Introduction

Pinto abalone (Haliotis kamtschatkana) are ecologically important grazers for the intertidal and subtidal ecosystems they inhabit and are prized for their economic and subsistence value1.While pinto abalone can be found from southeast Alaska, USA, to Baja California, Mexico, their populations have declined drastically along much of this range2. In Washington State (USA), pinto abalone experienced a 97% decline at 10 permanent index stations monitored for population trends from 1992 to 20173,4 and in 2019, they were listed as a state endangered species2,5,6.

Pinto abalone have historically been harvested to varying degrees by indigenous and non-indigenous people throughout their range2. While densities never supported a commercial harvest in Washington, their decline was noted; the State pinto abalone sport fishery closed in 1994, yet the population has not recovered3,7. The continued decline of pinto abalone could be caused by a combination of disease, illegal harvest, or changing water conditions, but the principal reason is their low population density1,3,8. The sparse distribution of reproductive adults hinders successful fertilization due to insufficient aggregation3. Gascoigne & Lipcius9, in their exploration of the Allee effect in marine ecosystems, emphasized that broadcast spawners and heavily exploited populations, both characteristics of pinto abalone, are particularly susceptible to exhibiting the Allee effect10.

Pinto abalone populations in Washington are unlikely to recover without human intervention. To rebuild the pinto abalone population to historic levels, hatchery-based restoration efforts were initiated two decades ago. These efforts have been led by the Washington Department of Fish and Wildlife (WDFW) and Puget Sound Restoration Fund (PSRF), with support from various other partners. Pinto abalone broodstock are spawned, and their gametes are combined to produce genetically diverse families. Larvae are raised through settlement competency, and juveniles are grown in nursery tanks for one to two years before they are outplanted to locations within the San Juan Archipelago and surrounding waters7.

As restoration occurs, however, climate change is already impacting the waters where vulnerable pinto abalone populations reside. Climate change, caused by increasing atmospheric CO2 concentrations, contributes to ocean warming, as well as ocean acidification through CO2 absorption by the oceans11,12,13. While previous research has advanced our understanding of optimal environmental conditions for pinto abalone, there is increasing recognition that looking at single stressors in isolation will not give an accurate picture of what will occur in the natural environment. Depending on the direction, extent, and mechanism in which each stressor affects a given species, multiple stressors can act in additive, antagonistic, or synergistic ways14. By understanding the impact of multiple stressors at an individual level, we can scale up findings to make more accurate projections about how climate change will impact entire populations. However, it is essential to recognize that these impacts depend on the contexts of specific communities and ecosystems15.

In the San Juan Islands of Washington, abalone already experience persistently low pH and are expected to experience higher temperatures more frequently in coming years16,17,18. Ocean acidification can reduce carbonate ion availability, or the mineral saturation state of seawater, and have detrimental consequences for species that use calcium carbonate to build their skeletons19. Early life stages of marine calcifiers such as shellfish have been found particularly vulnerable to the effects of ocean acidification20. Impacts such as shell deformities, mortality, delayed development, and reduced growth have been demonstrated when pinto and European abalone larvae were exposed to pCO2 conditions resembling those expected in 210021,22. Larvae of Ezo abalone had smaller shells and more shell deformities when exposed to increased pCO2 conditions23,24. One study found a threshold for these impacts at an aragonite saturation of around 1.124.

Simultaneously, increased ocean temperatures due to global warming can impact physiology and critical performance traits such as growth rate, reproductive output, and disease susceptibility of marine species25,26,27,28. Temperature tolerances of abalone have been extensively studied and are known to vary across different species and life stages29,30. Pinto abalone larvae showed a marked decline in performance at 24 °C compared to 21 °C, indicating a thermal tolerance limit within this 3 °C range31.

When experienced together, ocean acidification and warming also negatively impact larval abalone. For example, reddish-rayed abalone larvae had developmental failures, reduced calcification, and higher mortality when exposed to warmer (+ 2 °C and + 4 °C) and more acidified (-0.4 and − 0.6 pH units) conditions, but these stressors did not have interactive effects20. In European abalone, a 0.3-unit pH drop and 2 °C temperature increase caused significant negative impacts on larvae, especially in terms of shell length and calcification, but as with reddish-rayed abalone, no interactive effects of the stressors were found32.

Beyond survival, other important components of pinto abalone’s early life cycle may be impacted by climate change. Pinto abalone have a free-swimming larval phase after they hatch. After this period, they settle onto the substrate and metamorphose into juveniles (Fig. 1). Abalone larvae are lecithotrophic (non-feeding); thus, development progresses with finite energy reserves, and settlement occurs within a relatively short window of seven to 10 days33, depending largely on temperature. In the wild, this settlement is triggered by specific environmental cues, while in hatcheries, a chemical inducer (gamma-aminobutyric acid [GABA]) is commonly used34,35. In addition to studying the survival and size of abalone larvae exposed to different ocean warming and acidification scenarios, it is essential to assess how climate change will impact larval settlement. Ocean acidification may accelerate energy depletion as abalone larvae expend resources to regulate internal pH. Furthermore, ocean acidification can slow metabolism, which could delay settlement19,36,37,38,39. Negative impacts during early life stages such as these can create bottleneck effects on the overall population20. Despite their importance, early life stages in ecological studies of marine invertebrates are often considered a “black box” due to the difficulty in measuring larval behavior and survival in the natural environment40. Opening this “black box” is essential for understanding how variability in larval distribution and recruitment will impact abalone conservation under future climate change.

Fig. 1
figure 1

Abalone life cycle. Fertilized eggs hatch 1–3 days post spawn, then the larval phase (trochophore and veliger) lasts 7–10 days before settlement onto the benthos occurs and metamorphosis into spat occurs. Spat grow into juvenile and eventually adult abalone. Illustrations by Katie Craighill.

This study aimed to understand how climate change will affect the early life stages of pinto abalone to inform hatchery-based restoration practices. We studied how individual temperature and pH stressors and the combination of those stressors would affect larval abalone hatching, survival, settlement, and size. This study provides the first look at multiple stressor impacts of climate change on pinto abalone.

Methods

Spawning of broodstock

Fertilized gametes were acquired from the Kenneth K. Chew Center for Shellfish Research and Restoration (Chew Center) as part of their conservation breeding program. Broodstock were induced to spawn by submerging them in a 0.024% hydrogen peroxide bath for three hours and then returned to filtered seawater, a procedure commonly used in commercial abalone aquaculture41. Gametes from two males and two females were collected, and fertilizations were made with each possible cross of the adults (four total). Fertilizations were carried out before exposure to distinct temperature and pH treatments. Fertilization rates were assessed for each cross in the hatchery, showing significant variation (6%, 80%, 2%, and 83%). Notably, both families from one male exhibited considerably lower fertilization rates. Each experimental replicate received the same number of theoretical embryos (n = 3,750 assuming 100% fertilization) from each cross, estimated by volume, so ratios of embryos from different families were consistent between replicates. Given the transfer of 15,000 theoretical embryos (3,750 from each cross) into each replicate tank and the known fertilization rates for each cross, it is estimated that the total number of successfully fertilized embryos in each replicate was approximately 6,400 at the start of the experiment.

Experimental treatments

Embryos were divided into four distinct experimental groups based on temperature and pH combinations, each with three replicates. Two treatments were set at 14 °C (ambient temperature), which is the temperature normally used for larval rearing at the hatchery in Washington. The other two treatments were set at 18 °C (high temperature), representing a temperature rarely experienced in the San Juan Archipelago during summer months, but one that is expected to become more common with climate change. In future, this temperature could occur during the pinto abalone spawning season in late spring and early summer13,42,43. The Salish Sea Model44 predicts warming of 1.5–3.0 °C from 2000 to 2095 in the Strait of Juan de Fuca and San Juan Islands. While the largest predicted increase would bring the typical summer temperatures to around 15 °C, some sites already see peaks to 18 °C in summer, and by 2095, these sites may consistently experience temperatures reaching 18 °C during the abalone spawning season42,44.

Two of the treatments were maintained at the hatchery´s flow-through seawater system pH of 7.9-8.0 (ambient pH), while the other two used future-forecasted pH levels, targeting a pH of 7.6 (low pH). Expected atmospheric CO2 levels by 2050 could lead to an average drop of 0.3 pH units in oceans45. Coastal regions where abalone reside may experience even faster pH declines and greater pH variability45,46. The Salish Sea Model shows pH levels of 7.4–7.7 in a 2095 prediction around the San Juan Islands and Strait of Juan de Fuca, varying seasonally44. Therefore, the 7.6 pH treatment aligns with projections for both global ocean changes by 2050 and more localized regional changes by 209544,45.

Larval hatching and rearing

A total of 15,000 eggs (see above for fertilization rates and expected embryos per tank) were placed in three replicate tanks (~ 15 L) for each of the four treatments. These embryo tanks were fed by four header tanks (~ 150 L), one for each of the four treatments. Water outflow occurred at the top of the embryo tanks, enabling hatched larvae to ascend into the water column and move into a second tier of tanks (~ 15 L). Here, larvae were contained in silos— PVC tubes equipped with mesh bottoms that facilitated water flow while preventing larvae from being flushed out of the system (Fig. 2a). On the third day (experiment days are counted as days post-spawn [dpf]) of the experiment, we removed the embryo tanks from the system. This decision assumed that any larvae destined to hatch had already completed this process by that time. Additionally, this step aimed to minimize the risk of introducing bacteria from deceased embryos into the system. From days three through ten of the experiment, the header tanks fed directly into the larval tanks (Fig. 2b). Counts were performed on days one through three and days six through ten. Counts, in triplicate, were taken from each silo by removing a known volume of water, counting the larvae present, and extrapolating to the number in the whole silo using its known volume. Next, we added the numbers of larvae initially removed from the current and previous days’ survival counts and settlement trials. This approach ensured that the larvae removed for experimental purposes were not counted as mortalities. On day 10, after the final counts of larvae had been taken, the silos were cleaned, and the remaining abalone stuck either on the sides or mesh screens were removed forcefully with water. Flow rates in each replicate were modulated with irrigation drippers throughout the experiment (approximately 10 L per hour); see Supplementary Table 1 for more details.

Fig. 2
figure 2

(a) Schematic of experiment setup for days 0–3. One header tank was adjusted to each of the four temperature and pH level combinations. Each header fed three experimental tanks. Embryos were placed on the floor of each tank. As embryos hatched, larvae would be pulled through into the second tier of tanks, which included silos to prevent loss of abalone. Larval abalone lived in these silos until day 10 of the experiment or until they were removed for settlement trials. (b) Schematic of experimental setup for days 3–10. On the third day after fertilization, the embryo tanks were removed from the system, so the header tanks fed directly into the larval silos.

Larval settlement trials

A subset of larvae (50–200 depending on living larvae available in each replicate) from each treatment were removed and induced to set each day from 6 to 10 dpf. Larvae were placed in a glass mason jar of seawater with one µmol gamma-aminobutyric acid (GABA, Acros Organics), to induce settlement. The jars were filled with water of the corresponding treatment pH and temperature, sealed with plastic lids, and placed in a water bath for ~ 24 h to maintain those conditions. After this period, the jars were gently rinsed with seawater to collect larvae still in the water column and then rinsed aggressively with freshwater and propanol to remove larvae that had settled onto the sides and bottom of the jar. Counts of the swimming and settled larvae were used to calculate the percent settlement in each replicate jar for each of the five days. These larvae were not included as mortalities in the larval survival data.

Water chemistry

Temperature and pH in each of the four header tanks were measured continuously with Durafet probes. Seawater pH was controlled by the automated addition of CO2 with a solenoid valve. Submersion heaters (Finnex, 800 W) increased the temperature of ambient inflowing seawater in an initial header tank equipped with a degassing column. Seawater then passed through a UV sterilizer and into the four experimental header tanks equipped with chillers (Teco TK2000H) for temperature control.

In addition to the Durafet probe measurements, temperature, salinity, and pH were assessed daily. For each header and treatment tank, temperature and salinity probes were used, and water samples were taken and analyzed using m-cresol purple dye spectrometry to assess pH47. Additionally, water samples were taken for alkalinity analyses on three days (0, 6, and 9 dpf) during the experiment. These samples were preserved with mercuric chloride. Samples from days zero and nine were analyzed via titration with HCl using a Mettler Toledo T5 Excellence Titrator. Alkalinity values were used to calculate temperature-corrected pH values for each spectrometer measurement using the R package Seacarb48. Alkalinity, pH, salinity, and temperature were then used to calculate pCO2 and aragonite saturation state (Ωaragonite). Water chemistry variables for both larval and embryo tanks were compared using ANOVA to assess whether there were treatment differences.

Temperature loggers (Onset HOBO Pendant) were placed in one of the replicate tanks from each treatment throughout the experiment. Temperature loggers were also placed in the water baths used for the settlement jars on days six through 11 of the experiment (as larvae 10 dpf were left overnight for the final settlement trial, the experiment lasted 11 days) to ensure they had remained at the targeted high and low temperature levels.

Shell length

Abalone larvae from each replicate tank were sampled daily and preserved in 30% isopropyl alcohol. Larvae were photographed using a compound scope (Nikon Eclipse Ni) and a camera (Nikon DS-Fi3). Shell length along the longest axis was measured for 30 shells from each tank on their sides. If fewer than 30 surviving or settled larvae were available in a replicate on a given day, measurements were taken for as many shells as possible. Measurements were done for larvae on days three, seven, and 10 of the experiment. On day three, larvae from the flow-through silos that were used for counts were also measured. On days seven and 10, the larvae counted as “settled” in the jars were measured (the settlement trials for these larvae started at six and nine dpf, respectively).

One individual conducted the shell measurements to ensure a consistent protocol was applied, especially for shells with unclear edges or no clear distinction between shell and tissue. Shell length could be affected when shells were crushed, positioned on one side, fragmented, or missing. However, the first 30 abalone shells encountered in each replicate were photographed regardless of their condition. This was done to ensure consistency between treatments. When the shell could be distinguished from the abalone foot or other tissue protruding from the shell, only the shell length was measured. However, when the shell and tissue could not be distinguished, the entire length of the abalone was measured.

Data analysis

Data analyses were completed in R. To assess the impact of pH and temperature while considering the day of measurement, we used multi-model inference to determine the relative importance of different factors. Using the lme4 package in R, we used linear models for hatching data49. Using the glmer package, we used general linear mixed models to assess settlement, treating the proportion of hatchery larvae as a binomial outcome. For both survival and size, we used lme4 to create mixed-effects linear models and the lmerTest package to calculate p-values for the best fitting models. All models for settlement, survival, and size included a random effect of day, and there was an additional random effect in the size models to account for repeated measurements from the same tanks for size49. When included, temperature and pH were treated as categorical (factor) variables, and in certain models, combinations of the interactions between pH, temperature, and day were also included. Confidence intervals around parameter estimates were calculated with confint() in R. Residuals were assumed to be normally distributed.

For these analyses, 13 alternative models (see Supplementary Tables 2–5) were compared using AICc (Akaike Information Criterion, corrected for small sample size) for each response variable. In addition to AICc, model complexity was also considered when selecting the best models, with simpler models favored when a substantial drop in AICc was not noted with the addition of more terms. We elected to use multi-model comparison instead of standard likelihood ratios because our alternate hypotheses were not neatly nested within each other. QQ plots and residual vs. fitted value plots were assessed for any of the best-performing models that we considered in interpretation.

Results

Water chemistry

Flow rates were approximately 10 L per hour on average for each treatment. Details of flow rates for each tank and treatment are summarized in Supplementary Table 1.

Data for pH, temperature, salinity, alkalinity, aragonite saturation, and pCO2 based on our daily measurements are summarized for each treatment in Table 1. We successfully maintained temperature treatments just under 4 °C apart in both of the larval tanks (17.88 ± 0.18 °C and 14.11 ± 0.25 °C for the ambient pH treatments and 17.97 ± 0.17 °C and 14.04 ± 0.21 °C for the low pH treatments) and embryo tanks (17.75 ± 0.20 °C and 14.26 ± 0.21 °C for the ambient pH treatments and 17.80 ± 0.23 °C and 14.10 ± 0.20 °C for the low pH treatments). We also successfully maintained a pH difference of just over 0.3 units between treatments in larval tanks (7.92 ± 0.02 and 7.58 ± 0.05 for high temperature treatments and 7.95 ± 0.04 and 7.60 ± 0.04 for low temperature treatments) and in embryo tanks (7.92 ± 0.02 and 7.58 ± 0.04 for high temperature treatments and 7.97 ± 0.06 and 7.60 ± 0.04 for low temperature treatments). Daily temperature and pH levels during the experiment grouped by treatment are included in Supplementary Figs. 1–4. The aragonite saturation in our ambient pH treatments was just over 2, while the aragonite saturation for the low pH treatments was around 1.

Table 1 Mean and standard deviation of water chemistry values for each treatment in larvae and embryo tanks.

The continuous temperature loggers in the larval tanks showed minimal daily fluctuations in temperature (Supplementary Fig. 5). The temperature loggers in the settlement water baths showed rapid but minimal temperature fluctuations as the water chillers turned on and off to maintain temperature around the targeted levels (Supplementary Fig. 6). While we were not able to take measurements in the settlement jars, we are confident that with the lack of air space in each jar and submersion in water baths, temperature and pH remained close to the starting targeted values as jars were filled with water from each respective header tank.

Larval hatching

The number of hatched larvae was highest in the ambient treatment, followed by both single stressor treatments, with the multiple stressor treatment showing the lowest hatching success. On day three, the percent surviving hatched larvae compared to the estimated number of fertilized embryos was 74% in the control treatment, 63% in the high temperature treatment, 56% in the low pH treatment, and 35% in the multiple stressor treatment. Notably, hatching occurred earlier in the high temperature treatments compared to the low temperature treatments. One dpf, the control only had 2% hatched and swimming larvae and the low pH treatment had 3%, while the high temperature treatment had 66% and the high temperature low pH treatment had 59% (Fig. 3).

Fig. 3
figure 3

Approximated number of larvae in each replicate tank from days 1–10 of the experiment. Lines represent the mean number of larvae for each treatment, while colored dots show each of the replicates. The increasing numbers at the beginning are due to embryos hatching and more larvae entering the larval tanks from the embryo tanks. Embryo tanks were removed from the system on day 3, so days 6–10 track surviving larvae of those hatched on days 1–3. Grey = ambient, red = high temperature, blue = low pH, purple = high temperature/ low pH.

Model comparison revealed that pH, temperature, and day influenced hatching success, and further, that temperature influenced hatch timing, as was reflected visually in the data (Supplementary Table 2). Model parameter coefficients are summarized in Table 2 for the best fitting model. Model diagnostics showed a normal QQ plot and a slight trend of positive residuals all falling in the middle of the predictions range. Given the complicated biological processes at play with larval hatching and mortalities in the system, we felt this model satisfactorily represented the data. The model with temperature, day, and the interaction of temperature and day also had reasonable support, indicating that support for the effect of pH is minimal. The estimated effect sizes in our best fitting model also indicate a stronger effect of temperature than pH treatment.

Table 2 Summary of the best fitting hatching model.

Larval settlement

Larval settlement (measured as the proportion: larvae that settled divided by the sum of settled and swimming larvae) was much higher in the two ambient pH treatments than in the two low pH treatments. Averaged across the five days of settlement trials, we had 81% settlement in the ambient treatment, 89% settlement in the high temperature treatment, 58% settlement in the low pH treatment, and 50% settlement in the high temperature low pH treatment. High temperature may have slightly enhanced settlement and decreased the time to settlement in the ambient pH treatment. However, higher temperature did not appear to have any positive effect on settlement when combined with low pH (Fig. 4).

Fig. 4
figure 4

Fraction of larvae that settled during trials from days 7–11 of the experiment. Different larvae were used for each trial. Lines represent the mean settlement rate for each treatment, while each of the replicates are shown by colored dots. Grey = ambient, red = high temperature, blue = low pH, purple = high temperature/low pH.

For larval settlement, we compared 13 models using AICc (Supplementary Table 3). Two models had notable support: the most supported model included day, pH, temperature, pH*temperature, and temperature*day as factors impacting settlement. The second-best supported model additionally included the interaction between pH and day. In the best-performing model, residuals were randomly distributed and the QQ plot showed normality. Parameter coefficients for the best performing model are shown in Table 3, converted back to proportions from the log-odds output by the binomial models.

Table 3 Parameter coefficients for the best fitting settlement model (logit scale).

As our experimental design required us to maintain tanks of swimming larvae throughout the entire window for settlement, we anticipated that some larvae could settle onto the silos used for larval rearing during this time. The larvae stuck to the silos that were removed on day ten were not included in any statistical analysis for survival. There was no way to determine if these abalone had settled on the sides or bottom of the silos or if they had somehow become stuck in the silo mesh, nor was there a way to determine if they were alive or dead at the time the experiment ended. At the conclusion of the experiment, the mean number of larvae stuck in the silo was 157 in the control treatment, 246 in the high temperature treatment, eight in the low pH treatment, and four in the high temperature low pH treatment. There were significantly more larvae from both ambient pH treatments left in the silos than there were from either of the low pH treatments (Two-way ANOVA, F1,9=11.8, p = 0.007), but there was no significant effect of temperature on number of larvae stuck in the silos (Two-way ANOVA, F1,9=0.56, p = 0.475) (Supplementary Fig. 7).

Due to a spill of experimental replicates, we only had four samples instead of the usual twelve samples (three from each of the four treatments) on day 11 of the experiment. Two of these replicates were from the low pH treatment, one from the high temperature low pH treatment, one from the high temperature treatment, and none from the ambient treatment. This data loss may limit our statistical power but does not impact the accuracy of the study’s results.

Larval survival

Due to significant differences in the survival of hatched abalone before and after day three, and as this was also when the embryo tanks were removed and no new hatching could occur, we chose to analyze survival during the latter-larval stage separately from the hatching stage. While the number of living larvae was dependent on treatment during this larval phase, the declines in abalone numbers we saw across the larval phase were very similar among treatments. On day six of the experiment, mean larval survival compared to theoretical fertilized embryos in each tank was 84% in the ambient treatment, 64% in the high temperature treatment, 58% in the low pH treatment, and 31% in the high temperature low pH treatment (apparent increases in larval survival between days three and six are due only to variability in estimates from subsampling—no additional larvae could enter the system after day three when we removed the embryo tanks). On day 10, these numbers were 71%, 56%, 48%, and 27%; they had declined by 13%, 13%, 17%, and 15%, respectively, during the four-day period. Both pH and temperature impacted the maximum survival rate, but neither factor nor their interaction appeared to dramatically impact the rate of larval mortality in the latter half of the larval stage.

Our model comparison supported these findings. The random effect of day was very small in every model we tested. Model selection indicated strong support for the most complex model with random effect of day and fixed effects of pH, temperature, day, the interactions temperature*day, pH*day, and pH*temperature (Table 4, Supplementary Table 4). Parameter estimates for the interaction effects were not significant, while individual fixed effects were. The QQ plot showed normal results, and plotted residuals vs. fitted values showed a generally random distribution of residuals, with very slight heteroscedasticity.

Table 4 Parameter coefficients for the best-fitting survival model.

Shell length

Low pH appeared to have the greatest negative impact on the shell length of larval abalone across all three days where shell length was measured. Shells in the low pH treatments were 9%, 18%, and 16% smaller than shells in the two ambient pH treatments on days three, seven, and ten, respectively. Temperature had minimal impact on shell size, with shells being only 4%, 0.1%, and 0.6% smaller in the ambient relative to high temperature treatments on days three, seven, and ten, respectively (Fig. 5).

Fig. 5
figure 5

Abalone shell size along the longest axis was measured for 30 (or as many as available) larvae from each replicate tank on day 3 (a), day 7 (b), and day 10 (C), grouped by treatment. Comparison of linear mixed effects models with replicate tank as a random effect showed that temperature, pH, and temperature*pH were all relevant fixed effects.

Comparison of 13 mixed models showed that the full model with pH, temperature, day, and all three 2-way interactions, as well as the random effects of larval tank and day, had the lowest AICc (Table 5, Supplementary Table 6). The parameter coefficients in this model demonstrated the larger influence of pH, which we noted visually in the data, followed by the influence of temperature. The model with all parameters except the pH*temperature interaction also had good support based on AICc (Table 6). QQ plots for the best-fitting models indicated slightly heavy-tailed data, and comparisons of residuals and predicted values showed random distribution, with slightly larger residuals for the smaller predicted sizes.

Table 5 Parameter coefficients for the best-fitting size model.
Table 6 Parameter coefficients for the second best-fitting size model.

While our approach of measuring the entire abalone length when distinguishing between the shell and tissue was challenging and may have introduced some measurement bias by potentially overestimating length, it is noteworthy that abalone from low pH treatments were smaller and had shells that were harder to distinguish from tissue (due to shell breakage, malformation, or disintegration). This likely led to an overestimation of shell length more frequently in these smaller abalone. Consequently, any potential bias would have likely made the shell lengths of the low and ambient pH treatments appear more similar rather than artificially increasing the difference in shell length between treatments.

Discussion

This study investigated the interacting effect of two environmental stressors, temperature and pH, on early life stage pinto abalone, a species designated as endangered in Washington State. Our experimental results suggest that both high temperature and low pH have the capacity to negatively impact early developmental processes in pinto abalone in the near future. Warmer temperatures led to faster hatching of abalone embryos in our experiment, but overall, higher temperatures and lower pH led to fewer surviving hatched abalone three dpf. Mortality rates were relatively low in the latter half of the larval phase and were similar among treatments. pH stood out as the main stressor impacting abalone larval settlement and size.

In our study, pinto abalone hatching was negatively impacted by increased temperature and decreased pH. The high hatching rates in both high temperature treatments (ambient and low pH) one dpf were likely due to increased metabolism and faster development of embryos in the warmer temperature50. The positive relationship between temperature and rate of development has been noted for other abalone species such as donkey’s ear, white, South African, greenlip, and blacklip, among others51,52,53,54. In our study, this faster development, however, was less apparent after the embryonic phase. By three dpf, both low pH treatments (high and ambient temperatures) had the lowest number of larvae remaining. The data show a slower hatching rate in the low pH treatment, which is likely distinct from the lower immediate post-hatching survival seen in the high temperature low pH treatment, but our sampling interval is insufficient to confirm this difference. Results from other studies do not show clear trends that would support one of these scenarios over the other. A metanalysis of multiple stressors on marine embryos and larvae found that embryos are more resilient to environmental stressors than larvae55. However, negative impacts of ocean acidification have been seen in both stages. Delayed development of embryos, delayed hatching, and longer hatching duration have been seen in various invertebrates under low pH conditions56,57. Traits such as embryonic and larval development, larval size, and survival can be negatively impacted by low pH in marine molluscs37. For instance, a study on donkey’s ear abalone demonstrated that hatching and trochophore larval survival decreased with declining pH levels58. Based on our results and information gleaned from previous studies on marine invertebrate development, it is likely that the pinto abalone embryos in our study had faster development due to high temperatures and hindered development due to low pH. After the embryonic phase, hatched larvae seemed more susceptible to higher temperatures and continued to be negatively impacted by low pH in the first three dpf.

During the latter half of our experiment, minimal larval mortality occurred and was relatively consistent across all four treatments. It appears that the impacts of increased temperature and decreased pH on survival may have been more pronounced primarily during the early larval stage. The trend we saw could also have been due to variability in vulnerability between individual larvae—the less resilient individuals may have died quickly, leaving more resilient larvae that survived better throughout the rest of the experiment. Additional investigation is necessary to determine what specific periods of the embryonic and larval phases are most susceptible to ocean acidification.

In the wild, this vulnerability of larvae implies that changing ocean conditions could lead to reduced recruitment if, for example, a heat wave or period of upwelling coincided with abalone larval periods. As ocean conditions change, pH drops and temperature spikes may become more frequent or sustained13,59. In the hatchery, this also suggests that greater emphasis should be placed on buffering water during the early days of the larval phase to reduce mortalities.

pH but not temperature significantly impacted pinto abalone settlement timing and success in our study. Other investigations have also found that low pH can decrease larval settlement and increase developmental time in shelled molluscs and other invertebrate taxa37,60,61. A review of larval settlement under ocean acidification highlighted that lower pH conditions could alter how larvae perceive settlement substrates and cues, potentially leading to delayed or decreased settlement and metamorphosis across various taxa62. For pinto abalone, this delayed or decreased settlement, combined with the larvae’s inability to acquire more energy through feeding during the larval phase, could create a substantial bottleneck at this life stage. Larval survival in marine species often declines as larval duration increases63,64. While studies on other abalone species have shown that larvae can remain competent for several weeks, substantial delays in settlement due to ocean acidification may reduce post-settlement survival and growth rates65,66. In the Salish Sea, pH levels fluctuate seasonally due to physical and biological processes67. Higher pH values are typically observed during the spring and summer months, aligning with the spawning season of pinto abalone, potentially providing more favorable conditions for abalone reproduction and larval development.

We did not anticipate that temperature would have no clear effect during the settlement trials. Although we observed a very slight decrease in larval duration in the high temperature (ambient pH) treatment, temperature did not influence settlement statistically. Previous studies in white and South African abalone found faster settlement or increased competency to settle with increased temperature53,68. However, these results are likely only applicable within the species’ optimal temperature range. The abalone in our experiment were physiologically stressed by the 18 °C temperature treatment, as demonstrated by higher mortalities in the early larval stage. While our selected temperature did not result in complete mortality, the stress experienced by the surviving larvae might have delayed settlement, contrary to our expectations. Our experiment with two temperatures does not allow the construction of a complete thermal performance curve for pinto abalone. However, previous studies suggest that survival and other developmental metrics may not always align on the same thermal performance curve, meaning that performance failure and mortality could occur at different temperature thresholds69.

Larval duration also affects dispersal, with long larval duration increasing dispersal distances63,70. If low pH increases larval duration by reducing or delaying abalone competency, it could have important implications for abalone population connectivity, especially in a broadcast spawning species where the proximity of adults is critical for successful reproduction9,10. A widespread but sparse population could impact the efforts of restoration practitioners in Washington State. Abalone could end up further from optimal habitat and from each other, potentially complicating restoration efforts.

In our study, we used GABA to stimulate abalone settlement. While GABA is not a natural settlement inducer and cannot replicate the full spectrum of natural settlement cues, the data we obtained provide valuable insight into how environmental stressors may impact abalone settlement. As the chemicals in coralline algae that induce settlement are GABA-mimetic71, and GABA has proven effective for use in hatcheries34, it has become widely used in abalone culture and research. GABA triggers abalone larvae to drop out from the water column, settle, and metamorphose. However, it is important to note that GABA does not make larvae competent to settle if they are not developmentally ready. By exposing a new subset of larvae to GABA on different days, we simulated what would occur if larvae encountered a settlement cue at different stages of development. All treatments in our study were exposed to the same cue, so while this is not identical to the combination of chemical and biological cues provided by coralline algae and its associated microbiome61,72, the effect should have been consistent across all treatments. Our study was not able to assess the impact of temperature or acidification on the efficacy of GABA, but future studies could address this question.

With respect to shell length, our results were consistent with other studies that found reduced growth or increased abnormalities in low pH conditions for pinto abalone, European abalone, Ezo abalone, and reddish-rayed abalone20,21,23,24,36. A decrease in shell strength and growth in European abalone represented trade-offs necessary to sustain metabolism under low pH conditions36. Our methods, limited by the difficulty in distinguishing shell edges in some of the malformed low pH samples, may have unintentionally overestimated shell length in these samples. As a result, the actual difference between treatments could be more substantial than our results indicate. The lack of temperature impact on shell length in our study was counterintuitive given what is known about the relationship between metabolism, development, and temperature50,52,53. However, a study on tropical donkey’s ear abalone noted a decrease in larval size above a certain temperature threshold54. In our study, faster hatching rates suggest that abalone in the high-temperature treatment were developing faster, but perhaps physiological stress from those high temperatures prevented the faster shell growth we might expect at higher temperatures. Selective mortality may also have influenced our observed larval size data, a factor we cannot completely disentangle based on our study design. For instance, if smaller larvae died earlier in some treatments, the average size could appear larger, while if certain treatments favored the survival of smaller larvae, this could have skewed the average size to be smaller in those treatments. Because dead larvae would have decomposed and likely been flushed out of our larval silo system, we were unable to measure the size of dead larvae. Investigating size-selective mortality and its potential effects on average larval size in different oceanographic environments would be a worthwhile line of future investigation. Larval size has been linked to predation rates in other species73,74, so a decline in larval and spat size may increase predation risk for abalone. Further, a study on a bryozoan with nonfeeding larvae found a relationship between larval size and survival post-metamorphosis75, suggesting that smaller size at settlement could lead to lower survival rates through the juvenile stage.

Although water conditions can be controlled to some extent in a hatchery setting, the changing oceanographic conditions in pinto abalone habitats raise the question of how restoration practitioners should prepare abalone for maximum success in the wild. As climate change has increasingly affected shellfish and, specifically, the aquaculture industry, more research has been conducted into the efficacy of selective breeding on building climate resilience in various species76. Research has been conducted on the potential use of genetics or genomics for conveying heat tolerance in abalone and the potential of acclimation within generations77,78. In abalone, there have been fewer consistent results found regarding the potential impacts of acclimation to ocean acidification or possible transgenerational effects of exposure. In European abalone, no carryover effects were seen after parental exposure to ocean acidification, with larval and juvenile fitness negatively affected by a drop in pH79. Evidence from a recent experiment on red abalone indicated that exposure to ocean acidification during early life stages negatively affected adult growth and offspring survival80. More research is needed to understand whether exposure to ocean acidification during broodstock conditioning and early life stages could harden abalone against unfavorable conditions that they or their offspring might experience outside the hatchery. An alternative approach would be to prevent the exposure of offspring to ocean acidification at critical early life stages. The status of pinto abalone as a state-endangered species complicates the feasibility of selective breeding trials and achieving optimal genetic diversity in research. This challenge was evident in our study due to limitations in the available spawning broodstock. Expanding the broodstock to include other areas beyond the San Juan Archipelago could help increase the availability of spawning adults for research. However, this approach would need to be carefully managed to mitigate potential risks, such as disease spread and logistical complications.

When it comes to outplanting, decisions also need to be made regarding abalone genetics. If the goal is to restore abalone populations to their former abundance and distribution, it is essential to release abalone into the wild that possess the highest level of climate resilience possible. An alternative option, however, is to find sites that are natural refugia to ocean acidification and other damaging factors. In Mexico, a study on green abalone found substantial differences in juvenile survival and growth between two sites with different exposure to oceanographic stressors81. Targeting the best sites for outplanting abalone could enhance their survival rates. However, considering the rapid pace of climate change, this strategy might only postpone the inevitable impacts on the species. It is possible that abalone could be better equipped to handle these challenges with environmental priming or early life exposure to potentially stressful environmental conditions they will experience in their natural habitats.

Our study can help determine the best hatchery conditions for rearing pinto abalone larvae and help inform what conditions are essential to monitor at restoration sites in Washington. This study is an important step in understanding how climate change may affect abalone larval survival and settlement, which is essential for predicting population-level changes to pinto abalone due to climate change. Our results showed that ocean acidification will have a negative impact on larval survival, growth, and settlement. While the effects of temperature were not as pronounced, increased temperature still negatively impacted larval survival. Combined stressors had additive effects on larval survival when both low pH and high temperature were imposed concurrently. Understanding the impact of these stressors will help optimize the hatchery rearing of abalone, inform how they may survive and reproduce in the wild, and improve the chances of successfully restoring this endangered species.

Our study also offers a first look at how temperature and pH could differentially contribute to changes in pinto abalone development and survival. As we look forward, it will be necessary to study these impacts under variable pH and temperature conditions, as growing evidence suggests that variability, in addition to mean temperature, may have important physiological implications as the climate changes69,82. This was beyond the scope of our study due to laboratory constraints. Future research that simulates the natural environmental variability within pinto abalone habitats could shed light on broader consequences of warming and acidification in early life stages. Performance curves for development and survival can differ in shape and magnitude83, and our study may have revealed hints of this through distinct outcomes for hatching, settlement, survival, and size. Further investigation into these variations and their potential population-level impacts will be vital to understanding how climate change could impact pinto abalone recovery.