulvy et al., 2008), including benthivorous flatfish with ecological
516 traits similar to those examined in our study. The full implications of these shifts for the populations
517 concerned remain to be thoroughly assessed.

518
519 Acknowledgements:

520 The authors wish to thank the crew of each survey, as well as the technicians involved in the sampling
521 at sea during surveys and in the laboratory. The authors also wish to thank Stanislas Dubois, François
522 Le Loc'h, and Anik Brind'Amour for providing access to the data from the surveys they directed. We
523 thank Plymouth Marine Laboratory and Susan Kay for providing the POLCOMS-ERSEM projections.

524 Authors also would like to express their gratitude to Lindsay Higgins for her insightful proofreading. This
525 work was supported by a PhD grant from the project Bentroph, which was co-funded by the Brittany
526 region and IFREMER.

527
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## Highlights:

- Estimates of macrobenthic biomass exhibited coastal-to-offshore trend on the shelf.

- The sparse field data macrobenthic biomass correlated with data from models.

- In deeper areas, muddy sediments contained the greatest macrobenthic biomass.

- Body condition of benthivorous fish improved with increased macrobenthic biomass.

- Diet literature suggests biomass maps reflect food resources beyond macrofauna.
**Declaration of interests**

[x] 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.

[ ] The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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