Contemporary ocean acidification and marine heatwaves shape individual energetics and rates of herbivory in a dominant ecosystem engineer.

Contemporary ocean acidification and marine heatwaves shape individual energetics and rates of herbivory in a dominant ecosystem engineer. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Contemporary ocean acidification and marine heatwaves shape individual energetics and rates of herbivory in a dominant ecosystem engineer. Nathan Spindel, Maya Munstermann, Sam Karelitz, Rachele Ferraro, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8349339/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change can alter ecological interactions, including herbivory, and potentially alter thresholds for ecosystem collapse. Yet how multiple stressors and dynamic conditions (i.e., variability) shape these interactions remains unclear. This question is particularly pertinent in the coastal ocean, where factors such as ocean warming (OW) and acidification (OA) shape the physiology of dominant consumers, including sea urchins that can turn kelp forests into barrens. We experimentally quantified how present-day extreme conditions (including temperatures, temperature variability, and ocean acidification) affect herbivory and herbivore energetics of barren-forming purple sea urchins (Strongylocentrotus purpuratus). Metabolic and consumption rates nearly doubled across the range of currently experienced temperatures. When combined with present-day extreme OA conditions (pCO2 = 1200 μatm) sea urchins experienced a further doubling of both metabolic and consumption rates. Despite dramatic increases in consumption rates across these conditions, animals gained little to no growth or reproductive benefits. Energetic efficiency (i.e., growth and reproductive gains per unit energy consumed) declined substantially under contemporary warming, ocean acidification and variable temperature (i.e., El Niño-like dynamics). This acceleration in per capita grazing potential and shift in herbivore fitness has the potential to exacerbate effects of climate change but also may lead to unpredictable and volatile responses at the population and community level. Such results present a mechanistic warning for how extreme climatic events and multiple stressors, such as OA and OW, can drive when and where trophic interactions can lead to collapse of primary production ecosystems experiencing environmental change. Biological sciences/Ecology/Ecophysiology Biological sciences/Ecology/Climate-change ecology Earth and environmental sciences/Ecology/Ecophysiology Earth and environmental sciences/Climate sciences/Ocean sciences/Marine biology Biological sciences/Ecology/Biogeochemistry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Main Anthropogenic climate change has caused pervasive and adverse ecological, social, and economic impacts 1 . Severe impacts of ocean acidification (OA), ocean warming (OW) and heatwaves are currently widespread 2 . OW and OA continue to accelerate with extreme events in many systems reaching values once thought only to occur in 2100 or beyond 3,4 . While many studies focus on quantifying lethal thresholds of OW and OA, sublethal impacts may have severe impacts on populations, species interactions and ecosystems well before lethal limits are reached. Specifically, physiological effects of climate change can impact metabolism, reproduction, growth, and energy allocation, but also interaction strengths among species. For ectothermic consumers, these stressors can increase metabolic demands that can lead to increases in foraging 5,6 and exacerbate the prevalence of overconsuming plant or algae ecosystems 7 . Climate-driven stressors such as OA and OW can intensify herbivore metabolic demand, often without corresponding energetic gains for consumers. These shifts can set the stage for unsustainable consumptive pressure via declines in primary production and/or consumer populations. In ectotherms, OW generally leads to exponential increases in metabolic rates until physiological functions begin to fail 8-10 . Some ectotherms compensate for elevated metabolic demands by increasing consumption 11 , constrained ultimately by handling time 12 . Others may reduce energetic expenditure through behavioral inactivity 12,13 , metabolic downregulation 14 , or by sacrificing growth and reproduction 15-17 . Similarly, OA can lead to increases in energetic costs of calcification, digestion, and acid-base regulation 18 as well as impair mitochondrial functioning 19 .Species differ in their ability to maintain acid-base homeostasis - some ramp up metabolism to compensate for added energetic costs 20,21 , others prioritize resource conservation 22,23 , while others lack regulatory control 24 . While OA frequently compromises fitness in calcifying species 5 , such outcomes depend on both specific organismal physiology as well as interacting extrinsic factors 25 , including OW 5,26-31 . Thus, how dominant herbivores respond to OW, OA, and MHWs can vary dramatically both within and among species 29 . Effects of climate change on rates of herbivory will vary in part as a function of how multiple stressors shape consumer energetics and behavior. Climate drivers like OW and OA rarely occur in isolation 28 , yet most empirical response curves are derived from single-stressor experiments. For example, thermal response curves can offer useful insights into nonlinear physiological or behavioral responses, but they are inherently context dependent. Extrinsic factors such as food availability, salinity 4 , OA 5 , and dissolved oxygen 32 can shift these curves substantially. Moreover, interactions among stressors may modify energy intake, allocation, and conversion efficiency in ways that are not additive 33 , ultimately influencing consumer fitness and ecosystem productivity. Although prior studies have shown independent effects of OW and OA, their combined impacts on herbivore energetics and consumption remain poorly understood 29,34 . Quantifying how OW, OA, and marine heatwaves (MHW) interact to influence consumption 29,34 , energetic efficiency, and energy allocation is vital for correlating and scaling physiological responses to broader ecological outcomes. Purple sea urchin barrens in the northeast Pacific offer a striking example of how climate-driven increases in herbivory can trigger non-linear dynamics and ecosystem collapse 35,36 . While climate change is expected to alter sea urchin consumption rates with cascading impacts on kelp forest productivity and viability, the conditions under which this occurs – and the mechanisms involved – remain uncertain 29,34,37,38 . During recent MHWs in the region, extreme warming coincided with purple sea urchin population explosions and the large-scale conversion of productive, biodiverse kelp forests into unproductive barrens 35,39 . This collapse likely reflects not only increased urchin density but also elevated per capita consumption under thermal stress. Once formed, these barrens can persist for decades or more, partly due to the metabolic plasticity and stress tolerance of temperate sea urchins 40,41 , which allows them to survive – and continue consuming – under extreme and resource-limited conditions. This persistent consumption in turn suppresses kelp forest recovery despite the return of optimal ocean temperatures 42 . To understand the relationship between climate change, consumption, and individual fitness, we experimentally tested the combined effects of OW, OA, and thermal variability on herbivore energetics. We exposed Strongylocentrotus purpuratus , a dominant herbivore implicated in widespread kelp forest collapse, to a factorial combination of elevated pCO₂ (600 vs. 1200 μatm), six constant temperatures (10–20 °C), and two ecologically realistic variable temperature regimes simulating El Niño (21–18 °C) and La Niña (18–14 °C) conditions. These dynamic regimes were designed to capture thermal trajectories associated with recent ENSO events and marine heatwaves observed in eastern boundary current systems. For each treatment, we measured size-specific rates of consumption, structural growth, gonadal growth, metabolism, and growth efficiency. We hypothesized that elevated pCO 2 would increase the energetic costs of homeostasis and alter thermal performance responses, consistent with prior work 43 . We also hypothesized that variable thermal regimes would elicit different responses than constant temperatures due to both biological mechanisms (e.g., physiological limits to rapid acclimation) 44 and mathematical consequences of nonlinear thermal performance (i.e., Jensen’s inequality 45-47 , whereby fluctuations around an optimum reduce average performance). Results Metabolic and consumption rates in S. purpuratus increased in response to warming, especially between 10 and 13 °Cwith additional increases under elevated pCO 2 at cooler temperatures. However, these increases in consumption were not matched by gains in gonad production or skeletal growth, which declined above 13 °C. Responses to variable temperature regimes were treatment-specific: the La Niña cool water regime elevated consumption but reduced gonad production and food conversion efficiency, while the El Niño warm water regime suppressed skeletal growth without significantly affecting other metrics relative to matched constant temperatures. Metabolic rate (RMR) Metabolic rate increased with both temperature and body size, and was generally higher under elevated pCO₂, although this offset appeared to diminish at the highest temperature (Fig. 1a). Model selection favored a linear additive formulation over more complex alternatives, with the additive model receiving 91.5% of total model weight (Supplemental Table S1). In this best-supported model, oxygen consumption rose by roughly 13% per °C and 61% per unit increase in log body mass, while individuals exposed to 1200 µatm pCO₂ exhibited on average 1.6-fold higher metabolic rates (95 % HPD = 1.0–2.3) than those at 600 µatm after accounting for body size and temperature. Posterior estimates indicated positive effects of temperature (β = 0.13 ± 0.03 SE; 95 % HPD = 0.07–0.19), body mass (β = 0.61 ± 0.04 SE; 95 % HPD = 0.53–0.68), and elevated pCO₂ (β₁₂₀₀ = 0.46 ± 0.20 SE; 95 % HPD = 0.07–0.86). The model explained a substantial proportion of variation in metabolic rate, with a Bayesian of 0.82 (95% HPD: 0.78–0.85). Exploratory analysis using the full Gaussian process model, which permitted pCO₂-specific temperature responses, revealed similar overall patterns but indicated that the metabolic difference between pCO₂ treatments narrowed at approximately 20 °C (ratio = 0.9; 95 % HPD = 0.4–1.5). Under variable temperature regimes at ambient pCO₂ (i.e., El Niño and La Niña simulations), RMR declined over time in parallel with the progressive cooling imposed by the treatment (Fig. 1b). This temporal decline was particularly pronounced in the warm El Niño treatment, where respiration rates decreased substantially over time as temperatures cooled. After accounting for body size, mean RMR in the El Niño treatment was substantially lower than in the constant 20 °C treatment despite both having the same mean temperature over the experiment (conditional effect on mean RMR at 20 °C = -2.26 mg h -1 g -1 AFDM, 95% HPD = [-2.78] - [-1.72]). In contrast, there was no significant difference in RMR between the La Niña and corresponding constant 16 °C treatment (conditional effect on mean RMR at 16 °C = -0.11 mg h -1 g -1 AFDM, 95% HPD = [-0.62]- 0.42). Consumption rate Per capita consumption increased nonlinearly with temperature and was consistently elevated under high pCO₂ across cool to moderate temperatures, but this acidification-driven enhancement weakened and ultimately disappeared at the warmest thermal extreme (Fig. 2). Per capita consumption rates increased sharply between 10 and 13 °C, then plateaued through 16 °C, with limited further increase at 20 °C. Across temperatures below 20 °C, consumption was consistently higher under elevated pCO₂ (1200 μatm) relative to ambient conditions (600 μatm). However, this pCO₂ effect diminished at the warmest treatment: at 20 °C, consumption rates converged across pCO₂ levels, and the difference was no longer statistically significant (ratio = 1.32, 95% HPD = 0.78–1.89). In contrast, at elevated pCO₂, per capita consumption 2.14 times faster at 10 °C (95% HPD = 1.16-3.26), 1.74 times faster at 13 °C (95% HPD = 1.10-2.43), 1.59 times faster at 16 °C (95% HPD = 1.11–2.09), 1.58 times faster at 17 °C (95% HPD = 1.17–2.04), 1.53 times faster at 18 °C (95% HPD = 1.11–1.99) relative to ambient pCO₂ treatments at the same temperatures. Model selection favored a Gaussian process formulation with pCO₂-specific thermal responses and additive random tank effect (Supplemental Table S3). Variable thermal regimes had weak and context-dependent effects on per capita consumption under ambient pCO₂. In the La Niña treatment (18–14 °C), consumption was marginally higher than in the corresponding constant 16 °C treatment (mean difference = 0.40 g urchin -1 day -1 , 95% HPD = [–0.08]–[0.87]), though the credible interval overlapped zero. By contrast, no meaningful difference in consumption was detected between the warm El Niño treatment (21–18 °C) and its constant 20 °C counterpart (mean difference = 0.14 g urchin -1 day -1 , 95% HPD = [–0.34]–[0.60]). Assimilation efficiency Assimilation efficiency was governed primarily by a nonlinear temperature response and body size, with comparatively weak and context-dependent effects of pCO₂ (Supplemental Figure S7). Model selection strongly supported a pCO₂-specific thermal response captured by a Gaussian-process model including body size (stacking weight = 0.94), with good predictive diagnostics. Assimilation efficiency increased with temperature from 10 to ~16 °C and then plateaued, indicating diminishing gains at warmer conditions. Elevated pCO₂ did not produce a consistent main effect across temperatures: point estimates suggested slightly higher efficiency at high pCO₂ at cooler temperatures, but all pCO₂ contrasts overlapped zero, indicating weak support for a systematic CO₂ effect. In contrast, body size had a clear negative effect, with larger individuals assimilating a smaller proportion of ingested food. Gonad production and food conversion efficiency Gonad production peaked at intermediate temperatures (approx. 16 °C) under constant conditions and was further reduced under the variable La Niña-like regime (Fig.3 – previously shown for wet mass 48 ). However, elevated pCO 2 had no significant effect (conditional effect on mean = 0.10, 95% HPD [-0.48] - [0.64]), 20 °C (conditional effect on mean = 0.27, 95% HPD [-0.28-0.82]), and overall (conditional effect on mean = –0.07, 95% HPD = [–0.47]-0.35) (Supplemental Fig. S10). Production increased allometrically with body size (i.e., test volume), and model stacking weights favored a model including with an additive log-body size, Gaussian process model of temperature (not factored by pCO 2 level), and random tank effect (Supplemental Table S4). Food conversion efficiency (FCE) declined strongly across most temperatures and was consistently lower under high pCO 2 after accounting for body size. The magnitude of this pCO 2 -related difference weakened as temperatures increased(Fig. 4, Supplemental Table S5, Fig. S11). At the coolest temperatures (10–13 °C), FCE was less than half as efficient under elevated pCO₂ (10 °C odds ratio = 0.42, 95% HPD 0.32–0.52; 13 °C odds ratio = 0.46, 95% HPD 0.37-0.57). This pCO₂ effect diminished at intermediate temperatures (16–18 °C), where FCE remained lower at 1200 μatm pCO₂ but with smaller proportional differences (odds ratios ≈ 0.62–0.82, 95% HPD intervals not overlapping 1). By 20 °C, the contrast was highly uncertain: the posterior mean slightly favored higher FCE under elevated pCO₂ (odds ratio ≈ 1.09), but the 95% HPD interval (0.80–1.39) encompassed both substantial decreases and modest increases. This wide credible interval indicates that the pCO₂ effect essentially disappears at the warmest temperatures due to uniformly low FCE across both treatments. Skeletal growth Across all temperatures tested, skeletal growth was substantially higher under ambient pCO₂ (600 μatm) than under elevated pCO₂ (1200 μatm) (Fig. 5). At cooler temperatures (10–13 °C), individuals grew approximately 50% faster under moderate pCO₂ (ratios 1.49–1.50), with tight HPD intervals indicating high certainty. This disadvantage under elevated pCO₂ persisted at intermediate temperatures (16–18 °C), where growth remained 46–65% lower under 1200 μatm. The strongest contrast occurred at 20 °C, where skeletal growth under moderate pCO₂ was ~80% higher than under elevated pCO₂ (ratio = 1.79, 95% HPD = 1.32–2.28) (Supplemental Table S6, Fig. S12). Compared to their constant-temperature counterparts, thermal variability had divergent effects on skeletal growth. El Niño-like conditions (21–18 °C) reduced growth by approximately 38% relative to a constant 20 °C regime (ratio = 0.62, 95% HPD: 0.46–0.79). In contrast, skeletal growth under La Niña-like conditions (18–14 °C) did not differ credibly from the constant 16 °C treatment (ratio = 0.85, 95% HPD: 0.64–1.06) (Fig. 5). Discussion Climate change can reshape ecosystems by altering individual physiological performance, per-capita interaction strengths with impacts on both population and community dynamics. Understanding these mechanisms at the individual level can inform forecasts of ecosystem level change under accelerating global change. In marine ecosystems, ocean warming (OW), marine heatwaves (MHW), and acidification (OA) independently and interactively alter organismal fitness and stability of ecosystems. Beyond effects on survival, sublethal stress from combined stressors such as OW and OA can destabilize food webs 29,49,50 and impair ecosystem function 51 through altered consumer-resource dynamics. Yet for most species, the ways in which multiple stressors and variable environmental conditions shape such interactions remain poorly understood 29 . Here, we show that for a dominant herbivore in temperate nearshore ecosystems, OW and OA jointly elevate metabolic and consumption rates through additive effects (Fig. 1 & 2). Critically, these increases yielded little benefit to growth or fecundity for the consumer, suggesting a compensatory response to rising energetic demands. This decoupling of consumption from fitness suggests a climate-driven intensification of herbivory with potentially severe consequences for primary producers generally and the resilience of kelp forest ecosystems in particular. We know now that OA is no longer a future threat but a present and frequent reality, with conditions once projected for future decades now observed regularly in coastal environments. As nearshore monitoring improves, pCO 2 levels previously considered extreme are increasingly recorded. In the region surrounding our collection site, an upwelling- influenced zone, seasonal mean pCO 2 reaches approximately 600 μatm, with episodic events exceeding 1200 μatm (Supplemental Fig. S1) – values that surpass open-ocean seasonal averages (approx. 405 μatm 52 ) and the control set points in many early OA studies (e.g., 380 μatm 53 ). Notably, pCO 2 levels above 1200 μatm fall within end-of-century projections under Representative Concentration Pathway (RCP) 8.5 54 , suggesting that historical experimental baselines may underestimate present-day conditions in our region of study. Our results show that contemporary OA elevates metabolic demand and substantially increases per-capita consumption in S. purpuratus , a species implicated in the widespread collapse of kelp forests along the U.S. West Coast 35 . The observed pCO₂-driven rise in consumption (Fig. 2) tracked closely with increased resting metabolic rates (Fig. 1). At the same time, it occurred alongside a net decline in food conversion efficiency (FCE) (Fig. 4). Together, these patterns are consistent with compensatory feeding to offset elevated energetic costs. These findings offer a complementary mechanism for patterns observed in northern California and Oregon, where strong coastal upwelling and persistently high pCO 2 have coincided with massive S. purpuratus population booms and unchecked herbivory, despite poor food availability and widespread starvation 35 . In such environments, climate-driven increases in metabolic stress may intensify foraging pressure even as herbivores gain little energetic return, increasing the likelihood of persistent ecosystem degradation. In resource-limited environments such as overgrazed urchin barrens 55 , this imbalance could also heighten the incidence of “metabolic meltdown” 56 , where ectotherms fail to meet escalating maintenance demands of abiotic environmental stressors due to a lack of food. Elevated pCO 2 likely contributes to this burden by increasing the energetic costs of homeostatic regulation 43 through multiple, interacting physiological pathways. Not all sea urchin populations respond to acidification and warming by ramping up metabolism and feeding. For example, larval S. purpuratus exposed to combined high temperature and high pCO₂ show depressed metabolic rates rather than compensation 57 , and the tropical grazer Tripneustes gratilla reduces grazing under warming and ocean-warming–acidification scenarios, thereby lowering top-down pressure on seagrass 58 . These contrasting responses likely reflect differences in life stage, proximity to thermal limits, and resource environment: our urchins experienced relatively cool, food-replete conditions that allowed compensatory feeding, whereas larvae near their upper developmental limits or tropical adults near their upper thermal window may have little scope to upregulate metabolism, instead suppressing activity and growth. Seen together, these studies suggest that urchins can either increase or decrease feeding and metabolic rates under global change, depending on whether stressors push them into a compensatory or a depressive energetic regime. Importantly, even metabolically depressed sea urchins 40 at high densities continue to pose a threat to kelp forest recovery. A key vulnerability of many marine invertebrates in acidified conditions is thought to be increased cost of skeletal growth under elevated pCO 2 . While our experiment did not directly measure calcification rates, the observed decline in growth of skeletal elements such as Aristotle’s lantern points to a potential cost of maintaining or producing calcified tissue under elevated pCO 2 (Fig. 5). This anatomical structure may be more sensitive than others to acidification because it is exposed to ambient seawater and thus not protected by soft tissues that could locally buffer the microenvironment where calcification occurs 59 . Nevertheless, elevated pCO 2 is known to inhibit calcification by increasing the energetic demands of CaCO 3 precipitation 60 and by accelerating dissolution of existing structures 61 . Although a recent model estimates an approximate 10% rise in energetic costs for calcification under end-of-century OA projections 60 , this likely underestimates costs in nearshore systems, where seawater frequently reaches undersaturation of aragonite (Ω < 1) 62 . The energy-homeostasis framework 6,43 posits that organismal tolerance to environmental stress is governed not by survival alone, but by the ability to maintain a balanced energy budget in which limited metabolic resources are continuously reallocated among maintenance, growth, and reproduction under changing conditions. Consistent with an energy-homeostasis framework 6,43 , our findings parallel a wide range of studies showing that many marine consumers remain alive under near-future acidification 63 but incur substantial sublethal energetic costs. Many corals, for example, can partially ameliorate the effects of acidified seawater by actively elevating the pH of the calcifying fluid through proton removal and ion transport, but this buffering is metabolically expensive 64-66 . Mussels ( Mytilus galloprovincialis ) redirect ATP toward ion transport at the expense of growth 67 , oysters ( Crassostrea virginica ) elevate metabolic rate to maintain acid–base balance 21 , gastropods such as Concholepas concholepas increase maintenance costs under high pCO₂ 68 , intertidal crabs ( Petrolisthes cinctipes ) shift energy allocation during early development 69 , and even OA-tolerant fishes like temperate wrasse exhibit higher respiration along CO₂ gradients 70 . Across these diverse taxa, acidification tolerance often masks hidden energetic tradeoffs, including stress responses that preserve short-term survival but erode the energy available for growth and reproduction. Environmental variability is increasingly recognized as a fundamental axis of global change. Increased variability can alter physiological performance in ways that cannot be predicted from mean conditions alone 71,72 . In our experiment, thermal variability produced responses that diverged from constant-temperature treatments with the same mean, illustrating the principle that nonlinear thermal performance and thermal variability jointly shape organismal energetics. Experimental work across taxa shows that environmental variability can either buffer or amplify the impacts of stressors relative to constant exposures with the same mean 10,71,73 . This occurs because organisms respond to the full temporal structure of conditions rather than to averages alone. Our findings extend this broader pattern by showing that short-term extreme warming excursions can either suppress or leave unchanged fitness-related traits in sea urchins, depending on the trait in question and the structure of the fluctuation. The reduced skeletal growth and altered metabolic profiles we observed under variable regimes (Fig. 1&5) highlight that ectotherms integrate environmental experience over time, rather than responding to average conditions, and that transient exposures to stressful temperatures can impose energetic costs that reverberate through growth and resource allocation. More broadly, these results underscore that predictions based solely on static thermal means may overlook critical dynamics governing consumer energetics and, ultimately, the strength and stability of trophic interactions under climate change. Conclusion Overconsumption by dominant herbivores has amplified the ecological impacts of climate change across ecosystems ranging from grasslands 74 and boreal forests 75 to kelp forests 35,76 . Herbivores experiencing environmental stress can intensify consumptive pressure on primary producers to satisfy increasing energy demands. In recent decades, extreme climatic events have become longer, more intense, and more frequent 77-79 . These events often impose severe consequences for ecosystem engineers 80 , biodiversity and ecosystem services 81,82 . These trends are expected to worsen under continued anthropogenic climate change 3 . Our experiments show that contemporary ocean warming and acidification push a key kelp‐grazing urchin into an energetically costly state in which metabolic rate and feeding accelerate while skeletal and gonadal growth stall or decline, especially under high pCO₂ and extreme temperature regimes. This decoupling of consumption from fitness provides a concrete physiological mechanism by which climate change can intensify herbivory even when individual energetic returns are poor. These impacts thereby increasing the likelihood ecosystems tip into and/or persist in overgrazed states. By quantifying how present-day OA and realistic thermal variability interact to reshape metabolism, foraging, and growth, our study refines expectations for consumer energy budgets in coastal ecosystems and provides a foundation for models that link shifting environmental conditions to food web structure and the resilience or collapse of kelp forests. Methods We addressed our core objectives of assessing the independent and combined energetic effects of 1) high vs moderate pCO 2 crossed with a temperature gradient (i.e., six constant temperatures) and 2) a historical variable ENSO thermal regime using a multifactorial laboratory experiment. Field collections, acclimation, and initial biometrics Purple sea urchins, Strongylocentrotus purpuratus , were hand-collected by SCUBA in Ucluelet, British Columbia, Canada (Lat 48.94˚, Long -125.56˚) at a depth of 20–25 ft relative to mean tide (n = 515; mean test diameter = 51.26 mm; range = 25.21–69.23 mm) in September 2021. Individuals were immediately transported to the Hakai Institute Quadra Island Ecological Observatory (HIQIEO) in insulated, aerated 120 L containers. Upon arrival, urchins were transferred to flow-through 260 L fiberglass sea tables and allowed to recover for 48 hours. Seawater was drawn from 20 m depth (relative to chart datum low tide) using a MP Pumps Inc. system (Fraser, MI, USA), filtered (~100 µm mesh), UV sterilized, and delivered to the sea tables via a holding tank at ~2000 L hr⁻¹ (turnover rate = 184.62 d⁻¹; mean temperature = 13.0 °C ± 0.2 SD). To estimate body size–specific gonad mass in the source population, we sampled a subset of individuals not used in experimental treatments (see Supplemental Fig. S4; n = 35; mean test diameter = 56.09 mm; range = 42.12–69.46 mm). We fit an allometric model assuming an exponential relationship between log-transformed body size and gonad mass. Following initial recovery, we collected biometric data on experimental individuals prior to transfer to the mesocosm array for secondary acclimation. Measurements included test diameter and height (digital calipers) and wet weight (calibrated digital balance). To allow for repeated measures, each urchin was implanted with a uniquely coded passive integrated transponder (PIT tag; 8 × 1.4 mm, 0.027 g; 64-bit ID, FDX-B, 134.2 kHz, read-only) using a sterile injector and hypodermic needle (Oregon RFID, Portland, OR, USA). PIT-tagged individuals were subsequently tracked using a waterproof stick antenna and FDX proximity reader (Oregon RFID). Prior to the main experiment, we conducted a 30-day pilot study in August 2021 at HIQIEO to test the effects of PIT tagging on survivorship and feeding. No mortality or apparent feeding differences were observed between tagged (n = 60) and untagged (n = 60) individuals. Following biometry, urchins were randomly assigned to experimental mesocosms, with each mesocosm containing 15 individuals ranging from approximately 45–65 mm in test diameter. Mesocosm system and animal husbandry We used an array of 32 replicated 340 L acrylic mesocosms supplied with flow-through seawater, each capable of independent control of temperature and pCO 2 (see Supplemental Fig S13 , Integrated Aqua Systems, Inc., Vista, CA, USA). To minimize biases associated with photoperiodism 72 , we implemented a uniform lighting regime for all mesocosms using LED fixtures programmed to provide 10L:14D with 2-hour linear light intensity transition periods for dawn and dusk (0-100% from 07:00 to 09:00 “dawn”, and 100-0% from 17:00 to 19:00 “dusk”). We standardized food availability by supplying uniform dry pellets composed of several macroalgal species formulated for the aquaculture of purple sea urchins (Urchinomics Canada Inc., Halifax, NS, Canada). We supplied pellets to sea urchins in all mesocosms ad libitum , removing uneaten food and feces every 72 hours. To optimize access to abundant food, we enclosed subjects and food in aquaculture submerged baskets (61 cm X 61 cm X 21 cm, Morenot Canada Ltd., Campbell River, BC, Canada) such that food was always readily accessible, but movement was not impeded. Mesocosm thermoregulation To create ecologically relevant temperature treatments, we scaled our target set points in the aquaria based on historical benthic time series data for purple sea urchins habitat in California, USA (see Supplemental Fig. S8). Specifically, constant set points included 10 ℃, 13 ℃, 16 ℃, 17 ℃, 18 ℃, and 20 ℃. Variable treatments tracked two distinct temporal thermal patterns observed during El Niño (range: 21-18 ℃) and La Niña (range: 18-14 ℃) Southern Oscillation phases between September and December. Each mesocosm independently maintained thermal treatment conditions using a heat exchanger fitted with a titanium coil regulated by a dual stage digital temperature controller (resolution = 0.1 ℃, Dwyer Instruments, LLC. © , Michigan City, IN, USA). To supply heat exchangers with on-demand cold and warm fluid for down- and up-regulation of temperature, respectively, the mesocosm system employed central cooling and heating plants that distributed fluids in parallel closed loops to each mesocosm. Each mesocosm maintained temperature set points by dynamically operating solenoid valves connected to the heat exchanger. To control for potential sensor drift, we manually checked and re-calibrated, as needed, all temperature probes using traceable digital thermometers every 72 h. Carbonate chemistry manipulation We crossed our constant thermal treatments with two pCO 2 treatments, 600 μatm (local seasonal average) and elevated at 1200 μatm (frequent local extreme, which is equivalent to IPCC SSP5-8.5 scenario for 2050 83 ). To regulate carbonate chemistry in the mesocosm array, we used a central mass flow controller (see Supplemental Fig S6, accuracy ±0.05% of full scale. MFC, Alicat Scientific © ) system that delivered precise mixtures of pure CO 2 gas and dehumidified compressed air. Gas-impermeable airlines distributed these gas mixtures to ceramic diffusers equipped with needle valves (McMaster-Carr, Chicago, IL, USA) for fine-tuning in each mesocosm. To monitor the carbonate chemistry in each mesocosm, we used a combination of continuous pH measurement via electrodes (Durafet III, Honeywell International Inc. © ) calibrated every 72 hours and weekly discrete water sampling for analysis on a Burke-o-lator (Dakunalytics, LLC.), a device capable of simultaneously measuring pCO 2 , TCO 2 , temperature, and salinity. With these carbonate chemistry measurements, we were able to calculate the rest of the parameters of the system, including aragonite saturation state ( Ω a ) and total alkalinity ( A T ). Energetics Respirometry We measured respiration rates as a proxy for metabolic rate, using the resting metabolic rate (RMR) as an approximation of the energetic cost of homeostatic maintenance. We opted to use RMR as an estimate of metabolic state to avoid the confounding effects of active digestion 84,85 . To avoid such postprandial effects (i.e., specific dynamic action), we starved focal subjects for 24 hours prior to respirometry measurements. We quantified rates of respiration by measuring dissolved oxygen (DO) concentration time series using flow-through optical oxygen sensors paired with temperature probes (Presens Precision Sensing GmbH, BioPark, Regensburg, Germany) affixed to custom-built sealed acrylic chambers 40 equipped with gas impermeable tubing and submersible pumps (Eheim GmbH & Co. KG, Germany). To account for displacement volume, we calculated the internal test volume of the urchins (V) from test diameter (D) and height (H) assuming oblate spheroid geometry (V = 4/3π D 2 H) and subtracted this urchin volume from the total chamber system volume. We estimated the amount of metabolically active biomass for an individual by calculating ash-free dry mass (AFDM) for each subject. AFDM quantifies soft tissue biomass while excluding skeletal biomass that does not contribute meaningfully to changes in DO. We calculated AFDM as the difference between dry mass and post-combustion ash mass (i.e., skeletal mass). We measured all mass metrics by weighing samples on a calibrated digital scale. To measure dry mass, we first cracked the test of the urchins and discarded the coelomic fluid, then dried the carcasses for 24 hours at 60 ℃ in a drying oven then weighed the dried carcasses. To measure post-combustion ash mass, we combusted these dried carcasses for six hours at 450 ℃ in a muffle furnace, then weighed the resulting ashes of each carcass. We accounted for background DO dynamics by operating a simultaneous “blank” respiration chamber in parallel with every set of three focal chambers sensu 86 . The “blank” chamber was identical to the focal respiration chambers, except that it contained no focal sea urchin and instead only contained treatment seawater. Prior to analyzing measured rates of change in DO, we conducted quality control on raw instrument output data using the R package respR 87 . Consumption rate We estimated per capita consumption rates using individual and aggregated feeding trials with four replicate 48-hour aggregated feeding trials throughout the experiment. Because individual consumption can vary by body size, we also conducted one 48-hour solitary set of feeding trials to calibrate the aggregate trials to individual scales. At the beginning of each feeding trial, we supplied subjects with a pre-weighed standardized aliquot of dry food (Urchinomics Canada Inc., Halifax, NS, Canada), noting the time of day when the feeding trial was initiated. At the end of each feeding cycle, we collected all uneaten food in each mesocosm, noting the time of day, and dried it for 24 hours at 60 ℃ in a drying oven. We then calculated consumption rate as the difference in dry weight between the initial food aliquot and the final uneaten food per individual per day. We also measured rates of mass loss of food pellets due to exposure to water (i.e., dissolution and microbial degradation), irrespective of herbivory in each treatment. A one-way ANOVA comparing the effect of treatment on pellet mass loss rates revealed no statistically significant effect of temperature ( F 7, 120 = 0.87, P = 0.53), pCO 2 ( F 1, 119 = 0.11, P = 0.73), or their interaction ( F 5, 114 = 0.64, P = 0.67). To estimate per capita consumption rates, we fit models to data from individual and aggregated feeding trials. For individual feeding trials, urchins were fed in isolation in flow-through tanks set within their larger mesocosms. The individual feeding trials served to estimate an allometric scaling effect of body size (i.e., test volume) on consumption rate and to estimate treatment effects on assimilation efficiency (see section Assimilation efficiency below). Aggregated feeding trials were set up such that replicate aggregations of seven or eight individuals were contained within standard aquaculture trays. Gonad mass To quantify changes in body size specific gonadal biomass, we calculated the difference between an estimated initial and measured final state. We estimated initial body size specific gonadal mass using an empirical gonadal mass to body size regression based on wild individuals sampled from the same time and place as the experimental subjects (n = 35, mean test diameter = 56.09 mm, range test diameter = 42.12 – 69.46 mm, Bayesian R 2 = 0.21 [95% CI = 0.03 – 0.40]). To evaluate treatment effects on change in gonadal mass, we calculated the difference between the estimated initial and measured final gonadal mass in each treatment. Food conversion efficiency (FCE) We quantified food conversion efficiency to gonadal biomass as the ratio between gonad mass change, G , and total dry food consumption, R , between the beginning and end of the experiment (FCE = G / R , based on 88 ). We accounted for minor differences in the duration of experimental treatments (± 3 hr, maximum) by using units of hours in the denominator for calculations of gonad mass change and total dry food consumption. Skeletal growth We estimated body size specific skeletal growth rates using a mark-recapture technique 89,90 designed to precisely quantify growth of a calcified jaw structure, the “demipyramid” (i.e., a component of the jaw that supports the teeth, known as Aristotle’s lantern). The initial chemical mark involved injecting the antibiotic tetracycline, which has been shown to have no effect on performance in sea urchins 91,92 . Tetracycline is rapidly incorporated into skeletal tissue during calcification and fluoresces under ultraviolet light. To apply the initial mark, we administered tetracycline injections (tetracycline hydrochloride USP, 5 g L autoclaved and 1 μm filtered seawater -1 ) through the peristomal membrane using syringes equipped with sterile 25-gauge hypodermic needles. After the experiment, we dissected the Aristotle’s lantern of experimental subjects and dried them for 24 hours at 60 ℃ in a drying oven and subsequently stored the dried Aristotle’s lantern in a -20 ℃ freezer for later analysis on a stereoscopic microscope equipped with an ocular micrometer. We measured growth as the distance between the initial mark on the jaw structure (demipyramid) to the top of the newly added calcareous tissue above the mark (Supplemental Fig S5). Assimilation efficiency To quantify assimilation efficiency (i.e., “absorption efficiency” 93 ), A , we measured individual rates of ingestion, I , and egestion, E , and expressed A as a ratio between net food retained versus food ingested following the method described in 94 ( A = ( I - E )/ I ). We measured individual rates of ingestion and egestion by temporarily housing urchins in individual containers with mesh tops that permitted free exchange of water, but not fecal pellets or food (Fig S5) for a period of 48 hours. To quantify egestion, we siphoned feces from individual containers onto a 400 µm sieve. We collected fecal pellets from the sieve using a micro spatula and transferred them to pre-weighed aluminum weigh boats. We then measured dry weight of feces after heating samples at 60 ℃ for 24 h in a drying oven. Statistical analyses We estimated body size specific respiration rate, per capita consumption, assimilation efficiency (i.e., “absorption efficiency” 93 ), gonad production, food conversion efficiency, and skeletal growth in response to temperature and pCO 2 treatments using Bayesian regression models. Efficiency metrics were continuous proportions bounded between zero and one, so we modeled them using a Beta likelihood distribution with a logit link function for the mean and log link function for the precision parameter. All other metrics were continuous and positive, so we modeled them using a Gamma likelihood and log link function. We used vague priors on all models (see below for details). To control for uncertainty in model formulae, we compared candidate model structures using approximate leave-one-out cross validation (loo) and selected model structures based on weights estimated using stacking 95,96 . This method is designed to maximize predictive accuracy of candidate models. We accounted for allometric scaling by including the log of body size metrics (test diameter or test volume) as a covariate. We compared parameter posteriors directly within selected models. Models included a nonparametric Gaussian process term (on the log-scale) for the effect of temperature factored by pCO 2 level. The Gaussian process also included standard deviation (controlling the response scale) and length-scale (controlling the smoothness) hyperparameters. We estimated model posteriors using Stan 97 via the R 98 package brms 99 . We implemented vague priors for all parameters (i.e., wide relative to the scale of expected potential parameter values). These included normal priors N (0, 1) on population-level (fixed) parameters and Student’s t priors (df = 3; mean = 0; scale = 2.5) on group-level (random) hyperparameters and intercepts 100 . To avoid overfitting six temperatures, we used an informative inverse-gamma prior for the Gaussian process length-scale hyperparameter specifically tuned to the covariates 99 . The shape parameter controlling the form of the Gamma likelihood was given a prior with shape, k = 0.1 and scale, ϴ = 0.1. We fit our models using 10,000 iterations across four chains, discarding the first half of the iterations per chain as a warm-up, resulting in a posterior sample of 20,000 iterations for each response. We checked that chains converged using visual inspection and each parameter estimate, we confirmed that Rhat (the potential scale-reduction factor) was less than 1.01 and the minimum effective sample size ( n eff ) was greater than 1,000 100 . To evaluate goodness of fit for our models, we evaluated graphical posterior predictive checks, searching for any systematic differences between model simulations and empirical data 100 and estimated Bayesian R 2 values 101 . Declarations Acknowledgements We thank Eric Peterson and the Hakai Institute for providing laboratory facilities at Quadra Island and technical support. This study was funded by a National Science Foundation grant to D. K. Okamoto (NSF OCE 2023649) and to L. Rogers-Bennett (NSF OCE 2023664) and the Tula Foundation. 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Bayesian Analysis 13 , 917-1003, doi:10.1214/17-Ba1091 (2018). Team, S. D. Stan Modeling Language Users Guide and Reference Manual, 2.29. (2024). Team, R. C. R: A language and environment for statistical computing. R Foundation for Statistical Computing (2024). Bürkner, P.-C. brms: An R Package for Bayesian Multilevel Models Using Stan. J Stat Softw 80 , 1 - 28, doi:10.18637/jss.v080.i01 (2017). Gelman, A. et al. Bayesian data analysis . (CRC press, 2013). Gelman, A., Goodrich, B., Gabry, J. & Vehtari, A. R-squared for Bayesian Regression Models. Am Stat 73 , 307-309, doi:10.1080/00031305.2018.1549100 (2019). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementalInformationNCC.docx Supplementary Information - Contemporary ocean acidification and marine heatwaves shape individual energetics and rates of herbivory in a dominant ecosystem engineer Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8349339","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":560789804,"identity":"0cd7d9ea-427d-4d5c-a6ae-c19e1f0ca4c4","order_by":0,"name":"Nathan 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Berkeley","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Okamoto","suffix":""}],"badges":[],"createdAt":"2025-12-12 23:40:30","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8349339/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8349339/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98325786,"identity":"70fb8d30-46b1-41fa-a8a9-6e3dfb48ba48","added_by":"auto","created_at":"2025-12-16 14:39:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140823,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea)\u003c/strong\u003e Resting metabolic rate as a function of different levels of pCO\u003csub\u003e2 \u003c/sub\u003eand temperature. Symbols represent empirical means for each tank replicate. Curves represent conditional modeled means, while dark and light bands represent 95% and 80% highest probability density intervals [HPD], respectively. Model predictions account for allometric scaling and are reported here conditional on a biomass (ash-free dry mass) value fixed at the mean. \u003cstrong\u003eb) \u003c/strong\u003eResting metabolic rate time series for variable temperature treatments. Symbols represent empirical means for each tank replicate. Curves represent conditional modeled means, while dark and light bands represent 95% and 80% highest probability density intervals [HPD], respectively. Model predictions account for allometric scaling and are reported here conditional on a biomass (wet mass) value fixed at the mean. Annotated arrows indicate mean temperature conditions at each sampling event.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8349339/v1/bde52a3a1071073b525c8b19.jpg"},{"id":98325780,"identity":"9ef06554-0fd9-4337-977b-47b0ae23b74c","added_by":"auto","created_at":"2025-12-16 14:39:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":149215,"visible":true,"origin":"","legend":"\u003cp\u003eHerbivory responses to temperature, pCO\u003csub\u003e2\u003c/sub\u003e, and variable versus constant thermal conditions. Symbols represent empirical means for each tank replicate. Curves represent conditional modeled means. Light and dark bands as well as thin and thick vertical bars represent 95% and 80% highest probability density intervals [HPD], respectively.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8349339/v1/abc60ce8b1deef14b94a4808.jpg"},{"id":98325781,"identity":"094353fb-8457-471d-ba9f-545ebd0c7e9e","added_by":"auto","created_at":"2025-12-16 14:39:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":151922,"visible":true,"origin":"","legend":"\u003cp\u003eGonad production responses to temperature, pCO\u003csub\u003e2\u003c/sub\u003e, and variable versus constant thermal conditions. Symbols represent empirical means for each tank replicate. Curves represent conditional modeled means. Light and dark bands as well as thin and thick vertical bars represent 95% and 80% highest probability density intervals [HPD], respectively. Model predictions account for allometric scaling and are reported here conditional on a body size (here test volume) value fixed at the mean.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8349339/v1/dfa35b303b547ba2741c643c.jpg"},{"id":98325787,"identity":"aa7031ab-a3ae-4263-b626-063780b83d8d","added_by":"auto","created_at":"2025-12-16 14:39:38","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161874,"visible":true,"origin":"","legend":"\u003cp\u003eFood conversion efficiency (FCE) responses to temperature, pCO2, and variable versus constant thermal conditions. Symbols represent empirical means for each tank replicate. Curves represent conditional modeled means. Light and dark bands as well as thin and thick vertical bars represent 95% and 80% highest probability density intervals [HPD], respectively. Model predictions account for allometric scaling and are reported here conditional on a body size (test volume) value fixed at the mean.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8349339/v1/48c2db61a384992dd4d1c729.jpg"},{"id":98325782,"identity":"9d7d55c8-9c16-4a0a-bc07-918e74ca5d84","added_by":"auto","created_at":"2025-12-16 14:39:37","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":169328,"visible":true,"origin":"","legend":"\u003cp\u003eJaw growth responses to temperature, pCO\u003csub\u003e2\u003c/sub\u003e, and variable versus constant thermal conditions (jaw structure referred to as “demipyramid”). Symbols represent empirical means for each tank replicate. Curves represent conditional modeled means. Light and dark bands as well as thin and thick vertical bars represent 95% and 80% highest probability density intervals [HPD], respectively. Model predictions account for allometric scaling and are reported here conditional on a body size (here test diameter) value fixed at the mean.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8349339/v1/bf802f4563d58ab8a0ab6995.jpg"},{"id":100369224,"identity":"e009023d-284f-467d-a8cb-6372681975bd","added_by":"auto","created_at":"2026-01-16 07:58:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1824477,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8349339/v1/c7cb5139-39d5-4eb8-b2cd-91e2e441bc47.pdf"},{"id":98325784,"identity":"dec01639-41ef-4198-ae99-6fe3c4f59435","added_by":"auto","created_at":"2025-12-16 14:39:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3046867,"visible":true,"origin":"","legend":"Supplementary Information - Contemporary ocean acidification and marine heatwaves shape individual energetics and rates of herbivory in a dominant ecosystem engineer","description":"","filename":"SupplementalInformationNCC.docx","url":"https://assets-eu.researchsquare.com/files/rs-8349339/v1/e4bef0101a051489d4d19f01.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Contemporary ocean acidification and marine heatwaves shape individual energetics and rates of herbivory in a dominant ecosystem engineer.","fulltext":[{"header":"Main","content":"\u003cp\u003eAnthropogenic climate change has caused pervasive and adverse ecological, social, and economic impacts \u003csup\u003e1\u003c/sup\u003e. Severe impacts of ocean acidification (OA), ocean warming (OW) and heatwaves are currently widespread \u003csup\u003e2\u003c/sup\u003e. OW and OA continue to accelerate with extreme events in many systems reaching values once thought only to occur in 2100 or beyond \u003csup\u003e3,4\u003c/sup\u003e. While many studies focus on quantifying lethal thresholds of OW and OA, sublethal impacts may have severe impacts on populations, species interactions and ecosystems well before lethal limits are reached. Specifically, physiological effects of climate change can impact metabolism, reproduction, growth, and energy allocation, but also interaction strengths among species. For ectothermic consumers, these stressors can increase metabolic demands that can lead to increases in foraging \u003csup\u003e5,6\u003c/sup\u003e and exacerbate the prevalence of overconsuming plant or algae ecosystems \u003csup\u003e7\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClimate-driven stressors such as OA and OW can intensify herbivore metabolic demand, often without corresponding energetic gains for consumers. These shifts can set the stage for unsustainable consumptive pressure via declines in primary production and/or consumer populations. In ectotherms, OW generally leads to exponential increases in metabolic rates until physiological functions begin to fail \u003csup\u003e8-10\u003c/sup\u003e. \u0026nbsp;Some ectotherms compensate for elevated metabolic demands by increasing consumption \u003csup\u003e11\u003c/sup\u003e, constrained ultimately by handling time \u003csup\u003e12\u003c/sup\u003e. Others may reduce energetic expenditure through behavioral inactivity \u003csup\u003e12,13\u003c/sup\u003e, metabolic downregulation \u003csup\u003e14\u003c/sup\u003e, or by sacrificing growth and reproduction \u003csup\u003e15-17\u003c/sup\u003e. Similarly, OA can lead to increases in energetic costs of calcification, digestion, and acid-base regulation \u003csup\u003e18\u003c/sup\u003e as well as impair mitochondrial functioning \u003csup\u003e19\u003c/sup\u003e.Species differ in their ability to maintain acid-base homeostasis - some ramp up metabolism to compensate for added energetic costs \u003csup\u003e20,21\u003c/sup\u003e, others prioritize resource conservation \u003csup\u003e22,23\u003c/sup\u003e, while others lack regulatory control \u003csup\u003e24\u003c/sup\u003e. While OA frequently compromises fitness in calcifying species \u003csup\u003e5\u003c/sup\u003e, such outcomes depend on both specific organismal physiology as well as interacting extrinsic factors \u003csup\u003e25\u003c/sup\u003e, including OW \u003csup\u003e5,26-31\u003c/sup\u003e. \u0026nbsp; Thus, how dominant herbivores respond to OW, OA, and MHWs can vary dramatically both within and among species \u003csup\u003e29\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEffects of climate change on rates of herbivory will vary in part as a function of how multiple stressors shape consumer energetics and behavior. Climate drivers like OW and OA rarely occur in isolation \u003csup\u003e28\u003c/sup\u003e, yet most empirical response curves are derived from single-stressor experiments. For example, thermal response curves can offer useful insights into nonlinear physiological or behavioral responses, but they are inherently context dependent. Extrinsic factors such as food availability, salinity \u003csup\u003e4\u003c/sup\u003e, OA \u003csup\u003e5\u003c/sup\u003e, and dissolved oxygen \u003csup\u003e32\u003c/sup\u003e can shift these curves substantially. Moreover, interactions among stressors may modify energy intake, allocation, and conversion efficiency in ways that are not additive \u003csup\u003e33\u003c/sup\u003e, ultimately influencing consumer fitness and ecosystem productivity. Although prior studies have shown independent effects of OW and OA, their combined impacts on herbivore energetics and consumption remain poorly understood \u003csup\u003e29,34\u003c/sup\u003e. Quantifying how OW, OA, and marine heatwaves (MHW) interact to influence consumption \u003csup\u003e29,34\u003c/sup\u003e, energetic efficiency, and energy allocation is vital for correlating and scaling physiological responses to broader ecological outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePurple sea urchin barrens in the northeast Pacific offer a striking example of how climate-driven increases in herbivory can trigger non-linear dynamics and ecosystem collapse \u003csup\u003e35,36\u003c/sup\u003e. While climate change is expected to alter sea urchin consumption rates with cascading impacts on kelp forest productivity and viability, the conditions under which this occurs – and the mechanisms involved – remain uncertain \u003csup\u003e29,34,37,38\u003c/sup\u003e. During recent MHWs in the region, extreme warming coincided with purple sea urchin population explosions and the large-scale conversion of productive, biodiverse kelp forests into unproductive barrens \u003csup\u003e35,39\u003c/sup\u003e. This collapse likely reflects not only increased urchin density but also elevated per capita consumption under thermal stress. Once formed, these barrens can persist for decades or more, partly due to the metabolic plasticity and stress tolerance of temperate sea urchins \u003csup\u003e40,41\u003c/sup\u003e, which allows them to survive – and continue consuming – under extreme and resource-limited conditions. This persistent consumption in turn suppresses kelp forest recovery despite the return of optimal ocean temperatures \u003csup\u003e42\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo understand the relationship between climate change, consumption, and individual fitness, we experimentally tested the combined effects of OW, OA, and thermal variability on herbivore energetics. We exposed \u003cem\u003eStrongylocentrotus purpuratus\u003c/em\u003e, a dominant herbivore implicated in widespread kelp forest collapse, to a factorial combination of elevated pCO₂ (600 vs. 1200 μatm), six constant temperatures (10–20 °C), and two ecologically realistic variable temperature regimes simulating El Niño (21–18 °C) and La Niña (18–14 °C) conditions. These dynamic regimes were designed to capture thermal trajectories associated with recent ENSO events and marine heatwaves observed in eastern boundary current systems. For each treatment, we measured size-specific rates of consumption, structural growth, gonadal growth, metabolism, and growth efficiency. We hypothesized that elevated pCO\u003csub\u003e2\u003c/sub\u003e would increase the energetic costs of homeostasis and alter thermal performance responses, consistent with prior work \u003csup\u003e43\u003c/sup\u003e. We also hypothesized that variable thermal regimes would elicit different responses than constant temperatures due to both biological mechanisms (e.g., physiological limits to rapid acclimation) \u003csup\u003e44\u003c/sup\u003e and mathematical consequences of nonlinear thermal performance (i.e., Jensen’s inequality \u003csup\u003e45-47\u003c/sup\u003e, whereby fluctuations around an optimum reduce average performance).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMetabolic and consumption rates in \u003cem\u003eS. purpuratus\u003c/em\u003e increased in response to warming, especially between 10 and 13 °Cwith additional increases under elevated pCO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eat cooler temperatures. However, these increases in consumption were not matched by gains in gonad production or skeletal growth, which declined above 13 °C. Responses to variable temperature regimes were treatment-specific: the La Niña cool water regime elevated consumption but reduced gonad production and food conversion efficiency, while the El Niño warm water regime suppressed skeletal growth without significantly affecting other metrics relative to matched constant temperatures.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eMetabolic rate (RMR)\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eMetabolic rate increased with both temperature and body size, and was generally higher under elevated pCO₂, although this offset appeared to diminish at the highest temperature (Fig. 1a). Model selection favored a linear additive formulation over more complex alternatives, with the additive model receiving 91.5% of total model weight (Supplemental Table S1). In this best-supported model, oxygen consumption rose by roughly 13% per °C and 61% per unit increase in log body mass, while individuals exposed to 1200 µatm pCO₂ exhibited on average 1.6-fold higher metabolic rates (95 % HPD = 1.0–2.3) than those at 600 µatm after accounting for body size and temperature. Posterior estimates indicated positive effects of temperature (β = 0.13 ± 0.03 SE; 95 % HPD = 0.07–0.19), body mass (β = 0.61 ± 0.04 SE; 95 % HPD = 0.53–0.68), and elevated pCO₂ (β₁₂₀₀ = 0.46 ± 0.20 SE; 95 % HPD = 0.07–0.86). The model explained a substantial proportion of variation in metabolic rate, with a Bayesian\u0026nbsp;\u0026nbsp;of 0.82 (95% HPD: 0.78–0.85). Exploratory analysis using the full Gaussian process model, which permitted pCO₂-specific temperature responses, revealed similar overall patterns but indicated that the metabolic difference between pCO₂ treatments narrowed at approximately 20 °C (ratio = 0.9; 95 % HPD = 0.4–1.5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnder variable temperature regimes at ambient pCO₂ (i.e., El Niño and La Niña simulations), RMR declined over time in parallel with the progressive cooling imposed by the treatment (Fig. 1b). This temporal decline was particularly pronounced in the warm El Niño treatment, where respiration rates decreased substantially over time as temperatures cooled. After accounting for body size, mean RMR in the El Niño treatment was substantially lower than in the constant 20 °C treatment despite both having the same mean temperature over the experiment (conditional effect on mean RMR at 20 °C = -2.26 mg h\u003csup\u003e-1\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e AFDM, 95% HPD = [-2.78] - [-1.72]). In contrast, there was no significant difference in RMR between the La Niña and corresponding constant 16 °C treatment (conditional effect on mean RMR at 16 °C = -0.11 mg h\u003csup\u003e-1\u003c/sup\u003e g\u003csup\u003e-1\u003c/sup\u003e AFDM, 95% HPD = [-0.62]- 0.42).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eConsumption rate\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003ePer capita consumption increased nonlinearly with temperature and was consistently elevated under high pCO₂ across cool to moderate temperatures, but this acidification-driven enhancement weakened and ultimately disappeared at the warmest thermal extreme (Fig. 2). Per capita consumption rates increased sharply between 10 and 13 °C, then plateaued through 16 °C, with limited further increase at 20 °C. Across temperatures below 20 °C, consumption was consistently higher under elevated pCO₂ (1200 μatm) relative to ambient conditions (600 μatm). However, this pCO₂ effect diminished at the warmest treatment: at 20 °C, consumption rates converged across pCO₂ levels, and the difference was no longer statistically significant (ratio = 1.32, 95% HPD = 0.78–1.89). In contrast, at elevated pCO₂, per capita consumption 2.14 times faster at 10 °C (95% HPD = 1.16-3.26), 1.74 times faster at 13 °C (95% HPD = 1.10-2.43), 1.59 times faster at 16 °C (95% HPD = 1.11–2.09), 1.58 times faster at 17 °C (95% HPD = 1.17–2.04), 1.53 times faster at 18 °C (95% HPD = 1.11–1.99) relative to ambient pCO₂ treatments at the same temperatures. Model selection favored a Gaussian process formulation with pCO₂-specific thermal responses and additive random tank effect (Supplemental Table S3).\u003c/p\u003e\n\u003cp\u003eVariable thermal regimes had weak and context-dependent effects on per capita consumption under ambient pCO₂. In the La Niña treatment (18–14 °C), consumption was marginally higher than in the corresponding constant 16 °C treatment (mean difference = 0.40 g urchin\u003csup\u003e-1\u003c/sup\u003eday\u003csup\u003e-1\u003c/sup\u003e, 95% HPD = [–0.08]–[0.87]), though the credible interval overlapped zero. By contrast, no meaningful difference in consumption was detected between the warm El Niño treatment (21–18 °C) and its constant 20 °C counterpart (mean difference = 0.14 g urchin\u003csup\u003e-1\u003c/sup\u003eday\u003csup\u003e-1\u003c/sup\u003e, 95% HPD = [–0.34]–[0.60]).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eAssimilation efficiency\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Assimilation efficiency was governed primarily by a nonlinear temperature response and body size, with comparatively weak and context-dependent effects of pCO₂ (Supplemental Figure S7). Model selection strongly supported a pCO₂-specific thermal response captured by a Gaussian-process model including body size (stacking weight = 0.94), with good predictive diagnostics. Assimilation efficiency increased with temperature from 10 to ~16 °C and then plateaued, indicating diminishing gains at warmer conditions. Elevated pCO₂ did not produce a consistent main effect across temperatures: point estimates suggested slightly higher efficiency at high pCO₂ at cooler temperatures, but all pCO₂ contrasts overlapped zero, indicating weak support for a systematic CO₂ effect. In contrast, body size had a clear negative effect, with larger individuals assimilating a smaller proportion of ingested food.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eGonad production and food conversion efficiency\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eGonad production peaked at intermediate temperatures (approx. 16 °C) under constant conditions and was further reduced under the variable La Niña-like regime (Fig.3 – previously shown for wet mass \u003csup\u003e48\u003c/sup\u003e). However, elevated pCO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003ehad no significant effect (conditional effect on mean = 0.10, 95% HPD [-0.48] - [0.64]), 20 °C (conditional effect on mean = 0.27, 95% HPD [-0.28-0.82]), and overall (conditional effect on mean = –0.07, 95% HPD = [–0.47]-0.35) (Supplemental Fig. S10). Production increased allometrically with body size (i.e., test volume), and model stacking weights favored a model including with an additive log-body size, Gaussian process model of temperature (not factored by pCO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003elevel), and random tank effect (Supplemental Table S4).\u003c/p\u003e\n\u003cp\u003eFood conversion efficiency (FCE) declined strongly across most temperatures and was consistently lower under high pCO\u003csub\u003e2\u003c/sub\u003e after accounting for body size. The magnitude of this pCO\u003csub\u003e2\u003c/sub\u003e-related difference weakened as temperatures increased(Fig. 4, Supplemental Table S5, Fig. S11). At the coolest temperatures (10–13 °C), FCE was less than half as efficient under elevated pCO₂ (10 °C odds ratio = 0.42, 95% HPD 0.32–0.52; 13 °C odds ratio = 0.46, 95% HPD 0.37-0.57). This pCO₂ effect diminished at intermediate temperatures (16–18 °C), where FCE remained lower at 1200 μatm pCO₂ but with smaller proportional differences (odds ratios ≈ 0.62–0.82, 95% HPD intervals not overlapping 1). By 20 °C, the contrast was highly uncertain: the posterior mean slightly favored higher FCE under elevated pCO₂ (odds ratio ≈ 1.09), but the 95% HPD interval (0.80–1.39) encompassed both substantial decreases and modest increases. This wide credible interval indicates that the pCO₂ effect essentially disappears at the warmest temperatures due to uniformly low FCE across both treatments.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eSkeletal growth\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAcross all temperatures tested, skeletal growth was substantially higher under ambient pCO₂ (600 μatm) than under elevated pCO₂ (1200 μatm) (Fig. 5). At cooler temperatures (10–13 °C), individuals grew approximately 50% faster under moderate pCO₂ (ratios 1.49–1.50), with tight HPD intervals indicating high certainty. This disadvantage under elevated pCO₂ persisted at intermediate temperatures (16–18 °C), where growth remained 46–65% lower under 1200 μatm. The strongest contrast occurred at 20 °C, where skeletal growth under moderate pCO₂ was ~80% higher than under elevated pCO₂ (ratio = 1.79, 95% HPD = 1.32–2.28) (Supplemental Table S6, Fig. S12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Compared to their constant-temperature counterparts, thermal variability had divergent effects on skeletal growth. El Niño-like conditions (21–18 °C) reduced growth by approximately 38% relative to a constant 20 °C regime (ratio = 0.62, 95% HPD: 0.46–0.79). In contrast, skeletal growth under La Niña-like conditions (18–14 °C) did not differ credibly from the constant 16 °C treatment (ratio = 0.85, 95% HPD: 0.64–1.06) (Fig. 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eClimate change can reshape ecosystems by altering individual physiological performance, per-capita interaction strengths with impacts on both population and community dynamics. Understanding these mechanisms at the individual level can inform forecasts of ecosystem level change under accelerating global change. In marine ecosystems, ocean warming (OW), marine heatwaves (MHW), and acidification (OA) independently and interactively alter organismal fitness and stability of ecosystems. Beyond effects on survival, sublethal stress from combined stressors such as OW and OA can destabilize food webs \u003csup\u003e29,49,50\u003c/sup\u003e and impair ecosystem function \u003csup\u003e51\u003c/sup\u003e through altered consumer-resource dynamics. Yet for most species, the ways in which multiple stressors and variable environmental conditions shape such interactions remain poorly understood \u003csup\u003e29\u003c/sup\u003e. Here, we show that for a dominant herbivore in temperate nearshore ecosystems, OW and OA jointly elevate metabolic and consumption rates through additive effects (Fig. 1 \u0026amp; 2). Critically, these increases yielded little benefit to growth or fecundity for the consumer, suggesting a compensatory response to rising energetic demands. This decoupling of consumption from fitness suggests a climate-driven intensification of herbivory with potentially severe consequences for primary producers generally and the resilience of kelp forest ecosystems in particular.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe know now that OA is no longer a future threat but a present and frequent reality, with conditions once projected for future decades now observed regularly in coastal environments. As nearshore monitoring improves, pCO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003elevels previously considered extreme are increasingly recorded. In the region surrounding our collection site, an upwelling- influenced zone, seasonal mean pCO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003ereaches approximately 600 μatm, with episodic events exceeding 1200 μatm (Supplemental Fig. S1) – values that surpass open-ocean seasonal averages (approx. 405 μatm \u003csup\u003e52\u003c/sup\u003e) and the control set points in many early OA studies (e.g., 380 μatm \u003csup\u003e53\u003c/sup\u003e). Notably, pCO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003elevels above 1200 μatm fall within end-of-century projections under Representative Concentration Pathway (RCP) 8.5 \u003csup\u003e54\u003c/sup\u003e, suggesting that historical experimental baselines may underestimate present-day conditions in our region of study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur results show that contemporary OA elevates metabolic demand and substantially increases per-capita consumption in \u003cem\u003eS. purpuratus\u003c/em\u003e, a species implicated in the widespread collapse of kelp forests along the U.S. West Coast \u003csup\u003e35\u003c/sup\u003e. The observed pCO₂-driven rise in consumption (Fig. 2) tracked closely with increased resting metabolic rates (Fig. 1). At the same time, it occurred alongside a net decline in food conversion efficiency (FCE) (Fig. 4). Together, these patterns are consistent with compensatory feeding to offset elevated energetic costs. These findings offer a complementary mechanism for patterns observed in northern California and Oregon, where strong coastal upwelling and persistently high pCO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003ehave coincided with massive \u003cem\u003eS. purpuratus\u003c/em\u003e population booms and unchecked herbivory, despite poor food availability and widespread starvation \u003csup\u003e35\u003c/sup\u003e. In such environments, climate-driven increases in metabolic stress may intensify foraging pressure even as herbivores gain little energetic return, increasing the likelihood of persistent ecosystem degradation. In resource-limited environments such as overgrazed urchin barrens \u003csup\u003e55\u003c/sup\u003e, this imbalance could also heighten the incidence of “metabolic meltdown” \u003csup\u003e56\u003c/sup\u003e, where ectotherms fail to meet escalating maintenance demands of abiotic environmental stressors due to a lack of food. Elevated pCO\u003csub\u003e2\u003c/sub\u003e likely contributes to this burden by increasing the energetic costs of homeostatic regulation \u003csup\u003e43\u003c/sup\u003e through multiple, interacting physiological pathways.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot all sea urchin populations respond to acidification and warming by ramping up metabolism and feeding. For example, larval \u003cem\u003eS. purpuratus\u003c/em\u003e exposed to combined high temperature and high pCO₂ show depressed metabolic rates rather than compensation \u003csup\u003e57\u003c/sup\u003e, and the tropical grazer \u003cem\u003eTripneustes gratilla\u003c/em\u003e reduces grazing under warming and ocean-warming–acidification scenarios, thereby lowering top-down pressure on seagrass \u003csup\u003e58\u003c/sup\u003e. These contrasting responses likely reflect differences in life stage, proximity to thermal limits, and resource environment: our urchins experienced relatively cool, food-replete conditions that allowed compensatory feeding, whereas larvae near their upper developmental limits or tropical adults near their upper thermal window may have little scope to upregulate metabolism, instead suppressing activity and growth. Seen together, these studies suggest that urchins can either increase or decrease feeding and metabolic rates under global change, depending on whether stressors push them into a compensatory or a depressive energetic regime. Importantly, even metabolically depressed sea urchins \u003csup\u003e40\u003c/sup\u003e at high densities continue to pose a threat to kelp forest recovery.\u003c/p\u003e\n\u003cp\u003eA key vulnerability of many marine invertebrates in acidified conditions is thought to be increased cost of skeletal growth under elevated pCO\u003csub\u003e2\u003c/sub\u003e. While our experiment did not directly measure calcification rates, the observed decline in growth of skeletal elements such as Aristotle’s lantern points to a potential cost of maintaining or producing calcified tissue under elevated pCO\u003csub\u003e2\u003c/sub\u003e (Fig. 5). This anatomical structure may be more sensitive than others to acidification because it is exposed to ambient seawater and thus not protected by soft tissues that could locally buffer the microenvironment where calcification occurs \u003csup\u003e59\u003c/sup\u003e. Nevertheless, elevated pCO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eis known to inhibit calcification by increasing the energetic demands of CaCO\u003csub\u003e3\u003c/sub\u003e precipitation \u003csup\u003e60\u003c/sup\u003e and by accelerating dissolution of existing structures \u003csup\u003e61\u003c/sup\u003e. Although a recent model estimates an approximate 10% rise in energetic costs for calcification under end-of-century OA projections \u003csup\u003e60\u003c/sup\u003e, this likely underestimates costs in nearshore systems, where seawater frequently reaches undersaturation of aragonite (Ω \u0026lt; 1) \u003csup\u003e62\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe energy-homeostasis framework \u003csup\u003e6,43\u003c/sup\u003e posits that organismal tolerance to environmental stress is governed not by survival alone, but by the ability to maintain a balanced energy budget in which limited metabolic resources are continuously reallocated among maintenance, growth, and reproduction under changing conditions. Consistent with an energy-homeostasis framework \u003csup\u003e6,43\u003c/sup\u003e, our findings parallel a wide range of studies showing that many marine consumers remain alive under near-future acidification \u003csup\u003e63\u003c/sup\u003e but incur substantial sublethal energetic costs. Many corals, for example, can partially ameliorate the effects of acidified seawater by actively elevating the pH of the calcifying fluid through proton removal and ion transport, but this buffering is metabolically expensive \u003csup\u003e64-66\u003c/sup\u003e. Mussels (\u003cem\u003eMytilus galloprovincialis\u003c/em\u003e) redirect ATP toward ion transport at the expense of growth \u003csup\u003e67\u003c/sup\u003e, oysters (\u003cem\u003eCrassostrea virginica\u003c/em\u003e) elevate metabolic rate to maintain acid–base balance \u003csup\u003e21\u003c/sup\u003e, gastropods such as \u003cem\u003eConcholepas concholepas\u003c/em\u003e increase maintenance costs under high pCO₂ \u003csup\u003e68\u003c/sup\u003e, intertidal crabs (\u003cem\u003ePetrolisthes cinctipes\u003c/em\u003e) shift energy allocation during early development \u003csup\u003e69\u003c/sup\u003e, and even OA-tolerant fishes like temperate wrasse exhibit higher respiration along CO₂ gradients \u003csup\u003e70\u003c/sup\u003e. Across these diverse taxa, acidification tolerance often masks hidden energetic tradeoffs, including stress responses that preserve short-term survival but erode the energy available for growth and reproduction.\u003c/p\u003e\n\u003cp\u003eEnvironmental variability is increasingly recognized as a fundamental axis of global change. Increased variability can alter physiological performance in ways that cannot be predicted from mean conditions alone \u003csup\u003e71,72\u003c/sup\u003e. In our experiment, thermal variability produced responses that diverged from constant-temperature treatments with the same mean, illustrating the principle that nonlinear thermal performance and thermal variability jointly shape organismal energetics. Experimental work across taxa shows that environmental variability can either buffer or amplify the impacts of stressors relative to constant exposures with the same mean \u003csup\u003e10,71,73\u003c/sup\u003e . This occurs because organisms respond to the full temporal structure of conditions rather than to averages alone. Our findings extend this broader pattern by showing that short-term extreme warming excursions can either suppress or leave unchanged fitness-related traits in sea urchins, depending on the trait in question and the structure of the fluctuation. The reduced skeletal growth and altered metabolic profiles we observed under variable regimes (Fig. 1\u0026amp;5) highlight that ectotherms integrate environmental experience over time, rather than responding to average conditions, and that transient exposures to stressful temperatures can impose energetic costs that reverberate through growth and resource allocation. More broadly, these results underscore that predictions based solely on static thermal means may overlook critical dynamics governing consumer energetics and, ultimately, the strength and stability of trophic interactions under climate change.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverconsumption by dominant herbivores has amplified the ecological impacts of climate change across ecosystems ranging from grasslands \u003csup\u003e74\u003c/sup\u003e and boreal forests \u003csup\u003e75\u003c/sup\u003e to kelp forests \u003csup\u003e35,76\u003c/sup\u003e. Herbivores experiencing environmental stress can intensify consumptive pressure on primary producers to satisfy increasing energy demands. In recent decades, extreme climatic events have become longer, more intense, and more frequent \u003csup\u003e77-79\u003c/sup\u003e. \u0026nbsp; These events often impose severe consequences for ecosystem engineers \u003csup\u003e80\u003c/sup\u003e, biodiversity and ecosystem services \u003csup\u003e81,82\u003c/sup\u003e. These trends are expected to worsen under continued anthropogenic climate change \u003csup\u003e3\u003c/sup\u003e. Our experiments show that contemporary ocean warming and acidification push a key kelp‐grazing urchin into an energetically costly state in which metabolic rate and feeding accelerate while skeletal and gonadal growth stall or decline, especially under high pCO₂ and extreme temperature regimes. This decoupling of consumption from fitness provides a concrete physiological mechanism by which climate change can intensify herbivory even when individual energetic returns are poor. \u0026nbsp;These impacts thereby increasing the likelihood ecosystems tip into and/or persist in overgrazed states. By quantifying how present-day OA and realistic thermal variability interact to reshape metabolism, foraging, and growth, our study refines expectations for consumer energy budgets in coastal ecosystems and provides a foundation for models that link shifting environmental conditions to food web structure and the resilience or collapse of kelp forests.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe addressed our core objectives of assessing the independent and combined energetic effects of 1) high vs moderate pCO\u003csub\u003e2\u003c/sub\u003e crossed with a temperature gradient (i.e., six constant temperatures) and 2) a historical variable ENSO thermal regime using a multifactorial laboratory experiment.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eField collections, acclimation, and initial biometrics\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003ePurple sea urchins, \u003cem\u003eStrongylocentrotus purpuratus\u003c/em\u003e, were hand-collected by SCUBA in Ucluelet, British Columbia, Canada (Lat 48.94˚, Long -125.56˚) at a depth of 20\u0026ndash;25 ft relative to mean tide (n = 515; mean test diameter = 51.26 mm; range = 25.21\u0026ndash;69.23 mm) in September 2021. Individuals were immediately transported to the Hakai Institute Quadra Island Ecological Observatory (HIQIEO) in insulated, aerated 120 L containers. Upon arrival, urchins were transferred to flow-through 260 L fiberglass sea tables and allowed to recover for 48 hours. Seawater was drawn from 20 m depth (relative to chart datum low tide) using a MP Pumps Inc. system (Fraser, MI, USA), filtered (~100 \u0026micro;m mesh), UV sterilized, and delivered to the sea tables via a holding tank at ~2000 L hr⁻\u0026sup1; (turnover rate = 184.62 d⁻\u0026sup1;; mean temperature = 13.0 \u0026deg;C \u0026plusmn; 0.2 SD).\u003c/p\u003e\n\u003cp\u003eTo estimate body size\u0026ndash;specific gonad mass in the source population, we sampled a subset of individuals not used in experimental treatments (see Supplemental Fig. S4; n = 35; mean test diameter = 56.09 mm; range = 42.12\u0026ndash;69.46 mm). We fit an allometric model assuming an exponential relationship between log-transformed body size and gonad mass.\u003c/p\u003e\n\u003cp\u003eFollowing initial recovery, we collected biometric data on experimental individuals prior to transfer to the mesocosm array for secondary acclimation. Measurements included test diameter and height (digital calipers) and wet weight (calibrated digital balance). To allow for repeated measures, each urchin was implanted with a uniquely coded passive integrated transponder (PIT tag; 8 \u0026times; 1.4 mm, 0.027 g; 64-bit ID, FDX-B, 134.2 kHz, read-only) using a sterile injector and hypodermic needle (Oregon RFID, Portland, OR, USA). PIT-tagged individuals were subsequently tracked using a waterproof stick antenna and FDX proximity reader (Oregon RFID). Prior to the main experiment, we conducted a 30-day pilot study in August 2021 at HIQIEO to test the effects of PIT tagging on survivorship and feeding. No mortality or apparent feeding differences were observed between tagged (n = 60) and untagged (n = 60) individuals.\u003c/p\u003e\n\u003cp\u003eFollowing biometry, urchins were randomly assigned to experimental mesocosms, with each mesocosm containing 15 individuals ranging from approximately 45\u0026ndash;65 mm in test diameter.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eMesocosm system and animal husbandry\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe used an array of 32 replicated 340 L acrylic mesocosms supplied with flow-through seawater, each capable of independent control of temperature and pCO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e(see Supplemental Fig\u0026nbsp;\u003ca href=\"#_heading=h.iz0g30ro1lu7\"\u003eS13\u003c/a\u003e, Integrated Aqua Systems, Inc., Vista, CA, USA). To minimize biases associated with photoperiodism \u003csup\u003e72\u003c/sup\u003e, we implemented a uniform lighting regime for all mesocosms using LED fixtures programmed to provide 10L:14D with 2-hour linear light intensity transition periods for dawn and dusk (0-100% from 07:00 to 09:00 \u0026ldquo;dawn\u0026rdquo;, and 100-0% from 17:00 to 19:00 \u0026ldquo;dusk\u0026rdquo;).\u003c/p\u003e\n\u003cp\u003eWe standardized food availability by supplying uniform dry pellets composed of several macroalgal species formulated for the aquaculture of purple sea urchins (Urchinomics Canada Inc., Halifax, NS, Canada). We supplied pellets to sea urchins in all mesocosms \u003cem\u003ead libitum\u003c/em\u003e, removing uneaten food and feces every 72 hours. To optimize access to abundant food, we enclosed subjects and food in aquaculture submerged baskets (61 cm X 61 cm X 21 cm, Morenot Canada Ltd., Campbell River, BC, Canada) such that food was always readily accessible, but movement was not impeded.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eMesocosm thermoregulation\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u0026nbsp;To create ecologically relevant temperature treatments, we scaled our target set points in the aquaria based on historical benthic time series data for purple sea urchins habitat in California, USA (see Supplemental Fig. S8). Specifically, constant set points included 10\u0026nbsp;℃, 13\u0026nbsp;℃, 16\u0026nbsp;℃, 17\u0026nbsp;℃, 18\u0026nbsp;℃, and 20\u0026nbsp;℃. Variable treatments tracked two distinct temporal thermal patterns observed during El Ni\u0026ntilde;o (range: 21-18\u0026nbsp;℃) and La Ni\u0026ntilde;a (range: 18-14\u0026nbsp;℃) Southern Oscillation phases between September and December. Each mesocosm independently maintained thermal treatment conditions using a heat exchanger fitted with a titanium coil regulated by a dual stage digital temperature controller (resolution = 0.1\u0026nbsp;℃, Dwyer Instruments, LLC.\u003csup\u003e\u0026nbsp;\u0026copy;\u003c/sup\u003e, Michigan City, IN, USA). To supply heat exchangers with on-demand cold and warm fluid for down- and up-regulation of temperature, respectively, the mesocosm system employed central cooling and heating plants that distributed fluids in parallel closed loops to each mesocosm. Each mesocosm maintained temperature set points by dynamically operating solenoid valves connected to the heat exchanger. To control for potential sensor drift, we manually checked and re-calibrated, as needed, all temperature probes using traceable digital thermometers every 72 h.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eCarbonate chemistry manipulation\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe crossed our constant thermal treatments with two pCO\u003csub\u003e2\u003c/sub\u003e treatments, 600 \u0026mu;atm (local seasonal average) and elevated at 1200 \u0026mu;atm (frequent local extreme, which is equivalent to IPCC SSP5-8.5 scenario for 2050\u0026nbsp;\u003csup\u003e83\u003c/sup\u003e). To regulate carbonate chemistry in the mesocosm array, we used a central mass flow controller (see Supplemental Fig S6, accuracy \u0026plusmn;0.05% of full scale. MFC, Alicat Scientific\u003csup\u003e\u0026copy;\u003c/sup\u003e) system that delivered precise mixtures of pure CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003egas and dehumidified compressed air. Gas-impermeable airlines distributed these gas mixtures to ceramic diffusers equipped with needle valves (McMaster-Carr, Chicago, IL, USA) for fine-tuning in each mesocosm. To monitor the carbonate chemistry in each mesocosm, we used a combination of continuous pH measurement via electrodes (Durafet III, Honeywell International Inc.\u003csup\u003e\u0026copy;\u003c/sup\u003e) calibrated every 72 hours and weekly discrete water sampling for analysis on a Burke-o-lator (Dakunalytics, LLC.), a device capable of simultaneously measuring pCO\u003csub\u003e2\u003c/sub\u003e, TCO\u003csub\u003e2\u003c/sub\u003e, temperature, and salinity. With these carbonate chemistry measurements, we were able to calculate the rest of the parameters of the system, including aragonite saturation state (\u003cem\u003e\u0026Omega;\u003c/em\u003e\u003csub\u003ea\u003c/sub\u003e) and total alkalinity (\u003cem\u003eA\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eEnergetics\u003c/strong\u003e\u003c/h2\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eRespirometry\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe measured respiration rates as a proxy for metabolic rate, using the resting metabolic rate (RMR) as an approximation of the energetic cost of homeostatic maintenance. We opted to use RMR as an estimate of metabolic state to avoid the confounding effects of active digestion \u003csup\u003e84,85\u003c/sup\u003e. To avoid such postprandial effects (i.e., specific dynamic action), we starved focal subjects for 24 hours prior to respirometry measurements. We quantified rates of respiration by measuring dissolved oxygen (DO) concentration time series using flow-through optical oxygen sensors paired with temperature probes (Presens Precision Sensing GmbH, BioPark, Regensburg, Germany) affixed to custom-built sealed acrylic chambers \u003csup\u003e40\u003c/sup\u003e equipped with gas impermeable tubing and submersible pumps (Eheim GmbH \u0026amp; Co. KG, Germany). To account for displacement volume, we calculated the internal test volume of the urchins (V) from test diameter (D) and height (H) assuming oblate spheroid geometry (V = 4/3\u0026pi; D\u003csup\u003e2\u003c/sup\u003eH) and subtracted this urchin volume from the total chamber system volume. We estimated the amount of metabolically active biomass for an individual by calculating ash-free dry mass (AFDM) for each subject. AFDM quantifies soft tissue biomass while excluding skeletal biomass that does not contribute meaningfully to changes in DO. We calculated AFDM as the difference between dry mass and post-combustion ash mass (i.e., skeletal mass). We measured all mass metrics by weighing samples on a calibrated digital scale. To measure dry mass, we first cracked the test of the urchins and discarded the coelomic fluid, then dried the carcasses for 24 hours at 60\u0026nbsp;℃\u0026nbsp;in a drying oven then weighed the dried carcasses. To measure post-combustion ash mass, we combusted these dried carcasses for six hours at 450\u0026nbsp;℃\u0026nbsp;in a muffle furnace, then weighed the resulting ashes of each carcass. We accounted for background DO dynamics by operating a simultaneous \u0026ldquo;blank\u0026rdquo; respiration chamber in parallel with every set of three focal chambers sensu\u0026nbsp;\u003csup\u003e86\u003c/sup\u003e. The \u0026ldquo;blank\u0026rdquo; chamber was identical to the focal respiration chambers, except that it contained no focal sea urchin and instead only contained treatment seawater. Prior to analyzing measured rates of change in DO, we conducted quality control on raw instrument output data using the R package respR\u0026nbsp;\u003csup\u003e87\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eConsumption rate\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe estimated per capita consumption rates using individual and aggregated feeding trials with four replicate 48-hour aggregated feeding trials throughout the experiment. Because individual consumption can vary by body size, we also conducted one 48-hour solitary set of feeding trials to calibrate the aggregate trials to individual scales. At the beginning of each feeding trial, we supplied subjects with a pre-weighed standardized aliquot of dry food (Urchinomics Canada Inc., Halifax, NS, Canada), noting the time of day when the feeding trial was initiated. At the end of each feeding cycle, we collected all uneaten food in each mesocosm, noting the time of day, and dried it for 24 hours at 60\u0026nbsp;℃\u0026nbsp;in a drying oven. We then calculated consumption rate as the difference in dry weight between the initial food aliquot and the final uneaten food per individual per day. We also measured rates of mass loss of food pellets due to exposure to water (i.e., dissolution and microbial degradation), irrespective of herbivory in each treatment. A one-way ANOVA comparing the effect of treatment on pellet mass loss rates revealed no statistically significant effect of temperature (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e7, 120\u003c/sub\u003e = 0.87, \u003cem\u003eP\u003c/em\u003e = 0.53), pCO\u003csub\u003e2\u003c/sub\u003e (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e1, 119\u003c/sub\u003e = 0.11, \u003cem\u003eP\u003c/em\u003e = 0.73), or their interaction (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e5, 114\u003c/sub\u003e = 0.64, \u003cem\u003eP\u003c/em\u003e = 0.67). To estimate per capita consumption rates, we fit models to data from individual and aggregated feeding trials. For individual feeding trials, urchins were fed in isolation in flow-through tanks set within their larger mesocosms. The individual feeding trials served to estimate an allometric scaling effect of body size (i.e., test volume) on consumption rate and to estimate treatment effects on assimilation efficiency (see section \u003cem\u003eAssimilation efficiency\u003c/em\u003e below). Aggregated feeding trials were set up such that replicate aggregations of seven or eight individuals were contained within standard aquaculture trays.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eGonad mass\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;To quantify changes in body size specific gonadal biomass, we calculated the difference between an estimated initial and measured final state. We estimated initial body size specific gonadal mass using an empirical gonadal mass to body size regression based on wild individuals sampled from the same time and place as the experimental subjects (n = 35, mean test diameter = 56.09 mm, range test diameter = 42.12 \u0026ndash; 69.46 mm, Bayesian \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = 0.21 [95% CI = 0.03 \u0026ndash; 0.40]). To evaluate treatment effects on change in gonadal mass, we calculated the difference between the estimated initial and measured final gonadal mass in each treatment.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eFood conversion efficiency (FCE)\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;We quantified food conversion efficiency to gonadal biomass as the ratio between gonad mass change, \u003cem\u003eG\u003c/em\u003e, and total dry food consumption, \u003cem\u003eR\u003c/em\u003e, between the beginning and end of the experiment (FCE = \u003cem\u003eG\u003c/em\u003e/\u003cem\u003eR\u003c/em\u003e, based on \u003csup\u003e88\u003c/sup\u003e). We accounted for minor differences in the duration of experimental treatments (\u0026plusmn; 3 hr, maximum) by using units of hours in the denominator for calculations of gonad mass change and total dry food consumption.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eSkeletal growth\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe estimated body size specific skeletal growth rates using a mark-recapture technique \u003csup\u003e89,90\u003c/sup\u003e designed to precisely quantify growth of a calcified jaw structure, the \u0026ldquo;demipyramid\u0026rdquo; (i.e., a component of the jaw that supports the teeth, known as Aristotle\u0026rsquo;s lantern). The initial chemical mark involved injecting the antibiotic tetracycline, which has been shown to have no effect on performance in sea urchins \u003csup\u003e91,92\u003c/sup\u003e. Tetracycline is rapidly incorporated into skeletal tissue during calcification and fluoresces under ultraviolet light. To apply the initial mark, we administered tetracycline injections (tetracycline hydrochloride USP, 5 g L autoclaved and 1 \u0026mu;m filtered seawater\u003csup\u003e-1\u003c/sup\u003e) through the peristomal membrane using syringes equipped with sterile 25-gauge hypodermic needles. After the experiment, we dissected the Aristotle\u0026rsquo;s lantern of experimental subjects and dried them for 24 hours at 60\u0026nbsp;℃\u0026nbsp;in a drying oven and subsequently stored the dried Aristotle\u0026rsquo;s lantern in a -20\u0026nbsp;℃\u0026nbsp;freezer for later analysis on a stereoscopic microscope equipped with an ocular micrometer. We measured growth as the distance between the initial mark on the jaw structure (demipyramid) to the top of the newly added calcareous tissue above the mark (Supplemental Fig S5). \u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eAssimilation efficiency\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTo quantify assimilation efficiency (i.e., \u0026ldquo;absorption efficiency\u0026rdquo; \u003csup\u003e93\u003c/sup\u003e), \u003cem\u003eA\u003c/em\u003e, we measured individual rates of ingestion, \u003cem\u003eI\u003c/em\u003e, and egestion, \u003cem\u003eE\u003c/em\u003e, and expressed \u003cem\u003eA\u003c/em\u003e as a ratio between net food retained versus food ingested following the method described in \u003csup\u003e94\u003c/sup\u003e (\u003cem\u003eA\u003c/em\u003e = (\u003cem\u003eI\u003c/em\u003e-\u003cem\u003eE\u003c/em\u003e)/\u003cem\u003eI\u003c/em\u003e). We measured individual rates of ingestion and egestion by temporarily housing urchins in individual containers with mesh tops that permitted free exchange of water, but not fecal pellets or food (Fig S5) for a period of 48 hours. To quantify egestion, we siphoned feces from individual containers onto a 400 \u0026micro;m sieve. We collected fecal pellets from the sieve using a micro spatula and transferred them to pre-weighed aluminum weigh boats. We then measured dry weight of feces after heating samples at 60\u0026nbsp;℃\u0026nbsp;for 24 h in a drying oven.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWe estimated body size specific respiration rate, per capita consumption, assimilation efficiency (i.e., \u0026ldquo;absorption efficiency\u0026rdquo; \u003csup\u003e93\u003c/sup\u003e), gonad production, food conversion efficiency, and skeletal growth in response to temperature and pCO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003etreatments using Bayesian regression models. Efficiency metrics were continuous proportions bounded between zero and one, so we modeled them using a Beta likelihood distribution with a logit link function for the mean and log link function for the precision parameter. All other metrics were continuous and positive, so we modeled them using a Gamma likelihood and log link function. We used vague priors on all models (see below for details). To control for uncertainty in model formulae, we compared candidate model structures using approximate leave-one-out cross validation (loo) and selected model structures based on weights estimated using stacking \u003csup\u003e95,96\u003c/sup\u003e. This method is designed to maximize predictive accuracy of candidate models. We accounted for allometric scaling by including the log of body size metrics (test diameter or test volume) as a covariate. We compared parameter posteriors directly within selected models. Models included a nonparametric Gaussian process term (on the log-scale) for the effect of temperature factored by pCO\u003csub\u003e2\u003c/sub\u003e level. The Gaussian process also included standard deviation (controlling the response scale) and length-scale (controlling the smoothness) hyperparameters. We estimated model posteriors using Stan \u003csup\u003e97\u003c/sup\u003e via the R \u003csup\u003e98\u003c/sup\u003e package brms \u003csup\u003e99\u003c/sup\u003e. We implemented vague priors for all parameters (i.e., wide relative to the scale of expected potential parameter values). These included normal priors \u003cem\u003eN\u003c/em\u003e(0, 1) on population-level (fixed) parameters and Student\u0026rsquo;s t priors (df = 3; mean = 0; scale = 2.5) on group-level (random) hyperparameters and intercepts \u003csup\u003e100\u003c/sup\u003e. To avoid overfitting six temperatures, we used an informative inverse-gamma prior for the Gaussian process length-scale hyperparameter specifically tuned to the covariates \u003csup\u003e99\u003c/sup\u003e. The shape parameter controlling the form of the Gamma likelihood was given a prior with shape, \u003cem\u003ek\u003c/em\u003e = 0.1 and scale, \u003cem\u003eϴ\u0026nbsp;\u003c/em\u003e= 0.1.\u003c/p\u003e\n\u003cp\u003eWe fit our models using 10,000 iterations across four chains, discarding the first half of the iterations per chain as a warm-up, resulting in a posterior sample of 20,000 iterations for each response. We checked that chains converged using visual inspection and each parameter estimate, we confirmed that Rhat (the potential scale-reduction factor) was less than 1.01 and the minimum effective sample size (\u003cem\u003en\u003csub\u003eeff\u003c/sub\u003e\u003c/em\u003e) was greater than 1,000 \u003csup\u003e100\u003c/sup\u003e. To evaluate goodness of fit for our models, we evaluated graphical posterior predictive checks, searching for any systematic differences between model simulations and empirical data \u003csup\u003e100\u003c/sup\u003e and estimated Bayesian \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e values \u003csup\u003e101\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; We thank Eric Peterson and the Hakai Institute for providing laboratory facilities at Quadra Island and technical support. This study was funded by a National Science Foundation grant to D. K. Okamoto (NSF OCE 2023649) and to L. Rogers-Bennett (NSF OCE 2023664) and the Tula Foundation. Additional support was provided by a Professional Association of Diving Instructors (PADI) Foundation Research Grant to N. B. 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R-squared for Bayesian Regression Models. \u003cem\u003eAm Stat\u003c/em\u003e \u003cstrong\u003e73\u003c/strong\u003e, 307-309, doi:10.1080/00031305.2018.1549100 (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8349339/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8349339/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Climate change can alter ecological interactions, including herbivory, and potentially alter thresholds for ecosystem collapse. Yet how multiple stressors and dynamic conditions (i.e., variability) shape these interactions remains unclear. This question is particularly pertinent in the coastal ocean, where factors such as ocean warming (OW) and acidification (OA) shape the physiology of dominant consumers, including sea urchins that can turn kelp forests into barrens. We experimentally quantified how present-day extreme conditions (including temperatures, temperature variability, and ocean acidification) affect herbivory and herbivore energetics of barren-forming purple sea urchins (Strongylocentrotus purpuratus). Metabolic and consumption rates nearly doubled across the range of currently experienced temperatures. When combined with present-day extreme OA conditions (pCO2 = 1200 μatm) sea urchins experienced a further doubling of both metabolic and consumption rates. Despite dramatic increases in consumption rates across these conditions, animals gained little to no growth or reproductive benefits. Energetic efficiency (i.e., growth and reproductive gains per unit energy consumed) declined substantially under contemporary warming, ocean acidification and variable temperature (i.e., El Niño-like dynamics). This acceleration in per capita grazing potential and shift in herbivore fitness has the potential to exacerbate effects of climate change but also may lead to unpredictable and volatile responses at the population and community level. Such results present a mechanistic warning for how extreme climatic events and multiple stressors, such as OA and OW, can drive when and where trophic interactions can lead to collapse of primary production ecosystems experiencing environmental change.","manuscriptTitle":"Contemporary ocean acidification and marine heatwaves shape individual energetics and rates of herbivory in a dominant ecosystem engineer.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 14:39:19","doi":"10.21203/rs.3.rs-8349339/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dcac65cc-cd77-4395-b037-d911afaf823d","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59705426,"name":"Biological sciences/Ecology/Ecophysiology"},{"id":59705427,"name":"Biological sciences/Ecology/Climate-change ecology"},{"id":59705428,"name":"Earth and environmental sciences/Ecology/Ecophysiology"},{"id":59705429,"name":"Earth and environmental sciences/Climate sciences/Ocean sciences/Marine biology"},{"id":59705430,"name":"Biological sciences/Ecology/Biogeochemistry"}],"tags":[],"updatedAt":"2026-01-13T15:21:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 14:39:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8349339","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8349339","identity":"rs-8349339","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}