Cold tolerance strategies of freshwater mussels across latitudes

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Lipińska, Paweł Adamski, Adam M. Ćmiel, Maria J. Golab, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5596428/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 6 You are reading this latest preprint version Abstract Freshwater mussels across Europe exhibit physiological and behavioural adaptations to survive winter conditions. Climate change projections, including more frequent extreme weather events, are expected to intensify pressures on these ecosystems. In this study, we tested the temperature-size hypothesis, which posits that larger body size in ectothermic organisms is an adaptation to colder climates. We predicted that Anodonta anatina populations in northern regions would have larger shells than those in central and southern regions. Additionally, we hypothesized that harsher winters in northern regions require mussels to maintain higher glycogen levels as an energy reserve. We also explored whether shell size varies between lowland and upland populations, following the temperature-size rule, and whether supercooling (SCP) occurs primarily in northern populations as a complementary survival strategy. Northern populations had the highest glycogen levels, reflecting adaptations to colder conditions. SCP was rare (2.5%) and observed predominantly in northern mussels, suggesting limited reliance on freeze avoidance. Instead, it is likely that mussels employ mixed strategies, such as metabolic reduction and burrowing, to withstand winter. These findings link shell size, glycogen levels, and SCP to specific survival strategies, providing new insights into the cold tolerance mechanisms of freshwater mussels and their potential vulnerability to climate change. Biological sciences/Ecology Biological sciences/Physiology Biological sciences/Zoology Anodonta anatina supercooling point frost resistance survival strategies overwintering climate change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Climate change projections indicate an increase in the frequency and intensity of storms, floods, heat waves, droughts, cyclones, and cold spells 1 ; 2 . Freshwater ecosystems, being highly sensitive to variations in temperature and water availability, face particular challenges under these changes 3 ; 4 . These challenges are compounded by significant alterations in the physical, chemical, and biological characteristics of lakes and rivers driven by climatic warming. For instance, climate change strongly influences terrestrial catchments, where inputs to freshwater systems can be dampened or amplified by in-lake processes. This can lead to seemingly counter-intuitive responses, such as the acidification of streams but the alkalinization of lakes in areas with limited base cation supplies 5 . Species inhabiting these environments, such as freshwater mussels of the order Unionida (hereafter referred to as "mussels"), are already experiencing significant declines due to habitat loss, pollution, and climate change 5 , 6 ; 7 , 8 . Mussels play a crucial role in freshwater and marine ecosystems, where they contribute to or provide many ecosystem services, such as nutrient cycling and storage, biofiltration, and food web modification 9 , 10 . However, they are particularly vulnerable to environmental fluctuations and extreme events 11 . As one of the fastest declining animal groups globally, mussels are especially vulnerable due to their long lifespans and limited mobility, which reduce their ability to escape adverse conditions 7 , 12 . Although freshwater habitats generally provide some thermal buffering, they are still susceptible to climate disturbances 13 . In particular, shallow streams, ponds, and the shores of larger water bodies are prone to freezing during winter months, exposing benthic organisms such as mussels to subzero temperatures 13 . Extreme cold can disrupt biological processes, leading to reduced activity or even cessation of essential functions 3 . These conditions can push temperatures beyond the thermal tolerances of some species, posing a serious threat to survival 14 . While high-temperature stress in bivalves has been extensively studied, knowledge about their responses to cold stress remains limited 15 , 16 . The adaptations of bivalves to low temperatures may involve a combination of structural, physiological, and behavioural traits, such as the insulating properties of their shells, which may include specialised microstructures 17 ; physiological mechanisms, such as changes in metabolic rate, modulation of oxygen demand, supercooling, or synthesis of cryoprotectants 14 , 18 – 20 and body size adjustments in line with the temperature-size rule 21 – 23 . The temperature-size rule posits that ectothermic organisms tend to develop larger body sizes in colder climates, as lower temperatures slow metabolic rates, allowing for more efficient growth and resource use over longer periods 23 – 25 . Such adaptations may confer advantages in resource-limited, colder environments by promoting energy conservation and enhanced cold resistance 23 . Behavioural responses to low temperatures, such as burrowing into the sediment or moving to deeper water, also contribute to their survival 26 , 27 . Cold-tolerance strategies can generally be categorised as either freeze avoidance or freeze tolerance 14 , 20 . Freeze avoidance involves mechanisms that prevent the formation of ice crystals in body fluids by lowering their freezing point, whereas freeze tolerance allows controlled ice formation in body tissues, thereby minimising cellular damage. However, these survival strategies remain poorly understood in large mussels, highlighting a significant gap in our knowledge of their cold-resistance mechanisms 20 . In species such as the duck mussel ( Anodonta anatina ), which inhabits diverse freshwater environments across the western Palearctic and Siberia, the ability to withstand cold temperatures likely varies in response to local climatic conditions 27 . This variation can manifest in several phenotypic traits, including shell size, physiological condition, and supercooling point (SCP). SCP measures how far below freezing temperatures body fluids can get before ice crystals form and is defined as the temperature at which ice crystals begin to develop. Mussels inhabiting colder environments are thought to have evolved or adapted physiological mechanisms, such as a lower SCP, to survive freezing conditions. Cold hardiness mechanisms are strongly linked to the metabolism of energy reserves, such as glycogen, which plays a key role in the synthesis of cryoprotectants, such as glycerol 28 – 31 . These cryoprotective substances help organisms survive freezing conditions by stabilizing cellular structures and minimizing damage caused by ice formation. The production and accumulation of cryoprotectants primarily depend on the availability of energy reserves and are critical for overwintering success 32 . The objective of this study is to investigate key adaptive traits in freshwater mussels, using Anodonta anatina as a model organism. Focusing on body size, energetic condition (in terms of glycogen concentration), and supercooling point (SCP) across environmental gradients - particularly latitude and altitude - this study examines how these traits vary within and among mussel populations. In this study, we propose several hypotheses to explain the observed variation in mussel traits across regions and environmental conditions. First, we hypothesize that environmental temperature influences mussel morphology and physiology in accordance with the temperature-size rule. Based on this, we predict that mussels in colder northern regions will have larger body sizes than those in central and southern Europe.. We also hypothesize that seasonal climatic conditions shape energy storage strategies in mussels. Therefore, we predict that mussels from northern populations will exhibit higher glycogen concentrations, as longer and harsher winters in these regions likely require greater energy reserves for survival. In addition, we hypothesize that both latitude and altitude interact to affect mussel size. Accordingly, we predict that mussels from Northern and upland areas will generally have larger shells than those from South of Europe and lowland areas due to temperature rule size (latitude) and lower ambient temperatures (altitude). Finally, we hypothesize that low-temperature resistance mechanisms vary across climatic gradients. We predict that supercooling (SCP) will be most frequently observed in mussels from northern regions, where exposure to freezing conditions is more common. By testing these hypotheses using A. anatina as a representative species, this study aims to shed light on the adaptive responses of mussels to different thermal environments. Our findings will contribute to a broader understanding of the ecological effects of temperature on species distributions, survival strategies, and the potential consequences of climate change in freshwater ecosystems. Material and methods Study sites and mussel collection To examine mussel responses to variation in climatic conditions, individuals were collected from sites differing in latitude, altitude, and thermal regimes during the winter season, a period of maximum physiological preparation for overwintering (Fig. 1 ). In total, 322 A. anatina individuals were collected during the winter season of 2023/2024, with 150 individuals from Northern Europe (Scandinavia; Norway and Sweden), 98 from Central Europe (Poland), and 74 from Southern Europe (Portugal). Mussels were collected from lowland (3 in Northern, 3 in Central, and 2 in Southern Europe) and upland habitats (3 in Northern, 1 in Central, and 2 in Southern Europe). In Northern Europe, mussels were collected at 6 sites, exclusively in lakes; in Central Europe, they were found at 4 sites: 3 reservoirs and 1 river; and in Southern Europe, they were found at 4 sites, only in rivers. In all locations, mussels were collected manually. The sampling protocol required the collector to gather the first 30 individuals encountered, regardless of their size, to avoid any bias in sampling related to mussel size. However, the number of mussels collected per site varied, as it was not always possible to gather the required number in less abundant populations. In some cases, slight overcollections occurred due to collector error. Climatic conditions To verify that mussels collected from different regions and altitudes experience distinct climatic conditions, we compared air and water temperature regimes at all collection sites. Climatic zones at mussel collection sites were visualised using high-resolution (1 km) Köppen-Geiger climate classification maps 33 . In Southern Europe, all sites were classified as “Csa” (hot-summer Mediterranean climate); in Central Europe, as “Cfb” (temperate oceanic climate); and in Northern Europe, the dominant class (5 out of 6 sites) was “Dfb” (warm-summer humid continental climate; Fig. 1 ). Mean monthly air temperature at each site was obtained from ClimateData.org 3434 , based on ECMWF data (0.1–0.25° resolution) collected between 1991 and 2021 and refreshed in May 2022. Mean monthly water temperatures were estimated using the Lake Model 35 over the period 1998–2009. The simulation incorporated site-specific meteorological and lake parameters, including wind components, air temperature, humidity, cloud cover, solar and thermal radiation, geographic coordinates, water depth (2 m), and assumed water transparency of 1 m. To statistically test for differences in climatic conditions between regions, a repeated measures ANOVA was used to compare both air and water temperatures across months. Additional details on the mussel collection sites, mean monthly air temperatures, and modelled water temperatures are provided in the Supplementary Materials (Tables S1–S3). Measurement of shell size and supercooling point (SCP) To assess variation in morphological and physiological traits relevant to cold adaptation, we measured shell size and supercooling point (SCP) in all collected individuals. Each mussel was aged and measured for shell size upon collection. Shell length, width, and height were recorded using callipers. The age of individuals was determined by counting annual growth rings visible on the shell 36 . To minimise the risk of error in age determination, counting was performed on the right shell valve whenever possible. If growth rings were not clearly visible, the left valve was used instead. To analyse variation in shell size across regions and habitats, we used a General Linear Model with region, habitat type, mussel age, and their interaction as predictor variables. The supercooling point (SCP) of each mussel was measured to assess cold tolerance following Sinclair et al. 19 . SCP was typically measured on 20–30 individuals per population to assess the distribution shape 19 . To obtain the SCP of mussel body fluids, a thermocouple (K-type, probe diameter 0.5 mm) was inserted into the posterior adductor muscle of each individual. The mussels were then placed in a deep freezer (Platilab 340 SV-3-STD; ALS Angelantoni Life Science deep freezer), with the temperature set to decrease from + 5°C to -20°C at a constant rate of 1°C/min, as recommended in previous SCP measurement studies 19 , 20 , 37 . The temperature decrease was continuously recorded, and the SCP was read off (with ± 0.1°C accuracy) as the last temperature indication just before the peak (rebound) caused by the heat of the exothermic crystallisation reaction. A freeze survival experiment was conducted at three sites in Central Europe. To assess post-freezing survival, after capturing the SCP, 8 mussels from each site were placed in water at 4°C. They were then kept for another 7 days or until evidence of mortality was confirmed. To explore potential physiological correlates of cold resistance, we used a linear regression to test for associations between SCP and both water temperature and glycogen concentration. Glycogen Concentration To assess physiological condition and energy reserves in mussels from different climatic regions, glycogen concentration was measured in soft tissues. Each animal sample (whole tissue without the shell) was homogenised with 3–4 mL of distilled water using an IKA homogeniser (30 s pulse, 100% amplitude, 5–8 times). The homogenates were then centrifuged (10 min, 4°C, 14,000 × g). The supernatants were used for further analysis. The ELISA Glycogen Assays (Sigma-Aldrich, No. MAK016 and MAK465, USA) were performed for the quantitative determination of glycogen. The Glycogen Assay Kit uses a single working reagent that integrates the enzymatic breakdown of glycogen with the measurement of glucose in a single step. The concentration of glycogen in the sample is proportional to the colour intensity of the reaction product, measured at 570 nm in flat-bottom clear 96-well plates. Each 96-well plate had its own glycogen standard curve, run in duplicate. Additionally, a blank and a negative control were included for each plate. Samples were added to the plate in duplicate technical replicates for statistical purposes. Both glycogen assays were performed according to the ELISA manufacturers' instructions, and the plates were then read at a wavelength of 570 nm using an Epoch 2 multimode microplate reader (BioTek Instruments, Inc., Winooski, VT, USA). Differences in glycogen concentration were analysed using a two-way ANOVA with region and habitat type as fixed effects and their interaction. Results Climatic conditions Repeated measures ANOVA showed that the highest mean yearly air temperature at collection sites was observed in Southern Europe (t mean =14.8 o C; SD = 5.9; Fig. 2 a). Whereas the mean yearly air temperature at collection sites in Central Europe (t mean =9.1 o C; SD = 7.7; Fig. 2 b) and Northern Europe (t mean =5.5 o C; SD = 7.7; Fig. 2 c) were lower, and the differences in mean yearly air temperature at collection sites among regions were significant (df = 2; F = 40.4; p < 0.0001). Also, the within effect (months) was significant (df = 11, F = 1690.1; p < 0.0001), as well as the interaction between region and within effect (months; df = 22; F = 21.6; p < 0.0001). Repeated measures ANOVA showed that modelled mean monthly water temperatures at collection sites also differed significantly among regions (Southern Europe: t mean =17.3 o C, SD = 6.2.; Central Europe: t mean =9.1 o C, SD = 7.6; Northern Europe: t mean =7.3 o C, SD = 7.3; df = 2; F = 151.4; p < 0.0001), with significant influence of within effect (months; df = 11, F = 1709.9; p < 0.0001) and the interaction between region and within effect (months; df = 22; F = 23.8; p < 0.0001; Fig. 2 D). Moreover, FLake model showed that the median number of weeks with ice covering the lake surface at collection sites was 15 in Northern Europe (range: 0–15), 2 in Central Europe (range: 0–3) whereas in Southern Europe ice cover on the lake surface did not occur at any of the collection sites. Shell length Descriptive statistics for mussel shell length are presented in Table 1 . The General Linear Model showed that all analysed factors: region, habitat, mussel age, and the interaction between region and habitat, had significant influence on mussel shell length (Fig. 3 a-c; detailed results in the Supplementary Materials, Table S1 ). Additionally, both age and the interaction between region and habitat had the highest influence on mussel shell length, whereas the influence of region and habitat on mussel shell length was much weaker (Supplementary Materials, Table S1 ). Mussels from Southern Europe were significantly longer than mussels from Central and Northern Europe, while no significant difference in length was observed between mussels from Central and Northern Europe (Fig. 3 a). Also, mussels from Central and Southern Europe were significantly larger in lowland areas than in highland areas (Fig. 3 b). However, the interaction between regions and habitat showed that mussels from Northern Europe were significantly longer in highland areas than in lowland areas. In contrast, mussels from Southern and Central Europe were significantly shorter in highland areas than in lowland areas (Fig. 3 c). Together, these results suggest that both latitude and altitude jointly influence mussel shell size, with opposite altitudinal trends observed in Northern Europe compared to Central and Southern Europe. Table 1 Mussel shell length in the different regions and habitat types. LQ – lower quartile, UQ – upper quartile, SD – standard deviation of mean. Region Habitat type N Mean [mm] Median [mm] Min [mm] Max [mm] LQ [mm] UQ [mm] SD Northern Europe Lowland 69 77.0 74.7 39.6 108.0 68.6 88.8 14.8 Highland 66 101.1 101.3 70.7 123.3 95.2 108.0 10.5 Central Europe Lowland 63 94.5 97.0 59.5 124.0 81.0 108.6 17.2 Highland 24 98.7 106.2 11.4 128.5 89.3 115.0 25.4 Southern Europe Lowland 42 114.1 109.4 51.1 175.0 91.5 147.5 31.4 Highland 36 85.3 85.5 36.2 126.1 76.2 95.4 16.2 Frost resistance Supercooling Point was detected in only 8 individuals, representing 2.5% of all collected mussels: 7 individuals from Northern Europe (5%) and 1 individual from Central Europe (1%). No SCP was detected in individuals from Southern Europe (more details in the Supplementary Materials, Table S2). For those mussels with a measurable SCP (n = 8), linear regressions were performed to examine whether SCP was related to water temperature at the collection site or to glycogen concentration. The higher the water temperature, the higher the SCP observed in mussels, but this relationship was not significant (linear regression; y = 0.75x − 1.7; p = 0.0828; Fig. 4 a). Similarly, mussels with higher glycogen concentration in their tissues had higher SCP values, however, this relationship was not significant (linear regression; y = 0.556x − 1.9; p = 0.1534; Fig. 4 b). In Central Europe, post-SCP measurement survival varied by site. At the Zapadlisko site, 4 out of 8 individuals survived; at the Drzewiczka site, 5 out of 8 survived; and at the Zajączek site, only 1 out of 8 individuals survived the experiment (Table 2 ). Table 2 Number of live individuals 24 hours, 48 hours, and 7 days after freezing (experimental group; Exp.) and in non-frozen mussels (control group). Site Number (%) of live individuals in time 24h 48h 7d Exp. Control Exp. Control Exp. Control Drzewiczka 8 (100%) 8 (100%) 8 (100%) 8 (100%) 5 (62.5%) 8 (100%) Zajaczek 8 (100%) 8 (100%) 8 (100%) 8 (100%) 1 (12.5%) 7 (87.5%) Zapadlisko 8 (100%) 8 (100%) 7 (87.5%) 8 (100%) 4 (50%) 7 (87.5%) Glycogen concentration Basic statistics of glycogen concentration in mussel tissues were presented in the Supplementary Materials (Table S3). Glycogen concentration significantly differed among regions (two-way ANOVA; df = 2; F = 194.2; p < 0.0001; Fig. 5 ), but not between habitats (two-way ANOVA; df = 1; F = 0.9; p = 0.3455; Fig. 5 ). The significant interaction between region and habitat (two-way ANOVA; df = 2; F = 3.4; p = 0.0361) showed that in Northern Europe, slightly higher glycogen concentrations were found in mussels from the lowland areas than mussels from the highland areas, while in Central and Southern Europe, the differences in glycogen concentrations in mussels from the lowland and highland areas were not significant (Fig. 5 ). Discussion and Conclusion Contrary to our prediction on mussel size, our findings indicate that the largest mussels were found in Southern Europe, the warmest region. We argue that this result can be explained by the longer growing season and more stable environmental conditions in southern climates, which support growth throughout the year, increase resource availability, and provide energy for development 38 – 40 . Additionally, longer growing seasons may allow mussels to continue somatic growth even after reaching maturity, provided that environmental conditions are favourable and food is abundant 41 .In mussel species such as A. anatina , warmer waters have been associated with continued growth after maturity and a more convex shell shape in females 42 . In contrast, colder regions are characterized by shorter growing seasons and limited resources, which can constrain growth by prioritizing energy investment towards survival rather than development 43 . The production of cryoprotectants and glycogen storage in colder climates may increase metabolic costs, reducing the energy available for shell growth. Furthermore, A. anatina does not exhibit a thermal compensation strategy, maintaining a consistently low energy requirement 27 . This physiological trait likely contributes to the smaller size observed in colder regions, despite the temperature-size rule. In addition to latitudinal variation, we also hypothesized that mussels from upland areas would have larger shells than those from lowland areas due to lower temperatures in upland regions. Our results showed that mussels in Southern and Central Europe were larger in lowland areas, likely due to higher trophic levels in rivers and lakes, slower water flow, warmer waters, and abundant microorganisms that collectively support growth 44 – 48 . In contrast, upland areas in these regions may present harsher growth conditions, such as greater variability in water flow and lower nutrient levels. Furthermore, biotic interactions, such as competition or facilitation (e.g., aquatic vegetation providing shelter or increasing food availability), may also contribute to observed growth variations. These biotic interactions, combined with abiotic factors, highlight the complexity of ecological processes shaping mussel growth at local scales and emphasize the importance of considering both environmental and community-level factors 7 , 8 , 10 . In Northern Europe, lowland areas may experience more extreme hydrological conditions, such as stronger currents and sudden water level changes 49 , 50 , leading mussels to allocate more energy to survival than growth. In contrast, Northern European highlands, characterized by clean, cold, well-oxygenated waters, provided favourable growth conditions despite lower temperatures. Taken together, these findings suggest that both latitude and altitude shape shell growth in A. anatina, although their effects vary depending on the regional context. Genetic differences among European mussel populations may also play a significant role in shaping their physiological responses to the environment. A. anatina is divided into distinct genetic clades, with Central and Northern populations sharing close genetic relationships, while the Iberian clade is more divergent 51 . Southern mussels may possess genotypes favouring faster growth, whereas Northern and Central populations may have evolved adaptations prioritizing energy storage over intensive growth, enabling them to survive longer and colder winters. This pattern aligns with previous studies showing that animals at higher latitudes often exhibit metabolic compensation in low temperatures, leading to comparable metabolic rates across different environmental conditions 52 . Phenotypic plasticity also plays a crucial role in local adaptation by allowing organisms to adjust to environmental variation without requiring genetic changes. Such plastic responses facilitate acclimation to new conditions and may contribute to long-term adaptation over generations 53 . For example, in A. anatina , plasticity may enable adjustments in growth rates, glycogen storage, and metabolic processes in response to climatic factors. However, while phenotypic plasticity provides immediate flexibility, long-term adaptation requires genetic changes that fine-tune physiological and morphological traits to local environments. Over time, plastic responses may become genetically encoded through genetic assimilation, as observed in other aquatic ectotherms with broad latitudinal distributions 54 . Further research on the genetic structuring of A. anatina populations could help disentangle the relative contributions of plasticity and genetic adaptation to environmental variation. Another hypothesis proposed that mussels from northern populations would exhibit higher glycogen concentrations than those in Central and Southern Europe, reflecting adaptations to harsher winters requiring greater energy reserves. Our findings partially support this hypothesis, as glycogen levels showed a positive correlation with latitude. This trend likely reflects more stable year-round food availability at lower latitudes, reducing the need for glycogen reserves, combined with slower glycogen utilization in colder waters 44 , 45 , 55 . Habitat type did not significantly affect glycogen levels, suggesting that temperature and food availability play a more critical role in glycogen accumulation than geographic elevation. Glycogen is primarily stored during spring and summer, when food availability is high, and is mainly used during gametogenesis 56 . Seasonal glycogen accumulation and depletion play a crucial role in mussel survival strategies, particularly in colder climates where metabolic demands fluctuate throughout the year. As expected, mussel age had a strong influence on shell length, which is expected, as mussels exhibit indeterminate growth 57 , and older individuals are generally larger than younger ones. The final hypothesis posited that supercooling would be most frequently observed in mussels from northern regions as an adaptive strategy to survive low temperatures. Our findings indicate that supercooling is rare in A. anatina , as only 2.5% of examined individuals exhibited this ability. This suggests that supercooling is not a primary survival strategy for freshwater mussels. Instead, mussels in milder climates likely rely on reducing metabolism 18 and burrowing into sediments for winter survival 27 , 58 . However, the presence of supercooling capacity in a small percentage of individuals in colder regions suggests an adaptation to occasional low temperatures. The rarity of supercooling may be due to its high metabolic costs. Organisms employing this method often exhibit seasonal variation in supercooling capacity, increasing it during colder periods 20 , 59 – 61 . Collecting mussels during the coldest part of winter, particularly after ice cover forms, could provide further insights into low-temperature adaptations in these populations. Furthermore, variation in survival strategies suggests that A. anatina populations may employ a form of bet-hedging strategy, described in the context of adaptation to variable environmental conditions 62 . A small proportion of the population invests in supercooling as an adaptive mechanism 63 , while the majority adopt less risky strategies, such as metabolic reduction and habitat selection. This "coin-flipping" strategy maximizes survival chances across different environmental scenarios. Studying mussel responses to extreme cold could further elucidate their adaptive mechanisms and their relevance in the context of global climate change 64 . Overall, our findings highlight the complexity of interactions between environmental conditions, physiological adaptations, and survival strategies in A. anatina . As global temperatures rise, longer growing seasons and warmer waters in Northern and Central Europe may lead to increased mussel sizes, potentially reducing current size differences between southern and northern populations. However, in Southern Europe, increasing water scarcity and more frequent droughts have already led to significant population declines 64 . Predictions indicate that rising air and water temperatures may exacerbate this trend, leading to further mortality and shifts in mussel distribution 65 , 66 . Declarations Data Availability Statement The data underlying this article are available in GitHub repository, at https://doi.org/10.5281/zenodo.13693199 Acknowledgements This publication is based upon work from COST Action CA18239: CONFREMU-Conservation of freshwater mussels: a pan-European approach, supported by COST (European Cooperation in Science and Technology). AML was supported by the National Science Centre, Poland Grant 2023/07/X/NZ9/00300 and partly by the statutory funds of the Institute of Nature Conservation, Polish Academy of Sciences, Kraków. AMĆ was supported by the statutory funds of the Institute of Nature Conservation, Polish Academy of Sciences, Kraków and partly by internal funding from INC PAS “Minigranty”. ML-L was funded by FCT - Fundação para a Ciência e a Tecnologia under contract (2020.03608.CEECIND). PI-F was supported by Grants4NCUStudents (90-SIDUB.6102.89.2023.G4NCUS7). JHM was supported by the Norwegian Research Council, through their basic funding for the Norwegian Institute for Nature Research (NINA), and internal funding from NINA. Author contributions AML: conceptualisation, field work, laboratory work, writing - original draft, writing - review and editing; PA, PAI-F, AN, SC: laboratory work, writing - original draft, writing - review and editing; AMĆ: shells size measurements, data analysis, visualisation of the data, writing - original draft, writing - review and editing; MJG, SS, DH, ML-L, JHM, AT, SV: field work, writing - original draft, writing - review and editing, MÖ: writing - original draft, writing - review and editing Competing interests The authors declare no competing interests. References Williams, G. et al. Come rain or shine: The combined effects of physical stresses on physiological and protein-level responses of an intertidal limpet in the monsoonal tropics. Funct. Ecol. 25 , 101–110 (2011). Weilnhammer, V. et al. 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Seasonal changes in the behaviour and respiration physiology of the freshwater duck mussel, Anodonta anatina. J. Exp. Biol. 217 , 235–243 (2014). Kim, Y. & Song, W. Effect of Thermoperiod and Photoperiod on Cold Tolerance of Spodoptera exigua (Lepidoptera: Noctuidae). Environ. Entomol. 29 , 868–873 (2000). Atapour, M. & Moharramipour, S. Changes of Cold Hardiness, Supercooling Capacity, and Major Cryoprotectants in Overwintering Larvae of Chilo suppressalis (Lepidoptera: Pyralidae). Environ. Entomol. 38 , 260–265 (2009). Atapour, M. & Moharramipour, S. Changes in supercooling point and glycogen reserves in overwintering and lab-reared samples of beet armyworm, Spodoptera exigua (Lep.: Noctuidae) to determining of cold hardiness strategy. (2011). Zheng, X. L. et al. Cold-hardiness mechanisms in third instar larvae of Spodoptera exigua Hübner (Lepidoptera: Noctuidae). Afr. Entomol. 22 , 863–871 (2014). Storey, K. B. & Storey, J. M. Freeze tolerant frogs: cryoprotectants and tissue metabolism during freeze–thaw cycles. Can. J. Zool. 64 , 49–56 (1986). Beck, H. E. et al. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci. Data . 10 , 724 (2023). ClimateData (2024). https://en.climate-data.org/ . [Accessed 03 October 2024]. Lake Model & Flake (2009). https://www.cosmo-model.org/content/model/cosmo/misc/flake/default.htm . [Accessed 09 November 2024]. Haukioja, E. & Hakala, T. Measuring growth from shell rings in populations of Anodonta piscinalis (Pelecypoda, Unionidae). Ann. Zool. Fenn. 15 , 60–65 (1978). Salt, R. W. Principles of Insect Cold-Hardiness. Ann. Rev. Entomol. 6 , 55–74 (1961). Blanchette, C. A., Helmuth, B. & Gaines, S. D. Spatial patterns of growth in the mussel, Mytilus californianus , across a major oceanographic and biogeographic boundary at Point Conception, California, USA. J. Exp. Mar. Biol. Ecol. 340 , 126–148 (2007). 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Hatcher, A., Grant, J. & Schofield, B. Seasonal changes in the metabolism of cultured mussels ( Mytilus edulis l. ) from a Nova Scotian inlet: the effects of winter ice cover and nutritive stress. J. Exp. Mar. Biol. Ecol. 217 , 63–78 (1997). Fearman, J. A. et al. Energy storage and reproduction in mussels, Mytilus galloprovincialis the influence of diet quality. J. Shellfish Res. 28 , 305–312 (2009). Vannote, R. L., Cummins, K. W., Minshal, G. W., Sedell, J. R. & Cushing, C. E. The River Continuum Concept. Can. J. Fish. Aquat. Sci. 130–137 (1980). Mouthon, J. Longitudinal organisation of the mollusc species in a theoretical French river. Hydrobiologia 390 , 117–128 (1998). Martello, A., Kotzian, C. & Erthal, F. The role of topography, river size and riverbed grain size on the preservation of riverine mollusk shells. J. Paleolimnol. 59 , 1–19 (2018). Nilsson, C. 9. Rivers and streams. Acta Phytogeographica Suecica 84, 135–148 (1999). Nilsson, C., Polvi, L. E. & Lind, L. Extreme events in streams and rivers in arctic and subarctic regions in an uncertain future. Freshw. Biol. 60 , 2535–2546 (2015). Lyubas, A. A. et al. Phylogeography and Genetic Diversity of Duck Mussel Anodonta anatina (Bivalvia: Unionidae) in Eurasia. Diversity 15 , 260 (2023). Sukhotin, A. A., Abele, D. & Pörtner, H. O. Ageing and metabolism of mytilus edulis: populations from various climate regimes. shre 25, 893–899 (2006). Noble, D. W. A., Radersma, R. & Uller, T. Plastic responses to novel environments are biased towards phenotype dimensions with high additive genetic variation. Proceedings of the National Academy of Sciences 116, 13452–13461 (2019). Johansson, F., Watts, P. C., Sniegula, S. & Berger, D. Natural selection mediated by seasonal time constraints increases the alignment between evolvability and developmental plasticity. Evolution 75 , 464–475 (2021). Makarieva, A. M., Gorshkov, V. G., Li, B. L. & Chown, S. L. Size- and temperature-independence of minimum life-supporting metabolic rates. Funct. Ecol. 20 , 83–96 (2006). Thompson, R. J. The reproductive cycle and physiological ecology of the mussel Mytilus edulis in a subarctic, non-estuarine environment. Mar. Biol. 79 , 277–288 (1984). Haag, W. R. North American Freshwater Mussels: Natural History, Ecology, and Conservation (Cambridge University Press, 2012). McMahon, R. F. & Wilson, J. G. Seasonal respiratory responses to temperature and hypoxia in relation to burrowing depth in three intertidal bivalves. J. Therm. Biol . 6 , 267–277 (1981). Bale, J. S. Insect cold hardiness: Freezing and supercooling—An ecophysiological perspective. J. Insect. Physiol. 33 , 899–908 (1987). Wang, J. & Wang, S. Variations of Supercooling Capacity in Intertidal Gastropods. Animals 13 , 724 (2023). Lipińska, A. M., Ćmiel, A. M., Olejniczak, P. & Gąsienica-Staszeczek, M. Constraints on habitat possibilities: overwintering of a micro snail species facing climate change consequences in a harsh environment. folia biol. (krakow) . 72 , 1–10 (2024). Olofsson, H., Ripa, J. & Jonzén, N. Bet-hedging as an evolutionary game: the trade-off between egg size and number. Proceedings of the Royal Society B: Biological Sciences 276, 2963–2969 (2009). Cooper, W. S. & Kaplan, R. H. Adaptive coin-flipping: a decision-theoretic examination of natural selection for random individual variation. J. Theor. Biol. 94 , 135–151 (1982). Lopes-Lima, M. et al. The silent extinction of freshwater mussels in Portugal. Biol. Conserv. 285 , 110244 (2023). Sousa, R. et al. Die-offs of the endangered pearl mussel Margaritifera margaritifera during an extreme drought. (2018). 10.1002/aqc.2945 Nogueira, J. G., Lopes-Lima, M., Varandas, S., Teixeira, A. & Sousa, R. Effects of an extreme drought on the endangered pearl mussel Margaritifera margaritifera: a before/after assessment. Hydrobiologia 848 , 3003–3013 (2021). Williams, G. A. et al. Come rain or shine: The combined effects of physical stresses on physiological and protein-level responses of an intertidal limpet in the monsoonal tropics. (2011). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 09 Jun, 2025 Reviews received at journal 30 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers invited by journal 14 May, 2025 Submission checks completed at journal 29 Apr, 2025 First submitted to journal 21 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5596428","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":456682847,"identity":"b2f1b1ea-a87c-4910-8abc-f35ccdc2c4dc","order_by":0,"name":"Anna M. Lipińska","email":"","orcid":"","institution":"Polish Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"M.","lastName":"Lipińska","suffix":""},{"id":456682848,"identity":"f716f1c2-b379-4fcb-a27c-878b2b056766","order_by":1,"name":"Paweł Adamski","email":"","orcid":"","institution":"Polish Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Paweł","middleName":"","lastName":"Adamski","suffix":""},{"id":456682849,"identity":"fe08f401-49fa-4f97-8db5-f479c51f94d1","order_by":2,"name":"Adam M. Ćmiel","email":"","orcid":"","institution":"Polish Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"M.","lastName":"Ćmiel","suffix":""},{"id":456682851,"identity":"b018fe14-34a6-4b33-9fcd-51ad7182f11d","order_by":3,"name":"Maria J. 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Mageroy","email":"","orcid":"","institution":"Norwegian Institute for Nature Research","correspondingAuthor":false,"prefix":"","firstName":"Jon","middleName":"H.","lastName":"Mageroy","suffix":""},{"id":456682863,"identity":"7b7e0f7f-3bd7-427e-a23b-146cce037a9e","order_by":7,"name":"Anna Nowakowska","email":"","orcid":"","institution":"Nicolaus Copernicus University","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Nowakowska","suffix":""},{"id":456682864,"identity":"4e57bd2c-14e1-4a66-aab8-4fce0b886d56","order_by":8,"name":"Martin Österling","email":"","orcid":"","institution":"Karlstad University","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Österling","suffix":""},{"id":456682865,"identity":"e5247d79-50f0-49f0-b17d-bea7b34ffd1d","order_by":9,"name":"Szymon Sniegula","email":"","orcid":"","institution":"Polish Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Szymon","middleName":"","lastName":"Sniegula","suffix":""},{"id":456682866,"identity":"29302071-521d-4efa-946c-1113ccf629c0","order_by":10,"name":"Amílcar Teixeira","email":"","orcid":"","institution":"CIMO, LA SusTEC, Instituto Politécnico de Bragança","correspondingAuthor":false,"prefix":"","firstName":"Amílcar","middleName":"","lastName":"Teixeira","suffix":""},{"id":456682867,"identity":"683c817b-ecba-4ccc-9719-b1aeb40f6139","order_by":11,"name":"Silvana Costa","email":"","orcid":"","institution":"MORE- Laboratório Colaborativo Montanhas de Investigação- Associação","correspondingAuthor":false,"prefix":"","firstName":"Silvana","middleName":"","lastName":"Costa","suffix":""},{"id":456682868,"identity":"e6bd5cbb-f43e-4e05-bb87-a1ad94ed4afa","order_by":12,"name":"Simone Varandas","email":"","orcid":"","institution":"University of Trás-os-Montes and Alto Douro","correspondingAuthor":false,"prefix":"","firstName":"Simone","middleName":"","lastName":"Varandas","suffix":""},{"id":456682869,"identity":"3aa84de8-d1ef-41b1-b713-0723ee1eabdf","order_by":13,"name":"Dariusz Halabowski","email":"data:image/png;base64,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","orcid":"","institution":"University of Lodz","correspondingAuthor":true,"prefix":"","firstName":"Dariusz","middleName":"","lastName":"Halabowski","suffix":""}],"badges":[],"createdAt":"2024-12-07 01:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5596428/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5596428/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-06450-7","type":"published","date":"2025-07-01T15:57:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82870308,"identity":"af27693f-c64f-4536-8b3b-5b8955d1fb46","added_by":"auto","created_at":"2025-05-16 08:42:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2682357,"visible":true,"origin":"","legend":"\u003cp\u003eSampling locations of \u003cem\u003eAnodonta anatina\u003c/em\u003e across Europe, superimposed on the Köppen-Geiger climate classification map \u003csup\u003e33\u003c/sup\u003e.Red and green circles indicate the sites where mussels were collected. The map provides a visual representation of the climatic zones. The following climate types are included: Af – Tropical rainforest, Am – Tropical monsoon, Bwh – Hot desert, Bwk – Cold desert, Csa – Mediterranean hot summer, Csb – Mediterranean warm summer, Cwa – Subtropical with dry winters and hot summers, Cwb – Subtropical with dry winters and warm summers, Cfb – Temperate oceanic, Cfa – Temperate with hot summers, Dsa – Cold with dry summers and hot summers, Dsb – Cold with dry summers and warm summers, Dwa – Cold with dry winters and hot summers, Dwb – Cold with dry winters and warm summers, Dfa – Cold with no dry season and hot summers, Dfb – Cold with no dry season and warm summers, ET – Tundra. Map source: Adapted from Beck, H. E. et al. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci. Data 10, 724 (2023), https://doi.org/10.1038/s41597-023-02549-6. The map has been modified and simplified. The original data is licensed under the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/).\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5596428/v1/a569e44ecfbbef5f50025c66.jpg"},{"id":82870611,"identity":"52b78130-0635-488f-bdca-963e050cbefc","added_by":"auto","created_at":"2025-05-16 08:50:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1884420,"visible":true,"origin":"","legend":"\u003cp\u003eMean monthly air temperatures (solid line) and range (minimum and maximum temperatures; dotted line) at collection sites in different climatic zones, located in Southern (\u003cstrong\u003ea\u003c/strong\u003e), Central (\u003cstrong\u003eb\u003c/strong\u003e), Northern (\u003cstrong\u003ec\u003c/strong\u003e) Europe (\u003csup\u003e34\u003c/sup\u003e). Solid red horizontal lines indicate mean yearly temperature, red dotted horizontal lines indicate standard deviation of mean yearly temperature. (\u003cstrong\u003ed\u003c/strong\u003e) mean monthly water temperatures at collection sites modelled with FLake function. \u0026nbsp;SE – Southern Europe, CE – Central Eurpe, NE – Northern Europe, Mer. – Mertola, Pat. – Pateira, VR – Villa Real, Mir. – Mirandela, Drz. – Drzewiczka,, Zaj. – Zajaczek, Zes. – Zeslawice, Zap. – Zapadlisko, Hal. – Hallaskog, Bov. – Boverbu, Jar. – Jarenvatnet, Mid. – Midsjovannet.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5596428/v1/1f419d553e38a5cbf9afe05e.jpg"},{"id":82870307,"identity":"32537c3c-9a46-4b79-8b45-4a9282dafad3","added_by":"auto","created_at":"2025-05-16 08:42:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1800455,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of the General Linear Model showing the differences in mean shell length between regions (\u003cstrong\u003ea\u003c/strong\u003e), habitat types (\u003cstrong\u003eb\u003c/strong\u003e) and the interaction between region and habitat type (\u003cstrong\u003ec\u003c/strong\u003e). NE – Northern Europe, CE – Central Europe, SE – Southern Europe, LL – lowlands, HL – highlands. Whiskers denote 95% confidence interval of the mean.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5596428/v1/2ba0616014d3bfb6926b6484.png"},{"id":82871590,"identity":"4f159e3d-f8df-4415-9aa1-1ddcfaedbd57","added_by":"auto","created_at":"2025-05-16 08:58:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5098452,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences in glycogen concentration in mussel tissues between regions and habitats.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5596428/v1/cea7faa4972b548a4e7a8492.png"},{"id":82870314,"identity":"9e36db49-953e-4731-8d89-fcc0f510f565","added_by":"auto","created_at":"2025-05-16 08:42:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1145273,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between water temperature at the collecting site and SCP (A) and glycogen concentration in mussel tissues and SCP (B).\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-5596428/v1/636388e0eae5f4cdec2c304a.png"},{"id":86179004,"identity":"74810dba-a089-4829-a44f-a97705ec736b","added_by":"auto","created_at":"2025-07-07 16:14:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18863475,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5596428/v1/15c869bf-b689-4fe0-bf40-ffe6d0b4f808.pdf"},{"id":82870310,"identity":"9db9c1a6-ca7e-4ab3-b1ae-18737fd8321e","added_by":"auto","created_at":"2025-05-16 08:42:24","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":22283,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5596428/v1/20fb8bc04289da112e9da085.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cold tolerance strategies of freshwater mussels across latitudes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change projections indicate an increase in the frequency and intensity of storms, floods, heat waves, droughts, cyclones, and cold spells \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e; \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Freshwater ecosystems, being highly sensitive to variations in temperature and water availability, face particular challenges under these changes \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e; \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. These challenges are compounded by significant alterations in the physical, chemical, and biological characteristics of lakes and rivers driven by climatic warming. For instance, climate change strongly influences terrestrial catchments, where inputs to freshwater systems can be dampened or amplified by in-lake processes. This can lead to seemingly counter-intuitive responses, such as the acidification of streams but the alkalinization of lakes in areas with limited base cation supplies \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Species inhabiting these environments, such as freshwater mussels of the order Unionida (hereafter referred to as \"mussels\"), are already experiencing significant declines due to habitat loss, pollution, and climate change \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e;\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMussels play a crucial role in freshwater and marine ecosystems, where they contribute to or provide many ecosystem services, such as nutrient cycling and storage, biofiltration, and food web modification \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, they are particularly vulnerable to environmental fluctuations and extreme events \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. As one of the fastest declining animal groups globally, mussels are especially vulnerable due to their long lifespans and limited mobility, which reduce their ability to escape adverse conditions \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Although freshwater habitats generally provide some thermal buffering, they are still susceptible to climate disturbances \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In particular, shallow streams, ponds, and the shores of larger water bodies are prone to freezing during winter months, exposing benthic organisms such as mussels to subzero temperatures \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Extreme cold can disrupt biological processes, leading to reduced activity or even cessation of essential functions \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. These conditions can push temperatures beyond the thermal tolerances of some species, posing a serious threat to survival \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. While high-temperature stress in bivalves has been extensively studied, knowledge about their responses to cold stress remains limited \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe adaptations of bivalves to low temperatures may involve a combination of structural, physiological, and behavioural traits, such as the insulating properties of their shells, which may include specialised microstructures \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e; physiological mechanisms, such as changes in metabolic rate, modulation of oxygen demand, supercooling, or synthesis of cryoprotectants \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and body size adjustments in line with the temperature-size rule \u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The temperature-size rule posits that ectothermic organisms tend to develop larger body sizes in colder climates, as lower temperatures slow metabolic rates, allowing for more efficient growth and resource use over longer periods \u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Such adaptations may confer advantages in resource-limited, colder environments by promoting energy conservation and enhanced cold resistance \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Behavioural responses to low temperatures, such as burrowing into the sediment or moving to deeper water, also contribute to their survival \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCold-tolerance strategies can generally be categorised as either freeze avoidance or freeze tolerance \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Freeze avoidance involves mechanisms that prevent the formation of ice crystals in body fluids by lowering their freezing point, whereas freeze tolerance allows controlled ice formation in body tissues, thereby minimising cellular damage. However, these survival strategies remain poorly understood in large mussels, highlighting a significant gap in our knowledge of their cold-resistance mechanisms \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In species such as the duck mussel (\u003cem\u003eAnodonta anatina\u003c/em\u003e), which inhabits diverse freshwater environments across the western Palearctic and Siberia, the ability to withstand cold temperatures likely varies in response to local climatic conditions \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This variation can manifest in several phenotypic traits, including shell size, physiological condition, and supercooling point (SCP). SCP measures how far below freezing temperatures body fluids can get before ice crystals form and is defined as the temperature at which ice crystals begin to develop. Mussels inhabiting colder environments are thought to have evolved or adapted physiological mechanisms, such as a lower SCP, to survive freezing conditions. Cold hardiness mechanisms are strongly linked to the metabolism of energy reserves, such as glycogen, which plays a key role in the synthesis of cryoprotectants, such as glycerol \u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These cryoprotective substances help organisms survive freezing conditions by stabilizing cellular structures and minimizing damage caused by ice formation. The production and accumulation of cryoprotectants primarily depend on the availability of energy reserves and are critical for overwintering success \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe objective of this study is to investigate key adaptive traits in freshwater mussels, using \u003cem\u003eAnodonta anatina\u003c/em\u003e as a model organism. Focusing on body size, energetic condition (in terms of glycogen concentration), and supercooling point (SCP) across environmental gradients - particularly latitude and altitude - this study examines how these traits vary within and among mussel populations. In this study, we propose several hypotheses to explain the observed variation in mussel traits across regions and environmental conditions. First, we hypothesize that environmental temperature influences mussel morphology and physiology in accordance with the temperature-size rule. Based on this, we predict that mussels in colder northern regions will have larger body sizes than those in central and southern Europe.. We also hypothesize that seasonal climatic conditions shape energy storage strategies in mussels. Therefore, we predict that mussels from northern populations will exhibit higher glycogen concentrations, as longer and harsher winters in these regions likely require greater energy reserves for survival. In addition, we hypothesize that both latitude and altitude interact to affect mussel size. Accordingly, we predict that mussels from Northern and upland areas will generally have larger shells than those from South of Europe and lowland areas due to temperature rule size (latitude) and lower ambient temperatures (altitude). Finally, we hypothesize that low-temperature resistance mechanisms vary across climatic gradients. We predict that supercooling (SCP) will be most frequently observed in mussels from northern regions, where exposure to freezing conditions is more common.\u003c/p\u003e \u003cp\u003eBy testing these hypotheses using \u003cem\u003eA. anatina\u003c/em\u003e as a representative species, this study aims to shed light on the adaptive responses of mussels to different thermal environments. Our findings will contribute to a broader understanding of the ecological effects of temperature on species distributions, survival strategies, and the potential consequences of climate change in freshwater ecosystems.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy sites and mussel collection\u003c/h2\u003e \u003cp\u003eTo examine mussel responses to variation in climatic conditions, individuals were collected from sites differing in latitude, altitude, and thermal regimes during the winter season, a period of maximum physiological preparation for overwintering (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn total, 322 \u003cem\u003eA. anatina\u003c/em\u003e individuals were collected during the winter season of 2023/2024, with 150 individuals from Northern Europe (Scandinavia; Norway and Sweden), 98 from Central Europe (Poland), and 74 from Southern Europe (Portugal). Mussels were collected from lowland (3 in Northern, 3 in Central, and 2 in Southern Europe) and upland habitats (3 in Northern, 1 in Central, and 2 in Southern Europe). In Northern Europe, mussels were collected at 6 sites, exclusively in lakes; in Central Europe, they were found at 4 sites: 3 reservoirs and 1 river; and in Southern Europe, they were found at 4 sites, only in rivers. In all locations, mussels were collected manually. The sampling protocol required the collector to gather the first 30 individuals encountered, regardless of their size, to avoid any bias in sampling related to mussel size. However, the number of mussels collected per site varied, as it was not always possible to gather the required number in less abundant populations. In some cases, slight overcollections occurred due to collector error.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClimatic conditions\u003c/h3\u003e\n\u003cp\u003eTo verify that mussels collected from different regions and altitudes experience distinct climatic conditions, we compared air and water temperature regimes at all collection sites.\u003c/p\u003e \u003cp\u003eClimatic zones at mussel collection sites were visualised using high-resolution (1 km) K\u0026ouml;ppen-Geiger climate classification maps \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In Southern Europe, all sites were classified as \u0026ldquo;Csa\u0026rdquo; (hot-summer Mediterranean climate); in Central Europe, as \u0026ldquo;Cfb\u0026rdquo; (temperate oceanic climate); and in Northern Europe, the dominant class (5 out of 6 sites) was \u0026ldquo;Dfb\u0026rdquo; (warm-summer humid continental climate; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMean monthly air temperature at each site was obtained from ClimateData.org\u003csup\u003e3434\u003c/sup\u003e, based on ECMWF data (0.1\u0026ndash;0.25\u0026deg; resolution) collected between 1991 and 2021 and refreshed in May 2022. Mean monthly water temperatures were estimated using the Lake Model \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003eover the period 1998\u0026ndash;2009. The simulation incorporated site-specific meteorological and lake parameters, including wind components, air temperature, humidity, cloud cover, solar and thermal radiation, geographic coordinates, water depth (2 m), and assumed water transparency of 1 m.\u003c/p\u003e \u003cp\u003eTo statistically test for differences in climatic conditions between regions, a repeated measures ANOVA was used to compare both air and water temperatures across months.\u003c/p\u003e \u003cp\u003eAdditional details on the mussel collection sites, mean monthly air temperatures, and modelled water temperatures are provided in the Supplementary Materials (Tables S1\u0026ndash;S3).\u003c/p\u003e\n\u003ch3\u003eMeasurement of shell size and supercooling point (SCP)\u003c/h3\u003e\n\u003cp\u003eTo assess variation in morphological and physiological traits relevant to cold adaptation, we measured shell size and supercooling point (SCP) in all collected individuals. Each mussel was aged and measured for shell size upon collection. Shell length, width, and height were recorded using callipers. The age of individuals was determined by counting annual growth rings visible on the shell \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. To minimise the risk of error in age determination, counting was performed on the right shell valve whenever possible. If growth rings were not clearly visible, the left valve was used instead.\u003c/p\u003e \u003cp\u003eTo analyse variation in shell size across regions and habitats, we used a General Linear Model with region, habitat type, mussel age, and their interaction as predictor variables.\u003c/p\u003e \u003cp\u003eThe supercooling point (SCP) of each mussel was measured to assess cold tolerance following Sinclair \u003cem\u003eet al.\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. SCP was typically measured on 20\u0026ndash;30 individuals per population to assess the distribution shape \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. To obtain the SCP of mussel body fluids, a thermocouple (K-type, probe diameter 0.5 mm) was inserted into the posterior adductor muscle of each individual. The mussels were then placed in a deep freezer (Platilab 340 SV-3-STD; ALS Angelantoni Life Science deep freezer), with the temperature set to decrease from +\u0026thinsp;5\u0026deg;C to -20\u0026deg;C at a constant rate of 1\u0026deg;C/min, as recommended in previous SCP measurement studies \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The temperature decrease was continuously recorded, and the SCP was read off (with \u0026plusmn;\u0026thinsp;0.1\u0026deg;C accuracy) as the last temperature indication just before the peak (rebound) caused by the heat of the exothermic crystallisation reaction.\u003c/p\u003e \u003cp\u003eA freeze survival experiment was conducted at three sites in Central Europe. To assess post-freezing survival, after capturing the SCP, 8 mussels from each site were placed in water at 4\u0026deg;C. They were then kept for another 7 days or until evidence of mortality was confirmed.\u003c/p\u003e \u003cp\u003eTo explore potential physiological correlates of cold resistance, we used a linear regression to test for associations between SCP and both water temperature and glycogen concentration.\u003c/p\u003e\n\u003ch3\u003eGlycogen Concentration\u003c/h3\u003e\n\u003cp\u003eTo assess physiological condition and energy reserves in mussels from different climatic regions, glycogen concentration was measured in soft tissues. Each animal sample (whole tissue without the shell) was homogenised with 3\u0026ndash;4 mL of distilled water using an IKA homogeniser (30 s pulse, 100% amplitude, 5\u0026ndash;8 times). The homogenates were then centrifuged (10 min, 4\u0026deg;C, 14,000 \u0026times; g). The supernatants were used for further analysis. The ELISA Glycogen Assays (Sigma-Aldrich, No. MAK016 and MAK465, USA) were performed for the quantitative determination of glycogen. The Glycogen Assay Kit uses a single working reagent that integrates the enzymatic breakdown of glycogen with the measurement of glucose in a single step. The concentration of glycogen in the sample is proportional to the colour intensity of the reaction product, measured at 570 nm in flat-bottom clear 96-well plates.\u003c/p\u003e \u003cp\u003eEach 96-well plate had its own glycogen standard curve, run in duplicate. Additionally, a blank and a negative control were included for each plate. Samples were added to the plate in duplicate technical replicates for statistical purposes. Both glycogen assays were performed according to the ELISA manufacturers' instructions, and the plates were then read at a wavelength of 570 nm using an Epoch 2 multimode microplate reader (BioTek Instruments, Inc., Winooski, VT, USA).\u003c/p\u003e \u003cp\u003eDifferences in glycogen concentration were analysed using a two-way ANOVA with region and habitat type as fixed effects and their interaction.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClimatic conditions\u003c/h2\u003e \u003cp\u003eRepeated measures ANOVA showed that the highest mean yearly air temperature at collection sites was observed in Southern Europe (t\u003csub\u003emean\u003c/sub\u003e=14.8\u003csup\u003eo\u003c/sup\u003eC; SD\u0026thinsp;=\u0026thinsp;5.9; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Whereas the mean yearly air temperature at collection sites in Central Europe (t\u003csub\u003emean\u003c/sub\u003e=9.1\u003csup\u003eo\u003c/sup\u003eC; SD\u0026thinsp;=\u0026thinsp;7.7; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) and Northern Europe (t\u003csub\u003emean\u003c/sub\u003e=5.5\u003csup\u003eo\u003c/sup\u003eC; SD\u0026thinsp;=\u0026thinsp;7.7; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) were lower, and the differences in mean yearly air temperature at collection sites among regions were significant (df\u0026thinsp;=\u0026thinsp;2; F\u0026thinsp;=\u0026thinsp;40.4; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Also, the within effect (months) was significant (df\u0026thinsp;=\u0026thinsp;11, F\u0026thinsp;=\u0026thinsp;1690.1; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), as well as the interaction between region and within effect (months; df\u0026thinsp;=\u0026thinsp;22; F\u0026thinsp;=\u0026thinsp;21.6; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRepeated measures ANOVA showed that modelled mean monthly water temperatures at collection sites also differed significantly among regions (Southern Europe: t\u003csub\u003emean\u003c/sub\u003e=17.3\u003csup\u003eo\u003c/sup\u003eC, SD\u0026thinsp;=\u0026thinsp;6.2.; Central Europe: t\u003csub\u003emean\u003c/sub\u003e=9.1\u003csup\u003eo\u003c/sup\u003eC, SD\u0026thinsp;=\u0026thinsp;7.6; Northern Europe: t\u003csub\u003emean\u003c/sub\u003e=7.3\u003csup\u003eo\u003c/sup\u003eC, SD\u0026thinsp;=\u0026thinsp;7.3; df\u0026thinsp;=\u0026thinsp;2; F\u0026thinsp;=\u0026thinsp;151.4; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with significant influence of within effect (months; df\u0026thinsp;=\u0026thinsp;11, F\u0026thinsp;=\u0026thinsp;1709.9; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and the interaction between region and within effect (months; df\u0026thinsp;=\u0026thinsp;22; F\u0026thinsp;=\u0026thinsp;23.8; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Moreover, FLake model showed that the median number of weeks with ice covering the lake surface at collection sites was 15 in Northern Europe (range: 0\u0026ndash;15), 2 in Central Europe (range: 0\u0026ndash;3) whereas in Southern Europe ice cover on the lake surface did not occur at any of the collection sites.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eShell length\u003c/h3\u003e\n\u003cp\u003eDescriptive statistics for mussel shell length are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The General Linear Model showed that all analysed factors: region, habitat, mussel age, and the interaction between region and habitat, had significant influence on mussel shell length (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-c; detailed results in the Supplementary Materials, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additionally, both age and the interaction between region and habitat had the highest influence on mussel shell length, whereas the influence of region and habitat on mussel shell length was much weaker (Supplementary Materials, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Mussels from Southern Europe were significantly longer than mussels from Central and Northern Europe, while no significant difference in length was observed between mussels from Central and Northern Europe (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Also, mussels from Central and Southern Europe were significantly larger in lowland areas than in highland areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). However, the interaction between regions and habitat showed that mussels from Northern Europe were significantly longer in highland areas than in lowland areas. In contrast, mussels from Southern and Central Europe were significantly shorter in highland areas than in lowland areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Together, these results suggest that both latitude and altitude jointly influence mussel shell size, with opposite altitudinal trends observed in Northern Europe compared to Central and Southern Europe.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMussel shell length in the different regions and habitat types. LQ \u0026ndash; lower quartile, UQ \u0026ndash; upper quartile, SD \u0026ndash; standard deviation of mean.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHabitat type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003e[mm]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003cp\u003e[mm]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003cp\u003e[mm]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003cp\u003e[mm]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLQ\u003c/p\u003e \u003cp\u003e[mm]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUQ\u003c/p\u003e \u003cp\u003e[mm]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNorthern\u003c/p\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e108.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e68.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e88.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHighland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e101.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e123.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e95.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e108.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e81.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e108.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHighland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e106.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e128.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e89.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e115.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSouthern\u003c/p\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e109.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e175.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e91.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e147.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHighland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e126.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e76.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eFrost resistance\u003c/h3\u003e\n\u003cp\u003eSupercooling Point was detected in only 8 individuals, representing 2.5% of all collected mussels: 7 individuals from Northern Europe (5%) and 1 individual from Central Europe (1%). No SCP was detected in individuals from Southern Europe (more details in the Supplementary Materials, Table S2). For those mussels with a measurable SCP (n\u0026thinsp;=\u0026thinsp;8), linear regressions were performed to examine whether SCP was related to water temperature at the collection site or to glycogen concentration.\u003c/p\u003e \u003cp\u003eThe higher the water temperature, the higher the SCP observed in mussels, but this relationship was not significant (linear regression; y\u0026thinsp;=\u0026thinsp;0.75x \u0026minus;\u0026thinsp;1.7; p\u0026thinsp;=\u0026thinsp;0.0828; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Similarly, mussels with higher glycogen concentration in their tissues had higher SCP values, however, this relationship was not significant (linear regression; y\u0026thinsp;=\u0026thinsp;0.556x \u0026minus;\u0026thinsp;1.9; p\u0026thinsp;=\u0026thinsp;0.1534; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Central Europe, post-SCP measurement survival varied by site. At the Zapadlisko site, 4 out of 8 individuals survived; at the Drzewiczka site, 5 out of 8 survived; and at the Zajączek site, only 1 out of 8 individuals survived the experiment (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of live individuals 24 hours, 48 hours, and 7 days after freezing (experimental group; Exp.) and in non-frozen mussels (control group).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eNumber (%) of live individuals in time\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e24h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e48h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e7d\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExp.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExp.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExp.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrzewiczka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZajaczek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e(12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(87.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZapadlisko\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(87.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e(87.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGlycogen concentration\u003c/h2\u003e \u003cp\u003eBasic statistics of glycogen concentration in mussel tissues were presented in the Supplementary Materials (Table S3). Glycogen concentration significantly differed among regions (two-way ANOVA; df\u0026thinsp;=\u0026thinsp;2; F\u0026thinsp;=\u0026thinsp;194.2; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), but not between habitats (two-way ANOVA; df\u0026thinsp;=\u0026thinsp;1; F\u0026thinsp;=\u0026thinsp;0.9; p\u0026thinsp;=\u0026thinsp;0.3455; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The significant interaction between region and habitat (two-way ANOVA; df\u0026thinsp;=\u0026thinsp;2; F\u0026thinsp;=\u0026thinsp;3.4; p\u0026thinsp;=\u0026thinsp;0.0361) showed that in Northern Europe, slightly higher glycogen concentrations were found in mussels from the lowland areas than mussels from the highland areas, while in Central and Southern Europe, the differences in glycogen concentrations in mussels from the lowland and highland areas were not significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion and Conclusion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003cp\u003eContrary to our prediction on mussel size, our findings indicate that the largest mussels were found in Southern Europe, the warmest region. We argue that this result can be explained by the longer growing season and more stable environmental conditions in southern climates, which support growth throughout the year, increase resource availability, and provide energy for development \u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Additionally, longer growing seasons may allow mussels to continue somatic growth even after reaching maturity, provided that environmental conditions are favourable and food is abundant \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.In mussel species such as \u003cem\u003eA. anatina\u003c/em\u003e, warmer waters have been associated with continued growth after maturity and a more convex shell shape in females \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In contrast, colder regions are characterized by shorter growing seasons and limited resources, which can constrain growth by prioritizing energy investment towards survival rather than development \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The production of cryoprotectants and glycogen storage in colder climates may increase metabolic costs, reducing the energy available for shell growth. Furthermore, \u003cem\u003eA. anatina\u003c/em\u003e does not exhibit a thermal compensation strategy, maintaining a consistently low energy requirement \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This physiological trait likely contributes to the smaller size observed in colder regions, despite the temperature-size rule.\u003c/p\u003e \u003cp\u003eIn addition to latitudinal variation, we also hypothesized that mussels from upland areas would have larger shells than those from lowland areas due to lower temperatures in upland regions. Our results showed that mussels in Southern and Central Europe were larger in lowland areas, likely due to higher trophic levels in rivers and lakes, slower water flow, warmer waters, and abundant microorganisms that collectively support growth \u003csup\u003e\u003cspan additionalcitationids=\"CR45 CR46 CR47\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In contrast, upland areas in these regions may present harsher growth conditions, such as greater variability in water flow and lower nutrient levels. Furthermore, biotic interactions, such as competition or facilitation (e.g., aquatic vegetation providing shelter or increasing food availability), may also contribute to observed growth variations. These biotic interactions, combined with abiotic factors, highlight the complexity of ecological processes shaping mussel growth at local scales and emphasize the importance of considering both environmental and community-level factors \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In Northern Europe, lowland areas may experience more extreme hydrological conditions, such as stronger currents and sudden water level changes \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, leading mussels to allocate more energy to survival than growth. In contrast, Northern European highlands, characterized by clean, cold, well-oxygenated waters, provided favourable growth conditions despite lower temperatures. Taken together, these findings suggest that both latitude and altitude shape shell growth in A. anatina, although their effects vary depending on the regional context.\u003c/p\u003e \u003cp\u003eGenetic differences among European mussel populations may also play a significant role in shaping their physiological responses to the environment. \u003cem\u003eA. anatina\u003c/em\u003e is divided into distinct genetic clades, with Central and Northern populations sharing close genetic relationships, while the Iberian clade is more divergent \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Southern mussels may possess genotypes favouring faster growth, whereas Northern and Central populations may have evolved adaptations prioritizing energy storage over intensive growth, enabling them to survive longer and colder winters. This pattern aligns with previous studies showing that animals at higher latitudes often exhibit metabolic compensation in low temperatures, leading to comparable metabolic rates across different environmental conditions \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePhenotypic plasticity also plays a crucial role in local adaptation by allowing organisms to adjust to environmental variation without requiring genetic changes. Such plastic responses facilitate acclimation to new conditions and may contribute to long-term adaptation over generations \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. For example, in \u003cem\u003eA. anatina\u003c/em\u003e, plasticity may enable adjustments in growth rates, glycogen storage, and metabolic processes in response to climatic factors. However, while phenotypic plasticity provides immediate flexibility, long-term adaptation requires genetic changes that fine-tune physiological and morphological traits to local environments. Over time, plastic responses may become genetically encoded through genetic assimilation, as observed in other aquatic ectotherms with broad latitudinal distributions \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Further research on the genetic structuring of \u003cem\u003eA. anatina\u003c/em\u003e populations could help disentangle the relative contributions of plasticity and genetic adaptation to environmental variation.\u003c/p\u003e \u003cp\u003eAnother hypothesis proposed that mussels from northern populations would exhibit higher glycogen concentrations than those in Central and Southern Europe, reflecting adaptations to harsher winters requiring greater energy reserves. Our findings partially support this hypothesis, as glycogen levels showed a positive correlation with latitude. This trend likely reflects more stable year-round food availability at lower latitudes, reducing the need for glycogen reserves, combined with slower glycogen utilization in colder waters \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Habitat type did not significantly affect glycogen levels, suggesting that temperature and food availability play a more critical role in glycogen accumulation than geographic elevation. Glycogen is primarily stored during spring and summer, when food availability is high, and is mainly used during gametogenesis \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Seasonal glycogen accumulation and depletion play a crucial role in mussel survival strategies, particularly in colder climates where metabolic demands fluctuate throughout the year.\u003c/p\u003e \u003cp\u003eAs expected, mussel age had a strong influence on shell length, which is expected, as mussels exhibit indeterminate growth \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, and older individuals are generally larger than younger ones.\u003c/p\u003e \u003cp\u003eThe final hypothesis posited that supercooling would be most frequently observed in mussels from northern regions as an adaptive strategy to survive low temperatures. Our findings indicate that supercooling is rare in \u003cem\u003eA. anatina\u003c/em\u003e, as only 2.5% of examined individuals exhibited this ability. This suggests that supercooling is not a primary survival strategy for freshwater mussels. Instead, mussels in milder climates likely rely on reducing metabolism \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and burrowing into sediments for winter survival \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. However, the presence of supercooling capacity in a small percentage of individuals in colder regions suggests an adaptation to occasional low temperatures. The rarity of supercooling may be due to its high metabolic costs. Organisms employing this method often exhibit seasonal variation in supercooling capacity, increasing it during colder periods \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Collecting mussels during the coldest part of winter, particularly after ice cover forms, could provide further insights into low-temperature adaptations in these populations.\u003c/p\u003e \u003cp\u003eFurthermore, variation in survival strategies suggests that \u003cem\u003eA. anatina\u003c/em\u003e populations may employ a form of bet-hedging strategy, described in the context of adaptation to variable environmental conditions \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. A small proportion of the population invests in supercooling as an adaptive mechanism \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e, while the majority adopt less risky strategies, such as metabolic reduction and habitat selection. This \"coin-flipping\" strategy maximizes survival chances across different environmental scenarios. Studying mussel responses to extreme cold could further elucidate their adaptive mechanisms and their relevance in the context of global climate change \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOverall, our findings highlight the complexity of interactions between environmental conditions, physiological adaptations, and survival strategies in \u003cem\u003eA. anatina\u003c/em\u003e. As global temperatures rise, longer growing seasons and warmer waters in Northern and Central Europe may lead to increased mussel sizes, potentially reducing current size differences between southern and northern populations. However, in Southern Europe, increasing water scarcity and more frequent droughts have already led to significant population declines \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Predictions indicate that rising air and water temperatures may exacerbate this trend, leading to further mortality and shifts in mussel distribution \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article are available in GitHub repository, at https://doi.org/10.5281/zenodo.13693199\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis publication is based upon work from COST Action CA18239: CONFREMU-Conservation of freshwater mussels: a pan-European approach, supported by COST (European Cooperation in Science and Technology). AML was supported by the National Science Centre, Poland Grant 2023/07/X/NZ9/00300 and partly by the statutory funds of the Institute of Nature Conservation, Polish Academy of Sciences, Kraków. AMĆ was supported by the statutory funds of the Institute of Nature Conservation, Polish Academy of Sciences, Kraków and partly by internal funding from INC PAS “Minigranty”. ML-L was funded by FCT - Fundação para a Ciência e a Tecnologia under contract (2020.03608.CEECIND). PI-F was supported by Grants4NCUStudents (90-SIDUB.6102.89.2023.G4NCUS7). JHM was supported by the Norwegian Research Council, through their basic funding for the Norwegian Institute for Nature Research (NINA), and internal funding from NINA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAML: conceptualisation, field work, laboratory work, writing - original draft, writing - review and editing; PA, PAI-F, AN, SC: laboratory work, writing - original draft, writing - review and editing; AMĆ: shells size measurements, data analysis, visualisation of the data, writing - original draft, writing - review and editing; MJG, SS, DH, ML-L, JHM, AT, SV: field work, writing - original draft, writing - review and editing, MÖ: writing - original draft, writing - review and editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWilliams, G. et al. 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(2011).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anodonta anatina, supercooling point, frost resistance, survival strategies, overwintering, climate change","lastPublishedDoi":"10.21203/rs.3.rs-5596428/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5596428/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFreshwater mussels across Europe exhibit physiological and behavioural adaptations to survive winter conditions. Climate change projections, including more frequent extreme weather events, are expected to intensify pressures on these ecosystems. In this study, we tested the temperature-size hypothesis, which posits that larger body size in ectothermic organisms is an adaptation to colder climates. We predicted that \u003cem\u003eAnodonta anatina\u003c/em\u003e populations in northern regions would have larger shells than those in central and southern regions. Additionally, we hypothesized that harsher winters in northern regions require mussels to maintain higher glycogen levels as an energy reserve. We also explored whether shell size varies between lowland and upland populations, following the temperature-size rule, and whether supercooling (SCP) occurs primarily in northern populations as a complementary survival strategy. Northern populations had the highest glycogen levels, reflecting adaptations to colder conditions. SCP was rare (2.5%) and observed predominantly in northern mussels, suggesting limited reliance on freeze avoidance. Instead, it is likely that mussels employ mixed strategies, such as metabolic reduction and burrowing, to withstand winter. These findings link shell size, glycogen levels, and SCP to specific survival strategies, providing new insights into the cold tolerance mechanisms of freshwater mussels and their potential vulnerability to climate change.\u003c/p\u003e","manuscriptTitle":"Cold tolerance strategies of freshwater mussels across latitudes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 08:42:19","doi":"10.21203/rs.3.rs-5596428/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-06-09T08:38:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-30T13:26:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30693512820760410766258821161668733133","date":"2025-05-19T18:27:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-14T17:15:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-29T12:40:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-21T08:09:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"57500903-f0d2-4650-9f2e-1310366bb757","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48540165,"name":"Biological sciences/Ecology"},{"id":48540166,"name":"Biological sciences/Physiology"},{"id":48540167,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2025-07-07T16:01:55+00:00","versionOfRecord":{"articleIdentity":"rs-5596428","link":"https://doi.org/10.1038/s41598-025-06450-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-01 15:57:03","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-05-16 08:42:19","video":"","vorDoi":"10.1038/s41598-025-06450-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-06450-7","workflowStages":[]},"version":"v1","identity":"rs-5596428","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5596428","identity":"rs-5596428","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}