Changing ocean temperatures are predicted to challenge marine organisms, especially when combined with other factors, such as ocean acidification. Acclimation, as a form of phenotypic plasticity, can moderate the consequences of changing environments for biota. Our understanding of how altered temperature and acidification together influence species’ acclimation responses is, however, limited compared with that of responses to single stressors. This study investigated how temperature and acidification affect the thermal tolerance and righting speed of the girdled dogwhelk, Trochia cingulata. Whelks were acclimated for 2 weeks to combinations of three temperatures (11°C: cold, 13°C: moderate and 15°C: warm) and two pH regimes (8.0: moderate and 7.5: acidic). We measured the temperature sensitivity of the righting response by generating thermal performance curves from individual data collected at seven test temperatures and determined critical thermal minima (CTmin) and maxima (CTmax). We found that T. cingulata has a broad basal thermal tolerance range (∼38°C) and after acclimation to the warm temperature regime, both the optimal temperature for maximum righting speed and CTmax increased. Contrary to predictions, acidification did not narrow this population's thermal tolerance but increased CTmax. These plastic responses are likely driven by the predictable exposure to temperature extremes measured in the field which originate from the local tidal cycle and the periodic acidification associated with ocean upwelling in the region. This acclimation ability suggests that T. cingulata has at least some capacity to buffer the thermal changes and increased acidification predicted to occur with climate change.

As the climate changes, marine species are predicted to experience higher thermal stress than terrestrial species, mostly as a result of narrower thermal safety margins and a lack of access to thermal refuges (Pinsky et al., 2019). Most marine species (∼93%) included in the analysis of Pinsky et al. (2019) were demersal (Richardson and Schoeman, 2019) but the effects of climate change on coastal ecosystems, which are becoming increasingly apparent, are likely to differ from those in the deeper, open ocean (Doney et al., 2012; Poloczanska et al., 2013; He and Silliman, 2019). There is also a need to study coastal species from southern Africa, where data are typically limited (Chambers et al., 2017) and unique climate trends are becoming apparent (Diffenbaugh and Giorgi, 2012). For example, despite the predicted global increase in ocean temperature (Pörtner et al., 2019), the west coast of South Africa is cooling as a result of intensified upwelling (Rouault et al., 2010; Lamont et al., 2018).

The ability to adjust physiology through evolutionary adaptation and phenotypic plasticity can moderate the consequences of changing temperatures (Armstrong et al., 2019). Thermal acclimation, typically defined as a form of reversible phenotypic plasticity that can alter trait responses following exposure to a particular temperature regime, can enable rapid responses of organisms to environmental change (Armstrong et al., 2019; Zhang et al., 2021). Thermal acclimation has been examined in a diversity of marine taxa and studies suggest that acclimatory responses can enhance survival when facing temperature variation associated with future warming scenarios (Wang et al., 2018; Leung et al., 2021). However, the magnitude and direction of responses to warm and cold acclimations vary within and between species (Kelly et al., 2012; Calosi et al., 2013; Tepolt and Somero, 2014; Gunderson and Stillman, 2015). Furthermore, climate change-related stressors include variables other than temperature, such as partial pressure of CO2 (PCO2), salinity and dissolved oxygen (Doney et al., 2012; Freitas et al., 2017; Leary et al., 2017). Investigating how these variables influence the performance of organisms in interactive ways can thus provide a more accurate indication of species' vulnerability to climate change (Breitburg et al., 2015; Gaylord et al., 2015; Buckley and Kingsolver, 2021).

Generally, simultaneous changes in temperature and increased dissolved CO2 (i.e. acidification) are predicted to narrow thermal tolerance as a result of combined effects increasing the sensitivity of organisms to temperature change (Minuti et al., 2021). Acidification can disrupt acid–base regulation by marine ectotherms, thereby lowering extracellular pH (pHe) and leading to protein degradation (Michaelidis et al., 2005; Parker et al., 2017). Acidification can also interfere with cellular pathways and energy turnover, depressing aerobic metabolism and activating anaerobic metabolism (Hofmann and Todgham, 2010; Francis Pan et al., 2015; Lefevre, 2016; Freitas et al., 2017). Thus, if a marine ectotherm is experiencing physiological stress, from either acidification or temperature, the resultant disruption in homeostasis, increased metabolic costs and limited aerobic scope may limit its physiological capacity to respond to additional stressors (Lanning et al., 2010; Melzner et al., 2013; Breitburg et al., 2015). For example, when reared in acidic conditions, the combination of reduced pHe, ventilation and circulatory capacities, and metabolic rate narrows the thermal tolerance of the Sydney rock oyster, Saccostrea glomerata (Parker et al., 2017). Similarly, sea urchin larvae expression of Hsp70 and abalone larvae survival in response to acute thermal stress are greatly reduced after an acidic rearing regime (O'Donnell et al., 2009; Zippay and Hofmann, 2010). In crustaceans, prior exposure to acidic conditions results in reduced arterial oxygen partial pressure, reduced Arrhenius breakpoint temperatures (the point at which performance rate sharply declines after increasing; Schulte, 2015), increased thermal sensitivity of cardiac function (Q10), and narrowed thermal tolerance windows by 1–5°C (Metzger et al., 2007; Walther et al., 2009; Harrington and Hamlin, 2019). In the maculated top shell, Trochus maculatus, the temperature of maximum metabolic rate increases in response to warm temperature acclimation. This response is maintained when the gastropod is acclimated to warm acidic conditions but the upper lethal temperature declines (Minuti et al., 2021). Similarly, the thermal tolerance and Hsp70 levels of the Chilean abalone, Concholepas concholepas, are negatively affected by the interactive effects of warming and acidification, while acclimation to cold-acidic conditions increases this species' thermal tolerance (Manríquez et al., 2020). However, although warming and acidification can have negative and synergistic effects, as these examples illustrate, some species have physiological and performance traits that are minimally affected by exposure to combined stressors or even show antagonistic responses (e.g. Lefevre et al., 2015; Gunderson and Leal, 2016; Stevens and Gobler, 2018). Several reviews highlight the context, trait and species dependency of these responses (Byrne and Przeslawski, 2013; Gunderson and Leal, 2016; Lefevre, 2016).

Coastal habitats are dynamic systems. During high tide, much of the intertidal environment is submerged and, because of the high thermal conductivity of water, temperature variation tends to be minimal (Helmuth et al., 2006). During low tide, however, organisms can be exposed to air (emersion) and generally experience greater thermal stress as a result of the wider range and fluctuations of air temperature. Importantly, the timing and duration of emersion are determined by the tidal cycle. For example, during neap tides, tidal variation is very moderate and so emersion periods tend to be short and only affect organisms located in the high intertidal zone. The most extreme tides occur during spring tides where even the low intertidal zone can be exposed and organisms are emersed for longer periods. This thermal heterogeneity can drive plasticity in intertidal organisms (Tomanek, 2010; Li et al., 2018), especially if temperature variation is predictable (Leung et al., 2020a; Barley et al., 2021). Predictability occurs when previous conditions reliably forecast future conditions (i.e. are autocorrelated) and allow organisms to prepare for adverse events (Helmuth et al., 2006). For example, temperatures in shaded microhabitats along the Chinese coastline are more predictable than those in sun-exposed microhabitats and thus enable intertidal gastropods to maintain their metabolism and survive thermal extremes better than those in less predictable sun-exposed microhabitats (Dong et al., 2017). Similarly, autocorrelated intertidal rock temperatures in Thailand result in temperatures close to 37°C, triggering oysters (Isognomon nucleus) into metabolic depression, promoting conservation of energy and survival in response to thermal stress (Hui et al., 2020).

The girdled dogwhelk, Trochia cingulata (Linnaeus 1771) is endemic to the west coast of southern Africa and occurs from the low intertidal to the subtidal zone (Wickens and Griffiths, 1985; Day et al., 1991). It has short generation times and is a direct developer (Branch et al., 2010), suggesting low connectivity between populations, as found in other whelk species (e.g. Nucella canaliculata; Kuo and Sanford, 2009). The survival of T. cingulata is markedly compromised by warming, with 98% mortality occurring after 2 weeks exposure to a 4°C increase in temperature (Martin et al., 2022). In contrast, T. cingulata copes well with longer-term exposure to cold temperatures, with higher survival in cold compared with mean seawater temperature (Martin et al., 2022). In addition, long-term exposure to acidification (0.5 unit decline in pH) does not affect this whelk's survival but cold acidic conditions reduce its shell strength and thickness, and change its shell shape (Martin et al., 2022). In this study, we aimed to assess how acclimation, and therefore short-term exposures, to combinations of temperature and acidification affects this whelk's righting speed and minimum and maximum critical thermal limits (CTmin and CTmax). As T. cingulata is endemic to a cool temperate upwelling region and experiences enhanced survival under long-term cooling (Martin et al., 2022), we predicted increased plastic responses to cold (shifts in the optimal temperature of righting speed, Topt, and in critical temperature limits, CTmin and CTmax) compared with warm conditions. Acidification is predicted to reduce the thermal tolerance of T. cingulata and limit the plasticity of its performance, as found in other marine invertebrates (gastropods: Manríquez et al., 2020; Leung et al., 2021; Minuti et al., 2021; oysters: Parker et al., 2017; crustaceans: Metzger et al., 2007, Walther et al., 2009, Harrington and Hamlin, 2019).

Species’ local environment and field microclimates

We studied the whelk population from Elands Bay, which is located on the west coast of South Africa (32°20′11″S, 18°18′54″E). This bay is downstream from the Cape Columbine upwelling cell, the most dominant upwelling cell in the region (Hutchings, 1992). The seasonal retention of nutrient-rich water within the bay periodically causes large phytoplankton blooms (Pitcher and Louw, 2021). High-resolution data of sea surface temperatures and pH at Elands Bay are limited, but coarser data indicate average temperatures of ∼13°C (Xavier et al., 2007; Pfaff et al., 2011; Smit et al., 2013) and pH levels within the bay generally range from 8.4 to 7.4 but can reach below 7 during intense phytoplankton blooms (Pitcher and Probyn, 2012). Typical of the South African coast, a semi-diurnal tidal cycle exists at Elands Bay (Schumann and Perrins, 1982), with a mean tidal range of 0.25–1.63 m above the lowest astronomical tide (South African National Hydrographer, https://www.sanho.co.za/, accessed 20 February 2023).

Trochia cingulata inhabit the low intertidal to subtidal zone, where they are typically found deep within dense mussel beds (Wickens and Griffiths, 1985; Day et al., 1991; Fig. S1A,B). We deployed temperature loggers (HOBO MX2201, Onset Computer Corporation, Bourne, MA, USA; ±0.5°C accuracy) at the collection site during a spring low tide, allowing access to the rocks in the lowest intertidal zone. This area was submerged for most data recordings, except spring tides (4% emersion, 192 h out of 5255 h of recording). Loggers were attached to the rock substrate under mussel beds using bolts (Fig. S1C,D). Small sections (±5 cm in diameter) of the mussel bed were lifted to deploy loggers and mussels were placed back at their original sites. Successive trips to retrieve data confirmed that mussels covered loggers, offering a similar and favourable microhabitat to whelks (Fig. S1D). Loggers (n=4) were placed at a minimum of 1 m apart. Temperature data were recorded at 30 min intervals from 13 February 2021 to 20 September 2021, and only two loggers were recovered.

Water samples were collected using 100 ml bottles on three consecutive days in July 2019, October 2019 and January 2020 to measure water pH and temperature [EcoSense 100A pH meter (±0.2 units) and temperature meter (±0.3°C), YSI, Yellow Springs, OH, USA], and salinity (BS eclipse salinometer, Lasec, Cape Town, South Africa) close to the whelk collection sites. To account for potential diurnal variability (Cornwall et al., 2013), five water sample replicates were collected within 1 h of dawn and 6 h after dawn and replicates were separated by at least 2 m.

Whelk collection, maintenance and acclimation regimes

Batches of whelks (n≈90 individuals/batch; size: 15–20 mm from the apex to the bottom of the shell) and mussels (n≈400/batch; Semimytilus patagonicus) were collected (Department of Environment, Forestry and Fisheries scientific investigation or practical experiment permit, permit number: RES2021/54) every 2 weeks at low tide and transported to Stellenbosch University between August 2020 and February 2021. In the laboratory, whelks were placed in individual 250 ml plastic containers and divided into acclimation regimes (n=10–15 whelks per collection batch per acclimation regime). There were six acclimation regimes in total: two pH levels (8.0 and 7.5) at each of three constant temperature regimes (11, 13 and 15°C). The intermediate temperature (13°C) approximates the current annual average seawater temperature at this location, which does not differ markedly between summer and winter (Smit et al., 2013). We chose 11 and 15°C as the cold and warm regimes, respectively, based on microclimate measurements (see Results). Temperature regimes were maintained by placing containers in walk-in climate rooms set to 11, 13 and 15°C and using artificial saltwater that had been chilled to these temperatures using digital chillers (HS-90A, Hailea, Guangdong, China). Air temperature in the climate rooms and water temperature in the containers were continually monitored using iButton loggers (DS2422, Dallas Semiconductor Maxim, Dallas, TX, USA; ±0.5°C). The loggers in water were protected with silicone capsules (SL-ACC06, Signatrol, Tewkesbury, UK). The two levels of pH were chosen based on global average pH levels (8.0) and the worst-case scenario RCP8.5 for 2100 (7.5) (Pörtner et al., 2019). Target pH levels were achieved by bubbling CO2 into artificial seawater.

Temperature, salinity and pHNBS, measured with instruments as previously described for field water samples, were monitored daily, while total alkalinity (TA) was measured weekly. TA was determined via potentiometric titration using a TIM860 Titration Manager (Radiometer). These measures were used to calculate seawater PCO2 using the program CO2SYS (Pelletier et al., 2007) with the dissociation constants of Mehrbach et al. (1973) refitted by Dickson and Millero (1987) and the KSO4 constant from Dickson (1990). The physicochemical parameters for each temperature and pH combination are summarised in Table 1.

Table 1.

Physico-chemical parameters of artificial seawater during acclimation periods

Physico-chemical parameters of artificial seawater during acclimation periods
Physico-chemical parameters of artificial seawater during acclimation periods

Air was supplied to each container using a pump to maintain adequate oxygenation (7.54±0.13 mg l−1; mean±s.e.m.). Whelks were held at a 12 h:12 h light:dark cycle and fed S. patagonicus ad libitum. Water was changed every alternate day with old or eaten mussels replaced. An acclimation period of 8–14 days was used and has been shown to be sufficient for acclimation responses to take place in other marine invertebrates (Faulkner et al., 2014; Drake et al., 2017; Salo et al., 2019). Whelks were then randomly assigned to either righting response or temperature tolerance trials.

Rate of righting response

The rate of righting at different temperatures was used as a measure of performance (Gaitán-Espitia et al., 2013; Manríquez et al., 2020). Whelks (n=3–11) were transferred to individual plastic containers immersed in programmable water baths (Grant Instruments GP 200-R4, Cambridge, UK) with artificial saltwater, set at their acclimation temperature (i.e. 11, 13 or 15°C) with pH maintained at 8.0±0.01 and salinity at 35±0.25 ppt. Whelks were held for 10 min to reduce handling stress, followed by a 20 min ramp to a test temperature (5, 7, 9, 11, 13, 15, 17 or 19°C). The ramping period was used to avoid heat injury for the warmest test temperatures and was maintained constant for all test temperatures to avoid bias due to time-mediated compensatory or detrimental effects (Terblanche and Hoffmann, 2020). Whelks were held at test temperatures for 10 min, after which they were inverted, and their time to right was recorded. Whelks were allowed 90 min to self-right and were fully submerged for the duration of the trial. Water temperature was recorded during the trial using thinly sealed copper–constantan thermocouples (Type T, secured next to the whelk on the bottom of the container) connected to a datalogger (PICO Technology, TC-08, thermocouple data logger, St Neots, UK). Dissolved oxygen levels in each container were monitored before and after each test temperature trial (YSI EcoSense ODO200 dissolved oxygen and temperature meter, ±1.5 mg l−1 accuracy; Table S1). On average, dissolved oxygen varied by less than 0.26 mg l−1 before and after trials. Following the trial, whelks were allowed to recover at their original acclimation temperature. Recovery was assessed by gently probing the whelk's foot with a sharp object within 90 min of the trial and up to 24 h after the trial for non-responding whelks. Following Sorte and Hofmann (2005), a score of zero was given for no response (i.e. dead), one if the whelk was moribund (only responded after repeated probing), two if whelks responded immediately and withdrew from the probe, and three if whelks had reattached to a surface. Only whelks scoring two and three were used in successive trials. The period between successive trials ranged between 2 and 24 h. To prevent systematic temperature effects and thermal injury, we randomly used one of the following order of test temperatures for whelks across acclimation regimes: series (1): 11, 9, 13, 17, 15, 5, 17, 19°C; (2) 13, 11, 15, 9, 17, 7, 5, 19°C; and (3) 15, 13, 17, 11, 9, 7, 5, 19°C. We aimed to test the same individual whelk across all temperatures but whelks that died or became non-responsive after a trial (i.e. those that scored zero or one) were replaced. Most whelks (91%) were tested at 4–7 test temperatures.

Critical thermal limits

A standard ramping protocol was used to determine the CTmax and CTmin of T. cingulata (e.g. Madeira et al., 2018; Armstrong et al., 2019). Whelks (n=2–9) were transferred to individual containers laid on their side, which allowed the whelks to attach to the inside wall. The containers were then carefully moved to programmable water baths set to 13°C. The inner wall of the containers was divided into three levels, i.e. bottom, middle and top, and the position of each whelk within these sections was recorded. One empty container per water bath had a copper–constantan thermocouple (type T) secured to each level and attached to a data logger to record temperature throughout the trial. Water was then poured into the containers to submerge the whelks in the following conditions: CTmin trials – temperature: 13.1±0.02°C, pH: 8.0±0.01, salinity: 36±0.2 ppt; CTmax trials – temperature: 13.0±0.02°C, pH: 8.0±0.01, salinity: 35±0.1 ppt. Whelks were first held at 13°C for 10 min. The temperature was then increased or decreased by 0.2°C min−1 until whelks lost attachment from the container wall, marked as the end points (Madeira et al., 2018). For CTmin trials, the temperature was ramped down to −5°C, as beyond this temperature, the water inside the containers started freezing. If whelks remained attached at this point, temperature was maintained at −5°C until the loss of attachment (n=14 out of 188). Once whelks detached, we recorded the container level at which the whelks were attached and the associated thermocouple temperature (i.e. critical thermal limit). During ramping, containers were lightly shaken every 5 min to consistently identify whelks that were attached versus those that had reached their critical limit (Sorte and Hofmann, 2005). Dissolved oxygen levels in containers were monitored at the beginning and end of each trial (Table S2). On average, dissolved oxygen varied by less than 0.14 and 1.77 mg l−1 before and after trials for CTmin and CTmax, respectively. For 97% of individuals (n=144), CTmax was scored after CTmin but a 3 day recovery period was provided between the two. After recovery from CT trials, whelks’ response to probing was assessed as described above (Sorte and Hofmann, 2005), and only whelks scoring two or three were used in CTmax trials. Additional individuals (n=48) were scored for CTmax only, to increase sample sizes, and these had a acclimation period of 9 days.

Statistical analyses

All statistical analyses were run in the R environment (version 4.1.0, http://www.R-project.org/). The ‘rTPC’ (Padfield et al., 2021) and ‘nls.multstart’ (https://CRAN.R-project.org/package=nls.multstart) packages were used to fit a series of models to the righting response rate data and build thermal performance curves. Average CTmin and CTmax data were calculated for each acclimation regime and were needed to establish zero function when model fits reflected non-biologically relevant curves (Battles and Kolbe, 2019). The Gaussian model (‘gaussian_1987’ in the ‘rTPC’ package, following Lynch and Gabriel, 1987) was used as it was the best-fit model (84% of cases) according to Akaike information criteria (AIC)-based model selection. The model with the lowest AICc (for small sample sizes) was considered the best-fit model and models with ΔAICc<2 were taken as equivalent models (Burnham and Anderson, 2002). The Gaussian model was defined as:
formula
(1)
where temp is the temperature (°C), rmax is the maximum performance rate, Topt is the optimal temperature at maximum performance and a is the standard deviation and defines the width of the curve. Topt, rmax and the thermal breadth (B80) were determined for each whelk. B80 represents the range of temperatures over which performance meets or exceeds 80% of rmax.

For parameters obtained from curves (Topt, rmax, B80) and critical limits (CTmin, CTmax), data points greater or smaller than three standard deviations from the mean were considered outliers and removed from datasets (<10% of the performance data and <2% of the tolerance data). For performance parameters, we also confirmed outliers by noting that these originated from unrealistic performance curves, which typically resulted from the absence of a data point between Topt and CTmax.

Generalized least-squares (GLS) models (package ‘nlme’; http://CRAN.R-project.org/package=nlme) were first compared with linear mixed-effects models (LMMs) (function lmer from the package ‘lme4’; Bates et al., 2015) to assess the importance of random effects (Zuur et al., 2009). We also determined the conditional variance (R2c, variance explained by both fixed and random effects) and marginal variance (R2m, variance explained by the fixed effects) of mixed-effects models (Nakagawa and Schielzeth, 2013). Random effects included the whelk collection batch and the container attachment level on containers of CT trials and these predictors were dropped from models if they did not contribute to the variance explained.

Separate linear mixed-effects models were used to assess whether acclimations influenced Topt, CTmax and CTmin. For Topt and CTmax, acclimation temperature (11, 13, 15°C), acclimation pH (7.5 and 8.0), whelk size (shell length in mm) and all interactions were included as fixed effects and batch (1–8) was kept as a random effect. For CTmin, the fixed effects remained the same but collection batch and attachment level were included as random effects. For both rmax and B80, GLS models were used to examine the influence of temperature and pH regimes, whelk size, and their interactions. Models were fitted with maximum likelihood (ML) for estimation of model selection of fixed effects. Best-fit models were determined using the dredge function from the package ‘MuMIn’ (https://CRAN.R-project.org/package=MuMIn) and interpretations of mixed-effects models were done using restricted maximum likelihood estimation (REML). Residuals were checked for normality and homoscedasticity. Type II Wald chi-square tests were used to assess the significance of fixed effects. Statistics are reported from the summary function with appropriate contrasts used to compare across acclimation levels.

To estimate the impact of current temperatures on the performance of this whelk, we calculated the percentage of environmental temperatures that fell within B80. As rTPC provides the B80 range but not the lower and upper B80 values, we determined Topt, rmax and a from the average thermal performance curve of individuals acclimated to average conditions (13°C and 8.0 pH) as there was no effect of acclimation on B80 (see Results). We then used the curve equation with a rate set to 0.8 to determine upper and lower B80 limits (Huey and Stevenson, 1979).

To examine the time-varying predictability in temperature at the collection site, we used the acf function from the package ‘stats’ (http://www.R-project.org/) and assessed the temporal autocorrelation of daily maximum, minimum and average temperatures recorded during the study period. Daily patterns of thermal variation were examined from a short time lag (24 h) to a maximum time lag of 28 days, the duration of a full tidal cycle (Helmuth et al., 2006).

Environmental temperature and pH

In the field, pH ranged from 7.2 to 8.3, with an average of 7.8±0.03 (Fig. S2). Microsite temperatures at Elands Bay ranged from 7.4 to 33.9°C, with an average temperature of 13.5±0.01°C (Fig. 1). The highest temperatures likely reflected air temperatures as the timing aligned with very low shore emersions, which accounted for 4% of recordings. The number of days that the recorded temperature either exceeded CTmax or was lower than CTmin was zero. The upper and lower B80 limits for the whelks acclimated to average conditions (13°C and 8 pH) were 11.95 and 15.50°C, respectively and 74.6% of recorded microsite temperatures fell within this range (Fig. 1B).

Fig. 1.

Microsite temperature recorded at Elands Bay. (A) Temperatures recorded from two HOBO temperature loggers deployed at microsites typically used by girdled dogwhelks, Trochia cingulata, during 2021. Asterisks indicate periods of emersion, and the insert is an expanded view of 3 days of data, illustrating how temperature fluctuated daily despite whelks (and loggers) being submerged. (B) The density distribution of temperatures recorded from HOBO loggers during the study period (February to September 2021) is depicted in dark grey. The blue, green and yellow lines represent the optimal temperature (Topt) after each temperature acclimation regime (11, 13 and 15°C acclimation). The area shaded in light grey indicates the temperatures that fall within the thermal performance breadth (B80). Critical thermal minimum (CTmin) and maximum (CTmax) values are provided on the left and right, respectively, for each temperature and pH combination. CTmax was affected by the pH acclimation regime (P<0.01, see Results) but CTmin was not (P>0.05) and is only included for illustrative purposes.

Fig. 1.

Microsite temperature recorded at Elands Bay. (A) Temperatures recorded from two HOBO temperature loggers deployed at microsites typically used by girdled dogwhelks, Trochia cingulata, during 2021. Asterisks indicate periods of emersion, and the insert is an expanded view of 3 days of data, illustrating how temperature fluctuated daily despite whelks (and loggers) being submerged. (B) The density distribution of temperatures recorded from HOBO loggers during the study period (February to September 2021) is depicted in dark grey. The blue, green and yellow lines represent the optimal temperature (Topt) after each temperature acclimation regime (11, 13 and 15°C acclimation). The area shaded in light grey indicates the temperatures that fall within the thermal performance breadth (B80). Critical thermal minimum (CTmin) and maximum (CTmax) values are provided on the left and right, respectively, for each temperature and pH combination. CTmax was affected by the pH acclimation regime (P<0.01, see Results) but CTmin was not (P>0.05) and is only included for illustrative purposes.

The daily average, maximum and minimum temperature data were highly autocorrelated at 1–5 day lags (Fig. 2). By contrast, only the daily average and maximum temperatures showed significant positive autocorrelations at ∼14 and ∼28 days (Fig. 2B,C).

Fig. 2.

Autocorrelograms of daily microsite temperatures recorded from two HOBO loggers deployed at Elands Bay, South Africa. Daily minimum (A), maximum (B) and average (C) temperatures are shown. Maximum lag was set to 28 days, reflecting a full tidal cycle. Horizontal dotted lines are 95% confidence intervals for significant autocorrelation.

Fig. 2.

Autocorrelograms of daily microsite temperatures recorded from two HOBO loggers deployed at Elands Bay, South Africa. Daily minimum (A), maximum (B) and average (C) temperatures are shown. Maximum lag was set to 28 days, reflecting a full tidal cycle. Horizontal dotted lines are 95% confidence intervals for significant autocorrelation.

Righting response

Whelks acclimated to 11°C had a significantly lower Topt (12.4±0.4°C) than whelks kept at 13°C (13.7±0.4°C; LMM, t=2.41, P<0.05) and 15°C (15.0±0.4°C, LMM, t=−5.48, P<0.0001) (Fig. 3). The Topt of whelks kept at 13 and 15°C also differed significantly (LMM, t=−2.88, P<0.01).

Fig. 3.

Thermal performance curves of the righting response rate of girdled dogwhelks (Trochia cingulata) exposed to the temperature acclimation regimes. (A,D) 11°C acclimation (n=22), (B,E) 13°C acclimation (n=20) and (C,E) 15°C acclimation (n=21). A–C present individual whelk righting rate curves while D–F provide the average righting rate per temperature acclimation group, with the lines and shaded bands indicating the predicted models and 95% bootstrap confidence intervals from Gaussian models. The vertical dashed lines in D–F represent the mean Topt of righting rate for each acclimation temperature. The righting rate Topt differed significantly among all temperature acclimation groups (P>0.05 in all cases, linear mixed-effects model).

Fig. 3.

Thermal performance curves of the righting response rate of girdled dogwhelks (Trochia cingulata) exposed to the temperature acclimation regimes. (A,D) 11°C acclimation (n=22), (B,E) 13°C acclimation (n=20) and (C,E) 15°C acclimation (n=21). A–C present individual whelk righting rate curves while D–F provide the average righting rate per temperature acclimation group, with the lines and shaded bands indicating the predicted models and 95% bootstrap confidence intervals from Gaussian models. The vertical dashed lines in D–F represent the mean Topt of righting rate for each acclimation temperature. The righting rate Topt differed significantly among all temperature acclimation groups (P>0.05 in all cases, linear mixed-effects model).

Acclimation temperature (GLS, P>0.05 in all cases), whelk size (GLS, t=0.49, P>0.05) and the interaction between the two (GLS, P>0.05 in all cases) did not significantly influence rmax, despite the best model retaining these variables (Table S2). For B80, we found that larger whelks had significantly larger B80 ranges than smaller whelks (GLS, t=2.16, P<0.05). The pH acclimation was not retained in any of the best-fit models testing for its effects on performance curve parameters (Topt, rmax and B80; see Table S2).

Critical thermal limits

Acclimation to 13°C resulted in a CTmin of −1.3±0.2°C, which did not differ from that of whelks exposed to 11°C (−0.9±0.4°C, LMM, t=0.82, P>0.05) but was significantly lower than the CTmin of whelks kept at 15°C (−0.3±0.4°C, LMM, t=2.07, P<0.05) (Fig. 4A). CTmin of whelks kept at 11 and 15°C did not differ (LMM, t=1.24, P>0.05). After CTmin trials, 92.55% of whelks fully recovered (score three), 2.66% of whelks were responsive (score two), 2.13% of whelks were moribund (score one) and 2.66% of whelks died (score zero). The pH acclimation was not retained in the best-fit model explaining CTmin (Table S2).

Fig. 4.

Critical thermal limits of girdled dogwhelks exposed to the temperature and pH acclimation regimes. (A) CTmin and (B) CTmax. Sample sizes (n) are indicated in the figure. Values and intervals in pink and blue are model estimates (means±s.e.m.) from linear mixed-effects models; boxplots are from original data. Different letters indicate values that differ significantly from each other (P<0.05, linear mixed-effects model).

Fig. 4.

Critical thermal limits of girdled dogwhelks exposed to the temperature and pH acclimation regimes. (A) CTmin and (B) CTmax. Sample sizes (n) are indicated in the figure. Values and intervals in pink and blue are model estimates (means±s.e.m.) from linear mixed-effects models; boxplots are from original data. Different letters indicate values that differ significantly from each other (P<0.05, linear mixed-effects model).

Both temperature and pH regimes affected CTmax. At pH 8.0 and 13°C, the CTmax of whelks was 36.7±0.3°C (Fig. 4B) and was not significantly different from that of individuals kept at 11°C (CTmax= 36.3±0.2°C, LMM, t=−1.29, P<0.05), but was significantly lower than the CTmax of those kept at 15°C (37.2±0.2°C, LMM, t=−2.51, P<0.05). Short-term exposure to acidic conditions significantly increased CTmax at all temperature regimes (LMM, t=3.20, P<0.01; Fig. 4B), and smaller whelks had significantly higher CTmax than larger whelks (LMM, t=−3.04, P<0.01). After CTmax trials, only 0.53% of whelks fully recovered (score three), 1.60% of whelks were responsive (score two), 12.83% of whelks were moribund (score one) and 85.02% of whelks died (score zero).

Despite a broad thermal tolerance range of ∼38°C, Trochia cingulata displayed plasticity in the optimal temperature for righting (Topt) and in critical thermal limits. The warm temperature regime induced the largest trait responses, increasing Topt from average conditions by 1.3°C, and CTmax and CTmin by 0.5 and 1°C, respectively, while exposure to the cold regime did not affect critical temperature limits (Fig. 4). Contrary to predictions, acidification increased CTmax by ∼0.5°C at all temperature regimes. The response from the combined effects of changes in temperature (warming or cooling) and acidification was additive, resulting in cross-tolerance rather than increased susceptibility as found in other studies (Gunderson and Leal, 2016). Depicting differences or commonalities of our findings with responses of other marine coastal invertebrates can provide insights on the drivers of plasticity of tolerance and optimal performance in these species and contribute to developing unifying concepts in this group (Gunderson and Stillman, 2015; Riebesell and Gattuso, 2015). In conjunction with data on field microsite temperatures and pH conditions, our findings also contribute to predictions of potential impacts of future climate change on this whelk species (Gunderson et al., 2017).

The cold tolerance of T. cingulata (approximately −1.3°C) aligns with lower temperature limits of other temperate intertidal and subtidal gastropods (Ansart and Vernon, 2003; Sinclair et al., 2004; Davenport and Davenport, 2005, 2007; Stickle et al., 2010). Increased upwelling along the west coast of South Africa has resulted in sea surface temperatures cooling by ∼0.5°C per decade (Rouault et al., 2010; Lima and Wethey, 2012; Lamont et al., 2018), with predictions of a further 4°C decrease by 2100. However, microsite data collected in situ showed that T. cingulata also experiences extreme high-temperature events, reaching 30–34°C (Fig. 1), likely occurring during aerial exposure when the low intertidal zone is emersed. These rare high-temperature events (4% of ∼7 months of in situ recordings) are likely to favour selection for high CTmax, despite the predominance of colder temperatures. Indeed, upper tolerance is often positively correlated with maximum habitat temperatures (Ravaux et al., 2016; Wang et al., 2019) and has been well documented in marine taxa (Stillman and Somero, 1996; Tomanek and Somero, 1999; Tomanek and Helmuth, 2002). In addition, T. cingulata is typically found deep within mussel beds, and is rarely observed moving to the top of mussel beds or exposed rock (Wickens and Griffiths, 1985; Branch and Steffani, 2004). This reduced mobility likely prevents behavioural thermoregulation to some extent (e.g. refuge selection, aggregation, shell orientation and posturing), which is employed by some intertidal gastropods (Ng et al., 2017). Furthermore, metabolic depression may also enable high heat tolerance as it reduces the energetic demand of operating at high temperatures (Guppy and Withers, 1999; Marshall and McQuaid, 2020) and is employed by some intertidal and subtidal gastropods (Marshall et al., 2011; Marshall and McQuaid, 2020). This strategy has been reported in several species belonging to the same family as T. cingulata, Muricidae (Kapper and Stickle, 1987; Liu et al., 1990; Riedel et al., 2012).

The temperature limits of T. cingulata were broader than those expected based on mortality data of whelks subjected to longer-term exposure (maximum 12 weeks) to various temperature and pH regimes (Martin et al., 2022). These differences support the importance of considering the rate, intensity and duration of heat exposure when assessing temperature tolerance (Kingsolver and Woods, 2016; Kovacevic et al., 2019; Terblanche and Hoffmann, 2020; Ørsted et al., 2022). For example, caridean prawns with high upper thermal limits are more vulnerable to chronic warming because of the greater metabolic costs incurred during these extended exposures (Magozzi and Calosi, 2015). Increased vulnerability to chronic exposure, despite high CTmax, can also be caused by the accumulation of damage and/or lack of recovery time required to compensate for this damage. The mortality of whelks after 2 weeks of exposure to 17°C (Martin et al., 2022) suggests that despite its high CTmax, T. cingulata cannot withstand prolonged exposure to this temperature.

Short-term exposure to warm temperatures (8–14 days) elicited shifts in Topt (by 1.3°C), CTmin (1°C) and CTmax (0.5°C) in the direction favourable to experienced conditions. In some gastropods, warm acclimation also elicits plastic responses in CTmax (Madeira et al., 2018; Armstrong et al., 2019; Manríquez et al., 2020; Leung et al., 2021), upper lethal limits (Peck et al., 2010; Marshall et al., 2018; Brahim and Marshall, 2020), heart function (Stenseng et al., 2005), the temperature of maximum metabolic rate (Minuti et al., 2021) and the temperature at which heat shock proteins are induced (Tomanek and Somero, 1999). In theory, predictable thermal variability drives acclimation capacity, as the ability to express phenotypes that compensate for environmental variability should be favoured by selection (Reed et al., 2010; Chevin and Lande, 2015; Leung et al., 2020a). For thermal variability to be predictable, it needs to have reliable cues such as the change in photoperiod across seasons (Beaman et al., 2016). The understanding of plasticity and predictable environmental variability in marine environments is still developing (Broitman et al., 2021; Nancollas and Todgham, 2022). In some areas, tidal cycles can lead to predictable thermal stress, but in others, they can cause unpredictable thermal variability (Helmuth et al., 2006; da Silva et al., 2019; Wang et al., 2020). The microsite temperature data from this study suggest that the thermal variability at Elands Bay is predictable at several temporal scales, particularly for the daily maximum and average temperatures (Fig. 2). This predictability is driven by the tidal cycle as temperatures are most strongly autocorrelated during spring tides. This strong tidal forcing likely results in temperatures being reliably high during spring tides as a result of the emersion of the low intertidal zone, potentially driving the plasticity of thermal physiology in T. cingulata.

The extent of plasticity of Topt for the righting response of T. cingulata (∼2.6°C) demonstrates the importance of examining shifts in full thermal performance curves and, more specifically, trait optima, and not just critical limits (Buckley et al., 2022). As Topt reflects the temperature at which the maximum rate of righting response can be achieved, shifts of Topt that match environmental conditions can increase species' performance. Interestingly, the maximum performance rate for this trait was consistent across acclimations, suggesting some degree of compensation enabling maintenance of locomotory performance across temperature and pH regimes rather than trade-offs (Havird et al., 2020). This could have important implications for overall performance as shell growth and maintenance can occur in sub-optimal conditions as long as these organisms maintain a positive energy budget (Leung et al., 2020b; Martin et al., 2022). However, this study only determined the Topt for the righting response, and Topt can vary among traits (Sinclair et al., 2016; Kellermann et al., 2019); thus, assessing the thermal sensitivity of multiple traits is required for a more integrated understanding of temperatures suitable for optimal performance and the extent of plasticity thereof (e.g. Clark et al., 2013).

Contrary to predictions that acidification would narrow the thermal tolerance range (Pörtner, 2008; Pörtner and Farrell, 2008; Byrne and Przeslawski, 2013; Kroeker et al., 2013), acidification increased CTmax. To our knowledge, no other studies on molluscs have found increased thermal tolerance after exposure to acidification but this has been reported for two coral reef damselfishes, Dischistodus perspicillatus and Acanthochromis polyacanthus (Clark et al., 2017). In gastropods, warming and acidification can negatively interact and cause greater reductions in upper thermal limits, heart rate and overall energy budget, when compared with either stressor on its own (Wang et al., 2018; Leung et al., 2021; Minuti et al., 2021), ultimately decreasing thermal performance and reducing acclimation capacity (Manríquez et al., 2020). However, increasing evidence suggests that organisms from environments in which low pH levels naturally occur may have physiological and metabolic adaptations that enable them to cope well with acidification (Duarte et al., 2015; Calosi et al., 2017; Vargas et al., 2017). For example, when exposed to acidic conditions, limpets (Scurria zebrina and Scurria viridula) from variable pH environments have higher growth rate plasticity and lower shell dissolution than those from more stable pH environments (Lardies et al., 2021). While better spatial and temporal pH sampling at Elands Bay will improve our understanding of this location's pH, the microsite data from our study indicate that pH below 7.5 occurs in summer (Fig. S2). This is likely driven by seasonal upwelling and the subsequent large phytoplankton blooms that take place in this area (Shannon and Pillar, 1986; Pitcher et al., 1998; Hutchings et al., 2012). As the physicochemical conditions that result from upwelling, including reduced pH, can impose strong selective pressure (Broitman et al., 2021; Ramajo et al., 2020), T. cingulata likely has the physiological mechanisms that enable it to withstand acidic conditions. Interestingly, this whelk builds its shell with calcite (Martin et al., 2022), which is less susceptible to dissolution in acidic conditions than the more commonly used aragonite (Morse et al., 2007).

Potential physiological adaptations that could explain why short-term acidic acclimation increased CTmax across temperatures include an acid–base regulatory system that can maintain optimal external and internal cellular ion and pH levels in the face of acidification (e.g. through specific ion transport proteins), as is common in coastal organisms that experience frequent carbonate system fluctuations (Pane and Barry, 2007; Gutowska et al., 2010; Marchant et al., 2010; Proum et al., 2017; Weihrauch and O'Donnell, 2017; Zlatkin and Heuer, 2019; Melzner et al., 2020). The Bohr effect may also explain how short-term acidification increased heat tolerance. When this effect is present, net offloading of oxygen into acidified tissues occurs as free hydrogen ions reduce the oxygen affinity of respiratory proteins (Bohr et al., 1904). Oxygen is thus more readily available for metabolism, helping to overcome the typical oxygen limitation that occurs as temperatures increase (Pörtner, 2001), and this may have enabled T. cingulata to maintain performance at higher temperatures. The often correlated hypoxic and hypercapnic conditions of coastal environments (especially during upwelling) have likely driven this adaptive response (Burnett and Stickle, 2001). The respiratory proteins of some gastropods do conform to the Bohr effect (Bugge and Weber, 1999) but the response of most respiratory proteins to pH variation is not known (Ghiretti, 1966). Gastropods of the subclass Caenogastropoda to which T. cingulata belongs, are thought to experience the greatest increase in heat tolerance with increased oxygen availability because of their low capacity to regulate oxygen uptake typical of fully aquatic gas exchange (Koopman et al., 2016). Higher oxygen levels increased the heat tolerance of the gastropods Nucella lapillus (Davenport and Davenport, 2007; Gardeström et al., 2007) and Bithynia tentaculata (Koopman et al., 2016). In addition to overcoming oxygen limitation, increased oxygen unloading due to the Bohr effect may have increased the CTmax of T. cingulata through more efficient induction of chaperone activity (e.g. heat-shock and thermo-defence proteins) and upregulation of antioxidant defence enzymes (e.g. superoxide dismutase and catalase) (Abele and Puntarulo, 2004) as is the case for N. lapillus (Gardeström et al., 2007). It is important to note, however, that the whelk's CTmax response to acidic conditions may differ if it experiences a longer exposure than that used in this study.

Species with both high tolerance and plasticity are often widely distributed taxa with high gene flow between populations (Vinagre et al., 2016). By contrast, T. cingulata is endemic to a small region of southern Africa and, despite its limited dispersal abilities, exhibits some buffering capacity to short-term temperature and pH variation. Our results reinforce the need to assess how species' full performance curves can shift when facing simultaneous changes in environmental variables and to examine the physiological mechanisms underlying these responses in species from understudied regions (Melzner et al., 2020).

K. Martin, R. Vorster, S. Vorster, M. Vorster, H. Lewis, M. Janssonius and M. Bezuidenhout are gratefully thanked for their help with field collections and laboratory work. We thank E. Nortje for providing technical assistance in the CIB/CL.I.M.E laboratory. We are grateful to the Department of Environment, Forestry and Fisheries for granting us the permit needed for animal collection and experimentation. We are thankful to the research facilities provided by the CIB/CL.I.M.E laboratory and the Department of Botany and Zoology, Stellenbosch University. Two anonymous reviewers are acknowledged for the thoughtful comments that helped to improve this paper.

Author contributions

Conceptualization: N.M., S.C.-T., T.B.R.; Methodology: N.M., S.C.-T.; Formal analysis: N.M., S.C.-T.; Investigation: N.M.; Data curation: N.M.; Writing - original draft: N.M.; Writing - review & editing: S.C.-T., T.B.R.; Visualization: N.M.; Supervision: S.C.-T., T.B.R.

Funding

This work was supported by the DST–NRF Centre of Excellence for Invasion Biology (PhD bursary to N.M.) and the National Research Foundation of South Africa (grant number: 116035 to T.B.R. and National Research Foundation incentive funding to S.C.-T.).

Data availability

All relevant data can be found within the article and its supplementary information.

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Competing interests

The authors declare no competing or financial interests.

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