Influence of salinity on the thermal tolerance of aquatic organisms

18 Aquatic organisms are challenged by changes in the external environment, such as temperature 19 and salinity fluctuations. The response of an organism to temperature changes can be modified 20 by salinity, thus pointing at the potential interaction of both variables. In the present study, we 21 tested this assumption for freshwater, brackish, and marine organisms, including algae, 22 macrophytes, heterotrophic protists, parasites, invertebrates, and fish. We reviewed the existing 23 body of literature on potential interactions between temperature and salinity and performed a 24 meta-analysis that compared the thermal tolerance (characterized by the temperature optima, 25 lower and upper temperature limits, and thermal breadth s). The final database includes 9 0 26 relevant publications (a lgae: 15; heterotrophic protists: 1; invertebrates: 43; and fish: 3 1). 27 Relevant publications for microphytes and parasites were not available. Overall, our results 28 show that decreasing salinity significantly increased the lower temperature limits and decreased 29 the upper temperature limits irrespective of the organism groups. These findings mainly reflect 30 the response to salinity changes in brackish and marine systems that dominate our database. 31 Although the number of studies on freshwater species was limited , they showed negative, 32 although statistically nonsignificant, effects of an increased salinity on the thermal tolerance of 33 these species (i.e. increased lower limits and decreased upper limits) . In addition, our meta-34 analysis shows nonsignificant differences in the responsiveness of thermal tolerance to salinity 35 changes among different groups of organisms, but the sensitivity of thermal tolerance to 36 salinity changes generally followed the order: algae > invertebrates > fish. Facing the impact 37 of climate change, our findings point at adverse effects of salinity changes on the temperature 38 tolerance of aquatic organism s. Further studies that investigate the thermal performance of 39 freshwater species at var ious salinity gradients are required to broaden the evidence for 40 interactions between salinity and temperature tolerance. This also applies to the influence of 41 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 3 parasitic infections, which have been found to modulate the temperature tolerance of aquatic 42 invertebrates and fish. 43

Co-tolerance; Thermal performance; Osmotic stress; Multiple stressors; Algae; 44 Invertebrates; Fish; Meta-analysis; Global changes 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 4 1. Introduction 63 Temperature and salinity are among the most important environmental variables governing 64 the distribution and development of organisms in aquatic ecosystems (Bonacina et al., 2023 ; 65 Duan et al., 2023). Aquatic systems are subjected to substantial fluctuations of both variables. 66 The underlying drivers of elevated water temperature include climate change, i.e. increases in 67 air temperature and anthropogenic heat emission (Liu et al., 2020) . Aquatic ecosystems are 68 additionally challenged by changes in ion concentration s. Salinity increases in freshwater 69 ecosystems, hereafter called salinisation (Reid et al., 2019) , are driven by climate change, 70 agriculture (associated with increased surface runoff), mining (associated with the discharge of 71 highly saline water), and road runoff (Canedo-Argüelles et al., 2016, 2013; Feld et al., 2023; 72 Schröder et al., 2015; Hintz et al., 2022) . Different patterns of salinity changes in marine 73 ecosystems have been reported. At high latitudes of the oceans, global warming-increased net 74 precipitation dilutes the concent ration of ions, contrasting with salinization due to decreased 75 net precipitation and elevated evaporation at low latitudes (Curry et al., 2003 ; Boyer et al., 76 2005). Saline rivers, estuaries, and salt -marshes are being diluted, mainly owing to runoff 77 decreases (Zhang et al., 2021). 78 Generally, responses of organisms to binary stressors can be classified into three types: 79 random co-tolerance (i.e. tolerance to stressors is unrelated), cross -tolerance (or positive co -80 tolerance), and cross-susceptibility (or negative co-tolerance) (Vinebrooke et al., 2004). Cross-81 tolerance occurs when the increase of one stressor enhances the tolerance to another (Todgham 82 et al., 2005). This phenomenon might result from triggering similar defence mechanisms or 83 response pathways (Isaza et al., 2021; Korkaric et al., 2015; MacMillan et al., 2009; Todgham 84 and Stillman, 2013; Vergauwen et al., 2013). By contrast, cross -susceptibility occurs when 85 exposure to one stressor reduces the tolerance towards the other stressor. Cross -susceptibility 86 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 5 may originate from trade-offs in the ability of organisms to tolerate each stressor (Cuenca‐87 Cambronero et al., 2021; Sinclair et al., 2013; Todgham and Stillman, 2013; Vinebrooke et al., 88 2004); or shared damage that lead s to disproportionately h igher effects compared to the 89 individual stressors (Cruz -Loya et al., 2021). For example, there is a trade -off between the 90 energy demands for osmoregulation and the ability to protect the organism against other 91 stressors such as changes in temperature (Vereshchagina et al., 2016). 92 Changes in temperature and salinity can jointly act on aquatic organisms as thermal and 93 salinity stressors activate the same cellular response, and trigger the expression of the same 94 genes in aquatic organisms (Lockwood et al., 2010; Lockwood and Somero, 2011). For aquatic 95 organisms, osmoregulation is crucial to sustain an osmotic balance between the internal fluids 96 and the external environment. Changes in the concentration of ions in the external environment 97 can increase the costs of osmoregulation. However, our understanding of the respons e of 98 thermal tolerance to salinity gradients is poor, and it is unknown whether the responses differ 99 systematically between organism groups. In the present study, we compare the thermal 100 tolerance of various groups of eukaryotic aquatic organisms at various salinity levels to identify 101 the type of co -tolerance per organism group. We hypothesize that due to the trade-offs in the 102 energy demand and shared damages, as explained above, the correlation be tween thermal 103 tolerance and salinity tolerance would be negative, i.e. negative co -tolerance. In other words, 104 we expect salinization would decrease the thermal tolerance of freshwater species (see Le et 105 al., 2023 ), while salinity dilution would negatively affect the thermal tolerance of marine 106 species. In addition, we hypothesize differences in the co -tolerance among organism groups. 107 Osmoregulatory capacities might be insufficient for microorganisms at low trophic levels, such 108 as algae and fungi, to cope wi th salinity changes. Therefore, we expect that their thermal 109 tolerance is more responsive to salinity fluctuations. 110 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 6 2. Materials and Methods 111 2.1. Database construction 112 A database of thermal tolerance at various salinity levels was built by retrieving the relevant 113 variables (e.g. optimum temperature, temperature limits, incipient temperature, and thermal 114 breadth) from peer-reviewed publications available through the Web of Science and Scopus. 115 The literature survey was conducted on 18th January 2024 using a combination of various terms 116 describing the responses of aquatic organisms to temperature and salinity changes ( Appendix 117 1: Database construction). After checking for duplicates, the retrieved records were screened 118 following a stepwise approach (Fig. 1). ASRreview abstract and manual abstract screening 119 were to remove irrelevant records (e.g. field or community studies), while manual full text 120 screening was to retrieve relevant records. The machine learning tool ASReview (Active 121 learning for Systematic Reviews) has been demonstrated to be an efficient reviewing tool (van 122 de Schoot et al., 2021; Quan et al., 2024). We used this tool for the first screening step that was 123 followed by manual abstract screening to further remove irrelevant records (e.g. studies on the 124 influence of temperature and/or salinity on another stressor). In the final screening step (i.e. 125 manual full text screening), r ecords are considered relevant when they met the following 126 criteria: 1) Records provide at least one indicator of thermal to lerance (Fig. 2; Table S1, 127 Supporting Information). The records that contain equations for determining at least of the 128 indicators were also considered relevant ; 2) Effects of temperature and salinity were 129 simultaneously investigated; 3) Data were reported for at least three salinity levels; 4) Data 130 were obtained from laboratory experiments or modelling research based on laboratory 131 experiments under controlled conditions in which other factors besides temperature and salinity 132 were kept constant among treatme nts; and 5) Data were based on measured responses of 133 populations (for algae and macrophytes) and individuals (for other organisms) in aquatic life 134 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 7 stages, i.e. excluding non -aquatic life stages, where applicable. We focussed on eukaryotic 135 aquatic organisms, covering aquatic algae (green algae, diatoms, and dinoflagellates); 136 heterotrophic protists; submerged or floating macrophytes; parasites; fungi (except food fungi), 137 invertebrates, and fish. We excluded studies in which thermal tolerance was determined under 138 irrelevant environmental conditions, for example, air exposure. A detailed description of the 139 steps for database construction is given in Appendix 1. 140 141 Fig. 1. Flow chart of database construction 142 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 8 143 144 Fig. 2. Indicators of thermal tolerance: (A) showing responses to temperature gradients before 145 mortality occurs; and (B) showing change in the survival probability to temperature gradients. 146 From each relevant record, we extracted the following information, if available, and 147 included them in our thermal tolerance database: species, organism group, habitat, acclimation 148 conditions, test conditions (especially test salinity), indicators of thermal tolerance (indicator, 149 definition, measurements, number of treatments, size of variations, measurement of variations, 150 and number of replicates in each treatment). Data was retrieved from narrative descriptions, 151 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 9 tables, figures (using WebPlotDigitizer) , and equations in the main text as well as in the 152 Supporting Information. When the experiments were repeated, the mean and its standard 153 deviation were calculated. The final database included 9 0 publications (organism groups: 154 algae: 15; heterotrophic protists: 1; invertebrates: 43; and fish: 3 1; habitats: freshwater: 1 7; 155 brackish: 21; and marine: 53 ) (Appendix 2: Database). Studies on parasites and macrophytes 156 that met our inclusion criteria were not available. Data on lower and upper ultimate incipient 157 lethal temperatures, as well as on lower and upper ultimate critical thermal minima and maxima 158 were relatively scarce (Table S1, Supporting Information). Therefore, they were omitted from 159 the analyses. Prior to data analysis, the other indicators of thermal tolerance were grouped into 160 six groups: optimum temperature (including optimum temperature and preferred temperature); 161 lower temperature limit (including critical thermal minimum and lower lethal temperature); 162 upper temperature limit (including critical thermal maximum and upper lethal temperature); 163 thermal breadth (including survival and physiol ogical thermal breadth); lower effect 164 temperature (including lower incipient lethal/physiological temperature and lower 100% 165 mortality temperature); and upper effect temperature (including upper incipient 166 lethal/physiological temperature and upper 100% mortality temperature). When both incipient 167 and 100% mortality temperatures were reported from the same study, the incipient temperature 168 was used in the analysis. 169 2.2. Statistical analyses 170 We aimed to quantify the influence of salinity upon the selected thermal indicators: 171 optimum temperature, lower and upper temperature limits, breadth of the temperature tolerance 172 range, and lower and upper effect temperatures. All measurements of ion concentrations were 173 converted to salinity in ppt. Electrical conductivity w as converted to salinity (ppt) using the 174 equation derived by Lewis and Perskin (1981). The effect of salinity on each response was 175 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 10 quantified by the slope coefficient of a linear regression model, while the slopes standard error 176 was used to quantify its variation. Since both the predictor (salinity) and the responses (thermal 177 tolerance indicator) were expressed on the same numerical scales across all reviewed studies. 178 We ran weighted random -effects meta-analyses for each thermal indicator as response using 179 the metafor package in R (Viechtbauer, 2010). The weight for each study was assigned based 180 on its number of replicates weight increasing with replicates. Additionally, we considered 181 effects of the following moderators: organism group, habitat, and acclimat ion conditions (i.e. 182 test organisms were acclimated to test salinity or not). We refrained from using more than one 183 moderator at a time owing to the relatively small sample size. For each response, models with 184 different moderators were compared based on the Akaike Information Criterion (AIC) (Akaike, 185 1974). We then proceeded to subgroup analyses, applying the same categories to display and 186 examine the differences between these su bgroups. Meta-analyses were conducted using 187 RStudio (version 2023.09.1+494 , https://github.com/rstudio/rstudio/tree/v2023.09.1+494), 188 and the packages: Tidyverse 2.0.0 (Wickham et al., 2019), lme4 1.1.35.1 (Bates et al., 2015), 189 blme 1.0.5 (Chung et al., 201 3), and metafor 4.4.0 (Viechtbauer, 2010). Responsiveness of 190 thermal indicators was classified using the effect sizes of the meta-analyses. Effect sizes were 191 considered significant when the 95% confidence interval excluded zero. 192 3. Results and Discussion 193 3.1. Salinity changes and thermal tolerance 194 In general, our analysis indicates a positive relationship between salinity changes and the 195 thermal tolerance of aquatic organisms (i.e. salinization extended the thermal tolerance and 196 salinity dilution contracted the thermal tolerance) . Increased salinity tends to broaden the 197 thermal tolerance of organisms, while decreased salinity narrows down the thermal tolerance 198 (Table 1). In particular, salinization significantly decreased the lower temperature limit (Fig. 199 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 11 3) and the lower effect temperature (Fig. S1, Supporting Information) as shown by negative 200 pooled effect sizes. Such effects of s alinity changes on these two indicators are of a similar 201 magnitude on these indicators as shown by the overlapping effect sizes (Table 1). By contrast, 202 the upper temperature limit (Fig. S2) and the upper effect temperature (Fig. S 3, Supporting 203 Information) significantly increased with salinization, as displayed by the positive pooled 204 effect sizes. These two indicators were influence d by salinity changes at the same order of 205 magnitude (Table 1). Moreover, the upper temperature limit was less responsive to salinity 206 gradients than the lower limit (Table 1), which is consistent with the findings of Botella-Cruz, 207 M. et al. (2016). This phenomenon has been defined as the asymmetric response of the thermal 208 tolerance (Botella-Cruz, M. et al., 2016). 209 Table 1. Effect of salinity on the indicators of thermal tolerance. Data availability regarding 210 each response is indicated in terms of both the number of studies and the number of species. 211 Effect sizes, 95% confidence intervals, and p-value (pmod) were derived from the meta-analysis 212 for the six thermal indicators. Significant effects are defined as the 95% confidence interval 213 not overlapping with zero. Heterogeneity is expressed by Cochran’s Q and its significance (pQ). 214 Thermal indicator Data availability Effect size (95% CI) pmod Q pQ Study Species Optimum temperature 28 31 0.05 (-0.00 – 0.10) < 0.05 1,303,585 <0.0001 Lower temperature limit 22 26 -0.10 (-0.16 – -0.04) < 0.001 396 <0.0001 Upper temperature limit 28 35 0.05 (0.02 – 0.09) < 0.005 9,099 <0.0001 Thermal breadth 15 19 0.11 (-0.03 – 0.24) 0.12 215 <0.0001 Lower effect temperature 41 46 -0.07 (-0.13 – -0.01) < 0.05 3,223 <0.0001 Upper effect temperature 46 51 0.06 (0.01 – 0.11) < 0.05 7,611 <0.0001 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 12 215 Fig. 3. The effect of salinity on the lower temperature limit. Mean (individual and pooled) 216 effect size and 95% confidence interval were estimated from the model. Their values are listed 217 on the right side of the figure. The polygons at the bottom of the plot represent the mean effect 218 size by different subgroups (organism group, habitat, and acclimation to test salinity). 219 Significant effects are defined as the 95% confidence interval not overlap ping with zero. 220 Heterogeneity statistics of the model are shown by the values of the Cochran’s Q, its degree of 221 freedom (df), p, I2, and τ2. 222 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 13 Brackish (21/90) and marine (53/90) species dominated in our database. This is in line with 223 Velasco et al. (2019) where the majority of experimental studies on multiple -stressor effects 224 also were from brackish and marine species. Hence, the above-mentioned results mainly reflect 225 the response of brackish and marine species (Fig. 3; Figs. S1-S3, Supporting Information). The 226 increased lower temperature limit and the decreased upper temperature limit of brackish and 227 marine species with salinity dilution are consistent with our hypothesis. Salinity decreases 228 might increase energetic costs for adjusting osmoregulation in marine f ish and invertebrates 229 (Botella-Cruz, M. et al., 2016; Röthig et al., 2023) . Osmoregulation is considered an 230 energetically expensive process as energy is diverted into acclimation and cellular protection 231 (Sokolova et al., 2012). Therefore, salinity dilution may limit the energy available for 232 thermoregulatory mechanisms, such as the production of heat shock proteins, thereby affecting 233 the thermal tolerance (Botella-Cruz, M. et al., 2016; Tomanek et al., 2011). Besides the effects 234 on energy acquisition and all ocation, salinity stress might lead to dehydration, thus causing 235 changes in membrane fluidity and consequently decreasing the thermal tolerance (Everatt et 236 al., 2013; Kikawada et al., 2006). 237 With these results, the thermal breadth is expected to be positively correlated with salinity 238 changes (i.e. extended by salinity increases and contracted by salinity dilution). That was also 239 revealed in our meta-analysis as the average effect size was clearly positive (Table 1; Fig. 4). 240 Notwithstanding, this corr elation was not statistically significant, because the confidence 241 interval contained zero (Table 1; Fig. 4). A similar pattern was found for the optimum 242 temperature (Table 1; Fig. S4). Conclusive judgments on the effect of salinity upon the 243 optimum temperature and the thermal breadth could not be obtained as the p-values were being 244 or close to significance (Table 1). Our analysis revealed indicator-specific responses of thermal 245 tolerance to salinity changes. According to previous studies, the pattern of correlation between 246 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 14 stressors depends on the gradient of individual stressors (Fischer et al., 2012; Earhart et al., 247 2022; Mack et al., 2022; Segurado et al., 2022) and the target organisms (Corcoll et al., 2015). 248 249 Fig. 4. Effect of salinity on the thermal breadth. Mean (individual and pooled) effect size and 250 95% confidence interval were estimated from the model. Their values are listed on the right 251 figure margin. The polygons at the bottom of the plot represent the mean effect size by different 252 subgroups (organism group, habitat, and acclimation to test salinity). Significant effects are 253 defined when the 95% confidence interval not overlap ping with zero. Heterogeneity statistics 254 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 15 of the model are shown by the values of the Cochran’s Q, its degree of freedom (df), p, I2, and 255 τ2. 256 Salinity increases exerted opposite effects on the thermal tolerance of freshwater (negative) 257 and of brackish/marine species (positive) (Fig. 3; Figs. S1 -S3, Supporting Information). The 258 thermal tolerance of freshwater species contracted, although not significantly, while the 259 thermal tolerance of brackish and marine species significantly expanded with increasing 260 salinity ( Fig. 3; Figs. S1 -S3, Supporting Information ). This suggests different types of 261 interactions between stressors in freshwater and marine systems, as demonstrated in p revious 262 meta-analyses (Crain et al., 2008; Harvey et al., 2013; Przeslawski et al., 2015; Jackson et al., 263 2016). Freshwaters and marine waters have dissimilar ion compositions with the former being 264 dominated by Ca2+ and HCO3- and the latter being dominated by Na + and Cl- (Wetzel, 2001). 265 Moreover, the ions have different physiological functions (Charmantier et al., 2009) . In 266 freshwater animals, Na + and Cl - are major osmolytes responsible for maintaining the 267 hyperosmotic state of their fluid (Bradley, 1987; Dietz, 1979; Evans, 2008; Wheatly and 268 Gannon, 1995) . Most freshwater animals are hyper -regulators that sustain higher ion 269 concentrations in their blood or hemolymph compared to the external environme nt to achieve 270 physiological homeostasis (Bradley, 2008). Most marine invertebrates are osmoconformers 271 (Solan and Whiteley, 2016 ). For these organisms, it has been suggested that energy is not 272 diverted to transport mechanisms in response to salinity changes as the osmolality of their 273 internal medium fluctuates with the changing osmolality of the external medium (Rivera -274 Ingraham and Lignot, 2017). This contrasts with our result that salinity dilution contracted the 275 thermal tolerance of marine species. 276 For freshwater organisms, experimental studies testing temperature effects at a range of 277 different salinity levels (ideally, more than five temperature levels covering the width of the 278 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 16 respective tolerance breadth of the tested species) are needed to address the central question of 279 the present study, i.e., whether salinity gradients affect thermal tolerance of aquatic organisms. 280 Even with such experimental designs, small shifts in temperature limits might be difficult to 281 detect. It is possible that a modification of the critical thermal minimum/maximum framework 282 applied for macroscopic organisms might be more suitable for addressing this question, but we 283 are not aware of applications of this concept to microscopic organisms. In addition, a model 284 linking cellular and molecular responses with responses at the organismal level might improve 285 our understanding further. 286 3.2. Variation in the sensitivity of thermal tolerance to salinity gradients among organism 287 groups 288 Our meta-analysis indicates insignificant variations of salinity effects on thermal tolerance 289 among different groups of organisms (Fig. 5), contrasting with our hypothesis. The 290 responsiveness of lower and upper temperature limits and the optimum temperature for 291 different organism groups was: algae > invertebrates > fish (although not significant) (Fig. 5). 292 It should be noted that the effect size in the present meta -analysis was estimated based on the 293 slope of a linear relationship between thermal tolerance and salinity gradients. This method 294 was selected becaus e in most of the studies available, less than four salinity levels were 295 included (Appendix 2). Mack et al. (2022) indicated that non -linear responses were more 296 common for organisms at high trophic levels. In other words, the highest value of the critical 297 thermal maximum is found at intermediate salinities. A non -linear relationship between the 298 critical thermal maximum and salinity has been commonly found in fish (King and Sardella, 299 2017; Matern, 2001; Haney and Walsh, 2003; Sardella et al., 2008; Rodgers and Isaza, 2022). 300 Such non-linear relationships might contribute to the dependence of co -tolerance on salinity 301 levels. For example, for Daphnia pulex, Chen and Stillman (Chen and Stillman, 2012) reported 302 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 17 cross-tolerance of temperature and salinity at interme diate salinity gradients and cross -303 susceptibility at extreme salinity variations. 304 305 Fig. 5. The effect size (mean; 95% confidence interval) of the influence of salinity changes on 306 thermal indicators of algae, heterotrophic protists, invertebrates, and fish. 307 The more responsive thermal tolerance of algae to salinity gradients may be attributed to 308 the higher sensitivity of algae to salinity gradients compared to other organism groups. 309 Compared to larger multicellular organisms, unicellular organisms are more impacted by 310 salinity as they have a limited capacity of osmotic buffering (Ishika et al., 2018). The size -311 dependent osmotic tolerance has been related to the different development of ionoregulatory 312 capacity (Amiri et al., 2009) as larger organisms such as crustaceans and fish have developed 313 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 18 osmoregulatory mechanisms (Charmantier et al., 2009; Evans and Claiborne, 2009). Further 314 studies on the effect of multicellularity on the interactions of thermal and osmotic stress might 315 provide more insights into the un derlying mechanisms contributing to the variations among 316 organism groups. 317 Only a few studies of algae (marine or freshwater, planktonic or benthic, macro - or 318 microalgae) have applied a treatment matrix or other approach capable of detecting the 319 interactive effects of both factors as illustrated in the present study. Bollen et al. (2016) found 320 significant interactions between salinity and temperature effects upon photosynthetic 321 maximum quantum yield for three (marine) kelp species, and for at least one species also upon 322 photosynthetic electron transport rates, chlorophyll -a content, xanthophyll pool size, and 323 antioxidant capacity. Bjaerke and Rueness (2004) did not find a significant interactive effect 324 of temperature and salinity on the growth and survival o f marine macroalga Heterosiphonia 325 japonica, though some of their observations could possibly be interpreted as a narrowing of 326 the thermal tolerance range at least at the highest desalination. In terms of microalgae studies, 327 Xu et al. (2017) unravelled a pattern of decreasing temperature tolerance range for Phaeocystis 328 globosa with suboptimal (decreasing) salinities. Similar observations were made for the marine 329 diatom Asteroplanus karianus (Shikata et al., 2015). These latter studies, however, all 330 investigated marine taxa and salinity concentrations below optimal level as a stressor, in 331 contrast to a freshwater situation. Salinity stress with concentrations above the optimal level 332 would be more relevant in the context of freshwater salinization. The publication of Le et 333 al. (2023) was the only study we were able to locate that tested the interactive effects of 334 temperature and salinity on the growth rate of six freshwater benthic diatom strains. In terms 335 of publicly available data sets, the GlobTherm database contains information on the thermal 336 tolerance of 274 red, brown, and green algal species, but none from the secondary 337 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 19 endosymbiotic groups of microalgae playing important roles in many aquatic ecosystems (like 338 diatoms, dinoflagellates; https://datadryad.org/stash/dataset/doi:10.5061/dryad.1cv08 last 339 accessed 11.12.2023). More importantly, from the point of view of this study, GlobTherm 340 contains no information on salinity conditions at which the individual thermal tolerances were 341 determined. 342 Regardless of indicators, salinity changes consistently had a limited influence on the 343 thermal tolerance of fish (Figs. 2 -6). In fish, the intracellular and extracellular osmolality 344 stabilises with a fluctuating osmolality of the external environment (Griffith, 2017; Urbina and 345 Glover, 2015). Walker et al. (2020) pointed out that there is an isosmotic point in fish, i.e., an 346 internal osmolality that is equal to the osmolality of the external environment, below which 347 less energy is required for osmoregulation. Moreover, fish species in our database are mostly 348 euryhaline and in some cases, even anadromous (e.g., salmon). These species have adapted to 349 changes in their osmotic environment and are less likely to be challeng ed by salinity stress. 350 Previous research indicated that other factors that can influence thermal tolerance in fish, 351 especially the upper thermal limit, are acclimation temperature, photoperiod, and duration 352 (Hines et al., 2019). The influence of photoperio d on thermal tolerance also varies between 353 species. Usually, longer photoperiods enhance thermal tolerance (Healy and Schulte, 2012; 354 Terpin et al., 1976); however, artificially long photoperiods can also act as a stressor, and 355 inhibit thermal tolerance (Hines et al., 2019; Newman et al., 2015). In the study of Hines et al. 356 (2019), the upper temperature tolerance decreased during a 400-day experiment from ~29°C to 357 ~26°C in all salinity treatments (Hines et al., 2019). In such long experiments, the effect could 358 be additionally affected by the substantial changes in body size (Recsetar et al., 2012). Effects 359 reported from the field (e.g., Morgan et al., 2019) are therefore likely related to additional 360 stressors such as energy limitation, predation, or pathogen pressure. 361 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 20 In contrast to algae and fish, the influence of salinity gradients on thermal tolerance in 362 invertebrates has been more intensively studied. But still, most of the studies have been mainly 363 conducted on marine species. More intensive research on marine species allows for conducting 364 meta-analyses. These studies yielded inconsistent patterns of interactions between temperature 365 and salinity. The meta -analysis in the study of Crain et al. (2008) indicated antagonistic 366 interactions as the most common pattern, while Przeslawski et al. (2015) suggested synergistic 367 interactions. Such differences might be attributed to the different life stages and phyla 368 considered in these two studies (Lange and Marshall, 2017) , and/or to the species -specific 369 response of osmoregulation capacities to temperature (Torres et al., 2021) . Combined effects 370 of temperature and salinity have been evaluated on some freshwater invertebrate species 371 (Kumlu et al., 2010; Venancio et al., 2023). For instance, Venancio et al. (2023) suggest ed 372 synergistic effects of these factors on the survival of Daphnia longispina. 373 3.3. Other confounding factors 374 Experimental conditions 375 Acclimation to test salinity significantly enhanced the thermal tolerance of aquatic 376 organisms ( Fig. 3; Figs. S1 -S3, Suppo rting Information ). Acclimation strongly affect ed 377 temperature and salinity tolerance in laboratory experiments (Botella-Cruz et al., 2016; 378 Fernandes et al., 2023), where – after a short acclimation period – the lower temperature limit 379 was above the limit t hat was observed under natural ( in situ) conditions (White et al., 2015). 380 The importance of acclimation to test salinity was also unravelled in our study as shown by the 381 significant difference in the effect size on the upper temperature limit between the acclimated 382 and unacclimated organisms. This result agrees with the acclimation -enhanced tolerance 383 reported in a number of previous studies (Loureiro et al., 2015; K umlu et al., 2010). For 384 instance, acclimation of fish from various habitats to high temperatures led to significantly 385 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 21 higher tolerance towards high temperatures (e.g., Davis et al., 2019; Matern, 2001), while the 386 acclimation to lower temperatures increased tolerance to low temperatures (King and Sardella 387 2017; Schofield et al., 2009). Acclimation temperature therefore had a strong effect on the 388 thermal tolerance, and the profound differences between the studies might have concealed a 389 general salinity effect. However, this moderator was not considered in the model because this 390 information is not given in many studies. 391 Relevance of parasite infections in evaluating stressor effects 392 An important group of organisms in the context of studies focusing on stress responses are 393 parasites. Parasites can directly and/or indirectly modulate ecological interactions, affecting, 394 for instance, mobility, habitat selection, foraging, reproduction, longevity, and morphology of 395 infected hosts (Fenton and Rands, 2006; Forbes, 1 993; Hurd et al., 2001; Miura et al., 2006) . 396 More importantly, parasites may alter host tolerance to stressors such as fluctuating salinity 397 and increasing temperature, sometimes with surprising outcomes (Sures et al., 2023) . For 398 instance, the acanthocephalan Polymorphus minutus enhanced the salinity tolerance of its host 399 Gammarus roeselii (Piscart et al., 2007). Studies dealing with environmental stressors should, 400 therefore, account for parasitic infections in their study organisms, or else they are at ri sk of 401 biased outcomes to some degree, as already seen in the ecotoxicological context (Grabner et 402 al., 2023). As studies analysed in the current work have not taken the influence of parasitism 403 into account, variations in the data could, for instance, be du e to unrecognized infections, and 404 the here-presented results should rather be interpreted in the context of uninfected individuals 405 or, at least of those not strongly affected by their parasites. 406 Although the final dataset did not contain any records on parasites, they would be an 407 important target group for the analyses presented here due to their overall complexity. Parasites 408 can be protists or metazoans, external (ectoparasites) or internal (endoparasites), have simple 409 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 22 (involving one host) or complex (involving multiple hosts) life cycles, be host generalist or 410 host specialist, and have differing life stages (Dobson et al., 2008; Lafferty, 2012) . All these 411 strategies can influence the outcome of parasite -stressor interactions, resulting in parasite 412 species, populations, or their life stages responding differently to salinity and temperature 413 changes (Sures et al., 2023). 414 For example, ectoparasites are directly exposed to the external environment. Hence, their 415 tolerance to temperature and salinity changes, which might be lower than that of the host, is 416 directly affected by environmental changes. This is exemplified by aquaculture practices in 417 which salt or freshwater baths are employed to treat hosts infected with freshwater and marine 418 parasites (Buchmann, 2022). On the other hand, endoparasites are protected from the external 419 environment by the host, therefore experiencing stressors somewhat indirectly. For instance, 420 host osmoregulation mitigates external changes in salinity, maintaining an environment suited 421 for the development of endoparasites (Möller, 1978) . Nevertheless, endoparasites may be 422 exposed to the external environment at some stage of their lifecycle. Spores (microparasites), 423 eggs (macroparasites), and free-living larval stages (micro - and macroparasites) are de facto 424 challenged by environmental changes (Sures et al., 2023) . Hence, most multiple stressors 425 studies on endoparasites focus on their free -living stages. However, these stages are rather 426 resistant to temperature and salinity changes, and the survival of endoparasites is often 427 indirectly constrained by the host's tolerance to stressors, but not by the direct impact of 428 stressors (Möller, 1978; Rogowski and Stockwell, 2006). 429 Despite their high relev ance and multifaceted life strategies, it seems that parasites have 430 often been seen as an additional stressor on their hosts rather than a separate entity on which 431 multiple-stressor effects should be assessed. Consequently, studies dealing solely with the 432 combined effects of temperature and salinity on freshwater parasites are scarce. In the future, 433 (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprintthis version posted July 16, 2024. ; https://doi.org/10.1101/2024.07.11.603038doi: bioRxiv preprint 23 more resources should be allocated to investigate the impact of environmental stressors on both 434 parasites and their hosts. 435 4. Conclusions 436 The influence of salin ity changes on the thermal breadth of aquatic organisms has been 437 investigated mostly on brackish and marine species. Studies on freshwater species are required 438 to achieve a more comprehensive evaluation on the influence of salinization. Our meta-analysis 439 indicates salinity changes -elevated effects of thermal stress, supporting the hypothesis that 440 energetically expensive osmoregulation in response to salinity changes might lead to effects on 441 the thermal tolerance of aquatic organisms. The effect of salinity on the thermal tolerance 442 differed, but not, significantly among groups of organisms with the following order of 443 responsiveness: algae > invertebrates > fish. Furthermore, the infection status of the host should 444 be considered in further analyses as the ther mal tolerance of the host might be affected by 445 parasitism. 446

447 This study was performed within the Collaborative Research Centre 1439 RESIST 448 (Multilevel Response to Stressor Increase and Decrease in Stream Ecosystems; www.sfb -449 resist.de) funded by the Deutsche Forschungsgemeinschaft (DFG, German Research 450 Foundation; CRC 1439/1, project number: 426547801). 451

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