# Physiological and metabolic response of the bloody cockle (Senilia senilis) to salinity in the Sine Saloum estuary, Senegal

Babacar Sané, Jonathan Flye-Sainte-Marie, Malick Diouf, Frédéric Jean, Etienne Poirier, Yoann Thomas

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## To cite this version:

Babacar Sané, Jonathan Flye-Sainte-Marie, Malick Diouf, Frédéric Jean, Etienne Poirier, et al.. Physiological and metabolic response of the bloody cockle (Senilia senilis) to salinity in the Sine Saloum estuary, Senegal. Regional Studies in Marine Science, 2025, 92, pp.104636. 10.1016/j.rsma.2025.104636. hal-05390640

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# Physiological and metabolic response of the bloody cockle (*Senilia senilis*) to salinity in the Sine Saloum estuary, Senegal

Babacar Sané<sup>a,*</sup>, Jonathan Flye-Sainte-Marie<sup>a</sup>, Malick Diouf<sup>b</sup>, Frédéric Jean<sup>a</sup>, Etienne Poirier<sup>a</sup>, Yoann Thomas<sup>a</sup>

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## ARTICLE INFO

**Keywords:**

*Senilia senilis*

Salinity tolerance

Osmoregulation

Estuarine ecology

Ecophysiology

## ABSTRACT

Estuaries are unique ecosystems that experience dynamic environmental conditions. Organisms in estuaries face significant and rapid fluctuations in salinity caused by tidal variations, evaporation, rainfall, and freshwater runoff. This is especially true for bivalve mollusks, which cannot move to escape unfavorable conditions. Consequently, variations in salinity can significantly influence their life-history traits. Here, we examined the response of the bloody cockle, *Senilia senilis*, to variations in salinity. This bivalve is predominant in the estuaries of West Africa and supports a significant artisanal fishing activity. Experiments were conducted in an in situ laboratory to assess the behavioural (valve opening/closing), physiological (respiration, filtration) and osmo-regulatory responses of the species to changes in salinity. The results help us define the species' tolerance and functional niche ranges: (1) closing behavior occurs at salinities above 12 and 60, (2) filtration stops at salinities above 25 and 40, and (3) respiration increases on either side of an optimal salinity of 40. Analysis of internal osmolarity confirms that S. senilis is an osmoconformer, as its osmolarity strongly correlates with that of seawater. These responses underline the adaptation of this species to an estuarine habitat subject to intense salinity fluctuations, while identifying tolerance thresholds beyond which its vital functions are impaired. These results are crucial for understanding the resilience of S. senilis in the context of global change and for anticipating the effects of increased estuarine salinity on benthic biodiversity and associated ecosystem services.

## 1. Introduction

Estuaries are particular ecosystems due to their location at the confluence of rivers and oceans (Dyer, 1995). As transition zones, they are characterized by dynamic and variable environmental conditions (Pinheiro Andutta et al., 2013). This interface position exposes estuaries to the constant influence of tides and freshwater inflows from rivers, which will modulate the physico-chemical properties of the water. Organisms are thus confronted with significant and sometimes rapid fluctuations in salinity due to tidal variations, evaporation, rainfall and freshwater runoff (Brambilla et al., 2018; Shumway, 1977).

Salinity can control organisms' physiological response and distribution in coastal ecosystems (Röthig et al., 2023). This is particularly the case for bivalve molluscs, which generally cannot move to escape unfavorable conditions (Pourmozaffar et al., 2020). For example, in some bivalves, salinity alone can explain 87 % of the variability in growth rate (Pourmozaffar et al., 2020). Variations in salinity may also be an

important factor in controlling the reproductive phenology of some bivalve species (Sané et al., 2023; Sühnel et al., 2023).

Most bivalves are to some extent osmoconformer, i.e. they do not actively regulate the osmolarity of their intra- and extracellular fluids, which adjusts to the salinity of their environment (Sokolov and Sokolova, 2019; Yancey, 2005). However, variations in salinity beyond the limits of osmoconformity can lead to differences in osmotic pressure between the surrounding environment and tissue cells, creating stress that can, in extreme situations, lead to their death (Barrett et al., 2022; Heilmayer et al., 2008; Tan et al., 2023). To cope with this stress, bivalves generally respond by closing their valves and/or burying themselves in the sediment to reduce tissue exposure to osmotic stress, which protects them in the short term but can also have negative consequences for their survival and energy balance (Woodin et al., 2013).

The bloody cockle *Senilia senilis* (Linnaeus 1758) is a species of bivalve that lives in intertidal mudflats, buried in the sediment at shallow depths. This species is widely distributed along the west coasts

<footnote><sup>a</sup> IRD, Univ Brest, Ifremer, CNRS, LEMAR, Plouzané 29280, France</footnote>

<footnote><sup>b</sup> Université Cheikh Anta Diop de Dakar, département de biologie animale, Dakar, Senegal</footnote>

<footnote>* Corresponding author.
E-mail address: babacar.sane@ird.fr (B. Sané).</footnote>
of Africa, from the Western Sahara to Angola (Cosel and Gofas, 2019). In the Sine Saloum estuary (Senegal), this species is a keystone species due to its dual ecological and socio-economic importance. Ecologically, *S. senilis* plays a crucial role in benthic–pelagic coupling by filtering large volumes of water, recycling nutrients, and serving as prey for higher trophic levels. Socio-economically, it sustains artisanal fisheries and provides a vital source of income and protein for local communities, particularly women who have harvested this resource for centuries (Carré et al., 2022; Hardy et al., 2016).

The bloody cockle is subject to significant variations in salinity throughout the year in this ecosystem. Indeed, the region's climate is characterized by the alternation of a dry season from November to June during which it does not rain, and a monsoon from July to October, marked by intense rainfall. As freshwater inflows are limited during the monsoon period, evaporation causes salt concentration to increase downstream of the estuary. This results in a salt gradient that can reach a salinity of 100 upstream, 100 km from the mouth at the end of the dry season, and 40 during the monsoon (Descroix et al., 2020). The estuary has been marked by a long period of reduced freshwater inflows resulting from a drought that began in the 1950s (Pages and Citeau, 1990). This dynamic exposes *Senilia senilis*, which inhabits the entire estuary, to a broad range of salinity conditions. For instance, in the downstream zones near the estuary mouth, salinity fluctuates between 10 during the monsoon and approximately 40 during the dry season. In contrast, in upstream areas near the species' distribution limit, salinity ranges from about 60 at the end of the monsoon to as high as 100 at the dry season's peak (Descroix et al., 2020; Panfili et al., 2004; Walter, 2006). Additionally, the estuary has experienced a long-term reduction in freshwater inflows due to a drought that began in the 1950s (Pages and Citeau, 1990), further amplifying the salinity extremes to which this species is exposed.

To date, very little is known about the ecophysiology of *S. senilis*, which would provide a better understanding of the consequences for the species of environmental variations associated with changes in the rainfall regime. Previous research has already provided important insights into the biology of *S. senilis*. Early works demonstrated its behavioral and osmotic responses to fluctuating salinities (Djangmah et al., 1979) Later, (Djangmah et al., 1980) highlighted its respiratory strategies, showing that *S. senilis* is one of the few bivalves with hemoglobin in its hemolymph. Despite these pioneering contributions, our knowledge of the ecophysiology of *S. senilis* remains limited, especially under the extreme salinity gradients characteristic of the Sine Saloum estuary. The ability of estuarine species to respond to salinity fluctuations through behavioral and/or physiological responses may determine the maintenance of populations (Domínguez et al., 2020). This is therefore a fundamental element in improving our understanding of this keystone species in the estuarine ecosystem, to guide its management and sustainably preserve its critical role both ecologically and socio-economically.

In this context, the aim of this study was to identify the physiological and metabolic response of *S. senilis* to variations in salinity. To do this, we performed experiments in controlled conditions in order to study the effect of salinity on (1) the behavioral response of the bloody cockles (2) the metabolic response of respiration and filtration and (3) the osmoregulation process.

## 2. Material and methods

All the experiments were carried out in a 40-foot container fitted out as an ecophysiology laboratory (named “Galou Xam Xam”, meaning “knowledge canoe” in Wolof). The laboratory was deployed in the Sine Saloum estuary in Senegal, in the village of Diakhanor (Lat: 13°58′5.20′N, Long: 16°45′32.01′W), in the immediate vicinity of the bloody cockle's natural habitat.

This experimental structure is fed by seawater pumped directly from the main branch of the Saloum and allows to carry out experiments an

open circuit. The water is pre-filtered with a 150 µm mesh to eliminate the largest particles before being stored in a 1000-L settling tank equipped with a stirring pump. A lift pump distributes the water from the settling tank to three tanks (two of 390 L and one of 660 L). These three tanks feed 9 experimental 40-Ltanks and an 80-L tank used for respiration and filtration measurements. Each experimental tank is equipped with a stirring pump and is thermoregulated. Water within the tanks is continuously renewed at a rate of twice their volume per 24 h.

### 2.1. Biological material and acclimatation

At the start of each experiment, the bloody cockles ranging from 32 to 35 mm (antero-posterior length) were collected from the foreshore near the experimental container, within a radius of around 1000 m from the container. Before being subjected to the experimental conditions, the animals were acclimatized for 24 h to the experimental structure, in conditions close to the natural environment (salinity of 38, temperature of 25°C) in a 40 L stirred experimental tank with a renewal of 2 times the volume per 24 h.

### 2.2. Behavioral response to salinity

The valve opening behavior of the bloody cockle was observed under increasing / decreasing salinity conditions. This experiment was carried out between January 2023 and February 2023. For this purpose, a triplicate of 30 L tanks each containing 60 individuals were subjected to decreasing salinities: 30, 25, 20, 15, 10, 5, 0 as a second triplicate were exposed to increasing salinities: 42, 47, 52, 57, 62, 67, 72, 86. Salinity levels were adjusted by dilution with freshwater extracted from the wells for low salinities, and by enrichment with salt from the Diakhanor salt flats for higher salinities. In both conditions, the temperature was maintained at 25°C. Each salinity level was maintained for 24 h. Daily, the number of animals with closed or open valves was counted.

In addition, a control tank containing 60 individuals was maintained at a temperature of 25°C and a salinity of 37. Each day, the number of dead animals was counted as well as the number of animals with closed valves.

To assess the salinity tolerance thresholds of the bloody cockles as a function of their closure behavior, a log-logistic model was used to model the sigmoid response according to the equation:

$$y = c + \frac{d - c}{1 + \exp(a(\loglogx - \loglog ED50))} \quad (1)$$

Where, ED50 represents the value of the factor inducing a median response between the upper limit *d* and the lower limit *c*. Thea depends on the slope of the response around the value *e*, thus providing a measure of the intensity of the response (Ritz and Streibig, 2005).

### 2.3. Respiration and filtration measurement

#### 2.3.1. Experimental system

The system used to measure the respiration rate of organisms in an open circuit (Fig. 1) is similar to the one described in Flye-Sainte-Marie et al. (2007). The bench consists of eight acrylic chambers, each chamber is traversed by a flow of water, the flow rate of which is controlled by a peristaltic pump. The first chamber, E1, was used as a control and contained no organism as the seven other chambers (E2 to E8) each contained three individuals. The water exciting each chamber passes through a set of computer-controlled solenoid valves which allows the water leaving specific chamber to be directed either towards an oximeter (WTW Multi3630, OXY 925 optical dissolved oxygen probe) and a fluorometer (TriLux fluorescence probe, Chelsea Technologies), or directly back to the experimental tank. This configuration enables oxygen concentration and food concentration to be measured sequentially at the outlet of each experimental chamber, alternating systematically
<figure><img src="image_5.png" /><figcaption>Fig. 1. (A) Schematic diagram of the system for measuring respiration rate and filtration of bloody cockles in an open circuit; (B) individual measurement chamber containing 3 individuals.</figcaption></figure>

with the control chamber.

### 2.3.2. Acclimation and experimental protocol

Filtration and respiration rates were assessed under 10 salinity conditions: 15, 20, 25, 30, 38, 40, 45, 50, 55, 60 during the month of May 2024. For each of these salinities, a batch of 35 individuals ranging from 30 to 35 mm was collected from the foreshore. Each batch was acclimated for 24 h to the natural conditions in a 30 L tank continuously fed with seawater (twice the tank volume per 24 h), homogenized by a mixing pump and thermoregulated at 25°C and a salinity of 37. This initial phase allowed the animals to recover from handling and stabilize their physiological state. Individuals were then acclimated for 48 h to the target experimental salinity at 25°C.

For each salinity treatment, respiration and filtration measurements were conducted simultaneously in seven replicate chambers (E2-E8), each containing three individuals. One additional chamber (E1) served as a control without animals. Thus, 21 individuals were tested per treatment. Measuring respiration with three individuals per chamber assumes additive contributions and that group behavior could affect metabolism. In our setup, chambers provided sufficient space per individual, minimizing interactions. Our goal was to estimate average physiological rates per individual across salinity treatments, and using small groups reduced measurement noise compared to single individuals. The water flow rate in each chamber ranged between 2.7 and 3 L·h⁻¹, and was checked at the start and end of each experiment. Oxygen and chlorophyll-a consumption in each chamber was measured every 70 min, for 5 min, over a 24-hour period. These repeated measurements were considered as successive temporal replicates, providing a robust estimate of physiological performance under each salinity condition. These measurements were conducted in unfiltered water that naturally contained phytoplankton.

The remaining animals were kept under the same salinity conditions for a further 24 h for osmolarity measurements.

### 2.3.3. Determination of respiration rate

For each record, the average oxygen concentration values of the effluent water from both the control and experimental chambers were calculated. Due fails in the pumping system, data for E6 at salinities of 15, 20 and 30, and data for E7 at salinity of 45 were excluded. The respiration rate (RR) per 5-minute measurement sequence was then calculated for each chamber using the following equation:

$$RR = \frac{1}{n}(O_t - O_a) \bullet FR \qquad (2)$$

With, $O_t$ the oxygen concentration (mgO₂·L⁻¹) measured in the control chamber, $O_a$ the oxygen concentration (mgO₂·L⁻¹) measured in

the chamber containing animals, FR the water flow rate (L·h⁻¹) circulating through the chamber and n the number of individuals in each chamber (here equal to 3). The respiration rate was calculated for each salinity condition, giving a total of 247 values for each chamber for each treatment.

### 2.3.4. Determination of filtration rate

The fluorescence data, collected during measurements of the clearance rate in the different food treatments, were first adjusted according to the fluorometer baseline, determined during its calibration. A cleaning step was applied to the fluorescence values to ensure data reliability. Some fluorescence peaks may have been caused by the passage of feces or pseudofeces through the fluorimeter, potentially distorting the measurements. To address this, outliers were identified and filtered out using the R *outliers*' package (Komsta, 2022), minimizing their impact on the final analysis. The average filtration rate per 5-minute measurement sequence was then calculated for each chamber using the following equation:

$$CR = \frac{1}{n}FR \bullet \frac{C_c - C_a}{Ca} \qquad (3)$$

Where $Cc$ is the chlorophyll-a concentration (µg·L⁻¹ of Chl-a) in the effluent water of the control chamber, $Ca$ is the chlorophyll-a concentration (µg·L⁻¹ of Chl-a) in the measurement chamber, and $FR$ is the flow rate (L·h⁻¹) through the chambers.

### 2.3.5. Standardization of respiration and filtration data

Physiological rates (e.g. respiration and filtration) depend on individual size (see e.g. Flye-Sainte-Marie et al., 2007; Savina and Pouvreau, 2004; Trigos et al., 2015). To allow comparison of physiological rates measured at each salinity for different animals, respiration and filtration rates were standardized for an individual with a dry flesh mass equal to 0.5 g, corresponding to an average height of 35 mm. The standardization was performed using the following equation (Bayne et al., 1987):

$$Y_s = Y_m \bullet \left( \frac{W_s}{W_m} \right)^b \qquad (4)$$

With, $Y_s$ the physiological rate (e.g. respiration, filtration) related to an individual with a dry flesh mass of 0.5 g, $Y_m$ the individual physiological rate measured, $W_s$ the standard dry flesh mass (0.5 g), $W_m$ the average dry flesh mass (g) of the individuals in the enclosure considered, b the allometric exponent of the physiological rate considered. In valves, a value close to b = 3/4 for respiration and b = 2/3 for filtration generally applies well (Flye-Sainte-Marie et al., 2007).
## 2.4. Biometric measurements

After the 24 h of measurement individual height, thickness and length were measured individually with a caliper (precision 0.01 mm). Individuals were then weighed (total weight, shell weight, fresh flesh weight) using a 0.005 g precision balance. The weight of the dry flesh was obtained after 72 h of freeze-drying. The water content ($W_c$) of the individuals was determined by the formula:

$$CI = 1 - \left( \frac{W_f}{W_d} \right) \cdot 100 \qquad (5)$$

Where $W_f$ is the mass of fresh flesh of an individual in grams and $W_d$, the mass of dry meat.

## 2.5. Measurement of Osmolarity

Haemolymph were sampled in the adductor muscle using a sterile syringe adult bloody cockles kept in acclimation tanks for 72 h under the same experimental conditions as those used for respiration and filtration measurements. Their average height was between 42 and 49 mm. Haemolymph samples were immediately stored in Eppendorf tubes and stored frozen at -18°C until analysis in the laboratory. At the same time, seawater samples with the same salinity were prepared and preserved in cryotubes. Haemolymph and seawater osmolarity was measured using a freezing point osmometer (osmomat 3000).

## 2.6. Data analyses

All analyses were performed using R software (R Core Team, 2022). Normality of the data and homogeneity of variance were tested using the Shapiro-Wilk test and Levene's test, respectively. As the respiration, osmolarity and water content data were not normally distributed, data were normalized applying a squared transformation. Following this transformation, normality and homogeneity of variance were re-tested using the same Shapiro-Wilk and Levene's tests. The effect of the different experimental conditions was tested using an ANOVA and pairwise comparisons were assessed using a Tukey test.

# 3. Results

## 3.1. Behavioral tolerance limits to salinity

Fig. 2 shows the behavioral response (shell opening) to salinity

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Fig. 2. Percentage of open bloody cockles after 24 h of exposure at salinities ranging between 0 and 86. The dots represent the observations (3 tanks per treatment; a decrease in salinity: closed dots and an increase in salinity: open dots) and the red line the log-logistic model fitted for each experiment (R2 = 0.98).

ranging from 0 to 86, after 24 h of exposure. No mortality was recorded during the experiment. The parameter ED50 of the log-logistic model, corresponding to the salinity at which 50 % of the bloody cockles closed, was 11.7 for the lower limit and 61.7 for the upper limit (Table 1). The response to closure (i.e., parameter a) is more pronounced at low salinities and becomes more gradual at high salinities, with values of 46.7 and 28.6, respectively (Table 1). Between 15 and 60 salinity, between 90 % and 100 % of the bloody cockles were opened.

## 3.2. Effect of salinity on respiration and filtration

Fig. 3A shows the standard respiration rate of bloody cockles exposed to different salinity conditions. The standard respiration rate varies on average between 0.4 mgO₂·h⁻¹·ind⁻¹ and 0.7 mgO₂·h⁻¹·ind⁻¹. The highest respiration rates were measured for the lowest salinities (i.e. 15 and 20) and for salinity 50. The lowest respiration rates were measured for salinities of 38 and 40. There is a significant decrease in the respiration rate above salinity 50. It is also worth noting that, although the trend is not statistically significant, the respiration rate markedly decreases at salinity 15, with greater inter-individual variability compared to the other salinity conditions, particularly in comparison to salinity 20.

The standard filtration rate shows a very marked pattern, with mean values between 0.1 L·h⁻¹·ind⁻¹ and 0.59 L·h⁻¹·ind⁻¹ (Fig. 2B). The filtration rate is high between salinities 25 and 40, with a maximum measured at salinity 40. Filtration rates fall sharply below and above this range, with values below 0.2 L·h⁻¹·ind⁻¹. Filtration rates are close to zero for low (15) and high salinities (50 and 55).

## 3.3. Energetic balance (Filtration/Respiration ratio)

The Filtration/Respiration (F/R ratio) varied significantly across salinity treatments (ANOVA, $p < 0.05$; Fig. 4). The lowest values were recorded at 15, 55, and 60, with medians close to zero. The ratio progressively increased between 20 and 30, reaching intermediate values (0.5–1.0). A clear maximum was observed at 40, with a median energetic balance of 1.5 L·mg·O₂⁻¹, significantly higher than all other treatments. Beyond 45, the ratio dropped sharply, with values at 50–60 comparable to those at 15. Post-hoc comparisons confirmed that 40 differed significantly from all other treatments, while 15, 55, and 60 formed the group with the lowest energetic balance.

## 3.4. Effect of salinity on water content and hemolymph osmolarity

Fig. 5A illustrates a strong linear relationship between seawater osmolarity and the organism's internal osmolarity, with a slope of 0.93 and an R² of 0.98. At lower salinities, the first two data points slightly deviate from the regression line. However, as salinity exceeds 30, the values closely align with the line. Above this threshold, the internal osmolarity and seawater osmolarity converge, with the regression line almost perfectly overlapping.

The water content of the bloody cockles also varied significantly

<caption>Table 1</caption>

Parameters of the log-logistic model of the behavioural response of bloody cockles to salinity. Parameter a is expressed as a percentage per unit of salinity, while c and d are expressed as percentages, and e is expressed in units of salinity.

<table><thead><tr><th rowspan="2">Parameters</th><th rowspan="2">Definition</th><th colspan="2">Valeur</th></tr><tr><th>low salinity</th><th>high salinity</th></tr></thead><tbody><tr><td>a</td><td>Relative slope of the response around the e value</td><td>-46,7</td><td>28,6</td></tr><tr><td>c</td><td>Lower limit of the model</td><td>-0,07</td><td>2,6</td></tr><tr><td>d</td><td>Upper limit of the model</td><td>96,6</td><td>98,3</td></tr><tr><td>e</td><td>Value of the factor producing a response halfway between d and c</td><td>11.7</td><td>61,7</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Individual respiration rate (A) and filtration rate (B) as a function of salinity. Red dots represent the mean. Data are standardized for an individual with a dry flesh mass of 0.5 g (corresponding to an average size of 35 mm). Letters indicate differences between treatments (Tukey test).</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Energetic balance of *Senilia senilis* across salinity treatments. Different letters denote significant differences among salinities (ANOVA, post-hoc test, *p* < 0.05).</figcaption></figure>

according to the experimental conditions. It is highest at the lowest salinities, with an average of 86 % at a salinity of 15, and reaches its lowest values at the highest salinities, with 77 % at a salinity of 60 (Fig. 5B).

## 4. Discussion

Marine bivalves living in estuarine and coastal areas are subject to both short-term (e.g., tidal cycles) and long-term (e.g., seasonal rainy periods) salinity variations, which represent major stressors for these organisms. Prolonged exposure to salinities near or outside their

tolerance limits has a significant impact on their growth, primarily because it triggers physiological and behavioral responses that affect energy balance (Peteiro et al., 2018). For instance, when salinity levels approach critical thresholds, bivalves often respond by closing their valves (Addis et al., 2021; Domínguez et al., 2020). This behavior serves as a protective mechanism to prevent abrupt changes in osmolarity in osmoconforming species (Solan and Whiteley, 2016). However, this response comes at a cost: valve closure halts feeding, reducing energy intake. Moreover, prolonged exposure to unfavorable salinity conditions forces bivalves to shift to anaerobic metabolism, increasing energy demands due to the activation of adaptive mechanisms (e.g., regulating cell volume by mobilizing and losing free amino acids) (McFarland et al., 2013). Over time, the combination of reduced energy intake and elevated energy expenditure leads to diminished growth rates (Navarro and Gonzalez, 1998). Our results show that *S. senilis* is able to adopt a valve closure behavior in order to isolate itself from extreme salinity conditions, as shown in numerous other bivalves species (see e.g. Addis et al., 2021; Domínguez et al., 2020). The salinity range in which we observed open bloody cockles extends from 12 to 62. This range is wider than the one found by Djangmah et al. (1979) who observed that bloody cockles were opened between 15 and 33.5 (at a temperature of 25°C). The wider range that we observed appears more consistent with the available information about the distribution of the species in the Sine-Saloum estuary, which shows that bloody cockles can be found in waters with salinities ranging from 10 during the monsoon to 60 at the end of the monsoon, at the upstream limit of the bloody cockle's distribution zone (Descroix et al., 2020; Panfili et al., 2004; Walter, 2006). Yet, filtration rates dropped to nearly zero at 15, 50, and 55 despite valves remaining open, showing that valve gape alone is not a reliable indicator of performance. Thus, *S. senilis* maintains survival across 12–62, but its functional niche is restricted to 25–40, where filtration supports positive energy balance and growth.
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Variation in (A) internal haemolymph and seawater osmolarity, (B) the linear relationship between internal haemolymph and seawater osmolarity, and (C) water content in the bloody cockle (*Senilia senilis*) as a function of salinity. Different letters indicate significant differences between treatments (Tukey test).</figcaption></figure>

## 4.1. *S. senilis* as an osmoconformer bivalve

Bivalve osmoconformers are organisms that do not actively regulate their internal concentration of osmolytes. Instead, their internal osmolarity passively equilibrates with that of the surrounding water, primarily through the balance of ions and other osmolytes (Ruppert et al., 2004). In a high salinity environment, an osmoconformer bivalve has the ability to adjust the concentration of its internal fluids to achieve osmotic equilibrium with the surrounding environment. As the external salinity increases, the external osmotic pressure exceeds that of the bivalve's internal fluids. In response, the water in the bivalve's tissues is naturally drawn outwards to rebalance the osmotic concentrations between the inside and outside of the body. This leads to a decrease in water content in the animal's tissues (Peteiro et al., 2018; Shumway, 1977).

The decrease in water content observed in bivalves under hypersaline conditions is therefore a passive response to osmotic variations, rather than an active regulation aimed at maintaining a constant water content. This phenomenon is typical of osmoconformers, which do not possess sophisticated osmotic regulation mechanisms such as osmotic regulators (Stakowian and Freire, 2024). Consequently, these bivalves tolerate water loss in high salinity environments, but this remains within limits where they can maintain their physiological functions (Pierce, 1982).

One of the aims of this study was to see if the *S. senilis* bloody cockles is an osmoconformer by assessing the osmolality of their haemolymph, and their tissue water content. In this context, our results suggest that the bloody cockles is a perfect osmoconformer as described by Djangmah et al., (1979). Indeed, when the shell valves remain open within the tolerance limits (salinities between 15 and 60), *S. senilis* almost perfectly adjusts the osmolarity to match that of the surrounding water. Like most bivalves, internal osmolarity tends to follow variations in the salinity of the external environment (Chen et al., 2021; Pourmozaffar et al., 2020). The exchange of water internal environment and external environment is also confirmed by the measurement of an inverse relationship between water content and osmolarity in *S. senilis*. When tissue water content decreases, osmolarity increases. The same pattern has been observed in other osmoconformer bivalves species, in particular euryhaline bivalves such as *Chlarnys opercularis*, *Modiolus modiolis*, *Mya arenaria*, *Mercenaria mercenaria* and *Crassostrea tulipa* (Peteiro et al., 2018; Shumway, 1977). Our results also revealed that osmolarity at lower salinities (15 and 20),

the first two data points slightly deviate from the regression line. This difference can be explained as the reduction in available energy may constrain certain physiological processes, including passive osmotic adjustments, leading to slight deviations. Similar patterns have been observed in other euryhaline bivalves. For example, in *Crassostrea gigas*, a reduction in filtration rate was observed under hypoosmotic stress, resulting in slower adjustment of hemolymph osmolarity relative to the surrounding water (Fuhrmann et al., 2018). Likewise, in *Mya arenaria*, a decrease in the aerobic capacity of the foot muscle was noted under osmotic stress, affecting protein and amino acid homeostasis in the tissues (Haider et al., 2019).

## 4.2. Bloody cockle respiration and filtration response to salinity

In this study, we also examined the physiological response of *S. senilis* to variations in salinity, focusing on two key metabolic processes: respiration and filtration rate. Our results metabolic response is highly dependent on salinity in bloody cockles from the Sine Saloum estuary. The higher respiration rates at lower salinities reflect an increased energy demand linked to reaching the physiological limit, which has also been observed in behavior and osmoregulation. In *Crassostrea tulipa*, for example, an increase in respiration has also been observed in hypoosmotic environments, with the bivalves having to compensate for the difference in osmotic pressure with their environment, resulting in a higher metabolic cost (Bougrier et al., 1995; David et al., 2005). In addition, similar work on *Mytilus edulis* and *Ruditapes philippinarum* confirms that low salinities lead to increases in oxygen consumption due to additional osmoregulatory efforts (Liu et al., 2024; Stickle et al., 1989). The same effect was noted for high salinity. This phenomenon has been observed in other bivalve species, where an increase in salinity has been correlated with an increase in respiration rates due to hormonal stimulation (Pourmozaffar et al., 2020). Sokolova and Lannig (2008) have also reported that variations in salinity can affect the physiological responses of bivalves, leading to an increase in energy consumption. Conversely, the lowest respiration rates, observed at 38 and 40 salinity, suggest that the bloody cockles are in an optimal zone, where the energy costs associated with osmoregulation are minimal, a common pattern in osmoconformers in conditions close to their ideal osmotic tolerance (Bayne, 1976).

By integrating filtration and respiration, the filtration-to-respiration ratio provides a direct indicator of energetic balance. This indicator, first
formalized in the context of the “scope for growth” concept, integrates energy acquisition and expenditure to assess whether individuals are in a positive or negative energy balance (Bayne and Newell, 1983; Griffiths, 1981). Our results show that salinity 40 supports the highest energetic balance, with a median F/R ratio of 1.5 L·mg O₂⁻¹, significantly higher than all other treatments. This value indicates that at this salinity, filtration greatly outweighs respiration, providing a strong positive energy budget. Compared to other bivalves, this value falls within the upper range of reported performances. In *Mytilus edulis*, field and experimental studies have shown maximum F/R values between 1.2 and 1.6, depending on environmental conditions and individual size (Vajedsamiei et al., 2021; Vismann, 1990). Similarly, in the black mussel *Choromytilus meridionalis*, Griffiths (1980) observed ratios ranging from 0.8 to 1.5, with the maximum reached under optimal conditions. More recently, Khosravi et al. (2023) reported values close to 1.5 in unparasitized *Mytilus*, confirming that this ratio can reach such levels in the absence of stress. Finally, the syntheses of Riisgård (2001) on various filter-feeding organisms indicate that maximum F/R values generally range between 1.0 and 2.0, and rarely exceed this.

Outside this range, filtration declined sharply, approaching zero at salinities of 15, 50, and 55. This reduction occurred despite 90–100 % of individuals maintaining open valves, indicating that valve gape alone is not a reliable indicator of functional performance. Instead, *S. senilis* appears to reduce or suppress pumping activity under osmotic stress, thereby limiting feeding despite valve opening. This observation highlights a crucial distinction between the survival niche of *S. senilis* (12–62) and its functional niche, which is restricted to 25–40, the only range where positive energy balance supports growth and reproduction. Comparable results have been reported in *Mytilus edulis* and *Ruditapes decussatus*, where the scope for growth was positive only within a narrower salinity window despite broader survival limits (Bayne and Newell, 1983; Wang et al., 2011).

The energetic trade-off observed here provides an explicit link between osmotic stress, metabolic imbalance, and potential fitness consequences. Increased respiration at extreme salinities primarily reflects costs associated with osmotic regulation rather than feeding, as has also been shown in other bivalves (Pourmozaffar et al., 2020). This imbalance is consistent with the “metabolic allocation” framework proposed by Bayne and Newell (1983), whereby stressful environments increase maintenance costs at the expense of growth and reproduction. Indeed, a recent study on *Anadara kagoshimensis* confirmed that respiration can increase even under fasting conditions, indicating that stress-induced oxygen demand is prioritized over feeding (Vialova, 2022).

The results of this study on *Senilia senilis* reveal that respiration rates increase at high salinity levels, illustrating the physiological challenges that these bivalves must overcome in environments where salinity fluctuates considerably. Although this increase in respiration may initially enable organisms to respond to osmotic stress, extreme or prolonged levels of salinity could have harmful consequences for the survival and reproduction of individuals (Sokolova and Lannig, 2008). At the same time, the reduction in filtration rates observed at very low and very high salinity levels could also affect their ability to feed effectively, compromising the long-term health of these populations (Casas et al., 2018).

From an ecological perspective, reductions in filtration at extreme salinities also imply reduced ecosystem functioning. Bivalves are recognized as ecosystem engineers due to their role in benthic-pelagic coupling, nutrient cycling, and water quality regulation (Gutiérrez et al., 2003). Stress-induced reductions in filtration, as observed in *S. senilis*, therefore limit both individual performance and community-level processes. Woodin et al. (2020) emphasized that such behavioral shifts under stress (e.g., reduced pumping) can cascade into altered trophic interactions, reduced bioturbation, and changes in benthic community structure.

In the context of climate change, it is also essential to consider the effects of combined environmental stress, particularly the interaction

between salinity and temperature. In estuarine systems such as the Sine Saloum, salinity variation often interacts with temperature, hypoxia, or pollution. Rising temperatures are known to amplify osmotic stress by further elevating maintenance metabolism, sometimes at the expense of reproduction and growth (Rainer et al., 1979; Sokolova and Lannig, 2008). More recent studies confirm that combined stressors can have synergistic effects: for example, exposure to fluctuating salinity and elevated temperature reduces reproductive output in oysters (Yuan et al., 2016), while contaminants under variable salinity increase oxidative stress and metabolic costs in estuarine bivalves (Matoo et al., 2013). For *S. senilis*, such interactions may further narrow the functional niche identified here, shifting or reducing the salinity window that supports positive energy balance.

Taken together, our results highlight that the metabolic responses of *S. senilis* to salinity are shaped not only by the limits of osmotic tolerance but also by the energetic balance between feeding and respiration. This study emphasizes the importance of distinguishing between survival and functional niches when assessing species resilience. Future work should incorporate factorial experimental designs combining salinity with temperature or pollution to better predict how this ecologically and socio-economically important species will respond to ongoing environmental change.

### CRediT authorship contribution statement

**Jonathan Flye-Sainte-Marie:** Writing – review & editing, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Malick Diouf:** Writing – review & editing, Supervision. **Babacar Sané:** Writing – review & editing, Writing – original draft, Software, Methodology, Investigation, Formal analysis, Data curation. **Etienne Poirier:** Resources, Investigation, Data curation, Conceptualization. **Yoann Thomas:** Writing – review & editing, Supervision, Project administration, Investigation, Funding acquisition, Conceptualization. **Frédéric Jean:** Writing – review & editing, Supervision, Investigation, Conceptualization.

### Declaration of Competing Interest

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

### Acknowledgements

This work was supported by the French National Research Agency (ANR) as part of the IROCWA project (grant ANR-19-CE32-0003–01), as well as by the LMI ECLAIRS2. The authors would like to thank Barama Djouane (Fallou Galass) and Abdou Karim Houmenou for their valuable assistance in the field. They also thank Malika Kerhervé for her help with data processing.

### Data availability

Data will be made available on request.

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