1. Introduction
As temperatures increase due to climate change, numerous organisms across various locations become closer to their upper thermal tolerance limits [1,2]. Extreme events like marine heatwaves and El Niño are happening more often and becoming stronger [3], which makes it more likely that temperatures will rise in some places to levels that could change ecosystems and processes like reproduction and growth during their occurrence [4]. Among marine species, most benthic marine invertebrates tend to be more susceptible to regional changes in temperature due to their inability to escape unfavorable conditions [2]. Their ability to deal with stress depends on their coping mechanisms (phenotypic plasticity), which could include metabolic depression, anaerobic energy production, and stress protection systems [5].
Phenotypic plasticity depends on the evolutionary background of populations (genetic adaptation) and the ecological experiences of individuals [6,7,8]. The contributions of these two components could significantly impact the future trajectories of populations and species. Grasping these factors is essential for predicting their vulnerability to different climate change scenarios. Even though it frequently presents significant challenges, a commonly employed method is the use of common garden experiments, which involves raising descendants from various populations (genetic) under uniform conditions (ecological). Moreover, to understand how organisms respond to environmental extremes and fluctuating temperatures linked to the ongoing climate change, experimental designs based on organisms exposed to constant temperatures are frequently insufficient [9,10,11,12], as determinations can reveal performance differences as compared to fluctuating temperatures such as daily oscillations [13,14]. Some evidence also suggests that different types of variability, such as regular oscillations vs. stochastic, unpredictable variability, may cause different responses in experimental organisms, even if the average and ranges of temperature are the same [13,14].
This study examines the two previously described issues by analyzing organisms from different populations (genetic component) that have grown in the same environment (ecological history) and are exposed to various temperature challenges. For these experiments, we used the lion’s paw scallop, Nodipecten subnodosus (Sowerby, 1835), as a study model, given that its management in laboratory and basic culture conditions is relatively well known [15].
The Giant Lion’s Paw, or lion’s paw scallop, is a highly appreciated fish product distributed along the Eastern Central Pacific, from Peru to Baja California, Mexico, where it sustained a regionally important artisanal fishery between 1990 and 2010 [16]. The Baja California fishery collapsed due to yet formally unknown reasons, and capture remains banned to this day. Within northwest Mexico, wild populations occur inside the major bays along the Baja California Peninsula, with genetic differences between those from the west (open Pacific Ocean) and east (Gulf of California) coasts [17]. As well as having different genes, organisms from both coasts have been shown to have different survival rates, growth rates, and heat tolerance in crossover field experiments [18,19]. Data suggests that the Gulf of California populations, which are exposed to higher temperatures and temperature variability amplitudes, are less vulnerable to abnormal warming events than those from the west coast. However, the experiments did not specifically assess the relative contribution of genetic adaptation and environmental conditions, nor did they look at how different modes of temperature variability affected the organism’s performance.
2. Results
Only 2 of the 18 raw variables were normally distributed. After transformations, 12 variables were normally distributed and had homogenous variances (Table 1). An ANOVA for those 12 variables showed that the source of the population did not have a significant effect on any of the response variables, but the thermal regime (oscillatory, stochastic, and constant) did have a significant effect on eight variables, including the rates of respiration, ingestion, and assimilation; the scope for growth; the carbohydrate and total lipid levels in the muscle; and the oxidative stress indicators SOD and CAT in the muscle. The hyperthermia challenge also significantly affected the respiration rate, the scope for growth, and the levels of carbohydrates, total lipids, and CAT in the muscle. Finally, significant interactions were also obtained between the population and the thermal variability regime for SOD in the muscle and between the thermal variability regime and hyperthermia challenge for carbohydrates and CAT in the muscle. Regarding the variables that did not meet the ANOVA assumptions, Kruskal–Wallis analysis revealed a significant effect of the population only for carbohydrates in the mantle, while the thermal variability regime effect was significant in carbohydrates in the mantle, GPx in the muscle and mantle, and TBARS in the muscle, and the thermal challenge on TBARS in the muscle and CAT in the mantle (Table 2).
The post hoc pairwise comparisons, following ANOVA (Tukey’s HSD) or Kruskal–Wallis (Dunn), revealed significant differences between both variable thermal conditions (regular and stochastic) and the control (constant temperature) for ingestion and assimilation rates, the scope for growth, and carbohydrates, total lipids, SOD, CAT, GPx, and TBARS in the muscle, as well as carbohydrates and GPx in the mantle. On the other hand, the comparison of the two thermal variable regimes (RO and ST) showed significant differences only in the respiration rate, carbohydrates in the muscle and in the mantle, and GPx in the mantle (Table 3). However, as indicated previously by a significant interaction for SOD in the muscle (Table 2), the effect of thermal variability regime was significant only for the BLA population. For the acute hyperthermia challenge, significant differences were observed for the respiration rate, scope for growth, carbohydrates, total lipids, TBARS in the muscle, and CAT in the muscle and mantle (Table 3), although the interaction indicated that this effect was significant only for stochastic oscillation in the case of carbohydrates and for constant temperature in the case of CAT in the muscle (see also Supplementary Figure S1).
Figure 1 depicts the magnitude of the statistically significant differences in means between groups, as percentage, with panel a including the variables analyzed through ANOVA (Tukey’s HSD) and panel b those analyzed through Kruskal–Wallis (Dunn). For panel a (parametric), the major differences for all variables, except for the respiration rate, occur between the regular oscillation and the constant temperature, being positive (bigger in the regular oscillation than in the constant temperature) for all except total lipids, where it is negative. The comparison of stochastic variability and the constant temperature follows the same pattern but with a slightly smaller magnitude. For the non-parametric groups, the largest differences also occur between both variability regimes (regular and stochastic) and the constant temperature, being large positive values in TBARS and GPx in the muscle and small negative values in carbohydrates and GPx in the mantle (only for regular oscillation vs. constant). In contrast, the comparison between the regular and stochastic regimes shows only small negative values for the respiration rate and carbohydrates in the mantle, and positive values for carbohydrates in the muscle and GPx in the mantle. The acute hyperthermia challenge shows a relatively modest positive effect in the respiration rate, total lipids, CAT, and TBARS in the muscle, and negative for scope for growth, carbohydrates in the muscle, and CAT in the mantle.
3. Discussion
In this study, young lion’s paw scallops from different genetic backgrounds, but raised in the same environment (common garden approach [20]), were exposed to different temperature regimes for 15 days and then exposed to hyperthermia for 17 h. The responses were measured through metabolic rates, biochemical composition of tissues, and proxies of oxidative stress to explore three main questions: How adaptive evolution and environmental growing conditions influenced the animal’s responses. How the thermal regime (constant, regular oscillations, and stochastic variability) affected the organism’s performance. How well the groups responded to an acute hyperthermia challenge after having been exposed to the different thermal regimes.
Regarding the first question, we found that most traits were unaffected by the geographical origin of the broodstock from which the experimental organisms were produced (called “population” for practical purposes in this work).
Prior studies indicated that mitochondrial DNA analyses reveal that the Ojo de Liebre group is genetically distinct from scallops found in various locations within the Gulf of California, including Bahía de Los Angeles, likely due to approximately 5 million years of geographical isolation, coinciding with the formation of the Gulf of California [17]. At the ecological time scale, laboratory-determined optimal and critical temperatures point to the idea that different growth performance of Pacific and Gulf of California lion’s paw scallop populations might be influenced by differences in their environments, especially because the gulf is warmer and has a stronger seasonal amplitude [21]. Moreover, on-site aquaculture studies comparing both populations and their reciprocal transplants indicate a higher performance of the Gulf of California population in terms of growth and survival [18] and net growth efficiency [19]. Together, this evidence suggests that fitness differences among populations are linked to their evolutive history.
In contrast, we present evidence that, based on our experimental design, there is no distinction across populations in 17 out of 18 assessed variables, which we take as indicative of the absence of evolutionary changes in physiological performance. We contend that, as acknowledged by the authors of previous field studies granting the gulf population a relative superiority over the Pacific one in terms of growth [18] or energy conservation [19], food availability could be playing a major role in shaping the performance differences. Similar results of an apparent higher adaptive capacity of a Gulf of California scallop population with respect to the Pacific coast have been reported for the Pacific calico, or catarina scallop (Argopecten ventricosus), and attributed to the combination of thermal stress, due to higher absolute temperatures and higher variability, as well as lower food availability [22,23]. Indeed, the major differences in several traits (growth, survival, SFG, and reproductive performance) between Pacific and Gulf of California populations of N. subnodosus [18,19] and A. ventricosus [22,23] were observed when grown at the gulf sites, where unfavorable thermal conditions were combined with limited food.
In our laboratory-based study, food was provided ad libitum and never affected the performance, which we believe allowed us to better detect the lack of evolutionary differences in temperature-driven performance.
A second important observation, associated with how the thermal regime affected the performance, was the strong effect of the temperature variability with respect to the constant temperature, independently of the variability pattern (regular or stochastic). Previous studies in marine organisms, including the California mussel Mytilus californianus [14], the common starfish Asteria rubens, the Brush-clawed crab Hemigrapsus takanoi [24], the Atlantic salmon (Salmo salar), and the Labontan Cutthroat Trout (Oncorhynchus clarki henshawi; [10], have already shown that fluctuating thermal regimes influence growth, feeding activity, thermal tolerance, and the overall physiological performance, although comparisons are typically carried out between fluctuating and constant regimes. In our case, only four indicators showed significant differences between the regular and the stochastic variability challenges, including a 5% higher RR for the stochastic variability as compared to the regular oscillation regime and, also, modest differences in carbohydrates in the muscle and mantle, and GPx in the mantle. By far, the major differences occurred between both modes of temperature variability and the constant temperature. Compared to constant conditions, ectotherms exposed to variable thermal regimes may grow and survive more readily [25] and acquire wider physiological plasticity [26]. We observed that the free energy beyond the basal maintenance, as indicated by the SFG, was higher in individuals exposed to variable temperatures than in those maintained at constant conditions, indicating that thermal variability is a more favorable condition than constant temperature. Our results are supported by a previous report by Sicard-González [27], who measured a 15% to 20% higher SFG in organisms under an oscillatory temperature regime than those under constant conditions. However, while Sicard-González [27] found that the higher SFG under variable temperature was associated with a 10% higher RR, we found no significant difference in the RR and, instead, we associate the increase in SFG solely with increased feeding and digestive/absorption efficiency. Long-term experiments are needed, however, to assess if the real growth rate is enhanced by diel temperature variation.
In addition to the energy balance, the levels of reserves were also affected, as revealed by higher levels of carbohydrates and lower levels of lipids in the muscle of scallops exposed to both thermal variability regimes. The higher carbohydrate content probably resulted from the higher food assimilation. In contrast, the decreased concentration of muscle lipids, which are mainly represented by structural phospholipids and only to a minor extent by energy-reserve triglycerides [28,29], could be more related to membrane homeoviscous compensatory mechanisms involving a structural rearrangement of phospholipid and fatty acid classes [30,31,32]. Further research measuring intra- and inter-tissular lipid turnover and comparing constant conditions with thermal variability in addition to decreasing and increasing temperatures may shed light on this mechanism.
A seemingly contradictory observation comes from the increased antioxidant enzyme activity (SOD, CAT and GPx) and oxidative stress levels (TBARS) in the muscle under both regular oscillation (RO) and stochastic (ST) thermal regimes, compared to those maintained at constant temperature (CT). In particular, TBARS indicates that the production of reactive oxidative species (ROS) under thermal variability was not neutralized by endogenous antioxidant capacity, as lipid damage was observed at the end of the 15-day exposure to the thermal regimes. Several studies on bivalves have shown that gradual or abrupt changes in water temperature induce oxidative stress responses and antioxidant enzyme activity or RNA expression [33,34,35]. Based on survival rates and oxidative stress indicators in hemocytes, Rhaman et al. [36] reported that organisms less exposed to temperature shifts, such as the subtidal Mediterranean mussel (Mytilus galloprovincialis) and the Ridged Venus sand-buried cokle (Katelysia rhytiphora), showed higher thermal tolerance, with antioxidant enzyme activity responding to higher stress thresholds, than the intertidal oyster (Crassostrea gigas). Given the ecological niche of Nodipecten subnodosus in relatively deep waters [18], it is not surprising that high temperature variability affected the experimental organisms. In this sense, the 8 °C diel variability may signify a level of fluctuation that the species rarely experience in natural environments, despite its common occurrence in shallower aquaculture systems. Thus, further studies are needed to explore the influence of daily thermal variability of different amplitudes related to climate change scenarios. In addition, the studies previously referred to used constant high-temperature exposure for a maximum of 35 days [33]. Long-term experimental simulations of different scenarios of diel temperature variation are thus needed to determine whether oxidative stress is ultimately compensated for or leads to depressed biological performance in terms of resistance to pathogens, growth, and reproduction. Such further studies should extend to reproductive and larval stages, as spawning occurs in the warm season [37], summer mortalities may occur during El Niño events [18], and acute hyperthermia induces a higher energetic cost in late than in early gonad development [38].
Although we observed no significant differences in the whole organism RR between variable and constant thermal regimes at the end of the 15-day trial, it seems likely that the frequent exposure to stressing thermal conditions occurring daily under both regular and stochastic variability regimes caused increases the oxygen consumption at the tissular level, especially in metabolically active tissues such as muscle, resulting in peaks in ROS production. Similar tissue-specific responses to those observed in our experiment (muscle but not mantle) have already been reported in previous studies [34], occurring not only after temperature increases but also after it decreases [35], both replicated in our variable temperature regimes.
Based on metabolic balance and tissular composition indicators, we posit that, overall, the performance of the lions’ paw scallop is better under variable temperature than under constant thermal conditions, although oxidative stress is present. During the 15 days of exposure, temperature periodically reached temperatures of 17 and 25 °C [39], which are probably closer to the pejus levels (i.e., beyond the optimum, when performance declines but before reaching critical thresholds where aerobic metabolism collapses [40]) than to the physiological optima. We propose that further studies where oxidative stress activity is evaluated under a narrower, less extreme thermal variability range, and for longer periods than that of our trial, are necessary to ultimately prove that variable thermal conditions are better for the species than staying constant near the physiological optimum.
Regarding the response to an acute hyperthermia challenge after having been exposed to different thermal regimes, we observed a significant effect of the hyperthermia challenge in seven of the indicators, but there was an interactive effect of the hyperthermia challenge and the thermal regime from which the organisms where previously exposed only in two variables, suggesting that the acclimation conditions did not substantially affect the response capacity to acute hyperthermia.
An increase in the RR was observed after exposure to hyperthermia, as previously documented after a thermal increase ramp experiment for the same species [41] and for other marine ectotherms (see [42] and the references therein). Likely, this change in the RR caused a concomitant decrease in the SFG, which fits with the general assumption that energy is reallocated from conservation to compensation (i.e., growth vs. maintenance) when individuals are facing a stressful condition, such as pejus temperature [43]. Although it is difficult to extrapolate SFG to real growth within such a short period of time (17 h), 28 °C is clearly considered within the pejus interval of the temperature range for the species [21].
The observed increase in metabolism points to enhanced catabolic pathway activity to produce energy, which coincides with the observed use of carbohydrate reserves. However, this compensates only partially for the ATP, energy charge, and arginine phosphate decreases that occur under hyperthermia [41,44].
We observed an increase in total lipid levels in the muscle after an acute exposure to hyperthermia. Marine invertebrates have been shown to synthesize lipids de novo (lipogenesis) from carbohydrates, adjusting fatty acid profiles in response to environmental temperature shifts [30,32,45]. This process supports the formation of both membrane and storage lipids and is considered an adaptive trait in ectothermic species due to their thermal sensitivity. Our findings in juvenile N. subnudosus under hyperthermic conditions corroborate this, revealing a significantly greater increase in total muscle lipids. Yoon et al. [45] suggest that glucose for this de novo lipid synthesis is derived from stored carbohydrates in the same tissue, which is also consistent with our observation of an approximate 25% reduction in carbohydrate levels in the muscle. Another potential explanation involves the mobilization of lipids from the digestive gland, as a lipid reserve tissue [46], to the muscle, satisfying its metabolic demand. Unfortunately, lipid classes and fatty acids across different tissues, which could help at documenting such mechanisms, were not measured in the present work.
Under heat stress conditions, as a direct consequence of increased tissue oxygen consumption and enhanced ATP production to meet heightened energy demands, the observed changes in CAT in the muscle, together with a significant increase in TBARS, are in line with the increase in ROS and consequent uncompensated lipid damage produced by an acute increase in temperature [33,34,35]. This also aligns with the findings in A. irradians [47], where increased enzyme activity used to mitigate stress proves insufficient to reduce H2O2 at high temperatures, revealing a limit to the antioxidant capacity. A significant interaction for hyperthermia and the thermal regime was registered for CAT in muscle but was apparently mostly linked to scallops coming from the constant temperature regime, a pattern also observed for TBARS in the muscle (Supplementary Figure S1). In other words, under baseline conditions of constant temperature, oxidative stress is minimal but increased with exposure to hyperthermia, while in organisms coming from temperature-variable regimes, an effect of acclimation appears to exist, which is known to reduce thermal sensitivity [48].
Several questions arising from our study warrant further investigation. For example, although we compared descendants from two populations and interpreted the lack of differences in performance proxies as indicative of no evolutionary divergence in physiological performance, additional steps could strengthen this conclusion. Specifically, replicating the experiment with wild individuals would complete the common garden approach. If significant differences are detected between populations in wild individuals but not in their descendants, it would further support the hypothesis that an individual’s physiological response capacity is primarily shaped by its environmental history (i.e., phenotypic plasticity), rather than by evolutionary adaptations to temperature variability.
Moreover, incorporating additional physiological indicators would provide a more comprehensive understanding of the mechanisms involved. While we measured a relatively broad set of metabolic and stress-related variables, transcriptomic analyses could offer deeper insights. Notably, we preserved samples from the original experiment, which we plan to process for transcriptomic profiling in future work.
Another important consideration is the temporal scale of our experiment. The design was aimed at detecting physiological responses over a relatively short duration. Although a 15-day exposure is generally sufficient to achieve metabolic stabilization and yield reliable physiological responses, it may not capture longer-term acclimation dynamics, recovery processes, or chronic stress effects associated with natural warming phenomena, such as El Niño events (lasting several months to years) or marine heatwaves (usually weeks to months). Long-duration experiments would help reveal additional response mechanisms beyond those identified here and enable the use of performance indicators not suited to short-term studies—such as growth rates or reproductive output across life stages. The latter warrants special attention, as no studies have been conducted on this species, other from the documentation of summer mortalities coinciding with the spawning season [18] and the synergistic impact of reproductive effort and short-term hyperthermia on cellular energetics [38]. In other species, such as Octopus maya, high temperatures impair reproductive output by producing eggs with less yolk, resulting in lower-sized hatchlings that are more susceptibility to thermal stress [49]. Moreover, the impact of hyperthermia on the next generation seems to be attributed to a direct effect, such as general lower viability of eggs and ROS in embryos inherited form females during yolk synthesis [50] or through an epigenetic mechanism [51].
Finally, it is important to recognize that in natural settings, temperature rarely acts in isolation. It often interacts with other environmental drivers—such as oxygen availability, food quantity and quality, and pH—in complex ways, including additive, synergistic, antagonistic, or conditional interactions. Recreating such multifactorial scenarios experimentally is technically demanding and resource-intensive. However, by applying efficient experimental design frameworks such as MEDDLE [52], we believe it is possible to progressively unravel these interactions and gain a deeper understanding of organismal responses under realistic environmental conditions.
4. Materials and Methods
4.1. Source Organisms and Experimental Design
To separate the effect of the growth site conditions from the genetic factor (source population) on individual performance, this study used juvenile organisms of N. subnodosus born in the laboratory as the offspring of adults from two genetically different populations [17] that were then raised in the same environment.
The breeders came from Bahía de Los Angeles (BLA; 28°57′17.867″ N, 113°33′24.039″ W), which is located in the Gulf of California, where the average temperature is 22.7 °C, with a historical range of 12.8 to 32.3 °C, and Laguna Ojo de Liebre (LOL; 27°42′10.871″ N, 114°10′28.937″ W), on the Pacific coast, where the mean temperature is 19.4 °C and the thermal range spans from 13.1 to 26.4 °C [53]; Figure 2). Adult scallops were transported to the Northwestern Biological Research Center (CIBNOR) facilities in La Paz, Mexico, where they were maintained under controlled laboratory conditions to promote spawning induction and subsequent fertilization, adhering to established protocols [54].
After 142 days in the lab, the 3–5 mm larvae from both source populations were taken to Laguna Ojo de Liebre and kept in a suspended culture system (less than 2 m depth) for seven months until their shells were about 40 mm long. Thereafter, they were sent back to the lab for the acclimation phase. All juvenile individuals were maintained for 15 days in the laboratory at a constant temperature of 21 ± 1 °C and a photoperiod of 12:12 (light/dark). During this period, the scallops were fed a microalgal diet consisting of Isochrysis galbana and Chaetoceros calcitrans at a concentration of 150,000 cells mL−1. Daily monitoring of acclimation parameters encompassed food intake, measured via a particle counter (Multisizer, Beckman, Brea, CA, USA), water salinity (35–36 ppm; Extech Instruments, Waltham, MA, USA), pO2 (90% air saturation; Microx TX2, Presens, Regensburg, Germany), and temperature (HOBO data logger Onset UA-002-64, Bourne, MA, USA). No mortality was registered during the acclimation period.
For the bioassay phase after the acclimation, the organisms were segregated into experimental groups and subjected to different thermal variability regimes, designed according to the approximate mean and variability range of the sea surface temperature observed in the field. One group was exposed to regular circadian thermal oscillations (RO), averaging 21.05 °C and ranging from 17 °C to 25 °C. The second group was exposed to an unpredictable, stochastically variable regime (ST), also averaging 20.94 °C and ranging from 17 °C to 25 °C. The third group was maintained as a control at a constant temperature (21 ± 2.87 °C).
For segregation, animals from each population source were randomly distributed into three replicates per thermal variability regime and placed in 140 L experimental tanks (N = 10 per tank, for a total of nine replicated tanks, three per thermal regime, by two population sources) for 15 days (a period long enough to detect physiological responses [39,55,56]). After the thermal variability regime treatment, the temperature was raised from 21 °C to 28 °C at a rate of 1.75 °C per hour and then kept at 28 °C for 17 h as an acute hyperthermia challenge (see Supplementary Figure S2). According to personal observations (analysis not included), the 1.75 °C per hour warming rate used for the hyperthermia challenge occurs in the field with a relatively high frequency (9% of the time). An automated system made up of previously standardized computer-controlled heaters and coolers was used to reach the desired temperature conditions for thermal variability and the hyperthermia challenges. Temperature, salinity, dissolved oxygen, and survival were monitored continuously during the entire experiment.
Determinations occurred firstly at the end of the 15-day exposure and secondly after the 17 h exposure to the acute TC. A total of 120 individuals were used for physiological rate determinations: 10 for each thermal variability regimen (3 to 4 per replicate) at the end of the 15 days and 10 at the end of the TC, for each population (Supplementary Figure S2). After the physiological determinations, animals were dissected, and samples of each tissue, mantle and muscle, from each organism, were frozen at −80 °C until their analysis.
4.2. Energy Balance and Scope for Growth
Experimental individuals were placed in 300 mL tightly sealed glass incubation chambers mounted in a water bath inside 0.6 m2 tanks at a controlled temperature of 21 °C and incubated for two hours before measurements. Each chamber was connected to a microvalve to maintain an 80 ± 2 mL constant inflow of oxygen-saturated water at 21 °C with a food concentration of 300,000 cells mL−1 C. calcitrans. Outflow was used for dissolved oxygen (DO) and ammonia determination and then discharged. One chamber per treatment was maintained without an organism as a control to correct for microbial metabolic activity.
Using a Microx TX2 oximeter (PreSens, Regensburg, Germany) inside a T-cell placed in the water outflow of each chamber, the concentration of DO was measured every two seconds for one minute. The readings were then averaged over time. We computed the respiration rate (RR) as follows:
RR = F × (DOblank − DOorg)/DTW(1)
where F is the water flow (L h−1), DOblank is the DO concentration from the blank chamber (without an organism), DOorg is the lecture from the chamber with an organism, and DTW is the dry tissue weight of the organism. DTW was obtained by the lyophilization (Telstar Cryodos Model 50 lyophilizer, Barcelona, Spain) of soft tissue and weighed on a precise analytical balance (Model XT 220A, Dietikon, Switzerland) with an accuracy of 0.01 g. The RR values (O2 g−1 h−1) were transformed to energy units (J g−1 h−1), based on the assumption that 1 mL of O2 equals 20.2 Joules [57].The ingestion rate (IR) was estimated as the difference between the food concentrations in the outflow water from the blank chamber and the chamber with the organism, per unit of time. To measure the concentration of food, two 20 mL replicated samples were taken at the outlet from each chamber, and the number of phytoplankton cells was measured with a cell counter, Beckman Multisizer 3. The IR, expressed as cel−1 h−1, was converted to energy units using particulate matter equivalent to 23,500 J g−1, considering a cell weight of C. calcitrans as 39 pg [58].
Absorption efficiency (AE) was estimated using the Conover [59] method, based on the ratio between organic and inorganic matter in feces. After two hours of incubation, feces were recovered from the chamber and filtered with Micro Kitasato equipment, equipped with 0.75 μm constant-weight glass fiber filters and a vacuum pump. Filters containing feces samples were treated with a 3% ammonium formate solution to remove salts and were then dried in a convection oven at 65 °C for 48 h until a constant weight was achieved. Organic matter content was determined by weight difference. The filters were then incinerated in a muffle furnace at 540 °C for 12 h to obtain a constant weight, and the ash-free dry weight was calculated by weight difference, following Sorokin’s method [60].
To estimate the excretion rate, three replicated subsamples of water outflow were collected at the end of the two-hour incubation, placed in 2 mL Eppendorf tubes, and frozen at −80 °C for later processing. The measurement was then performed using a microplate adaptation of the Solórzano et al. [61] method.
The scope for growth, which is a proxy of the energy in the organism in excess of basal metabolic needs, results from the following equation:
SFG = (I × AE) − (RR + ER)(2)
where SFG is the scope for growth, typically in units of energy per time, I is the ingestion rate (energy consumed per unit time), AE is the absorption efficiency (a proportion), RR is the respiration rate (used metabolic energy), and ER is the excretion rate, mainly as nitrogenous waste [62].4.3. Biochemical Analyses of Tissues
Muscle and mantle samples were lyophilized (TELSTAR Cryodos, Terrassa, Spain) at 0.05 mBar and −50 °C and ground to a fine powder with a ball mill mixer (MM400, Retsch, Haan, Germany). Approximately 0.02 g of dry tissue was rehydrated and homogenized in 1 mL of distilled water to obtain the working extract for the determination of biochemical components. For the quantification of total carbohydrates, 100 μL of the working extract was mixed with 100 μL of 20% trichloroacetic acid (TCA). The samples were centrifuged, and 25 μL of the supernatant was collected for each assay. Absorbance was measured at 630 nm. Total lipid content was determined following the methodology of Barnes [63], adapted for a microplate format. A 25 μL aliquot of the working extract was combined with 250 μL of concentrated H2SO4 and incubated in a water bath at 90 °C for 10 min. Subsequently, 20 μL of this reaction mixture was transferred to a microplate and mixed with 200 μL of phospho-vanillin reagent, and absorbance was measured at 540 nm. For total protein quantification, alkaline digestion was conducted using 10 μL of the working extract and 90 μL of 0.1 N NaOH. From this mixture, 10 μL was taken and analyzed using the bicinchoninic acid (BCA) method, adapted for microplate format. Absorbance was measured at 562 nm.
4.4. Oxidative Stress Indicators
Approximately 1 g of fresh tissue was homogenized in 0.6 mL of phosphate-buffered solution (50 mM, pH 7.5), EDTA (50 mM), and PMSF (1 mM). From the homogenates, the activities of three antioxidant enzymes, namely superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx), were evaluated, along with oxidative lipid damage assessed through the quantification of thiobarbituric acid reactive substances (TBARS). All measurements were performed in three technical replicates, and results were normalized to total protein content using the Bradford method [64].
SOD activity determination followed the method of Suzuki [65], using the Cayman kit (Cayman Chemical Company, Ann Arbor, MI, USA; Cat. No. 706002). The analysis uses the xanthine/xanthine oxidase system as a source of superoxide radicals (O2−), which reduce nitroblue tetrazolium (NBT) salt, producing detectable coloration. The presence of SOD inhibits this reaction, and absorbance is then measured at 560 nm using a Thermo Scientific Multiskan spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). One unit of SOD activity (U) was defined as the amount of enzyme required to inhibit 50% of the O2−-mediated reduction in NBT. Results were expressed as U mg−1 of protein.
CAT activity was determined according to Aebi [66], using the Cayman kit (ref. 707002). This method measures the consumption of hydrogen peroxide (H2O2) in a potassium phosphate buffer (50 mM, pH 7.0) and contains EDTA (0.5 mM), as described by Hermes-Lima & Storey [67]. Absorbance was recorded at 240 nm with a Thermo Scientific Multiskan spectrophotometer. One unit of CAT activity (U) was defined as the amount of enzyme catalyzing the decomposition of 1 µmol of H2O2 per minute at 25 °C.
GPx activity was quantified following the method of Flohé & Günzler [68], adapted for a microplate format. This assay measures the oxidation of NADPH to NADP⁺ in the presence of H2O2 as a substrate, resulting in a decrease in absorbance at 340 nm, which is directly proportional to GPx activity. Readings were taken every 60 s for 5 min using a Thermo Scientific Multiskan spectrophotometer. One unit of GPx activity (U) was defined as the amount of enzyme oxidizing 1 nmol of NADPH per minute at 25 °C. Results were expressed as U mg−1 of protein.
Oxidative lipid damage was evaluated by determining the content of thiobarbituric acid reactive substances (TBARS) [69], as adapted by Persky et al. [70]. Absorbance of the reaction products was measured at 535 nm using a Thermo Scientific Multiskan spectrophotometer. TBARS concentrations were calculated through a linear regression model from a standard curve (absorbance vs. concentration) using malondialdehyde (MDA) as the standard. Results were expressed as nmol of TBARS mg−1 of protein.
4.5. Statistical Analyses
The Anderson–Darling test was used to see if all the response variables were normally distributed (p > 0.05). If normality was not found, a routine was used to test different transformations (logarithmic, square root, and Box–Cox) until normality was found. Variables that proved not normal after the transformations were excluded from further parametric analyses. The homogeneity of variances’ assumption was tested through a Levene test and was met after data transformation. A three-way ANOVA was conducted, considering the population (the offspring groups from LOL and BLA), temperature variability regime (regular oscillation (RO), stochastic variability (ST), and constant temperature (C)), and the measurements before and after the acute hyperthermia challenge (hyperthermia) as factors, followed by a pairwise Tukey’s HST post hoc test to assess the significance of mean differences between groups (p < 0.05). For variables that did not meet the ANOVA requirements, separate Kruskal–Wallis tests were performed for each factor, followed by pairwise comparisons using Dunn’s test. All analyses were performed in the R environment [71]. Finally, for comparative purposes, all significant differences in means between groups were expressed as proportions:
% Change = (G1 − G2)/G2 × 100(3)
where G1 is the mean value of the first group being compared, and G2 is the mean value of the second group.5. Conclusions
The evidence presented in this report indicates that although the N. subnodosus populations from BLA and LOL are genetically independent, they exhibit similar responses, via phenotypic plasticity, to varying thermal regimes (constant, regular oscillations, and stochastic variability) and acute hyperthermia exposure. Multiple factors suggest that organisms display better performance under thermal variability compared to constant temperature conditions; however, the similar respiration rates among organisms subjected to various thermal regimes (constant, regular oscillations, and stochastic variability) imply that compensation was attained in every scenario by optimizing the utilization of oxygen and enhancing ingestion and assimilation rates. Our findings indicate that following an abrupt temperature rise, energy demand increases, and compensation is hindered; indicators of oxidative stress, elevated metabolism (increase in the respiration rate), depletion of total carbohydrates in the muscle, and a substantial reduction in free energy (scope for growth) suggest a condition of physiological maintenance and survival under stress.
Conceptualization, N.G.J.-M., S.E.L.-C. and I.S.R.; methodology, N.G.J.-M., D.P.C.-L. and S.A.U.; formal analysis, N.G.J.-M., S.E.L.-C. and I.S.R.; writing—original draft preparation, N.G.J.-M., S.E.L.-C. and I.S.R.; writing—review and editing, S.A.U. and D.P.C.-L.; funding acquisition, S.E.L.-C. All authors have read and agreed to the published version of the manuscript.
The dataset used in this study can be made available on request from the authors.
Technical support was provided by Rosa Isela Vázquez Sánchez, Armando Monge Quevedo, José Luis Ramírez Arce, Roberto Hernández Herrera, and Rosalinda Salgado García. Jorge Cobos Anaya and Felipe Javier Rosas were instrumental for ensuring the functioning of the experimental facilities.
The authors declare no conflicts of interest.
The following abbreviations are used in this manuscript:
| BLA | Bahía de Los Ángeles |
| LOL | Laguna Ojo de liebre |
| SFG | Scope for growth |
| RR | Respiration rate |
| AR | Absorption rate |
| IR | Ingestion rate |
| SOD | Superoxide dismutase |
| CAT | Catalase |
| GPx | Glutathione peroxidase |
| TBARS | Thiobarbituric acid reactive substances |
Footnotes
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Figure 1 Proportional differences in means between pairwise groups: regular oscillation—constant temperatures (orange); stochastic variability regime—constant temperature (light blue); regular oscillation—stochastic variability (dark blue); and post—pre hyperthermia challenge (gray). Change is expressed as % change relative to the first group being compared. In all cases, N = 10 for each population–thermal regime–thermal challenge combination.
Figure 2 Sites of origin of the N. subnodosus broodstock used in this study: Bahía de Los Ángeles (BLA; red) and Laguna Ojo de Liebre (LOL; blue).
Normality tests for raw and normality-transformed variables. Significant values are marked in bold italics (p-value > 0.05, N = 120).
| Variable | p Value | Transformation | p Value |
|---|---|---|---|
| Respiration rate | 0.000 | Log | 0.907 |
| Ingestion rate | 0.035 | Square Root | 0.107 |
| Assimilation rate | 0.036 | Square Root | 0.266 |
| Scope for growth | 0.005 | Square Root | 0.158 |
| Carbohydrates in muscle | 0.054 | None | 0.054 |
| Total proteins in muscle | 0.247 | None | 0.247 |
| Total lipids in muscle | 0.000 | Log | 0.060 |
| Carbohydrates in mantle | 0.000 | None | 0.000 |
| Total proteins in mantle | 0.000 | Log | 0.148 |
| Total lipids in mantle | 0.000 | Log | 0.534 |
| SOD in muscle | 0.003 | Log | 0.170 |
| CAT in muscle | 0.000 | Log | 0.309 |
| GPx in muscle | 0.000 | None | 0.000 |
| TBARS in muscle | 0.000 | None | 0.000 |
| SOD in mantle | 0.006 | Square Root | 0.358 |
| CAT in mantle | 0.000 | None | 0.000 |
| GPx in mantle | 0.000 | None | 0.000 |
| TBARS in mantle | 0.000 | None | 0.000 |
Results from the ANOVA (parametric variables) and Kruskal–Wallis test (non-parametric variables). Significant responses (p < 0.05) are marked in bold italics. In all cases, N = 10 for each population–thermal regime (T regime)–thermal challenge (HT) combination.
| ANOVA | Population | T Regime | HT | Population T Regime | Population HT | T Regime HT | Population T Regime HT |
|---|---|---|---|---|---|---|---|
| Respiration rate | 0.383 | 0.018 | 0.000 | 0.470 | 0.614 | 0.633 | 0.572 |
| Ingestion rate | 0.420 | 0.000 | 0.266 | 0.089 | 0.326 | 0.443 | 0.124 |
| Assimilation rate | 0.345 | 0.000 | 0.214 | 0.119 | 0.630 | 0.859 | 0.050 |
| Scope for growth | 0.272 | 0.000 | 0.040 | 0.143 | 0.538 | 0.830 | 0.070 |
| Carbohydrates in muscle | 0.541 | 0.000 | 0.000 | 0.826 | 0.634 | 0.004 | 0.164 |
| Total proteins in muscle | 0.664 | 0.329 | 0.417 | 0.454 | 0.911 | 0.898 | 0.864 |
| Total lipids in muscle | 0.402 | 0.000 | 0.008 | 0.537 | 0.905 | 0.058 | 0.571 |
| Total proteins in mantle | 0.920 | 0.347 | 0.253 | 0.678 | 0.830 | 0.995 | 0.330 |
| Total lipids in mantle | 0.072 | 0.341 | 0.311 | 0.326 | 0.326 | 0.990 | 0.161 |
| SOD in muscle | 0.567 | 0.000 | 0.161 | 0.038 | 0.739 | 0.372 | 0.958 |
| CAT in muscle | 0.918 | 0.009 | 0.003 | 0.400 | 0.884 | 0.000 | 0.768 |
| SOD in mantle | 0.425 | 0.103 | 0.068 | 0.698 | 0.452 | 0.162 | 0.299 |
| Kruskal-Wallis | |||||||
| Carbohydrates in mantle | 0.003 | 0.003 | 0.854 | ||||
| GPx in muscle | 0.832 | 0.000 | 0.967 | ||||
| TBARS in muscle | 0.657 | 0.000 | 0.003 | ||||
| CAT in mantle | 0.613 | 0.784 | 0.025 | ||||
| GPx in mantle | 0.935 | 0.000 | 0.607 | ||||
| TBARS in mantle | 0.852 | 0.684 | 0.556 |
Post hoc analyses for groups with significant effects. Significant (p < 0.05) differences in means for Tukey’s HDS test (parametric variables) and Dunns’s test (non-parametric variables) are marked in bold italics. In all cases, N = 10 for each population–thermal regime (T regime)–thermal challenge (HT) combination.
| Tukey’s HSD Test | OR-Control | SR-Control | RO-ST | HT-T Regime |
|---|---|---|---|---|
| Respiration rate | 0.806 | 0.109 | 0.020 | 0.000 |
| Ingestion rate | 0.000 | 0.005 | 0.437 | 0.270 |
| Assimilation rate | 0.000 | 0.004 | 0.373 | 0.217 |
| Scope for growth | 0.000 | 0.007 | 0.286 | 0.042 |
| Carbohydrates in muscle | 0.000 | 0.000 | 0.001 | 0.000 |
| Total lipids in muscle | 0.000 | 0.000 | 0.168 | 0.008 |
| SOD in muscle | 0.000 | 0.000 | 0.310 | 0.352 |
| CAT in muscle | 0.019 | 0.045 | 0.881 | 0.004 |
| Dunn’s tests | ||||
| Carbohydrates in mantle | 0.004 | 1.000 | 0.004 | 0.427 |
| GPx in muscle | 0.000 | 0.003 | 0.363 | 0.483 |
| TBARS in muscle | 0.000 | 0.000 | 0.488 | 0.002 |
| CAT in mantle | 0.975 | 0.736 | 1.000 | 0.013 |
| GPx in mantle | 0.000 | 0.501 | 0.004 | 0.304 |
| TBARS in mantle | 0.750 | 1.000 | 0.617 | 0.278 |
Supplementary Materials
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; Racotta Ilie S. 2
; Carreño-León, Diana P 2
; Ulaje, Sergio A 3
; Lluch-Cota, Salvador E 4
1 Programa de Posgrado, Centro de Investigaciones Biológicas del Noroeste, S.C., La Paz 23096, Mexico; [email protected]
2 Programa de Acuicultura, Centro de Investigaciones Biológicas del Noroeste, S.C., La Paz 23096, Mexico; [email protected] (I.S.R.); [email protected] (D.P.C.-L.)
3 Departamento Académico de Ingeniería en Pesquerías, Universidad Autónoma de Baja California Sur, La Paz 23085, Mexico; [email protected]
4 Programa de Ecología Pesquera, Centro de Investigaciones Biológicas del Noroeste, S.C., La Paz 23096, Mexico