<figure><img src="image_1.png" /></figure>

# Physiological costs of infection by the invasive parasitic copepod *Mytilicola intestinalis* accumulate across temporal scales in the blue mussel *Mytilus edulis*

Felicitas Demann<sup>a</sup>, Christian Buschbaum<sup>a</sup>, Christian Bock<sup>b</sup>, Gaël Guillou<sup>c</sup>, Benoit Lebreton<sup>c</sup>, Gisela Lannig<sup>b</sup>, Markus Molis<sup>d</sup>, K. Mathias Wegner<sup>a,∗</sup>

## ARTICLE INFO

### Keywords:

- Parasitism

- Host-parasite interaction

- Biological invasion

- Energy budget

- Metabolomics

- Wadden Sea

## ABSTRACT

Exploitation of host resources by parasites can have profound impacts on infected hosts. When prevalence is high parasite infection can even feed back on host population dynamics. Especially when parasites are invasive species, their new native hosts can suffer from exploitation due to a lack of co-evolutionary history. Nevertheless, energetic costs are often subtle at the level of the individual and hard to link to population level effects. Sublethal energetic costs accumulate over time and can in sum be traded off against fitness over longer time scales. To study temporal accumulation of infection costs, we used a series of controlled infection experiments to assess the physiological effects exerted by the invasive parasitic copepod *Mytilicola intestinalis* on its newly acquired native host, the blue mussel *Mytilus edulis* in the North Sea, where the parasite can reach prevalences > 70 %. To link short-term physiological responses to long term components of fitness, we combined several methodological approaches measuring changes of physiological traits that act on different time scales. Stable isotope analyses over different seasons and environments revealed a direct consumption of host tissue by *M. intestinalis*. Tissue repair increased energy demand that could directly be observed in accelerated heartbeat rates, and changes of amino acid metabolism measured by ¹H NMR spectroscopy. Although these effects were comparatively small in size, the resulting altered energy budget of the mussel host led to a lower body condition index under controlled laboratory conditions on medium time scales (months) and slower growth in the field over longer time scales (1 year). In combination, our experimental results show that small short-term physiological changes can translate to fitness relevant negative effects on life history traits when integrated across temporal scales.

## 1. Introduction

By definition parasites harm their host (Combes, 2001), and show-case examples like host castration (Lafferty and Kuris, 2009) or induced suicide (Thomas et al., 2002) clearly demonstrate this. Also, emerging diseases resulting from invasive parasites that lack co-evolutionary history with their newly acquired hosts can cause substantial harm in marine invertebrates (Goedknegt et al., 2016). However, these examples rather represent the extreme end of the distribution of costs incurred by parasite infections, and in most cases the costs resulting from parasite infection are more subtle and often only manifest in sublethal costs

resulting from the diversion of host energetic resources (Sanchez et al., 2018; Shanebeck et al., 2022). Diversion costs can arise from mounting an immune response (Bonneaud et al., 2003; McKean et al., 2008) or from repairing tissue damaged by the infection (Loukas et al., 2016). Resource shortage can lead to lower body condition and growth (Sanchez et al., 2018), and thus directly affect individual fitness. Therefore, it is also important to understand how sublethal exploitation by parasites translates into fitness costs of the host (Shanebeck et al., 2022). These energetic costs manifest on different time scales and small immediate physiological effects can accumulate over time (Booth et al., 1997). As a consequence, energetic costs can be traded off against fitness

<footnote><sup>a</sup> Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Wadden Sea Station Sylt, Hafenstraße 43, D-25992 List/Sylt, Germany</footnote>

<footnote><sup>b</sup> Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Integrative Ecophysiology, Am Handelshafen 12, D-27570 Bremerhaven, Germany</footnote>

<footnote><sup>c</sup> UMR 7266 Littoral, Environment and Societies (CNRS - University of La Rochelle), Institut du Littoral et de l'Environnement, 2 rue Olympe de Gouges, 17000 La Rochelle, France</footnote>

<footnote><sup>d</sup>UiT - The Arctic University of Norway, Institute of Arctic Marine Biology, Muninbakken 21, 9019 Tromsø, Norway</footnote>

<footnote>* Corresponding author.
E-mail address: mathias.wegner@awi.de (K.M. Wegner).</footnote>
leading to, for instance, reduced growth and reproduction on longer time scales. However, the linkages of exploitation effects across time are only poorly understood since the long-term costs of exploitation are more challenging to quantify and only indirectly correlate to short-term responses (McElroy and de Buron, 2014). To translate short-term energetic costs into reduced fitness, the effects on physiological traits along an exploitation cascade need to be combined to integrate the costs over different time scales.

Under field conditions uncontrolled environmental variation can mask the more subtle costs of parasite infection, and manipulative experiments are necessary to unravel a putative causality between the physiological cost of infection and fitness costs affecting life history traits (Keymer et al. 1991). However, natural host-parasite systems that can be used for controlled experimental infections are rare, especially when the parasite is an invasive species.

One host-parasite system where experimental manipulation is possible is the invasive copepod *Mytilicola intestinalis*, which infects the intestines of mytilid mussels (Feis et al., 2016). *M. intestinalis* probably invaded the North Sea from the Mediterranean Sea in the 1930s (Caspers, 1939; Feis et al., 2019). In its new range it is specialized on the blue mussel *Mytilus edulis* as its principal host (Feis et al., 2022; Pogoda et al., 2012). Previous studies showed that *M. intestinalis* attaches to the gut wall with hook-like appendages (Bignell et al., 2008). This causes constant wounding, which in turn can lead to cancer-like changes of the gut epithelium (Moore et al., 1978). On top of that, high infection intensities of *M. intestinalis* alone could block the mussel gut (Gee and Davey, 1986a) suggesting that infection can severely impair gut function. In addition, reduced filtration rates combined with increased metabolic demands (Gresty and Quarby, 1991) can lower mussel condition and may even kill the host in case of high parasite load (Theisen, 1987).

Other studies suggest that the exploitation of mussel resources is too small to harm the host (Davey and Gee, 1988; Dethlefsen, 1975), even at high parasite infection intensities (Bolster, 1954). However, these studies investigated different physiological traits in different host and parasite populations. Furthermore, some were experimental while others were observational, which complicates linking of parasite infection effects across temporal scales. Indeed, all studies that concluded that *M. intestinalis* is not a harmful parasite came from field observations, where numerous potentially confounding effects can mask the effect of infection. In field caught hosts, infection history, i.e. how long has an individual been infected, is unclear. It is also unknown whether low condition is a direct result of parasite infection or a sign of bad nutritional status leading to few resources available to fight off parasite infection. In lab experiments, on the other hand, infection history and nutrition can be controlled and eliminate these confounding effects. Therefore, we complement the existing data with a series of controlled infection experiments to connect short-term physiological changes to long-term effects on individual fitness and document cascading effects of physiological costs across different time scales in a single experimental population.

In the first step (step 1), we used stable isotope analyses to reveal trophic relations between the host (i.e., the blue mussel) and *M. intestinalis* in different feeding situations, i.e. different seasons when quality of mussel food sources can be expected to differ (Pinnegar and Polunin, 2000). Inside the mussel gut *M. intestinalis* can share excess food of the host and be a commensal (Dethlefsen, 1975). Alternatively, *M. intestinalis* could adopt a parasitic lifestyle and compete with the host for food resources or directly consume host tissue. Such changes in feeding lifestyles can also be flexible and depend on food source quality. For example, the parasite could shift from host food sources to host tissues when quality or quantity of host food sources are low. The more *M. intestinalis* relies on host tissue the higher the $\delta^{15}N$ values should be relative to those of the mussel host, and much higher relative to the algae food of the mussels, indicating that the parasite is on a higher trophic level (Post, 2002). Direct consumption of host tissue will

intensify costs for the host due to impaired function of the affected tissue and additional energetic resources needed to repair tissue damage.

We then investigated how infection affects heartbeat rate as an immediate physiological response (step 2). Heartbeat rate is an indicator of metabolic activity that can react quickly and therefore acts on nearly immediate timescales (Green, 2011). If an infection by *M. intestinalis* exerts additional energy demands on the host, we would expect to see an increase in metabolic rate to meet that demand. Changes in metabolic rates can also shift metabolomic processes in different tissues dependent on the tissue specific functions. Bivalve tissues can thus serve as recorders of environmentally induced changes in metabolic pathways (Liénart et al., 2024). In ecophysiological studies, untargeted metabolomic approaches are a useful tool to analyze tissue-specific metabolic shifts and link them to whole-animal responses (Bock et al., 2024; Gotze et al., 2025). Using untargeted ¹H NMR spectroscopy, we investigated metabolite profiles of different host tissues to identify parasite induced metabolomic responses related to inflammation and impaired energy metabolism. We used different host tissues to determine whether metabolic shifts occur at the level of the whole animal or whether they are tissue-specific and only affect a subset of tissue-specific traits (step 3).

When high metabolic rates and metabolomic shifts persist over longer time periods, less energy reserves can be built up by the host. In *M. edulis* this can manifest in lower body condition, which we measured after several months under controlled lab conditions (step 4).

Finally, we incubated experimentally infected mussels in the field for a year to evaluate whether short-term physiological shifts and depletion of energy reserves can translate to lower growth over longer time scales under natural conditions (step 5). This combination of different analytical techniques in the same set of animals allowed us to integrate the effect of infection across different stages of parasite exploitation, and could ultimately provide a link from accessible short-term physiological effects of parasite infection to hard-to-quantify long-term fitness costs. The level of detail applied in an experimental setting can thus help to refine the impact assessment of this invasive parasite.

## 2. Materials and methods

### 2.1. Experimental infections and maintenance of blue mussels

For experimental infections, we used mussels from the western shore of the island of Sylt (North Sea, Germany, 54°56'35.61"N, 8°18'59.21"E). Here, mussels grow on wavebreaker groins, where high currents prevent transmission and lead to very low infection levels with *M. intestinalis* (parasite prevalence: 6.7 %, mean ± se infection intensity: 1.1 ± 0.07 parasites per infected host) and other parasites like trematodes. Mussels in a size range of 3 to 5 cm were collected from this low-infection source population and epibionts (e.g. barnacles) were removed. For maintenance, mussels were kept individually in 200 mL plastic bottles at a constant temperature of 17 °C. Each bottle was fed by a unidirectional flow of filtered seawater (50 µm) supplied by a constant supply of algal concentrate (3x10⁹ cells for all animals per day of Instant algae shellfish diet, Reed Mariculture Inc., U.S.A.) was added. For infection, mussels from a high prevalence source bed located on intertidal flats at the eastern side of the island of Sylt (Oddewatt, Sylt, Germany, 55°1'42.62"N, 8°26'2.94"E) were dissected and ripe egg sacs were collected from ovigorous *M. intestinalis* females. Prevalence in this bed ranged from 77-100 % with mean infection intensities ranging from 4.2 – 10.4 parasites (maximal intensity 42 parasites, Feis et al., 2022). Egg sacs were put individually into wells (3.5 mL) of 24 well cell culture plates filled with sterile (0.45 µm filtered) seawater and incubated at room temperature until hatching of copepods. Stage I copepods were collected just after hatching and added to single mussels placed in a new plastic bottle with filtered seawater. Mussels were kept overnight without flow-through to ensure that most of the copepods were ingested (Gee and Davey, 1986b). Uninfected control mussels—from the
same size class as infected ones—were treated in the same way but no
copepodids were added. Experimentally infected and control mussels
were then put back into the flow-through system. All mussels used
subsequently in laboratory and field experiments came from this set of
infected mussels.

## 2.2. Trophic level: measurement of δ¹⁵N values

Differences of δ¹⁵N values were measured between mussels, their food source (i.e., suspended particulate organic matter, SPOM) and *M. intestinalis* infecting the mussels. We collected material during two seasons in the field (winter vs. summer) to estimate the effect different SPOM qualities on the trophic level of the parasite. SPOM quality was determined by C/N ratios, and we expected SPOM to be of lower quality in winter. To compare field data with our laboratory setting we also added samples from the laboratory infection experiments. Mussels, SPOM and parasites were either collected in the field in winter (January 2015, n = 3) and in summer (July 2016, n = 5) or came from the laboratory infection experiment (n = 5).

During field surveys, suspended particulate organic matter and mussels were collected from the same mussel bed where *M. intestinalis* females used for infections came from (Oddewatt, Sylt, Germany). Suspended particulate organic matter was collected by filtering surface seawater on pre-combusted (450 °C, 4 h) glass microfiber filters (GF/F Whatman™). *M. intestinalis* were collected in the gut of mussels. Analyzed infected mussel hosts consisted of whole animals after removal of gut content. All samples were freeze-dried and stored at -80 °C until further analyses. For the laboratory experiment, five mussels from the low infection source were infected with 25 copepodids each and maintained for 2 ½ months in the flow-through systems before mussel and parasite samples were collected. Suspended particulate organic matter in the experimental tanks was collected on pre-combusted glass fiber filters by filtering the incoming water containing the algal food suspension. All samples were freeze-dried and stored at -80 °C until further analyses.

Mussel and parasite samples were ground to a fine powder using a ball mill (MM 300, Retsch). Mussels were analyzed individually and all parasite individuals that were found in one mussel were pooled for analyses. The surface of filters with suspended particulate organic matter was scrapped to reduce the amount of glass fibers in samples. All samples were placed in tin capsules and analyzed using an elemental analyzer (Flash EA 1112, Thermo Scientific, Milan, Italy) equipped with the smart EA option and an autosampler (Zero Blank, Costech, Valencia, CA, United States). The elemental analyzer was connected via a Conflo IV peripheral to an isotope ratio mass spectrometer (Delta V Advantage, Thermo Scientific, Bremen, Germany). Analyses were carried out at the LIENSs stable isotope facility of the University of La Rochelle, France. Data are reported in per mil (‰) using the delta (δ) notation and were normalized using the air content of N₂ as a reference material following the formula: δ¹⁵N = [(Rsample/Rstandard) – 1] x 10³, where R is the ratio of ¹⁵N to ¹⁴N. Calibration was done using reference materials (IAEA-N2, –NO-3, –600). Analytical precision based on the analysis of acetanilide (Thermo Scientific) and peptone (Sigma Aldrich) used as laboratory internal standards was < 0.15 ‰.

## 2.3. Short-term response: heartbeat rate measurements

Three months after infection, heartbeat rate was measured to estimate a physiological response of mussels to *M. intestinalis* infection. Heartbeat rate was non-invasively quantified in 24 uninfected (i.e. control) mussels and 24 mussels experimentally infected with 25 copepodids each, using a heartbeat logger (Arduino UNO board) wired to a watertight infrared sensor (CNY70, Gandra et al., 2015). The sensor head was fixed with cyanoacrylate glue at the position of the pericardium onto the shell of each mussel. Mussels were then transferred back into their 200 mL plastic bottles to acclimate for 60 min to experimental

conditions before recording heartbeat rate for 20 min. Logged data were
plotted and rhythmic cardiac sequences ranging from 2 – 59 consecu-
tive heartbeats were selected from which heartbeat rates were quanti-
fied as beats per min (bpm).

## 2.4. Intermediate-term response: untargeted NMR based metabolic profiling

Twenty uninfected blue mussels (3.6–5 cm shell length) were used to evaluate the impact of *M. intestinalis* infection on the metabolic profiles of specific tissues of *M. edulis*. In the laboratory, half of the mussels were infected with 50 *M. intestinalis* copepodids to maximize parasite impact, while the other half was not exposed to infective stages (control and parasitized mussels, n = 10). After an infection period of 4 ½ months under laboratory conditions, posterior adductor muscle, foot, gill and mantle tissues of all mussels were sampled. Infection load of each mussel was determined by dissection on ice, and tissue samples were flash frozen in liquid N₂ and stored at –80 °C for further analyses. Polar metabolites of the frozen tissue samples were extracted using the methanol dichloromethane extraction technique for NMR samples(Wu et al., 2008) with slight modifications for small samples (Tripp-Valdez et al., 2017). Each frozen tissue sample (≈ 100 mg) was homogenized in 400 µL of methanol and 85 µL of deionized water for 20 s at 6.000 rpm and 4 °C using a Precellys 24 (Bertin Technologies, France). Afterwards 400 µL of chloroform and 440 µL of deionized water were added and the sample was incubated on ice for 10 min. After centrifugation (10 min at 3.000 rpm and 4 °C), three phases were observed. The upper methanol layer containing polar metabolites from the cytosol was transferred into a 2 mL tube and dried at room temperature by vacuum centrifugation overnight (RVC 2–18, Martin Christ Freeze dryers, GmbH, Germany). For untargeted metabolic profiling based on ¹H NMR spectroscopy, dried samples were resuspended with D₂O (3x volume of tissue weight) containing 0.1 % trimethylsilylpropanoic acid (TSP, Sigma-Aldrich, St. Louis, USA), an internal reference marker acting as a chemical shift reference. All ¹H NMR spectra were acquired with a 9.4 T Avance III HD 400 WB spectrometer equipped with a high-resolution magic angle spinning (HRMAS) ¹H-³¹P-¹³C probe using the TOPSPIN 3.2 software for data acquisition and processing (Bruker Biospin GmbH, Germany). Before the NMR measurements, 70 µL of the resuspension was transferred to a 4 mm standard HRMAS zirconia rotor. A Carr-Purcell-Meiboom-Gill sequence with water pre-saturation (protocol cpmgr1d) were used for the acquisition of metabolic profiles at 20 °C using the following parameters: flip angle 90°, acquisition time 4.01 s, relaxation delay 4 s, sweep width 8.803 Hz, 64 scans with 4 dummy scans. An exponential line broadening of 0.5 Hz was applied prior Fourier transformation and a zero-filling of 64 k was used for processing. All spectra were manually corrected for phase and baseline shifts and calibrated using TSP as internal standard at 0.0 ppm. Specific metabolites were quantified using Chenomx NMR Suite 8.1 software (Chenomx Inc., 2014). Signals were assigned to specific metabolites using the database provided by Chenomix in combination with literature data (Ellis et al., 2014; Podbielski et al., 2016; Zittier et al., 2015).

## 2.5. Intermediate-term response: mussel condition in the laboratory

To evaluate the medium-term effects of *M. intestinalis* infection under controlled laboratory conditions, we infected mussels with different *M. intestinalis* exposure doses. Mussels were exposed to either 0, 25 or 50 copepodids to evaluate dose dependence of parasite induced impact. This resulted in three experimental groups consisting of control with no infection (n = 12), moderate infection (n = 12) and heavy infection (n = 10), respectively. Mussels were held under laboratory conditions for 4 ½ months. After the experimental period, we dissected all mussels, recorded the number of adult parasites (males and females), and measured the length of blue mussel shells (maximum anterior-posterior axis) with digital calipers (0.01 mm). Dry weight of blue mussel soft tissue was
determined after drying at 50 °C until constant weight. The condition index (CI) of blue mussels was determined following the formula $CI = \frac{DWF}{L^3}$, where DWF is blue mussel soft body dry weight and L is length of the blue mussel shell (Petersen et al., 2004).

## 2.6. Long-term response: mussel growth in the field

A field experiment was conducted from June 2015 to May 2016 to explore long-term effects of *M. intestinalis* infection on mussel growth. A total of 72 mussels from the low infection source in the size range 3.9 – 4.8 cm shell length were used for controlled infection experiments. Half of the mussels were exposed to 25 *M. intestinalis* copepodids each, and the other half served as control. After the infection procedure and just before starting the field exposure, maximum shell length was measured to the nearest 0.01 mm using digital calipers. Infected and uninfected mussels were placed individually into cages (23 x 23 cm plastic net pockets with a mesh size of 1 cm), which were fixed with an iron rod in the field within an existing mixed oyster and mussel reef. At the end of the experimental period, maximum length of each mussel was measured again to calculate growth increment. After that, mussels were dissected to determine parasite load.

## 2.7. Statistical analyses

All statistical calculations were performed with the software (R Core Team, R 2024). We used parasite abundance, i.e., the number of parasites in each mussel host including those without parasites, as a measure of parasite load, and analyzed parasite load with Poisson-distributed general linear models (GLM). Due to the small number of comparisons, we analyzed the differences of δ¹⁵N values across the different sampling events between mussels and parasites as well as between mussels and food sources by using a Kruskal-Wallis H-test. Heartbeat rate, growth, and condition index were analyzed by linear models. Since the number of intervals with high quality heartbeat signals used to calculate heartbeat rate varied between measurements of individual mussels, we weighed each measurement by the square root transformed number of heartbeat signals to account for possible variation in estimates of heartbeat rate resulting from differences in measurement intervals. The model for growth also contained initial length to control for the size-dependence of growth. Model fit was assessed by visual inspection of residual-vs-predicted plots and normality of residuals with QQ-plots. The browser-based free software Metaboanalyst 6.0 (Xia and Wishart, 2016) based on R was used to analyze and compare the metabolic profiles between uninfected and infected mussels. A log-transformation was applied to the metabolite concentrations to reduce the variance across samples prior to univariate and multivariate statistics. Unsupervised principal component analysis (PCA) in combination with heatmaps were applied between groups to identify potential outliers. A partial least squares classification for high dimensional metabolomic data (PLS-DA) was used to find differences between the metabolomic profiles of parasitized and non-parasitized mussels and to identify the most relevant metabolites responsible for significant differences. In addition, we tested differences in metabolite concentrations on significance with a t-test and SAM (Significance Analysis of Metabolites), assuming a FDR of < 0.1.

# 3. Results

## 3.1. Trophic level: nitrogen isotopic compositions

All experimentally infected mussels (n = 5) had parasite infections and the average parasite load (± SE) was 16 ± 2 parasites per mussel. We found no parasites in the control hosts. δ¹⁵N values of mussel food source (i.e., suspended particulate organic matter) were the lowest and ranged from 8.1 to 10.3 ‰ (Fig. 1). Mussel and parasite δ¹⁵N values

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Trophic relationship between host (*M. edulis*) and parasite (*M. intestinalis*): δ¹⁵N values (mean ± standard deviation) of suspended particulate organic matter (SPOM, i.e., algae food in laboratory experiment and suspended particulate organic matter in the seawater in the field), blue mussel tissue (*M. edulis*), and parasite tissue (*M. intestinalis*) sampled from the field in winter and summer and during a laboratory infection experiment. Much higher δ¹⁵N values of the parasite compared to the host suggest direct consumption of host tissue rather than food of the host.</figcaption></figure>

ranged from 11.5 to 12.8 ‰ and from 14.3 to 15.7 ‰, respectively. The difference of δ¹⁵N values (3.0 ± 0.1 ‰) between mussels and parasites was consistent in the three experiments and no statistical differences were found (Kruskal-Wallis test, H = 3.165, df = 2, *p* = 0.206). The difference of δ¹⁵N values between mussel food sources and mussels ranged from 2.6 to 4. ‰ and was, therefore, more variable and showed significantly lower difference observed in the laboratory and the winter field experiment compared to the summer field experiment (Fig. 1, Kruskal-Wallis test, H = 7.467, df = 2, *p* = 0.024). The lower C/N ratios in the field in winter (10.3 ± 0.5) compared to summer field experiment and the laboratory experiment (13.1 ± 0.4 and 13.1 ± 1.6, respectively, Kruskal-Wallis test, H = 6.303, df = 2, *p* = 0.043) suggest a higher quality of SPOM in winter, but did not contribute to the differences observed between parasites and hosts. The consistent enrichment of δ¹⁵N in the parasite tissue suggests that the parasite directly consumes mussel tissue rather than using the same algal food source as the mussels.

## 3.2. Heartbeat rate

Out of the 24 control mussels, three had a prior infection of one parasite each, which was significantly lower than the mean (± se) of 14.2 ± 1 parasites per mussel recovered from the exposed mussels, where 23 out of 24 mussels were infected (Poisson GLM, infection slope estimate = 4.733 ± 0.580, *z* = 8.164, *p* < 0.001). Parasite infection accelerated the heartbeat rate of infected mussels by 21 % (Gaussian GLM weighed by measurement duration, infection slope estimate = 3.631 ± 1.358, *t* = 2.675, *p* = 0.012, Fig. 2). This effect was independent of the size of the mussel since adding size to the model did not yield an
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Infection by *M. intestinalis* infection leads to increased heartbeat rates of the blue mussel (*M. edulis*) after a three-month infection period under laboratory conditions (control mussels: exposure dose = 0 in grey and infected mussels: exposure dose = 25 in red). White dots display the median, the 25 % and 75 % quantiles are shown as black boxes and 5 % and 95 % quantiles as whiskers with the density distribution of the data as violins. Single heartbeat rate measurements are shown as overlaid dots with the dot size corresponding to the square root of the duration of measured heartbeats. Higher heartbeat rates in infected mussels suggests that infected mussels have higher metabolic rates than control mussels.</figcaption></figure>

improved fit ($F_{1,31} = 0.825, p = 0.371$). High heartbeat rates (>25 bpm) were exclusively found in short measurement intervals leading to a negative relationship between heartbeat rate and measurement interval length (estimate: $-3.042 \pm 0.852, t = -3.573, p = 0.001$, Fig. 2). Measurement intervals were however similarly distributed between treatment groups (estimate $= -3.158 \pm 32.596, t = -0.097, p = 0.923$) and also the slope of the relationship between heartbeat rate and measurement interval length in both infection groups showed no significant difference (interaction estimate $= 0.1649 \pm 1.5299, t = 0.108, p = 0.915$) indicating that high heartbeat rates were mainly found with short measurement intervals with lower precision. To account for the lower measurement precision associated with short measurement intervals we weighed the analyses by interval length. Furthermore, removing the short, low-confidence measurement intervals from the analyses resulted in a stronger and quantitatively similar difference between infected and control mussels (estimate $= 3.880 \pm 1.071, t = 3.624, p = 0.006$).

### 3.3. Untargeted NMR based metabolic profiling

Only 3 out of 10 control mussels were infected by a single parasite, whereas from the experimental group 10 out of 10 mussel were infected with a significantly higher parasite load with an average (± SE) of $22 \pm 2.1$ parasites per mussel (Poisson GLM, estimate $= 4.303 \pm 0.584, z = 7.37, p < 0.001$). We identified 31 distinct metabolites within blue mussel tissues (Fig. 3). Based on the heatmap and PCA results, one sample from the foot and one sample from the muscle tissue were identified as outliers. Differences in the metabolite profiles between infected and uninfected mussels could only be partly characterized using partial least square analyses. Although the PLS-DA showed a clear separation of the metabolite groups between infected and uninfected mussels for the first three components in all tissues, a cross validation showed a trend for overfitting. The SAM analysis identified 15 metabolites with a significantly different concentration between infected and uninfected mussels, ranging from 2 metabolites in gill to 8 metabolites in

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Metabolic shifts induced by *M. intestinalis* infection in different tissues of the blue mussel (*M. edulis*) after a 4 ½ month infection period under laboratory conditions. The heatmap shows the difference of scaled metabolite concentrations between control mussels (exposure dose = 0 parasites) and infected mussels (exposure dose = 50 parasites). Red colors show higher concentrations in infected mussels while grey colors indicate higher concentrations in control mussels and white indicates no difference. Metabolites are arranged according to functional groups, and boxes outlined in black indicate significant differences in metabolite concentrations between infected and control mussels. Numbers behind functional groups and tissues show the percentage of significant changes. In response to infection, the amino acids histidine and serine were the only metabolites that showed significant increase in response to infection, while the other significant changes in metabolite concentration all showed a decrease in concentration. Furthermore, histidine was the only metabolite that showed a consistent infection-induced increase over all tissues, indicating that most metabolomic responses to infection rather affected specific tissues than the whole organism.</figcaption></figure>

foot tissue (Fig. 3). In the gill tissue of infected mussels, levels of the amino acids glycine and alanine were reduced. Muscle tissue of infected mussels showed an increase in histidine and serine levels, while levels of isoleucine, the organic osmolyte *sn-glycero*-3-phosphocholine, and of the membrane metabolite O-phosphocholine were reduced. Mantle tissue of infected mussels showed significant reductions in the levels of the amino acids isoleucine, glycine, valine, leucine, and aspartate along with lower levels of succinate. Most infection-induced changes were observed in foot tissue. Here, levels of the amino acids isoleucine, glycine, alanine, valine, glutamate, and hypotaurine, as well as succinylacetone (carbohydrate metabolism) were all reduced in infected compared to uninfected mussels. Despite tissue-specific differences in the metabolic profiles, when the same metabolite was affected in different tissues the infection-induced changes were always in the same direction. For most
metabolites this entailed a significant decrease in the concentrations in tissues of infected mussels (Fig. 3). Interestingly, only histidine and serine levels increased in infected mussels, and this was particularly obvious for histidine, which showed a similar relative increase in concentration in all tissues of the infected mussels, although the difference was only significant in the foot and muscle tissues in single tests.

### 3.4. Mussel condition in the laboratory

After the experimental time period of 4 ½ months, we found parasites in 7 out of the 12 control mussels leading to an average (± SE) parasite load of 2.2 ± 2.6 per mussel. Most of these parasites were juveniles (58 %) and only one was identified as a mature female, while the remaining ones were males or immature individuals. In contrast, in the infected group, we only found up to 3 % juveniles and 47 % were mature females, indicating that the infections found in the control mussels were acquired during the lab experiment despite our efforts to filter the flow through water. Nevertheless, compared to control mussels, the average parasite load was significantly higher in the mussels exposed to 25 copepods with an average of 19.2 ± 2.6 parasites per mussel (Poisson GLM, infection slope estimate = 2.180 ± 0.207, *z* = 10.537, *p* < 0.001), as well as the ones exposed to 50 copepods, where we found a mean parasite load of 28.4 ± 7.4 parasites per mussel (Poisson GLM, infection slope estimate = 2.573 ± 0.2049, *z* = 12.559, *p* < 0.001). Infection by *M. intestinalis* lowered the condition index (CI) of infected mussels. Compared to the CI of uninfected controls, CI of mussels exposed to 25 copepods was reduced, on average, by 12 % (LM, infection dose slope estimate = -0.728 ± 0.367, *t* = -1.985, *p* = 0.056), and, on average, by 25 % on mussels exposed to 50 copepods (LM, estimate = -1.527 ± 0.385, *t* = -3.971, *p* < 0.001, Fig. 4A). This linear decrease with exposure dose suggests a dose dependent response of conditions index to parasite infection. This was also confirmed by the negative correlation between CI and observed parasite load at the end of the experiment (LM, parasite load slope estimate = -0.059 ± 0.026, *t* = -2.217, *p* = 0.038, Fig. 4B) in the groups exposed to parasites.

### 3.5. Mussel growth in the field

After one year in the field, 51 out of the 72 cages with individual mussels could be retrieved from the field (i.e., 26 with lab exposed mussels and 25 with unexposed control mussels). Out of these, two mussels with prior exposure died, but all of the retrieved control mussels were still alive, leaving 24 infected and 25 control mussels. Only one of

the control mussels did not pick up infections in the field, resulting in a mean (± se) parasite load of 6 ± 0.9 parasites in control mussels. Average parasite load of lab exposed mussels was approximately twice as high with 12 ± 1.2 parasites per mussel (Poisson GLM, infection slope estimate = 0.028 ± 0.004, *z* = 6.884, *p* < 0.001). Lab exposed mussels with a higher parasite load grew significantly slower than control mussels (LM, infection slope estimate = -0.014 ± 0.035, *t* = -3.921, *p* < 0.001, Fig. 5), even when considering that larger mussels had smaller growth increments (LM, initial length slope estimate = -0.521 ± 0.219, *t* = -2.381, *p* = 0.021). In accordance with the CI measurements in the laboratory experiment, growth of blue mussels in the field correlated negatively with parasite load (LM, initial length estimate = -0.531 ± 0.232, *t* = -2.920, *p* = 0.027, parasite load slope estimate = -0.232 ± 0.079, *t* = -2.948, *p* = 0.005). This indicates that a higher parasite load throughout the experimental period diverted resources from growth to other traits in a load-dependent manner also under field conditions.

## 4. Discussion

Our experiments show that an infection with the invasive parasite *M. intestinalis* changes metabolic and energetic budgets of *M. edulis*, culminating in reduced growth under natural conditions. During the initial invasion into the blue mussel populations of the North Sea infection with *M. intestinalis* was associated with increased mortalities (Korringa, 1950) but later studies could not observe detrimental effects and questioned the parasitic nature of an *M. intestinalis* infection (Davey, 1989; Dethlefsen, 1975). In contrast, all of our applied methods, which were staged along the duration of the host-parasite interaction, indicate an increased energetic demand arising from the consumption of host tissue allowing us to integrate the subtle instantaneous costs into components of fitness over the life history of the host – even decades after the first introduction of the parasite.

### 4.1. Enrichment in ¹⁵N indicates host tissue is the main food source

Understanding the feeding strategy of *M. intestinalis* and its diet can provide important information to determine its ecological role within its mussel host. Several food sources are principally available to *M. intestinalis* in the mussel intestine, including undigested phytoplankton (i.e., the major food source of blue mussels), breakdown products produced by the host but also tissue of host gut epithelium. Here, the much higher δ¹⁵N values of *M. intestinalis* compared to the mussel are very close to the trophic fractionation factors (TFF) observed

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** A) Infection by *M. intestinalis* reduces condition index of the blue mussel *M. edulis* after a 4 ½ month infection period under laboratory conditions (control mussels: exposure dose: 0 (grey) and infected mussels: exposure dose: 25 (red) and 50 (dark red)). White dots display the median, the 25 % and 75 % quantiles are shown as black boxes, and 5 % and 95 % quantiles as whiskers with the density distribution of the data as violins. The decrease in body condition over exposure doses suggests a linear dose dependence of the infection. B) The linear relationship (dashed line) between parasite load at the end of the experiment and condition index supports the linear dose dependence in mussels exposed to parasites (red squares = exposure dose 25, dark red dots = exposure dose 50).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 5.** A) Effect of *M. intestinalis* infection on growth of the blue mussel *M. edulis* after 12 months under field conditions (control mussels: exposure dose = 0 in grey; infected mussels: exposure dose = 25 in red). White dots display the median, the 25 % and 75 % quantiles are shown as black boxes, and 5 % and 95 % quantiles as whiskers with the density distribution of the data as violins. Infection by *M. intestinalis* reduces growth rates under natural conditions. B) Linear relationship (dashed line) between growth and observed infection load at the end of the observation period. Control mussels (grey triangles, exposure dose = 0) collected several new infections while exposed in the field, matching the relationship of previously exposed mussels (red squares, exposure dose = 25).</figcaption></figure>

between two trophic levels for invertebrates in aquatic food webs (Minagawa and Wada, 1984; Post, 2002; Zanden and Rasmussen, 2001), and clearly indicate that this parasite at a higher trophic level than its host. Consequently, *M. intestinalis* is not consuming the same food sources as the mussel, but is directly or indirectly feeding on host tissue (i.e., gut tissue) or its breakdown products (Gresty and Quarmby, 1991). Together with the energy needed to repair the damage that *Mytilicola*-attachment causes to the host's intestinal wall (Moore et al., 1978), feeding on host tissue is likely to have consequences on the host health and can thereby alter its energy budget (Goedknegt et al., 2018a). In addition, the differences of δ¹⁵N values between *M. edulis* and suspended particulate organic matter (2.5 to 4.1 ‰) are in the range of the TFFs generally observed between primary consumers and their food sources (2.52 ± 2.50 ‰, Zanden and Rasmussen, 2001), validating that mussels were feeding on the SPOM in the field and in our experimental set up. Although, we observed a difference of C/N ratios between winter and summer, the generally high quality of SPOM did not affect the observed TFFs. Further investigations using a larger range of C/N ratios and available food quantities could target the effects of food quality on the host-parasite relationships between *M. edulis* and *M. intestinalis* more directly. The congeneric *Mytilicola orientalis* was also enriched in ¹⁵N compared to *Mytilus edulis*, but to a lower extent (i.e., 1.22 ‰), suggesting a more opportunistic feeding behavior, with a mixed reliance on host gut tissue and host ingested food sources (Goedknegt et al., 2018b). Such a difference of δ¹⁵N values between two closely related parasite species using the same host species could be related to differences in availability or in quality of food sources in host guts, but also highlights the complexity in understanding host-parasite relationships. TFFs between hosts and parasites can indeed vary greatly, e.g., ranging from -6.7 to 9.0 (Thieltges et al., 2019). Such a range of TFFs is evidently related to the diversity of ways parasites obtain resources from their hosts. These can range from absorption of amino acids through teguments (e.g. cestodes, trematodes, larval nematodes) to active consumption of host blood (e.g. louses, leeches, lampreys) or host tissues (e. g. *M. intestinalis*), which will inevitably lead to different TFFs. Excretion and digestion processes in *Mytilicola* are likely similar to those of free-living copepods (Hockley, 1951), which could explain why TFFs in copepod parasites are comparatively high (Thieltges et al., 2019) and close to those observed in classic trophic interactions. The consistent enrichment of δ¹⁵N across seasons and laboratory settings supports that *M. intestinalis* obtains its food resources mainly from its host.

## 4.2. Heartbeat rate is accelerated in infected mussel hosts

The increased heartbeat rate in infected mussels suggests that infection increases metabolic rates since increasing heartbeat rate can function as a rapid response to increased energy demand. The use of heartbeat rate as a proxy for metabolic rate (Green, 2011) is based on the correlation observed in marine invertebrates including limpets (Marshall and McQuaid, 1992) and mussels (Bakhmet, 2017). Although, the correlation between heartbeat rate and metabolic rate can vary between invertebrate populations (Bruning et al., 2013), we can assume that the increase in heartbeat rate for the experimentally infected mussels from the same population investigated here is a reliable indicator for increased metabolic rate and a higher energy demand resulting from the infection by *M. intestinalis*. An elevated heartbeat rate not only indicates an increased energy metabolism, it may reduce the scope to respond to additional stress. In bivalves including *M. edulis* increasing temperatures initially lead to a faster heartbeat rate (Bakhmet et al., 2022). However, once a critical threshold temperature is reached, heartbeat rate drops again leading to mismatches between metabolic processes and the oxygen needed to supply them with energy (Eymann et al., 2020). This suggests that thermal tolerance is partly determined by the range between resting and maximal heartbeat rate. When resting heart rate is already increased by parasite infection the scope of thermal tolerance will be limited (Braby and Somero, 2006). The increase of an average of three beats per minute observed here could reduce the critical temperature by 4–5 °C and substantially limit thermal scope of infected mussel hosts (Bakhmet et al., 2022).

## 4.3. Tissue-specific metabolic profiles are altered in infected mussel hosts

The effect of the parasite infection on the host's metabolism was also reflected in the observed changes of the ¹H NMR spectra obtained from different tissues of *M. edulis*. The differentiation of the metabolic profiles between control and infected mussels was mainly caused by changes in amino acid concentrations that were observed in all tissue types. *M. intestinalis* is thus capable of affecting the metabolic homeostasis of its host and might directly benefit from mussel metabolites for nutrition (Sobecka, 2012).

### 4.3.1. Functional shifts in the foot

Most of the significant changes in metabolite concentrations were observed in the foot. One of the main functions of the foot is to secrete byssus threads, which are needed for attachment and bed formation
(Farrell and Crowe, 2007). Production of byssus threads is highly energy demanding (Aguilera et al., 2017), and former studies showed that the byssus threads can be weakened by *M. intestinalis* infection (Brienne, 1964). The reduced levels of five amino acids in foot of infected mussels measured here might constitute a functional link between infection and weaker byssus threads as amino acids are a key component of byssus threads (Lucas et al., 2002). Alanine and hypotaurine are involved in the stabilization of proteins (Bishop et al., 1983), while branched amino acids (BBAs, leucine, isoleucine and valine) stimulate protein synthesis (e.g. for byssus production). Weakening of byssus attachment strength by *M. intestinalis* could enhance blue mussel susceptibility to wave-induced detachment, which could directly affect blue mussel population dynamics itself but also indirectly the associated community structure of mussel beds at both soft-bottom and rocky shores (Buschbaum et al., 2009). The lower levels of these amino acids could therefore affect the general ecological role of *M. edulis* as an important habitat engineer at both rocky and sedimentary coasts. Future studies could reveal how metabolomic shifts induced by parasite infection impair byssus formation and affect habitat properties in mussel beds.

### 4.3.2. Shifts in metabolites associated with oxidative stress

Alanine, hypotaurine, leucine, succinylacetone, and isoleucine play an important role in redox homeostasis. Changes in succinylacetone concentration can activate the biosynthesis of the antioxidant enzymes glutathione peroxidase and superoxide dismutase (SOD) as defence against oxidative stress. A decrease in succinylacetone concentration was, for example, observed in the phasic muscle of scallops as a response to stress induced by ocean warming and acidification (Bock et al., 2024). The shift in metabolites involved in oxidative stress and defense might be directly related to the oxidative stress caused by the reactive oxygen based immune response mounted against *M. intestinalis* (Feis et al., 2018).

### 4.3.3. Biomineralization and energy metabolism in gills and mantle

The gills and the mantle provide a large surface able to absorb nutrients that can be incorporated into the mussels' tissues and further processed via metabolic pathways. Especially the gills are responsible for the first uptake of amino acids, which are essential for viable organ functions of organisms (Siebers and Winkler, 1984; Wu, 2009).

The mantle tissue of *M. edulis* is mainly responsible for secreting the shell via biomineralization processes. The shell of *Mytilus* is the first line of defense against predators (Freeman, 2007; Leonard et al., 1999) and pathogens (Canesi et al., 2002), and metabolic shifts of amino acids might also affect shell formation. Generally, free α-amino acids are involved in many physiological processes, and altered concentrations might therefore indicate stress within the organism (Zurburg et al., 1989). The decrease in the majority of metabolite concentrations in infected compared to control mussels, such as the decrease in alanine concentrations in gills and foot tissue, indicates that the cellular energy budget is impaired in infected mussels. Alanine is the main end product in the breakdown of glucose in bivalves (Stokes and Awapara, 1968). The small changes in the markers for anaerobic energy production in (e. g. fumarate, succinate, lactate) infected mussels seem not to suffer from impaired aerobic energy metabolism (Eymann et al., 2020; Georgoulis et al., 2022). The observed changes of the Krebs cycle intermediate (i.e., lowered levels of succinate) in the mantle tissue of infected mussels rather indicate an increased aerobic cellular turnover and mitochondrial ATP production to counterbalance the elevated costs of infection. In addition, aspartate may be used as an energy source under environmental stress (Haider et al., 2020), and could explain the lower concentrations in mantle tissue of infected mussels. Such shifts in mantle tissue might in turn impair shell formation and slow down growth.

### 4.3.4. Organism-wide increase of histidine

The only common change in all tissues was the elevated concentration of histidine in infected mussels. Histidine is an essential amino acid

that is particularly important for protein biosynthesis but is also involved in the oxidative stress response (Diaz de Cerio et al., 2020) and immune response of *Mytilus* (Frizzo et al., 2021). Furthermore, the importance of histidine-rich-glycoproteins and their potential role for shell growth, immune responses, and antimicrobial activity in blue mussels has been highlighted (Sforzini et al., 2020), and the latter two can be triggered by an infection with *M. intestinalis* (Demann and Wegner, 2019; Feis et al., 2018). Finally, histidine is used to repair damaged tissues in general (Kim et al., 2019) and could thus reflect the increased need to repair tissue damaged by *M. intestinalis*. The combined function of histidine in tissue repair and scavenging reactive oxygen species suggests that histidine could serve as biomarker for infection in blue mussels.

Overall, the metabolomic results indicate changes of functional properties like attachment and shell formation, a disturbed redox homeostasis accompanied with oxidative stress, and a potential immune response in parasite infected mussels. In line with the heartbeat rate data, metabolic shifts in parasite infected mussels also indicate higher metabolic rates reducing the amount of cellular energy available to maintain energy reserves (body condition) and growth of mussels over longer periods.

### 4.4. Metabolic shifts lead to reduced condition and growth

In line with our expectations, we found that infection reduces body condition after 4.5 months under controlled lab conditions in a dose dependent manner. The reduction of 12 % in the treatment with 25 copedids is in accordance with estimates obtained from the same host population (Feis et al., 2016) and blue mussels from the Southern Wadden Sea infected with similar infection doses of the congeneric parasite *M. orientalis* (Goedknegt et al., 2018a). An exposure dose of 50 copepods was associated with a 25 % reduction in body condition demonstrating a nearly linear dose dependence of parasite inflicted energy drain. This negative correlation between parasite load and body condition in infected mussels may reflect the direct effect of the energy drain exerted by the parasite.

On the other hand, a larger reduction in body condition with higher parasite load could also be explained by increasing amounts of energy needed to mount an immune response against a larger number of parasites. Mussels infected by *M. intestinalis* mount a reactive oxygen mediated immune response (Feis et al., 2018) and divert immune resources away from cellular immunity (Demann and Wegner, 2019) indicating that resources are limited within the immune response, but could also contribute to resource limitations for other traits like reserve build-up and growth.

Consequently, we observed significantly slower growth in the infected mussels in the field, which is in accordance with other parasites of *M. edulis* (Thieltges et al., 2006). Among the physiological responses we measured, growth is the most reliable component of fitness for *M. edulis* since size correlates with reproductive output (Rodhouse et al., 1986; Sprung, 1983). Faster growth can also maintain spatial dominance (Petraitis, 1995) and allow mussels to outgrow the preferred prey size ranges of predators and thereby increase survival (Paine, 1976; Sommer et al., 1999).

### 4.5. Conclusion

Exploitation by parasites can have profound impacts on host populations (Ebert et al., 2000), but in many cases the direct energetic costs are only subtle and difficult to link to life history traits affecting fitness (Booth et al., 1997). These subtle physiological effects can accumulate over time and, in sum, be traded off on longer time scales against components of fitness like growth and reproductive output. By combining several methodological approaches, we could demonstrate how consumption of blue mussel *Mytilus edulis* tissue by the intestinal parasitic copepod *Mytilicola intestinalis* changes several physiological
traits acting on different time scales along the life history of the mussel hosts. The direct trophic connection between *M. intestinalis* and its mussel host seen in the enrichment in ¹⁵N of the parasite and the altered amino acid composition in the tissue of *M. edulis* suggest that reduced growth, as the final consequence of the exploitation chain, is a result of direct tissue disruption within parasitized mussels. The direct energetic exploitation of blue mussel hosts by *M. intestinalis* is complemented by the increase of somatic maintenance costs. The increased concentration of the tissue repair marker histidin could be a sign for the repair of lesions from parasite attachment to host gut walls (Davey and Gee, 1988; Moore et al., 1978). Furthermore, the high infection intensities (up to 41 parasites) combined with the large size of females (up to 10 mm) in the comparatively small host gut can impair nutrient uptake and intestinal function (Gee and Davey, 1986b). In combination, this suggests that a high infection intensity of *M. intestinalis* will have high energetic costs and ultimately limit the resources available for growth as a decisive component of fitness. As we base our conclusions on a single set of simultaneous experimental infections where we successfully controlled parasite load and environmental conditions, we can eliminate the uncontrolled sources of environmental variation (e.g. infection history, exposure dose) inherent to earlier observational field studies failing to find detrimental effects of *M. intestinalis* on mussel hosts (Dethlefsen, 1975). Our experimental data now suggests that *M. intestinalis* incurs negative effects on host fitness-relevant life history traits also under field conditions. The interaction between *M. intestinalis* and its mussel host differs between populations since different traits are involved in the mechanistic interaction along the distinct invasion routes (Feis et al., 2016). Temperature also has a strong impact on the energetic costs of infection (Jolma et al., 2025; Jolma et al., 2024). Differences in environmental conditions and the mechanistic basis of the host-parasite interaction will introduce variation in the amount of energetic cost of the infection. It is however safe to assume that subtle energetic costs will exist in all cases, which then can manifest in reduced fitness over longer time scales. Such an impact of parasite infection on mussel host fitness via reduced growth can influence host population persistence (Paine, 1976; Sommer et al., 1999). Habitat engineering species like *M. edulis* create hot spots of biodiversity (Buschbaum et al., 2009). Therefore, impacts of non-native parasites like *M. intestinalis* on these ecosystem engineers can potentially affect the occurrence and diversity of a wide variety of associated species with feedbacks on the whole community.

### CRediT authorship contribution statement

**Felicitas Demann:** Writing – review & editing, Methodology, Investigation, Conceptualization.
**Christian Buschbaum:** Writing – review & editing, Methodology, Investigation, Funding acquisition, Conceptualization.
**Christian Bock:** Writing – review & editing, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Gaël Guillou:** Methodology, Investigation, Data curation.
**Benoit Lebreton:** Writing – review & editing, Validation, Methodology, Investigation, Funding acquisition, Data curation, Conceptualization.
**Gisela Lannig:** Writing – review & editing, Methodology, Investigation, Data curation, Conceptualization.
**Markus Molis:** Writing – review & editing, Methodology, Investigation, Formal analysis, Conceptualization.
**K. Mathias Wegner:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

### Declaration of competing interest

The authors declare no competing interests.

### Acknowledgements

We thank Kaibil Escobar Wolf, Eike Petersen and Anette Tillmann for

help with the animals and in the lab. FD was financially supported by the Deutsche Bundesstiftung Umwelt (DBU) and KMW received funding from the DFG SPP1399 Host-Parasite Coevolution (We4641/1-2). BL was supported by a Fellowship from the Hanse Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany. The stable isotope facility of the joint research unit LIENSs got financial supports from the French state ("Contrat de Projet Etat-Région 2007-2013"), from the "région Poitou-Charentes" ("FEDER Poitou-Charentes") and from the "région Nouvelle-Aquitaine" ("Projet plateformes mutualisées 2017-2021")

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