Contrasting behavioural response to concurrent stressors in an aquatic snail: importance of stress type and combination

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In ecosystems, animals often face multiple stressors simultaneously. Their behavioural responses may vary when exposed to each stressor individually, as synergistic, additive, or antagonistic effects can result from the interaction of multiple stressors. Therefore, it is imperative to conduct studies that take into account the common occurrence of multi-stress scenarios in aquatic ecosystems. We tested the effects of three sources of stress (low water pH, toxicity (acetone) and conspecific chemical cues) on the behaviour of the aquatic nail Potamopyrgus antipodarum (Tateidae, Mollusca). We evaluated the impact of each stressor, as well as in combinations of two or three stressors simultaneously. The highest time to start movement was shown by the animals of the low water pH (acid treatment-A) followed by A plus toxic treatment (AT). The linear models showed an increase in the cumulative differences in time to start movement for the snails of A, AT, and T treatments over time. This indicates that snails in these treatments took longer to begin sliding movements compared to the control animals. On the contrary, animals of the conspecific chemical cues (S) and ST treatments showed an increased in the time to start sliding with time in comparison with control snails. It is conclude that behaviour was altered depending on the source and combination of stress, with antagonistic effects when simultaneously exposing the animals to an acidic environment and conspecific signals. toxicant cues environmental stress pollution invertebrate Figures Figure 1 Figure 2 Introduction Behaviour is a basic parameter to understand the fitness of animals, being a good indicator of exposure to different sources of stress (Warner et al. 1966 ; Bryan et al. 1995 ; Leung et al. 2015 ; Mahabir and Gerlai 2017 ; Suski et al. 2019 ; Ågerstrand et al. 2020 ). Ecosystems are subjected to natural and human stressors, including both biotic and abiotic ones (Heugens et al. 2001 ; Leung et al. 2015 ; Bertram et al. 2022 ). Behavioural responses are essential to face those stressors in the environment, as behaviour is an integrative whole-organism response with an important role in most of the acclimatization processes (Hellou 2011 ; Melvin and Wilson 2013 ; Bertram et al. 2022 ). Furthermore, behavioural changes typically occur rapidly, making them useful indicators of stress. Hence, these sentinel variables offer a distinct advantage for scientific disciplines like ecotoxicology, ecology, and for comprehending the impacts of global warming (Hellou 2011 ; Melvin and Wilson 2013 ; Nagelkerken and Munday 2016 ; Manz et al. 2023 ). In natural ecosystems, animals are subjected to various sources of stress at the same time (Heugens et al. 2001 ; Clements et al. 2021 ; Bertram et al. 2022 ). Hence, the behavioural response may differ from that to each source of stress individually, as synergistic, additive, or antagonistic responses can occur when multiple environmental changes coincide (Kungolos et al. 1999 ; Coors and Meester 2008 ; Yoo et al. 2021 ). Therefore, the study of multiple stress on animal behaviour contributes to improving the realism of laboratory studies in ecotoxicology and ecology, especially when behaviour is a sensitive parameter to environmental stress (Bertram et al. 2022 ). Animal behaviour is highly sensitive to disruption by chemical exposure (Ågerstrand et al. 2020 ; Bertram et al. 2022 ). However, the majority of studies in behavioural ecotoxicology focus on the effects of toxicants on animal behaviour, without considering concurrent stressors (Bryan et al. 1995 ; Kungolos et al. 1999 ; Hellou 2011 ; Melvin and Wilson 2013 ; Bertram et el. 2023). In recent decades, some studies have incorporated additional sources of stress for aquatic animals, including, among others, low pH, predation, parasitism, and competition (Beketov and Liess 2006 ; Coors and Meester 2008 ; Johnston and Keough 2003 ; Clements et al. 2021 ). Instead of these useful improvements, there is still a need to increase the number of environmental studies, considering multi-stress scenarios that are common in aquatic ecosystems. Individuals in natural populations are exposed to a wide variety of stressors, and changes in behaviour, such as burrowing, aggregating, and increasing or reducing activity, serve as efficient mechanisms to cope with stress (Baillieul et al. 1998 ; Alonso and Camargo 2009 ; Clements et al. 2021 ). However, certain behavioural changes may confer an advantage in response to one form of stress while proving disadvantageous in response to another. For instance, heightened activity aids in distancing from a contaminated area but may also elevate the risk of detection by a predator, potentially leading to predation (Araújo et al. 2019). Furthermore, the observation that an isolated source of stress can decrease a behaviour while a different source of stress can enhance it may suggest that the combined effect of the two stressors counterbalances the isolated behavioural alterations. For instance, the avoidance pattern of Atyaeohyra desmarestii shrimp exposed to copper was reduced in the presence of predators ( Danio rerio ) compared to when they were in a predator-free environment (Araújo et al. 2019). Therefore, studies that aim to enhance the mechanistic understanding of how the complexity of multiple stressors affects behaviour, while also improving environmental realism, are essential (Bertram et al. 2022 ). Among aquatic animals, gastropods are a useful group in behavioural ecology, as they are easily manageable in the laboratory, and their behaviour has been extensively studied in different species and under various stressors (Alonso and Camargo 2009 ; Leung et al. 2015 ; Manz et al. 2023 ). Behaviours associated with movement hold significant ecological importance, as they are linked to vital functions for animal fitness, such as evading predators and pollutants, as well as foraging for resources (Beketov and Liess 2006 ; Alonso et al. 2016 ; Araujo et al. 2019 ). Consequently, these endpoints used in ecology and ecotoxicology contribute to a more realistic assessment of stressor effects. Therefore, in the present study we focus on the combined effects of three sources of stress for the aquatic mud snail Potamopyrgus antipodarum (Tateidae, Mollusca). This mollusc has been broadly used in ecotoxicology and ecology, including several studies with behavioural variables, mainly due to its sensitivity and easy cultivation in laboratory (Alonso and Camargo 2009 ; Myrick 2009 ; Alonso et al. 2016 ; Ruppert et al. 2016 ; Alonso and Valle-Torres 2018 ; Heye et al. 2019 ). We use three stress sources: low water pH, toxicity stress (acetone) and conspecific chemical cues. These stressors have previously demonstrated adverse effects on the behaviour of aquatic animals, including P. antipodarum (Jacobsen and Stabell 2004 ; Hansen and Fair 2014 ; Leung et al. 2015 ; Alonso et al. 2016 ). Specifically we aimed in the assessment of the effect of each stressor individually, and in the assessment of all combination of two and three stressors simultaneously. The detrimental effects on mortality and behaviour caused by the three selected stressors were compared to determine whether they exhibited additive, antagonistic, or synergistic effects. We hypothesized that combining stressors with contrasting effects on behaviour (i.e., increasing activity vs. decreasing activity) would result in minimal or no impact on behaviour compared to the effect of each individual stressor. Methods Animal culture Animals used for the bioassay were obtained from a culture started in 2009 at the University of Alcalá (Laboratory of Ecology, Department of Life Sciences). The culture was started with animals collected in the upper reach of the Henares River (Guadalajara, Spain). They were kept in 60 L aquaria, fed with 0.10 mg of dry food per animal per day (50% fish food Tetra- Menü© GmbH, Melle, Germany + 50% Sera © Spirulina Tabs GmbH, Heinsberg, Germany). The water culture was renewed every 2 weeks (10% renewal) to maintain good water quality, and aeration was provided using aquarium filters. With these procedures, the animals grow and reproduce adequately, ensuring the proper quality of the physicochemical parameters of the water, along with balanced feed to complete their life cycle. Efforts were made to minimize any potential harm to the invertebrates during handling, experimentation, and subsequent care. All procedures were designed to ensure the well-being of the snails while achieving the scientific objectives of the study and to minimize the number of animals sacrificed. Reagents and water culture Standardized USEPA water (96 mg NaHCO 3 , 60 mg CaSO 4 ·2H 2 O, 4 mg KCl, 122.2 mg MgSO 4 , per liter of deionized water plus 10 mg CaCO 3 per litre) was used for the culture and bioassays (USEPA 2002). As chemical compounds, hydrochloric acid (Panreac, Lot. 0000026930, Spain, HCl 37%) was used for the treatment of acid solution (see below), and acetone (Fisher Chemical, Lot. 1202412, UK, 99.98% purity) was used for the toxic treatment of the bioassay (see below). Experimental design Eight treatments were used in the bioassay. The control (C) group consisted of standardized USEPA water (USEPA 2002). The acid treatment (A) group was exposed to a solution of hydrochloric acid in standardized USEPA water at a pH between 6.41 and 6.5. For this purpose, 3 ml of hydrochloric acid was diluted in 500 ml of USEPA water, and 1 ml of the resulting solution was further diluted in 500 ml of USEPA water, which was then used for the acid treatment. The toxic treatment (T) group was exposed to a sublethal concentration of 1000 mg acetone/l based on prior research with Potamopyrgus antipodarum (Alonso et al. 2016 ). The conspecific signal (S) group was exposed to chemical cues from conspecifics. To create the cues, 64 adult P. antipodarum were placed in a test tube with 32 ml of USEPA (2 animals per ml). The individuals were gently crushed using a glass stick once their shells were completed broken. The test tube was then shaken in a vortex mixer for 1 minute, followed by settling of shell pieces. Next, 1 ml of the supernatant was extracted using a glass pipette for each replicate (See below). This procedure was previously tested in our laboratory to verify the effectiveness of the behavioral reactions. These three treatments (A, T and S) were combined in pairs ( AT , AS , ST ) and all together ( AST ). In the pair combinations, the acetone solution was prepared in the acid solution ( AT ), the chemical signal was added to the acid solution ( AS ), and the conspecific signal was added to the acetone solution ( ST ). In the three-component combination ( AST ), the chemical signal was added to the acid solution with the same acetone concentration as the toxic treatment (T). In each treatment, the number of replicates ranged from 27 to 33. Each replicate consisted of a glass vessel with 20 ml of USEPA water and one adult Potamopyrgus antipodarum . Each replicate was covered with a perforated cap to reduce water evaporation and was then introduced into a climatic chamber set at 18ºC (ANSONIC). The photoperiod was 12.5h of light and 11.5h of darkness. Animals were allowed to acclimate for 24h in the climatic chamber and glass vessels. Variables and monitoring time Three variables were monitored in the bioassays: mortality, immobility, and activity. Activity referred to the time taken to initiate normal movement (in seconds) and was monitored as the duration each animal to start sliding (Alonso 2021 ). In each replicate, a snail was picked up using forceps and placed in the center of the vessel with the operculum downwards, after that, the time to start activity was monitored with a chronometer (Alonso 2021 ). If the snail remained immobile for 120 s, it was considered inactive, which served as another monitoring variable in the study ( immobility ). Lastly, an animal was considered as dead if the animal was motionless, and its soft body did not react when gently touched with forceps on the operculum ( mortality ). Two extra variables were calculated, the immobility plus mortality that was defined as affected animals and the cumulative differences of activity . For this last parameter, the mean activity of control for each monitoring time was calculated. At each monitoring time, the differences between the activity of each individual and the mean control activity for that monitoring time were calculated. Finally, the cumulative differences were calculated over the monitoring period. This allows us to estimate whether the individuals under stress are moving faster or slower than the control group throughout the course of the experiment. The study variables were monitored at 24h and 48h of the acclimatization period for all animals used in the bioassays. In each period, animals’ activity was measured twice. After 48h of acclimatization, all treatments were applied to the animals, and the study variables were monitored again after 24 hours of exposure. This monitoring procedure was then repeated at 48, 72, 96, and 168 hours of treatment exposure. Throughout all monitoring times, activity measurements were taken twice. Statistical analysis The impact of treatments on the mortality and affected animals was minimal so no statistical test was applied to these variables (see Results). To assess the effects of treatment and time (and their interactions) on the dependent variables (activity and the cumulative differences of activity) a two-way repeated-measures ANOVA was conducted. Time (0, 1, 2, 3, 4, and 7 days) was used as intrasubject factor and the treatments (A, AS, AST, AT, C, S, ST, T) were used as intersubject factors. ANOVA analysis was conducted using the ´ez´ package with the function “ezANOVA” of R (Lawrence 2011 ; R Core Team 2021 ). After the two-way repeated-measures ANOVA, treatments were compared with control for the activity and cumulative differences of activity through a post hoc tests on the trimmed means using the `lincon` function of the “WRS” package (Mair and Wilcox 2020 ). Finally, the cumulative differences of activity and time were related by means of a linear model with the function `lm´, the coefficient of determination and its significance was also calculated. All statistical analyses were conducted using R 4.0.5 Software (R Core Team 2021 ). Results The cumulative mortality was less than 10% in treatments and the cumulative affected animals (mortality plus inactivity) was less than 13% at the end of the bioassay. The mean activity of snails in each monitoring time and for each treatment is shown in Figure 1 . The applied treatments caused significant contrasting effects on behavioural activity (p<0.05; two-way repeated-measures ANOVA; post hoc test on the trimmed means) ( Fig. 1, Table 1 ). The interaction of both factors was also significant (p<0.05; two-way repeated-measures ANOVA) ( Table 1 ). In general, activity of treatments changed significantly with time (p<0.05; two-way repeated-measures ANOVA) ( Figure 1 ). The highest activity was shown by the acid treatment (A) followed by acid plus toxic treatment (AT). A high activity means a long time taken to start sliding. The other treatments (AS, AST, S, ST, T) showed a similar behaviour than that of the control animals (C) ( Figure 1 ). Table 1 Results for the repeated-measures two-way ANOVA where the treatment (A, AS, AST, AT, C, S, ST, T) was the intersubject factor, time (=Time) (0, 1, 2, 3, 4 and 7 days) was the intrasubject factor, and the dependent variables were activity and cumulative differences in activity. Source of variation df a F p ACTIVITY Intrasubject factor Time 4.55 5.19 <0.001 Time x Treatment 31.9 2.73 <0.001 Intersubject factors Treatment 6 5.83 <0.001 CUMULATIVE DIFFERENCES Intrasubject factor Time 1.342 6.03 <0.001 Time x Treatment 8.06 7.75 <0.001 Intersubject factors Treatment 6 4.74 <0.001 df degrees of freedom a df were corrected for sphericity using the Grennhouse-Geisser approach The linear models between the cumulative differences in activity and time are shown in Figure 2 . Differences in activity increased significantly with time in animals of the A, AT and T treatments (p0.85), which means that snails of those treatments taken a longer time to start sliding movements than control animals (positive differences) ( Figure 2 ). This effect was more intense in snails of the A and AT treatments. On the contrary, S and ST animals showed an increased activity with time in comparison with control snails (negative differences) (p0.85) ( Figure 2 ). The behaviour of the others treatments did not show differences, as their differences with control were nearly zero. Differences between treatments for this variable was very similar to the behavioural activity ( Figure 2 ). Discussion Our study has shown that the behavior of Potamopyrgus antipodarum changed depending on the source of stress and the combination of stressors to which the animals were exposed. Specifically, exposure to an acidic environment slowed down this species' ability to initiate sliding movement. In the presence of conspecific signals, and the conspecific signals plus toxicant, the adverse effects of the acidic environment were counteracted, which show antagonistic effects of both sources of stress. The presence of the toxicant plus the acid environment did not result in an increase in the adverse effects of the acid alone. On the other hand, the conspecific signals alone caused a reduction in the time to initiate sliding movement; this trend persisted in an environment containing conspecific signals despite the presence of the toxicant. Aquatic animals are exposed to different combinations of stress in natural ecosystems (Heugens et al. 2001 ; Coors and Meester 2008 ; Nagelkerken and Munday 2016 ; Alonso 2021 ; Clements et al. 2021 ). However, there is limited understanding of the effects of multiple stressors on behaviours. In our study, the acidic environment slowed down the snails' activity, with the effects being compensated in the presence of conspecific signals in the environment. This was clearly showed in the cumulative differences of snail activity. Previous studies have demonstrated the impact of acid environment on contrasting behaviours of molluscs (Watson et al. 2014 ; Leung et al. 2015 ; Manz et al. 2023 ; McGarrigle et al. 2023 ). Manz et al. ( 2023 ) found that water acidification impaired the ability of mud snails ( Ilyanassa obsoleta ) to detect food cues in seawater, resulting in reduced foraging success. The gastropod Nassarius festivus showed a reduction in several behavioural variables, including locomotor activity, when it was exposed to low pH (Leung et al. 2015 ). A similar reduction in activity was found in the gastropod Bembicium auratum in extreme acidified water (pH < 5.0) (Amaral et al. 2014 ). However, this specie showed an increase in avoidance behaviour in waters with pH between 6.2 and 7.0 (Amaral et al. 2014 ). In general, low pH caused several impartments on molluscs. Acid waters may cause an impairing of physiological regulation and a metabolic depression as short-term response strategies to water acidification (Ellis and Morris 1995 ; Amaral et al. 2014 ; Leung et al. 2015 ). Among physiological effects, acidification may affect to invertebrates, which are characterized by a low capacity to compensate for disturbances in extracellular ion and acid–base status (Pörtner 2008 ). Additionally, water pH reduction may also modify the nervous system (Watson et al. 2014 ). In fact, acidification caused the interference with the function of neurotransmitter receptors, which might be responsible for the impartment of predator-escape behaviour of snails (Watson et al. 2014 ). A likely cause of the increase in the reaction time of Potamopyrgus antipodarum may be the alteration of the nervous system, as this has a direct relationship with behavioural alterations involved in movement (Watson et al. 2014 ; Leung et al. 2015 ). However, the joint action of other causes cannot be ruled out, such as the reduction of available energy due to the need to compensate for physiological alterations (Pörtner et al. 2000) or damage on soft tissues (Dove and Sammut 2007 ; Dell’Acqua et al. 2019 ) or reduction of oxygen uptake (Leung et al. 2015 ). In our study, the adverse effects of acidic environment were counteracted by the combined exposure to acid and conspecific signals, but the presence of the toxicant did not change the effect of the acidic environment. Overall, conspecific signals led to a reduction of the time to initiate sliding movement of P. antipodarum , which means an increase in activity; this effect counteracted the deleterious acid environment in the combined exposure as snail showed a similar behaviour than that of control animals. The presence of conspecific chemical or predator signals in the aquatic environment can elicit different responses in molluscs (Jacobsen and Stabell 1999 , 2004 ; Wyeth 2019 ). On one hand, there are studies showing an increase in animal activity, most likely associated with the searching for refuge or for avoiding predator water (Dix and Hamilton 1993 ; Jacobsen and Stabell 1999 ; Jacobsen and Stabell 2004 ; Keppel and Scrosati 2004 ). Conversely, the opposite trend has also been observed, where animals decrease their activity or withdraw their soft body or spent more time in their refuges, which may be related to a lower risk of being detected by potential predators (Richardson and Brown 1992 ; Jacobsen and Stabell 1999 ). While our results highlight the primary factor, it is also conceivable that even small remnants of conspecifics could act as a food source, potentially mitigating part of the energy losses resulting from exposure to the acidic environment. However, conspecific treatment alone increase snail activity, so everything suggests that conspecific signals are causing an increase in activity, either due to having a food source or due to the modification of activity by chemical signals. In our study, the toxicant (i.e. acetone) did not cause a significant modification of behaviour, nor did it contribute to behaviour modification in combination with other sources of stress. The results were unexpected, as a previous study indicated that acetone caused sublethal changes in snail behaviour (Alonso et al. 2016 ). However, it's worth noting that the acetone concentration was lower, and the exposure method differed from that employed in the study by Alonso et al. ( 2016 ). Acetone may affect to the metabolic detoxification systems and metabolic activities at long-term exposure (Hutchinson et al. 2006 ; LeBlanc 2007 ; Leoni et al. 2006). Acetone is a highly volatile compound, such that to maintain constant concentrations in the water a frequent solution renewal is necessary. In our study, acetone was applied only once at the beginning of the 7-day period, potentially leading to minor adverse effects but enabling a swift recovery for the animals. This may explain the minor effect of toxicant treatment on behaviour. Despite this fact, the toxic treatment slightly altered the behaviour, as evidenced by the slight tendency towards a decrease in snail reaction time. This slight effect of acetone on its own may account for the absence of more severe consequences for behaviour when combined with any of the other stress sources, or with all of them together. Despite the undeniable usefulness of conventional approaches applied to the study of the effects of contaminants on ecosystems, they do not take into account the great complexity of natural systems. Ecosystems are subjected to numerous contaminants and novel entities, such as microplastics, which make the extrapolation from traditional bioassays to real scenarios very difficult (de Souza Machado et al. 2019 ; Bertram et al. 2022 ). In addition to this, there is the complexity of ever-changing aquatic ecosystems due to the planet's experiencing climate change (Ferreira et al. 2010 ; Pinheiro et al. 2021 ). The need to consider ecological complexity in ecotoxicology, despite its importance, poses a challenge for its implementation into environmental risk assessment and standardization processes. However, in recent years, several promising advancements have been made in this regard. For example, prioritizing behaviours crucial for species and their fitness, integrating individual behaviour, considering the most impactful variables associated with climate change, modelling multi-exposure to toxicants, and incorporating interactions in ecotoxicological bioassays, all represent promising advances in enhancing this complexity (Amaral et al. 2014 ; Araujo et al. 2019 ; De Souza Machado et al. 2019 ; Clements et al. 2021 ; Pinheiro et al. 2021 ; Poiverino et al. 2021 ; Bertram et al. 2022 ). Additionally, behavioural parameters provide a good connection from suborganismal processes to population level processes (Ågerstrand et al. 2020 ). Therefore, behaviour is a valuable parameter to chemical regulation and ecological studies (Hellou 2011 ; Ågerstrand et al. 2020 ; Bertram et al. 2022 ). Conclusions It is concluded that the behavioural responses of the aquatic snail Potamopyrgus antipodarum are altered depending on the source and combination of stress, with antagonistic effects observed when simultaneously exposing the animals to an acidic environment and conspecific signals. However, the activity of snail did not respond to toxicant exposure, which did not increase or counteract the adverse effects of the other stress sources. Therefore, to increase the realism of bioassays the exposure to several sources of stress should be included, since antagonistic effects can counteract the harmful effects of an isolated stress source. Declarations Acknowledgements Part of this work was carried out within the sabbatical period of Álvaro Alonso as a professor in the University of Alcalá for the 2021-2022 academic year. Funding MC Llandres-Díez was supported by a research assistant contract “Ayudas para la promoción de empleo joven e implantación de la garantía juvenil en I+D+i” funded by the Ministerio de Ciencia, Innovación y Universidades . P Cruces-Estepa was supported by a research assistant contract “Empleo juvenil y la iniciativa de Empleo Juvenil” funded by the “Comunidad de Madrid” and “Fondo Social Europeo”. Funds for this research came from the University of Alcalá (research projects CCG2013/EXP-054, CCG2016/EXP-054 and CCG2018/EXP-074), and the projects CGL2011˗16388/BOS, CGL2015-65346R, INTERTOX RTI2018-096046-B-C21 (MCIU/AEI/FEDER, UE) and EXARBIN RTI2018-093504-B-I00 (MCIU/AEI/FEDER, UE) of the Ministerio de Economía y Competitividad of Spain. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Consent for publication Author provides consent for publication Data availability Data that support the findings of this study are available from the corresponding author upon reasonable request Code availability Rcode available from the corresponding author on reasonable request Authors Contributions: A Alonso: Conceptualization, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Writing – Original draft, Writing – Review & editing, Visualization, Project administration, Validation, Supervision. MC Llandres-Díez: Formal analysis, Writing – Review & editing. Investigation, Methodology. P Cruces-Estepa: Formal analysis, Writing – Review & editing. Investigation, Methodology. 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Aquat Toxicol 76: 69-92 https://doi.org/10.1016/j.aquatox.2005.09.008 Kungolos A, Samaras P, Kipopoulou A, Zoumboulis AI, Sakellaropoulos GP (1999) Interactive toxic effects of agrochemicals on aquatic organisms. Water Sci Technol 40: 357-364. https://doi.org/10.1016/s0273-1223(99)00384-4 Jacobsen HP, Stabell OB (1999) Predator-induced alarm responses in the common periwinkle, Littorina littorea : dependence on season, light conditions, and chemical labelling of predators. Marine Biol 134: 551–557. https://doi.org/10.1007/s002270050570 Jacobsen HP, Stabell OB (2004) Antipredator behaviour mediated by chemical cues: the role of conspecific alarm signalling and predator labelling in the avoidance response of a marine gastropod. Oikos 104: 43–50. https://doi.org/10.1111/j.0030-1299.2004.12369.x Johnston EL, Keough MJ (2003) Competition modifies the response of organisms to toxic disturbance. Mar Ecol Prog Ser 251: 15–26. https://doi.org/10.3354/meps251015 Keppel E, Scrosati R (2004) Chemically mediated avoidance of Hemigrapsus nudus (Crustacea) by Littorina scutulata (Gastropoda): effects of species coexistence and variable cues. Anim Behav 68: 915–920. https://doi.org/10.1016/j.anbehav.2003.11.020 Lawrence MA (2011) ez: Easy analysis and visualization of factorial experiments. R package version 4.0-0. http://CRAN.R-project.org/package=ez LeBlanc GA (2007) Crustacean endocrine toxicology: a review. Ecotoxicology 16: 61–81. https://doi.org/10.1007/s10646-006-0115-z Leoni B, Bettinetti R, Galassi S (2008) Sub-lethal effects of acetone on Daphnia magna . Ecotoxicology 17: 199–205. https://doi.org/10.1007/s10646-007-0184-7 Leung JYS, Russell BD, Connell SD, Ng JCY (2015) Acid dulls the senses: impaired locomotion and foraging performance in a marine mollusc. Anim Behav 106: 223-229. doi: 10.1016/j.anbehav.2015.06.004. Mahabir S, Gerlai R (2017) The importance of holding water: salinity and chemosensory cues affect zebrafish behavior. Zebrafish 14: 444-458. doi: 10.1089/zeb.2017.1472. Mair P, Wilcox R (2020) Robust statistical methods in R using the WRS2 package. Behav Res Methods 52: 464-488 Manz M, Lord J, Morales M (2023) Ocean acidification impedes foraging behavior in the mud snail Ilyanassa obsoleta . Journal of Marine Science and Engineering 11: 623. https://doi.org/10.3390/jmse11030623 McGarrigle SA, Bishop MM, Dove SL, Hunt HL (2023) Effects of sediment and water column acidification on growth, survival, burrowing behaviour, and GABAA receptor function of benthic invertebrates. J Exper Mar Biol Ecol 566: 151918. https://doi.org/10.1016/j.jembe.2023.151918 Melvin SD, Wilson SP (2013) The utility of behavioral studies for aquatic toxicology testing: A meta-analysis. Chemosphere 93: 2217–2223. https://doi.org/10.1016/j.chemosphere.2013.07.036 Myrick CA (2009) A low-cost system for capturing and analyzing the motion of aquatic organisms. J N Amer Benthol Soc 28: 101–109. https://doi.org/10.1899/08-067.1 Nagelkerken I, Munday PL (2016) Animal behaviour shapes the ecological effects of ocean acidification and warming: moving from individual to community-level responses. Glob Change Biol 22: 974–989. https://doi.org/10.1111/gcb.13167 Pinheiro JPS, Windsor FM, Wilson RW, Tyler CR (2021) Global variation in freshwater physico-chemistry and its influence on chemical toxicity in aquatic wildlife. Biol Rev 96: 1528–1546. https://doi.org/10.1111/brv.12711 Poiverino G, Martin JM, Bertram MG, Soman VR, Tan H, Brand JA, Mason RT, Wong BBM (2021) Psychoactive pollution suppresses individual differences in fish behaviour. Proc R Soc B: Biol Sci 288: 20202294. https://doi.org/10.1098/rspb.2020.2294 Pörtner H-O (2008) Ecosystem effects of ocean acidification in times of ocean warming: a physiologist’s view. Mar Ecol Prog Ser 373: 203–217. https://doi.org/10.3354/meps07768 R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/ Richardson TD, Brown KM (1992) Predation risk and feeding in an intertidal predatory snail. J Exper Mar Biol Ecol 163: 169–182. https://doi.org/10.1016/0022-0981(92)90047-E Ruppert K, Geiß C, Ostermann S, Theis C, Oehlmann J (2016) Comparative sensitivity of juvenile and adult Potamopyrgus antipodarum (Mollusca: Hydrobiidae) under chronic exposure to cadmium and tributyltin. J Environ Sci Health A, Tox substances environ engin 51: 736–743. https://doi.org/10.1080/10934529.2016.1170443 Suski CD, Philipp MA, Hasler CT (2019) Influence of nutritional status on carbon dioxide tolerance and avoidance behavior in a freshwater teleost. T Am Fish Soc 148:914-925. doi: 10.1002/tafs.10179. Warner RE, Peterson KK, Borgman L (1966) Behavioral pathology in fish: a quantitative study of sublethal pesticide toxication. J Appl Ecol 3:223-247 Watson SA, Lefevre S, McCormick MI, Domenici P, Nilsson GE, Munday PL (2014) Marine mollusc predator-escape behaviour altered by near-future carbon dioxide levels. Proc R Soc B: Biol Sci 281: 20132377. https://doi.org/10.1098/rspb.2013.2377 Wyeth RC (2019) Olfactory navigation in aquatic gastropods. J Exp Biol 222, UNSP jeb185843. https://doi.org/10.1242/jeb.185843 Yoo J, Cho H, Lee K, Won E, Lee Y (2021) Combined effects of heavy metals (Cd, As, and Pb): Comparative study using conceptual models and the antioxidant responses in the brackish water flea. Comp Biochem PhysiolC Toxicol Pharmacol 239: 108863. https://doi.org/10.1016/j.cbpc.2020.108863 Supplementary Files dataactivity.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4319021","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":295083810,"identity":"184a27c8-8326-4dd5-9023-a1a901860a99","order_by":0,"name":"Alvaro Alonso","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACdijNByYrGBgMCGphhtJsYPIMyVoY24jQwt/MY/i5ooJBno299/CHj/MOy5szMB/+gE+LxGEeY8kzZxgM23jOpUnO3HbYcGcDW5oEXmsOsyVINrYBnSSRY8bMu+0w44YDPGZ4dcgfZkv+CdRiD9Ri/Jl3zmH7DQf4P+N1mMFh5mMgWxKBWgykeRsOJwJtYcDrMEOgFsuGMxLJbTxnzCRnHEtP3nCYzQyvFrnjjc03GypsbPvZe4w/fKixtt1wvPkxXodBAbKxzDhVjYJRMApGwSggFgAA8fdD2yGmEh8AAAAASUVORK5CYII=","orcid":"","institution":"Universidad de Alcala","correspondingAuthor":true,"prefix":"","firstName":"Alvaro","middleName":"","lastName":"Alonso","suffix":""},{"id":295083811,"identity":"410db653-6a43-4a0c-b766-94087602fd21","order_by":1,"name":"M Celeste Llandres-Díez","email":"","orcid":"","institution":"Universidad de Alcala","correspondingAuthor":false,"prefix":"","firstName":"M","middleName":"Celeste","lastName":"Llandres-Díez","suffix":""},{"id":295083812,"identity":"08a13733-3d19-4d2d-92ca-abcfead84b82","order_by":2,"name":"Paula Cruces-Estepa","email":"","orcid":"","institution":"Universidad de Alcala","correspondingAuthor":false,"prefix":"","firstName":"Paula","middleName":"","lastName":"Cruces-Estepa","suffix":""}],"badges":[],"createdAt":"2024-04-24 14:44:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4319021/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4319021/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55564117,"identity":"06ee7b37-0d25-4e63-883a-032360cd07c7","added_by":"auto","created_at":"2024-04-30 03:35:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33204,"visible":true,"origin":"","legend":"\u003cp\u003eActivity (in seconds) of \u003cem\u003ePotamopyrgus antipodarum\u003c/em\u003e for each treatment (upper capital letters) and each time (0, 1, 2, 3, 4 and 7 days). Black horizontal lines in each box represent the median, the limits of the coloured box represent the upper and lower 75 and 25% quartiles, and bars represent the maximum and minimum values excluding outliers. All plots are showing the activity of each monitoring animal. Lowercase letters (bottom) represents differences among treatments by means of the post hoc test on the trimmed means (p\u0026lt;0.05) (Mair and Wilcox 2020).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4319021/v1/912db5f7eec78130df3d4f67.png"},{"id":55564673,"identity":"94a4af8c-527a-4146-9313-9a1d7b0ea4c3","added_by":"auto","created_at":"2024-04-30 03:43:33","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":443674,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative differences in the activity of \u003cem\u003ePotamopyrgus antipodarum\u003c/em\u003e for each treatment (capital letters). The differences were calculated as the difference between the activity of each animal of each treatment and the mean activity of control for each time (0, 1, 2, 3, 4 and 7 days). Letters between parentheses represents differences among treatments by means of the post hoc test on the trimmed means (p\u0026lt;0.05) (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4319021/v1/c0b7a3a021344bbeb6079aff.jpeg"},{"id":57229291,"identity":"efc54052-8b66-4bf8-ae69-bd6fc8314c01","added_by":"auto","created_at":"2024-05-27 20:34:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1442764,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4319021/v1/89db1a04-75d5-4a3c-a36d-d3aead0d64fc.pdf"},{"id":55564123,"identity":"4fdf27bc-2e3f-4b98-b271-a21f36618eae","added_by":"auto","created_at":"2024-04-30 03:35:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":347129,"visible":true,"origin":"","legend":"","description":"","filename":"dataactivity.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4319021/v1/b4085b2eff24b3f0e16b010b.pdf"}],"financialInterests":"","formattedTitle":"Contrasting behavioural response to concurrent stressors in an aquatic snail: importance of stress type and combination","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBehaviour is a basic parameter to understand the fitness of animals, being a good indicator of exposure to different sources of stress (Warner et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1966\u003c/span\u003e; Bryan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Leung et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mahabir and Gerlai \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Suski et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; \u0026Aring;gerstrand et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Ecosystems are subjected to natural and human stressors, including both biotic and abiotic ones (Heugens et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Leung et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bertram et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Behavioural responses are essential to face those stressors in the environment, as behaviour is an integrative whole-organism response with an important role in most of the acclimatization processes (Hellou \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Melvin and Wilson \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bertram et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, behavioural changes typically occur rapidly, making them useful indicators of stress. Hence, these sentinel variables offer a distinct advantage for scientific disciplines like ecotoxicology, ecology, and for comprehending the impacts of global warming (Hellou \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Melvin and Wilson \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nagelkerken and Munday \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Manz et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn natural ecosystems, animals are subjected to various sources of stress at the same time (Heugens et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Clements et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bertram et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hence, the behavioural response may differ from that to each source of stress individually, as synergistic, additive, or antagonistic responses can occur when multiple environmental changes coincide (Kungolos et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Coors and Meester \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Yoo et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, the study of multiple stress on animal behaviour contributes to improving the realism of laboratory studies in ecotoxicology and ecology, especially when behaviour is a sensitive parameter to environmental stress (Bertram et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnimal behaviour is highly sensitive to disruption by chemical exposure (\u0026Aring;gerstrand et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bertram et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the majority of studies in behavioural ecotoxicology focus on the effects of toxicants on animal behaviour, without considering concurrent stressors (Bryan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Kungolos et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Hellou \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Melvin and Wilson \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bertram et el. 2023). In recent decades, some studies have incorporated additional sources of stress for aquatic animals, including, among others, low pH, predation, parasitism, and competition (Beketov and Liess \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Coors and Meester \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Johnston and Keough \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Clements et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Instead of these useful improvements, there is still a need to increase the number of environmental studies, considering multi-stress scenarios that are common in aquatic ecosystems. Individuals in natural populations are exposed to a wide variety of stressors, and changes in behaviour, such as burrowing, aggregating, and increasing or reducing activity, serve as efficient mechanisms to cope with stress (Baillieul et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Alonso and Camargo \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Clements et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, certain behavioural changes may confer an advantage in response to one form of stress while proving disadvantageous in response to another. For instance, heightened activity aids in distancing from a contaminated area but may also elevate the risk of detection by a predator, potentially leading to predation (Ara\u0026uacute;jo et al. 2019). Furthermore, the observation that an isolated source of stress can decrease a behaviour while a different source of stress can enhance it may suggest that the combined effect of the two stressors counterbalances the isolated behavioural alterations. For instance, the avoidance pattern of \u003cem\u003eAtyaeohyra desmarestii\u003c/em\u003e shrimp exposed to copper was reduced in the presence of predators (\u003cem\u003eDanio rerio\u003c/em\u003e) compared to when they were in a predator-free environment (Ara\u0026uacute;jo et al. 2019). Therefore, studies that aim to enhance the mechanistic understanding of how the complexity of multiple stressors affects behaviour, while also improving environmental realism, are essential (Bertram et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong aquatic animals, gastropods are a useful group in behavioural ecology, as they are easily manageable in the laboratory, and their behaviour has been extensively studied in different species and under various stressors (Alonso and Camargo \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Leung et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Manz et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Behaviours associated with movement hold significant ecological importance, as they are linked to vital functions for animal fitness, such as evading predators and pollutants, as well as foraging for resources (Beketov and Liess \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Alonso et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Araujo et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, these endpoints used in ecology and ecotoxicology contribute to a more realistic assessment of stressor effects.\u003c/p\u003e \u003cp\u003eTherefore, in the present study we focus on the combined effects of three sources of stress for the aquatic mud snail \u003cem\u003ePotamopyrgus antipodarum\u003c/em\u003e (Tateidae, Mollusca). This mollusc has been broadly used in ecotoxicology and ecology, including several studies with behavioural variables, mainly due to its sensitivity and easy cultivation in laboratory (Alonso and Camargo \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Myrick \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Alonso et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ruppert et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Alonso and Valle-Torres \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Heye et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We use three stress sources: low water pH, toxicity stress (acetone) and conspecific chemical cues. These stressors have previously demonstrated adverse effects on the behaviour of aquatic animals, including \u003cem\u003eP. antipodarum\u003c/em\u003e (Jacobsen and Stabell \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Hansen and Fair \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Leung et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Alonso et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Specifically we aimed in the assessment of the effect of each stressor individually, and in the assessment of all combination of two and three stressors simultaneously. The detrimental effects on mortality and behaviour caused by the three selected stressors were compared to determine whether they exhibited additive, antagonistic, or synergistic effects. We hypothesized that combining stressors with contrasting effects on behaviour (i.e., increasing activity vs. decreasing activity) would result in minimal or no impact on behaviour compared to the effect of each individual stressor.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimal culture\u003c/h2\u003e \u003cp\u003eAnimals used for the bioassay were obtained from a culture started in 2009 at the University of Alcal\u0026aacute; (Laboratory of Ecology, Department of Life Sciences). The culture was started with animals collected in the upper reach of the Henares River (Guadalajara, Spain). They were kept in 60 L aquaria, fed with 0.10 mg of dry food per animal per day (50% fish food Tetra- Men\u0026uuml;\u0026copy; GmbH, Melle, Germany\u0026thinsp;+\u0026thinsp;50% Sera \u0026copy; Spirulina Tabs GmbH, Heinsberg, Germany). The water culture was renewed every 2 weeks (10% renewal) to maintain good water quality, and aeration was provided using aquarium filters. With these procedures, the animals grow and reproduce adequately, ensuring the proper quality of the physicochemical parameters of the water, along with balanced feed to complete their life cycle. Efforts were made to minimize any potential harm to the invertebrates during handling, experimentation, and subsequent care. All procedures were designed to ensure the well-being of the snails while achieving the scientific objectives of the study and to minimize the number of animals sacrificed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eReagents and water culture\u003c/h2\u003e \u003cp\u003eStandardized USEPA water (96 mg NaHCO\u003csub\u003e3\u003c/sub\u003e, 60 mg CaSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;2H\u003csub\u003e2\u003c/sub\u003eO, 4 mg KCl, 122.2 mg MgSO\u003csub\u003e4\u003c/sub\u003e, per liter of deionized water plus 10 mg CaCO\u003csub\u003e3\u003c/sub\u003e per litre) was used for the culture and bioassays (USEPA 2002). As chemical compounds, hydrochloric acid (Panreac, Lot. 0000026930, Spain, HCl 37%) was used for the treatment of acid solution (see below), and acetone (Fisher Chemical, Lot. 1202412, UK, 99.98% purity) was used for the toxic treatment of the bioassay (see below).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExperimental design\u003c/h2\u003e \u003cp\u003eEight treatments were used in the bioassay. The \u003cb\u003econtrol (C)\u003c/b\u003e group consisted of standardized USEPA water (USEPA 2002). The \u003cb\u003eacid treatment (A)\u003c/b\u003e group was exposed to a solution of hydrochloric acid in standardized USEPA water at a pH between 6.41 and 6.5. For this purpose, 3 ml of hydrochloric acid was diluted in 500 ml of USEPA water, and 1 ml of the resulting solution was further diluted in 500 ml of USEPA water, which was then used for the acid treatment. The \u003cb\u003etoxic treatment (T)\u003c/b\u003e group was exposed to a sublethal concentration of 1000 mg acetone/l based on prior research with \u003cem\u003ePotamopyrgus antipodarum\u003c/em\u003e (Alonso et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The \u003cb\u003econspecific signal (S)\u003c/b\u003e group was exposed to chemical cues from conspecifics. To create the cues, 64 adult \u003cem\u003eP. antipodarum\u003c/em\u003e were placed in a test tube with 32 ml of USEPA (2 animals per ml). The individuals were gently crushed using a glass stick once their shells were completed broken. The test tube was then shaken in a vortex mixer for 1 minute, followed by settling of shell pieces. Next, 1 ml of the supernatant was extracted using a glass pipette for each replicate (See below). This procedure was previously tested in our laboratory to verify the effectiveness of the behavioral reactions. These three treatments (A, T and S) were combined in pairs (\u003cb\u003eAT\u003c/b\u003e, \u003cb\u003eAS\u003c/b\u003e, \u003cb\u003eST\u003c/b\u003e) and all together (\u003cb\u003eAST\u003c/b\u003e). In the pair combinations, the acetone solution was prepared in the acid solution (\u003cb\u003eAT\u003c/b\u003e), the chemical signal was added to the acid solution (\u003cb\u003eAS\u003c/b\u003e), and the conspecific signal was added to the acetone solution (\u003cb\u003eST\u003c/b\u003e). In the three-component combination (\u003cb\u003eAST\u003c/b\u003e), the chemical signal was added to the acid solution with the same acetone concentration as the toxic treatment (T).\u003c/p\u003e \u003cp\u003eIn each treatment, the number of replicates ranged from 27 to 33. Each replicate consisted of a glass vessel with 20 ml of USEPA water and one adult \u003cem\u003ePotamopyrgus antipodarum\u003c/em\u003e. Each replicate was covered with a perforated cap to reduce water evaporation and was then introduced into a climatic chamber set at 18\u0026ordm;C (ANSONIC). The photoperiod was 12.5h of light and 11.5h of darkness. Animals were allowed to acclimate for 24h in the climatic chamber and glass vessels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eVariables and monitoring time\u003c/h2\u003e \u003cp\u003eThree variables were monitored in the bioassays: mortality, immobility, and activity. \u003cb\u003eActivity\u003c/b\u003e referred to the time taken to initiate normal movement (in seconds) and was monitored as the duration each animal to start sliding (Alonso \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In each replicate, a snail was picked up using forceps and placed in the center of the vessel with the operculum downwards, after that, the time to start activity was monitored with a chronometer (Alonso \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). If the snail remained immobile for 120 s, it was considered inactive, which served as another monitoring variable in the study (\u003cb\u003eimmobility\u003c/b\u003e). Lastly, an animal was considered as dead if the animal was motionless, and its soft body did not react when gently touched with forceps on the operculum (\u003cb\u003emortality\u003c/b\u003e). Two extra variables were calculated, the immobility plus mortality that was defined as \u003cb\u003eaffected animals\u003c/b\u003e and the \u003cb\u003ecumulative differences of activity\u003c/b\u003e. For this last parameter, the mean activity of control for each monitoring time was calculated. At each monitoring time, the differences between the activity of each individual and the mean control activity for that monitoring time were calculated. Finally, the cumulative differences were calculated over the monitoring period. This allows us to estimate whether the individuals under stress are moving faster or slower than the control group throughout the course of the experiment.\u003c/p\u003e \u003cp\u003eThe study variables were monitored at 24h and 48h of the acclimatization period for all animals used in the bioassays. In each period, animals\u0026rsquo; activity was measured twice. After 48h of acclimatization, all treatments were applied to the animals, and the study variables were monitored again after 24 hours of exposure. This monitoring procedure was then repeated at 48, 72, 96, and 168 hours of treatment exposure. Throughout all monitoring times, activity measurements were taken twice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe impact of treatments on the mortality and affected animals was minimal so no statistical test was applied to these variables (see Results). To assess the effects of treatment and time (and their interactions) on the dependent variables (activity and the cumulative differences of activity) a two-way repeated-measures ANOVA was conducted. Time (0, 1, 2, 3, 4, and 7 days) was used as intrasubject factor and the treatments (A, AS, AST, AT, C, S, ST, T) were used as intersubject factors. ANOVA analysis was conducted using the \u0026acute;ez\u0026acute; package with the function \u0026ldquo;ezANOVA\u0026rdquo; of R (Lawrence \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; R Core Team \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). After the two-way repeated-measures ANOVA, treatments were compared with control for the activity and cumulative differences of activity through a post hoc tests on the trimmed means using the `lincon` function of the \u0026ldquo;WRS\u0026rdquo; package (Mair and Wilcox \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Finally, the cumulative differences of activity and time were related by means of a linear model with the function `lm\u0026acute;, the coefficient of determination and its significance was also calculated. All statistical analyses were conducted using R 4.0.5 Software (R Core Team \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe cumulative mortality was less than 10% in treatments and the cumulative affected animals (mortality plus inactivity) was less than 13% at the end of the bioassay. The mean activity of snails in each monitoring time and for each treatment is shown in \u003cstrong\u003eFigure 1\u003c/strong\u003e. The applied treatments caused significant contrasting effects on behavioural activity (p\u0026lt;0.05; two-way repeated-measures ANOVA; post hoc test on the trimmed means) (\u003cstrong\u003eFig. 1, Table 1\u003c/strong\u003e). The interaction of both factors was also significant (p\u0026lt;0.05; two-way repeated-measures ANOVA) (\u003cstrong\u003eTable 1\u003c/strong\u003e). In general, activity of treatments changed significantly with time (p\u0026lt;0.05; two-way repeated-measures ANOVA) (\u003cstrong\u003eFigure 1\u003c/strong\u003e). The highest activity was shown by the acid treatment (A) followed by acid plus toxic treatment (AT). A high activity means a long time taken to start sliding. The other treatments (AS, AST, S, ST, T) showed a similar behaviour than that of the control animals (C) (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Results for the repeated-measures two-way ANOVA where the treatment (A, AS, AST, AT, C, S, ST, T) was the intersubject factor, time (=Time) (0, 1, 2, 3, 4 and 7 days) was the intrasubject factor, and the dependent variables were activity and cumulative differences in activity.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003eSource of variation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003eACTIVITY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003eIntrasubject factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e5.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003eTime x Treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003eIntersubject factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003eCUMULATIVE DIFFERENCES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003eIntrasubject factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e1.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e6.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003eTime x Treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e8.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e7.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003eIntersubject factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e4.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.746031746031747%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.1657848324515%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.044091710758376%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003edf\u003c/em\u003e degrees of freedom\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e \u003cem\u003edf\u003c/em\u003e were corrected for sphericity using the Grennhouse-Geisser approach\u003c/p\u003e\n\u003cp\u003eThe linear models between the cumulative differences in activity and time are shown in \u003cstrong\u003eFigure 2\u003c/strong\u003e. Differences in activity increased significantly with time in animals of the A, AT and T treatments (p\u0026lt;0.05; R\u003csup\u003e2\u003c/sup\u003e\u0026gt;0.85), which means that snails of those treatments taken a longer time to start sliding movements than control animals (positive differences) (\u003cstrong\u003eFigure 2\u003c/strong\u003e). This effect was more intense in snails of the A and AT treatments. On the contrary, S and ST animals showed an increased activity with time in comparison with control snails (negative differences) (p\u0026lt;0.05; R\u003csup\u003e2\u003c/sup\u003e\u0026gt;0.85) (\u003cstrong\u003eFigure 2\u003c/strong\u003e). The behaviour of the others treatments did not show differences, as their differences with control were nearly zero. Differences between treatments for this variable was very similar to the behavioural activity (\u003cstrong\u003eFigure 2\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study has shown that the behavior of \u003cem\u003ePotamopyrgus antipodarum\u003c/em\u003e changed depending on the source of stress and the combination of stressors to which the animals were exposed. Specifically, exposure to an acidic environment slowed down this species' ability to initiate sliding movement. In the presence of conspecific signals, and the conspecific signals plus toxicant, the adverse effects of the acidic environment were counteracted, which show antagonistic effects of both sources of stress. The presence of the toxicant plus the acid environment did not result in an increase in the adverse effects of the acid alone. On the other hand, the conspecific signals alone caused a reduction in the time to initiate sliding movement; this trend persisted in an environment containing conspecific signals despite the presence of the toxicant.\u003c/p\u003e \u003cp\u003eAquatic animals are exposed to different combinations of stress in natural ecosystems (Heugens et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Coors and Meester \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Nagelkerken and Munday \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Alonso \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Clements et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, there is limited understanding of the effects of multiple stressors on behaviours. In our study, the acidic environment slowed down the snails' activity, with the effects being compensated in the presence of conspecific signals in the environment. This was clearly showed in the cumulative differences of snail activity. Previous studies have demonstrated the impact of acid environment on contrasting behaviours of molluscs (Watson et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Leung et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Manz et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; McGarrigle et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Manz et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that water acidification impaired the ability of mud snails (\u003cem\u003eIlyanassa obsoleta\u003c/em\u003e) to detect food cues in seawater, resulting in reduced foraging success. The gastropod \u003cem\u003eNassarius festivus\u003c/em\u003e showed a reduction in several behavioural variables, including locomotor activity, when it was exposed to low pH (Leung et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A similar reduction in activity was found in the gastropod \u003cem\u003eBembicium auratum\u003c/em\u003e in extreme acidified water (pH\u0026thinsp;\u0026lt;\u0026thinsp;5.0) (Amaral et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, this specie showed an increase in avoidance behaviour in waters with pH between 6.2 and 7.0 (Amaral et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In general, low pH caused several impartments on molluscs. Acid waters may cause an impairing of physiological regulation and a metabolic depression as short-term response strategies to water acidification (Ellis and Morris \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Amaral et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Leung et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Among physiological effects, acidification may affect to invertebrates, which are characterized by a low capacity to compensate for disturbances in extracellular ion and acid\u0026ndash;base status (P\u0026ouml;rtner \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Additionally, water pH reduction may also modify the nervous system (Watson et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In fact, acidification caused the interference with the function of neurotransmitter receptors, which might be responsible for the impartment of predator-escape behaviour of snails (Watson et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A likely cause of the increase in the reaction time of \u003cem\u003ePotamopyrgus antipodarum\u003c/em\u003e may be the alteration of the nervous system, as this has a direct relationship with behavioural alterations involved in movement (Watson et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Leung et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, the joint action of other causes cannot be ruled out, such as the reduction of available energy due to the need to compensate for physiological alterations (P\u0026ouml;rtner et al. 2000) or damage on soft tissues (Dove and Sammut \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Dell\u0026rsquo;Acqua et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) or reduction of oxygen uptake (Leung et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our study, the adverse effects of acidic environment were counteracted by the combined exposure to acid and conspecific signals, but the presence of the toxicant did not change the effect of the acidic environment. Overall, conspecific signals led to a reduction of the time to initiate sliding movement of \u003cem\u003eP. antipodarum\u003c/em\u003e, which means an increase in activity; this effect counteracted the deleterious acid environment in the combined exposure as snail showed a similar behaviour than that of control animals. The presence of conspecific chemical or predator signals in the aquatic environment can elicit different responses in molluscs (Jacobsen and Stabell \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Wyeth \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). On one hand, there are studies showing an increase in animal activity, most likely associated with the searching for refuge or for avoiding predator water (Dix and Hamilton \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Jacobsen and Stabell \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Jacobsen and Stabell \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Keppel and Scrosati \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Conversely, the opposite trend has also been observed, where animals decrease their activity or withdraw their soft body or spent more time in their refuges, which may be related to a lower risk of being detected by potential predators (Richardson and Brown \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Jacobsen and Stabell \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). While our results highlight the primary factor, it is also conceivable that even small remnants of conspecifics could act as a food source, potentially mitigating part of the energy losses resulting from exposure to the acidic environment. However, conspecific treatment alone increase snail activity, so everything suggests that conspecific signals are causing an increase in activity, either due to having a food source or due to the modification of activity by chemical signals.\u003c/p\u003e \u003cp\u003eIn our study, the toxicant (i.e. acetone) did not cause a significant modification of behaviour, nor did it contribute to behaviour modification in combination with other sources of stress. The results were unexpected, as a previous study indicated that acetone caused sublethal changes in snail behaviour (Alonso et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, it's worth noting that the acetone concentration was lower, and the exposure method differed from that employed in the study by Alonso et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Acetone may affect to the metabolic detoxification systems and metabolic activities at long-term exposure (Hutchinson et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; LeBlanc \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Leoni et al. 2006). Acetone is a highly volatile compound, such that to maintain constant concentrations in the water a frequent solution renewal is necessary. In our study, acetone was applied only once at the beginning of the 7-day period, potentially leading to minor adverse effects but enabling a swift recovery for the animals. This may explain the minor effect of toxicant treatment on behaviour. Despite this fact, the toxic treatment slightly altered the behaviour, as evidenced by the slight tendency towards a decrease in snail reaction time. This slight effect of acetone on its own may account for the absence of more severe consequences for behaviour when combined with any of the other stress sources, or with all of them together.\u003c/p\u003e \u003cp\u003eDespite the undeniable usefulness of conventional approaches applied to the study of the effects of contaminants on ecosystems, they do not take into account the great complexity of natural systems. Ecosystems are subjected to numerous contaminants and novel entities, such as microplastics, which make the extrapolation from traditional bioassays to real scenarios very difficult (de Souza Machado et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bertram et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition to this, there is the complexity of ever-changing aquatic ecosystems due to the planet's experiencing climate change (Ferreira et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pinheiro et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The need to consider ecological complexity in ecotoxicology, despite its importance, poses a challenge for its implementation into environmental risk assessment and standardization processes. However, in recent years, several promising advancements have been made in this regard. For example, prioritizing behaviours crucial for species and their fitness, integrating individual behaviour, considering the most impactful variables associated with climate change, modelling multi-exposure to toxicants, and incorporating interactions in ecotoxicological bioassays, all represent promising advances in enhancing this complexity (Amaral et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Araujo et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; De Souza Machado et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Clements et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pinheiro et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Poiverino et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bertram et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, behavioural parameters provide a good connection from suborganismal processes to population level processes (\u0026Aring;gerstrand et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, behaviour is a valuable parameter to chemical regulation and ecological studies (Hellou \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; \u0026Aring;gerstrand et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bertram et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIt is concluded that the behavioural responses of the aquatic snail \u003cem\u003ePotamopyrgus antipodarum\u003c/em\u003e are altered depending on the source and combination of stress, with antagonistic effects observed when simultaneously exposing the animals to an acidic environment and conspecific signals. However, the activity of snail did not respond to toxicant exposure, which did not increase or counteract the adverse effects of the other stress sources. Therefore, to increase the realism of bioassays the exposure to several sources of stress should be included, since antagonistic effects can counteract the harmful effects of an isolated stress source.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003ePart of this work was carried out within the sabbatical period of \u0026Aacute;lvaro Alonso as a professor in the University of Alcal\u0026aacute; for the 2021-2022 academic year.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e MC Llandres-D\u0026iacute;ez was supported by a research assistant contract \u0026ldquo;Ayudas para la promoci\u0026oacute;n de empleo joven e implantaci\u0026oacute;n de la garant\u0026iacute;a juvenil en I+D+i\u0026rdquo; funded by the \u003cem\u003eMinisterio de Ciencia, Innovaci\u0026oacute;n y Universidades\u003c/em\u003e. P Cruces-Estepa was supported by a research assistant contract \u0026ldquo;Empleo juvenil y la iniciativa de Empleo Juvenil\u0026rdquo; funded by the \u0026ldquo;Comunidad de Madrid\u0026rdquo; and \u0026ldquo;Fondo Social Europeo\u0026rdquo;. Funds for this research came from the University of Alcal\u0026aacute; (research projects CCG2013/EXP-054, CCG2016/EXP-054 and CCG2018/EXP-074), and the projects CGL2011˗16388/BOS, CGL2015-65346R, INTERTOX RTI2018-096046-B-C21 (MCIU/AEI/FEDER, UE) and EXARBIN RTI2018-093504-B-I00 (MCIU/AEI/FEDER, UE) of the \u003cem\u003eMinisterio de Econom\u0026iacute;a y Competitividad\u003c/em\u003e of Spain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eAuthor provides consent for publication\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eData that support the findings of this study are available from the corresponding author upon reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e Rcode available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Alonso: Conceptualization, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Writing \u0026ndash; Original draft, Writing \u0026ndash; Review \u0026amp; editing, Visualization, Project administration, Validation, Supervision. MC Llandres-D\u0026iacute;ez: Formal analysis, Writing \u0026ndash; Review \u0026amp; editing. Investigation, Methodology. P Cruces-Estepa: Formal analysis, Writing \u0026ndash; Review \u0026amp; editing. Investigation, Methodology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID:\u0026nbsp;\u003c/strong\u003e\u0026Aacute;lvaro Alonso https://orcid.org/0000-0002-7797-8045\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003e\u0026Aring;gerstrand M, Arnold K, Balshine S, Brodin T, Brooks BW, Maack G, McCallum ES, Pyle G, Saaristo M, Ford AT (2020)\u003c/strong\u003e Emerging investigator series: use of behavioural endpoints in the regulation of chemicals. Environ Sci Process Impacts 22: 49\u0026ndash;65. https://doi.org/10.1039/c9em00463g\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAlonso A (2021)\u003c/strong\u003e To eat or not to eat: the importance of starvation on behavioral bioassays. 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In ecosystems, animals often face multiple stressors simultaneously. Their behavioural responses may vary when exposed to each stressor individually, as synergistic, additive, or antagonistic effects can result from the interaction of multiple stressors. Therefore, it is imperative to conduct studies that take into account the common occurrence of multi-stress scenarios in aquatic ecosystems. We tested the effects of three sources of stress (low water pH, toxicity (acetone) and conspecific chemical cues) on the behaviour of the aquatic nail \u003cem\u003ePotamopyrgus antipodarum\u003c/em\u003e (Tateidae, Mollusca). We evaluated the impact of each stressor, as well as in combinations of two or three stressors simultaneously. The highest time to start movement was shown by the animals of the low water pH (acid treatment-A) followed by A plus toxic treatment (AT). The linear models showed an increase in the cumulative differences in time to start movement for the snails of A, AT, and T treatments over time. This indicates that snails in these treatments took longer to begin sliding movements compared to the control animals. On the contrary, animals of the conspecific chemical cues (S) and ST treatments showed an increased in the time to start sliding with time in comparison with control snails. It is conclude that behaviour was altered depending on the source and combination of stress, with antagonistic effects when simultaneously exposing the animals to an acidic environment and conspecific signals.\u003c/p\u003e","manuscriptTitle":"Contrasting behavioural response to concurrent stressors in an aquatic snail: importance of stress type and combination","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-30 03:35:15","doi":"10.21203/rs.3.rs-4319021/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78e30cfa-8849-44a3-af24-fc0c2675e16f","owner":[],"postedDate":"April 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-27T20:26:10+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-30 03:35:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4319021","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4319021","identity":"rs-4319021","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}