Breathing in the dark interactive influence of intrinsic and extrinsic factors on stygofauna metabolic rate

Breathing in the dark interactive influence of intrinsic and extrinsic factors on stygofauna metabolic rate | 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 Breathing in the dark interactive influence of intrinsic and extrinsic factors on stygofauna metabolic rate Sarah Boulamail, Lara Marastella Fumarola, Michele Onorato, Stefano Piraino, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6447063/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Metabolic rate is a key physiological trait shaping ecological and evolutionary processes, yet research on subterranean organisms remains scarce. With climate change increasingly impacting groundwater ecosystems, understanding how stygofauna respond to abiotic stressors is vital. We investigated the standard metabolic rate (SMR) of Spelaeomysis bottazzii Caroli, 1924, an endemic groundwater crustacean, under varying temperature and salinity conditions. Using constant volume respirometry, we measured oxygen consumption in 54 individuals across three temperatures (17, 21, and 25°C) and salinities (2, 4, and 6). We also assessed the effects of body mass, sex, and potential Consistent Individual Differences (CIDs) in SMR. S. bottazzii exhibited a notably low SMR compared to epigean related crustaceans, supporting the hypothesis of metabolic suppression in resource-limited environments. SMR scaled with temperature (E = 0.85 eV) and body mass (b = 0.44), though with lower explanatory power than in epigean species. Salinity, sex, and mass-temperature interactions had no significant effect, and no CIDs were detected. Long-term observations revealed slow growth, low mortality, and extended lifespan, indicating a slow-paced life history. These findings enhance our understanding of subterranean metabolic scaling and underscore the importance of studying stygofauna resilience under environmental change. Biological sciences/Ecology/Ecophysiology Earth and environmental sciences/Ecology Earth and environmental sciences/Ecology/Theoretical ecology climate change metabolism SMR stygofauna Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION The rate at which an organism expends energy (i.e., the individual metabolic rate) is one of its most fundamental traits, shaping life histories, reproductive strategies, and behavior (Brown et al., 2004 ; Brandl et al., 2023 ). Despite the central role of metabolism in ecology, the drivers of its variability in biological diversity remain debated. Classical models like the Metabolic Theory of Ecology (MTE) (Brown et al., 2004 ) propose universal scaling laws, predicting that metabolic rate increases allometrically with body mass and exponentially with temperature. However, newer frameworks challenge these generalizations (Glazier, 2022 ), instead viewing metabolism as a dynamic "hub trait" (sensu Glazier & Gjoni, 2024 ) that interacts with physiological, ecological, and evolutionary factors, including Consistent Individual Differences (CIDs) (sensu Dingemanse et al., 2010 ). Resolving these theoretical tensions requires broadening the empirical basis of metabolic ecology, moving beyond traditional model species (Jeltsch et al., 2025 ; Stark et al., 2025 ). In particular, integrating data from species with inherently low metabolic rates (such as subterranean fauna, deep-sea organisms, or polar invertebrates) can offer critical insights to refine metabolic scaling models and improve their applicability across ecosystems with contrasting energy constraints (Glazier, 2014 ). Groundwater ecosystems provide a unique opportunity to investigate the effects of extreme environmental adaptation on individual metabolism (Saccò, et al., 2024). These environments are characterized by distinct conditions, including perpetual darkness, low oxygen levels, resource scarcity, and relatively stable temperatures. Such conditions have driven the evolution of a set of convergent traits among groundwater inhabitants, collectively known as stygofauna. These traits include low metabolic, reproductive, and growth rates, high longevity, high foraging plasticity, decreased pigmentation, and vision, and reduced antipredator behavior (White & Culver, 2012 ). Research on stygofauna metabolism has historically been constrained by the inherent inaccessibility of groundwater habitats, which pose logistical and methodological challenges for in situ sampling and experimental studies (Saccò, et al., 2024). For example, a review by Di Lorenzo et al. ( 2024 a) found only 23 studies on aerobic metabolism of the stygofauna published by December 2023, in stark contrast to the 1,700 studies on surface-dwelling aquatic organisms that live on the surface available in PubMed. Existing studies generally suggest lower resting metabolic rates in stygofauna than in surface species (Wilhelm et al., 2006 ; Di Lorenzo et al. 2024 a), although some findings (Simčič and Sket, 2019 ) report no significant differences. Stygofauna metabolic rates also tend to be less responsive to warming and decline more rapidly at higher temperatures, suggesting vulnerability and lack of adaptability to warming (Di Lorenzo et al., 2024 a). At the interspecific level, metabolic rates scale allometrically with body mass, as predicted by the Metabolic Theory of Ecology (MTE). On the contrary, intraspecific studies report a lack of expected mass scaling (Wilhelm et al., 2006 ; Di Lorenzo et al., 2015 ; Di Lorenzo & Reboleira, 2022 ), which has been interpreted as a physiological adaptation to resource-limited environments specific to adult stygobites (Di Lorenzo et al., 2015 ; Hose et al., 2022 ). Although these pioneer studies provide important information, limited research still hinders a comprehensive understanding of metabolic patterns in the stygofauna and their responses to intrinsic and extrinsic factors (Castaño-Sanchez et al., 2020). The knowledge gap on stygofauna metabolic scaling patterns is particularly critical because these organisms not only serve as valuable models for understanding metabolic scaling across biological diversity, but also inhabit groundwater ecosystems increasingly threatened by rising temperature (Benz, et al., 2024), aridification and seawater intrusion (Richardson, et al., 2024) and other anthropogenic impacts (Becher et al., 2022 ). Environmental changes could endanger these highly specialized organisms, potentially disrupting their ecological stability and long-term survival (Vaccarelli et al., 2023 ). To better predict and mitigate these impacts, it is crucial to investigate how the metabolic rates of the stygofauna respond to multiple interacting environmental stressors (Hose et al., 2022 ). This study investigates the resting aerobic metabolic responses of the stygofauna to variations in temperature and salinity, as projected for coastal aquifers under climate change (Richardson et al., 2024 ). To this end, we used Spelaeomysis bottazzii Caroli 1924, a hypogean phreatic Stygiomysida endemic to Apulia (SE Italy), as a model organism. Constant volume respirometry (CVR) was employed to measure resting metabolic rates across factorial combinations of temperature and salinity. To assess metabolic rate as a "hub-trait" influenced by interacting intrinsic and extrinsic drivers, individuals of different body mass, a driver of metabolic rate and a proxy for age, and both sexes, were examined. Three repeated measurements per individual at varying salinity levels allowed us to investigate CIDs in metabolic rates. Furthermore, data on population demography, sexual dimorphism, somatic growth, and mortality rates were collected to explore covariation between metabolism and other traits. RESULTS Mass and Sex Distribution In total, 107 individuals were collected in a single sampling event using three traps with a 24-hour deployment period. Incubating females were not observed in the sample. The subset of specimens used for the respirometry analysis (the only ones that were sex and weighed) included 31 women and 21 males. Body mass of these individuals ranged from 0.58 mg to 6.67 mg, with an average of 2.93 mg ± 1.33 s.d. The four specimens that exceeded 4.5 mg were all females. There were no significant differences in average body mass (F 1,156 = 2.44, p-value = 0.12) between the sexes (Fig. 3 ). Since these specimens were randomly selected and represent the majority of individuals sampled in the field (excluding the six smaller individuals assigned to growth rate measurements), these statistics can be considered representative of the species' demographic characteristics at the sampling site. Standard Metabolic Rate Of the 162 planned respirometry measurements, three observations were missing due to accidental loss of a specimen during handling, and one was discarded due to a failure in the measurement system for a total of 158 observations. The SMR of the analyzed individuals ranged from 0.02 J day -1 to 6.07 J day -1 , averaging 0.69 J day -1 ± 0.92 s.d. The SMR exhibited an exponential increase with temperature following a Boltzmann-Arrhenius model (activation energy E = 0.85 eV ± 0.43 95% CI, p < 0.001, explaining 10% of the observed variance in the SMR) and an allometric scaling with body mass (scaling exponent b = 0.44 ± 0.35 95% CI, p < 0.016, explaining 5% of the observed variance in the SMR). No significant effects of salinity variation, sex, or interactions between fixed terms on SMR were detected, leading to their exclusion from the model. Additionally, we found no evidence of random consistent individual differences in SMR, either in baseline metabolic rates or in responses to salinity variations (Fig. 4 , Table 1 ). Table 1 Summary of the regression model that describes the scaling of the standard metabolic rate (SMR J day -1 ) with body mass (BM, mg) and temperature (T, K). The model was simplified through stepwise procedure using the Bayesian Information Criterion (BIC), which eliminated nonsignificant predictors (sex, salinity, interaction terms, random variation at individual level) to retain only those with a significant effect on SMR. The full version of the model can be found in Appendix 3 - Data Analysis. log(SMRJ) Predictors Estimates 95% CI p (Intercept) 31.98 14.90–49.06 < 0.001 BM [log] 0.44 0.08–0.79 0.016 -1/(kT) 0.85 0.42–1.28 < 0.001 Observations 158 R 2 / R 2 adjusted 0.153 / 0.143 Growth Rate The Von Bertalanffy growth model was fitted to five of the six individuals observed over a one-year period for growth rate measurements. One individual was excluded due to its initial size being too large, which prevented the nonlinear model from converging. The five individuals included in the analysis had an average length of 4.78 mm ± 0.82 s.d. on day 0 of measurements. After 260 days of observations, their average length reached 6.32 mm ± 0.45 s.d., corresponding to an average growth increase of 32%. (Fig. 5 ). Molting occurred, but was observed in only a few cases, as the exuviae were quickly consumed. The Von Bertalanffy growth model estimated a growth coefficient ( k ) of 0.25 years − 1 [95% CI: 0.17–0.33]. At the start of the measurement period, the estimated age based on the theoretical length at zero ( t 0 ) was 1.99 years [95% CI: 1.27–2.71]. Considerable random variation was observed among individuals, with the estimated time to reach 90% of the maximum length recorded in the field (13.5 mm) ranging from 5 to 13 years. The model predicted a strong covariance between t 0 and k at the individual level, likely an artifact arising from the unknown initial age of the specimens. Consequently, higher growth rates were estimated for the smaller individuals (Table 2 ). Table 2 Summary of the non-linear mixed regression model describing the individual growth in body length (mm) over time (year) observed over 5 individuals kept in laboratory as a von Bertalanffy Growth Function, specifying individual identity as a grouping factor for random variation in k and t 0 . Length (mm) Fixed Effects Estimates 95% CI p k 0.25 0.17–0.33 < 0.001 t 0 -1.99 -2.71 – -1.27 < 0.001 Random Effects StdDev Corr k 0.09 t 0 0.78 0.96 Residual 0.14 Individuals 5 Observations 86 Mortality in Laboratory Conditions Over more than a year of laboratory monitoring, only one of the 107 S. bottazzii individuals died of unknown causes, in addition to one accidental death and those sacrificed for measurements. No mortality occurred during experimental treatments or among 46 undisturbed individuals kept under constant natural conditions. As also observed by Ariani and Wittmann ( 2010 ), these latter individuals developed a visible brown fat deposit beneath their cuticle, and five females produced eggs visible in the egg pouch, although no reproduction events were recorded. DISCUSSION This study provides new information on the metabolic physiology of Spelaeomysis bottazzii . As expected for the stygofauna (White & Culver, 2012 ), the species showed slow metabolism, low mortality, and high longevity under laboratory conditions. SMR increased moderately with temperature and showed weak but positive scaling with body mass, a novel result for the stygofauna. S. bottazzii also exhibited high tolerance to environmental stressors: no mortality or stress signs were observed, and SMR remained unaffected by changes in salinity. No sexual dimorphism or consistent interindividual differences in SMR were detected. The measured SMR of S. bottazzii falls within the range reported for other stygobitic species (Wilhelm et al., 2006 ), suggesting that low metabolic rates may represent a common physiological feature among obligate groundwater fauna. It is approximately five times lower than what was reported for surface-dwelling sympatric Peracarida of similar size tested across a comparable temperature range (Shokri et al., 2025 ) and two times lower than taxonomically related surface-dwelling Mysisda at similar temperature (Chapina et al., 2020 ). Therefore, our study supports the interpretation that stygofauna exhibit extreme adaptations to the subterranean environment, minimizing energy expenditure in resting conditions (White & Culvier, 2012). Consistent with traditional expectations, we observed an exponential increase in S. bottazzii SMR with temperature and an independent allometric increase with body mass. However, the variance cumulatively explained by these two predictors (15% over 158 observations) is considerably lower than that commonly reported for surface-dwelling Peracarida (e.g., 78% over 75 observations, Shokri et al., 2025 ). No significant interaction between body mass and temperature in shaping S. bottazzii SMR was detected, despite expectations of the metabolic boundary hypothesis, which predicts such an effect due to constraints on oxygen supply and diffusion at higher metabolic demands (Glazier, 2010 ). This interaction has been observed in the surface-dwelling Peracarida (Shokri et al., 2025 ). A possible explanation is that the inherently low metabolic rates of the stygofauna do not approach the limitations imposed by internal oxygen diffusion, at least within the temperature range tested in this study. Our findings indicate that the SMR of the stygofauna, while generally conforming to the mass and temperature scaling principles, is also shaped by additional mechanisms likely linked to adaptations to the subterranean environment (Hose et al., 2022 ). For instance, subterranean organisms often exhibit long-term anaerobic metabolism capacity (Pop et al., 2023 ) which may further reduce the explanatory power of body mass and temperature in our scaling analyses based on oxygen consumption. The estimated activation energy (E) for metabolism in this study (0.85 eV ± 0.22, 95% CI) is higher than the average 0.48 eV reported by Jørgensen et al. ( 2022 ) for 314 ectothermic species within their permissive temperature range. Along with the relatively low variance in SMR explained by temperature scaling (approximately 10%), this supports the findings of Di Lorenzo et al. ( 2024 a), which suggest that stygofauna exhibit comparatively lower sensitivity to temperature increases within their physiological limits. S. bottazzii showed no metabolic decline or mortality between 17–25°C, indicating a greater thermal tolerance than typical for the stygofauna (Hose, et al., 2022; Di Lorenzo et al., 2024 a). This is consistent with observations that S. bottazzii migrates from deep, thermally insulated groundwater, where it reproduces, to thermally fluctuating surface waters to feed (Ariani & Wittmann, 2010 ). As a result, individuals experience substantial temperature variation throughout their lifespan and daily activities, which may have selected greater thermal plasticity compared to other, more strictly stygobitic species (MacLean et al., 2019 Fusi et al., 2024 ). Our observation of positive scaling of SMR with body mass contrasts with previous studies reporting a lack of intraspecific scaling in adult stygofauna (Di Lorenzo et al., 2015 ; Di Lorenzo & Reboleira, 2022 ; Wilhelm et al., 2006 ), despite detecting positive scaling trends among developing individuals (Di Lorenzo et al., 2015 ). The previously observed "ametric" scaling pattern (Di Lorenzo et al., 2015 ) has been hypothesized as a physiological adaptation to food-limited environments, potentially unique to the stygofauna (Hose et al., 2022 ). Reconciling our findings with this interpretation requires considering two key aspects: (a) our dataset likely includes a mix of adult and developing individuals, and (b) while the observed mass-scaling trend is statistically significant, it remains weak, accounting for only 5% of the variance. Furthermore, the estimated scaling exponent (0.44 ± 0.18, 95% CI) is lower than the 0.75 predicted by the Metabolic Theory of Ecology (MTE). Our observations suggest that while general SMR mass scaling principles apply to stygofauna, larger individuals may further suppress metabolic rates as an adaptation to the resource and oxygen constraints of subterranean environments. For example, our well-fed specimens developed brown fat deposits (probably an adaptation to sporadic resource availability in subterranean habitats, as suggested by Ariani & Wittmann, 2010 ), which contribute to body mass, but have low specific metabolic activity (Simčič and Sket, 2019 ), potentially influencing the observed scaling relationship. The stable metabolic response across salinity levels supports the view that S. bottazzii is euryhaline (Ariani, 1982; Pesce, 1982 ). Its coastal groundwater origins likely favored the evolution of physiological adaptations to salinity fluctuations, reducing osmoregulatory costs. This is consistent with Stock’s ( 1980 ) Regression Model, which posits that the 'talassoid' stygofauna evolved from isolated littoral ancestors during marine regressions, retaining their ancestral tolerance to salinity stress. Similar metabolic stability across salinity gradients has been observed in other cave-dwelling crustaceans, such as Typhlatya pearsei , which showed no significant variation in energy use across different salinities (Chávez-Solis et al., 2024). This suggests that metabolic resilience to osmotic stress may be a common trait among certain anchialine subterranean crustaceans, allowing them to thrive in dynamic groundwater environments where salinity and resource availability fluctuate over time. However, metabolic responses of the stygofauna can be highly variable, as studies have reported heterogeneous energy use patterns even among closely related syntopic species in anchialine caves (Shapouri et al., 2016 ; Chávez-Solis et al., 2024). Future experiments testing a broader salinity range beyond the limited conditions evaluated in this study are necessary to determine the full sensitivity and physiological limits of S. bottazzii to salinity fluctuations. Expanding these tests will provide a clearer understanding of its tolerance thresholds and adaptive capacity in response to changing groundwater salinity. The absence of pronounced sexual dimorphism in the SMR of S. bottazzii mirrors the patterns observed in other cave-dwelling species, such as Niphargus spp., where reduced metabolic and behavioral differences between sexes have been documented (Premate et al., 2024 ). This contrasts with surface dwelling species, where metabolic rates often show greater individual repeatability and sex-related differentiation (Killen et al., 2013 ; Auer et al., 2018 ), suggesting that the energetic constraints of subterranean environments can limit sexual dimorphism in metabolic rates. Consistently, the absence of individual repeatability in SMR in S. bottazzii points to a plastic metabolic phenotype, rather than a fixed intrinsic trait. One possible explanation is that, in resource-limited groundwater habitats, selection favors energy efficiency and convergence towards similar metabolic optima, homogenizing metabolic rates within populations. Based on observations from five individuals raised in the lab at natural levels of salinity and temperature, S. bottazzii should take on average more than 8 years (± 3 SD) to approximate the maximum field observed body length (13.5 mm). The slow growth rate observed in this study is in the lower range than that observed for other groundwater or cold water organisms (Gottstein et al., 2023 ). It must be considered that our estimates may be influenced by a combination of factors, including model assumptions (notably, the assumption of an asymptotic growth pattern and a uniform maximum size between individuals), the unknown age of the specimens, and the limited duration of the growth cycle covered in the study. Additionally, experimental conditions, such as restricted space (which, while artificial, mirrors the confined nature of groundwater habitats), a stable microclimate and diet, and the absence of interactions with conspecifics or other species, may have further shaped the observed growth patterns. However, they indicate that the growth rate of S. bottazzii is several times lower than most Pericarida species, which typically reach their maximum size within a few months (Fenwick, 1984 ). For example, under controlled laboratory conditions, the surface-dwelling mysid Gastrosaccus roscoffensis can grow nearly 7 mm in length over six weeks (Escánez et al., 2012 ). Our observation of disparity in the life history metrics among subterranean and surface-dwelling species (see Lunghi & Bilandžija, 2022 ) is further reinforced by the near-complete absence of mortality occurring both during the experimental trials and among larger, presumably older individuals that have been maintained in the laboratory, so far, for nearly a year. Given the very low mortality observed, our laboratory survival observations could probably extend beyond the 16 months reported by Ariani and Wittmann ( 2010 ). For comparison, similar experiments conducted on surface-dwelling Peracarida, such as Gammaridae, frequently report higher mortality rates due to stress from handling and susceptibility to infections (pers. obs.). The observed slow growth rate, low mortality, and high longevity indicate that the larger specimens collected in the field, which exceed 13 mm in length, could potentially be more than a decade old. Likewise, Mysida species inhabiting surface waters typically exhibit a maximal lifespan of just 1 to 2 years (Fenwick, 1984 ). Our observation of co-occurring low metabolic, growth, and mortality rates in S. bottazzii is consistent with theories of energy use and life history evolution, including the Pace of Life Syndrome (Réale et al., 2010 ), which links low metabolic rates to slow and prolonged life histories. It also aligns with the visual interactions hypothesis (Siebel & Drazen, 2007) and the Mortality Theory of Ecology (Glazier, 2025 ), which predict reduced metabolic rates and slow pace of life in species experiencing limited predation risk and, thus, reduced need for costly antipredator tissues (e.g. muscles), organs (e.g. eyes), and behaviors (e.g. movement). A low SMR also reflects reduced maintenance costs, allowing energy to be allocated to survival rather than rapid growth (Glazier, 2015 ). This creates a reinforcing feedback loop: slow metabolism restricts growth, while slow growth further reduces energy demands, sustaining long-term metabolic efficiency (Burton et al., 2011 ). The link between slow metabolism, longevity, and shallow metabolic scaling can be reinforced by reduced oxidative stress: maintaining consistently low and relatively size-independent metabolic activity likely minimizes free radical production, especially in larger and, likely, older individuals, reducing cellular damage and extending lifespan without requiring costly repair mechanisms (Paital et al., 2016 ). Our results highlight the remarkable tolerance of S. bottazzii to rising temperatures and salinity changes, as no mortality or signs of physiological stress were observed during the experimental treatments. The absence of sex differences in SMR across variations in temperature and salinity suggests that environmental changes are unlikely to differentially impact the demographic balance between sexes. Additionally, the rigid metabolic response to temperature, the weak response to body mass, and the lack of interaction between mass, temperature, and salinity indicate that climate change is unlikely to drive significant shifts in the species' size distribution. This physiological resilience, combined with the ancient origin of the Stygiomysida order, which has persisted through multiple climate fluctuations and marine transgressions since its emergence (Pesce, 1982 ), suggests that S. bottazzii is a warming and salt-tolerant species. From this perspective, the limited metabolic response to temperature and the absence of a response to salinity variations observed in our study may be related to the "buffer effect" of subterranean habitats against global warming (Kaandorp et al., 2019 ). This relative stability may have contributed to the evolution of a rigid metabolic strategy in S. bottazzii , allowing it to persist in fluctuating but generally buffered conditions of coastal aquifers over geological timescales. However, an alternative interpretation must be considered: the lack of metabolic plasticity could imply a lack of adaptability to a particularly rapidly changing environment, as could happen to groundwater under the current regime of climate change (Benz, et al., 2024). This could have significant ecological consequences. Even if environmental changes remain within the physiological tolerance range of the species, a fixed metabolic strategy can prevent individuals from optimizing energy use under new conditions (Norin & Metcalfe, 2019 ). For instance, rising temperatures and increased salinity due to groundwater depletion or marine intrusions can alter nutrient availability, oxygen solubility (Liso et al., 2020), and the pace of ecological interactions in subterranean ecosystems (Vaccarelli et al., 2023 ). Species with greater metabolic flexibility may gain a selective advantage, while those with constrained metabolic responses, like S. bottazzii , could face reduced fitness and population declines over time. Additionally, if metabolic rigidity extends to other physiological processes such as growth, reproduction, and behavioral responses, S. bottazzii may struggle to cope with long-term environmental instability. This is particularly concerning as groundwater buffering capacity against global warming can be weakened by additional anthropogenic pressures, especially in areas with impact on land use, including urban centers, industrial zones, and agricultural districts (Becher et al., 2022 ). Beyond S. bottazzii , our findings underscore the value of stygofauna as models for studying metabolic adaptation in extreme environments. Groundwater habitats offer a natural laboratory to explore how chronic resource scarcity, stability, and hypoxia shape metabolic strategies. With traits like low metabolic rates and long lifespans, stygofauna help test scaling laws and life history trade-offs in oligotrophic systems, including deep-sea analogs. However, our results show that the standard metabolic rate (SMR) alone is insufficient to assess energy strategies. To capture complete physiological plasticity, future research should measure active metabolic rate, metabolic scope, and anaerobic pathways (Chávez-Solis et al., 2024) and consider variation across life stages and timescales. In the face of growing threats such as climate change and groundwater depletion, understanding the metabolic limits of the stygofauna is vital. Integrating metabolic, ecological, and evolutionary perspectives will enhance both ecological theory and conservation efforts for these vulnerable ecosystems. MATERIALS AND METHODS Model organism Spelaeomysis bottazzii Caroli, 1924 is an anophthalmic stygobiont endemic to Apulia (Pesce, 1982 , Fig. 1 ). It belongs to the Stygiomysida order within Peracarida, a group exclusively composed of highly specialized species for groundwater habitats. Stygiomysida is closely related to Mysida, a predominantly surface dwelling order that is more widely distributed (Höpel et al., 2022 ). S. bottazzii is widely distributed across the coastal aquifers of the Apulia region (Boulamail et al., 2025 ). It is an omnivorous detritivore, primarily grazing on diatoms and microbial films while opportunistically scavenging for additional food sources (Ariani & Wittmann, 2010 ). To release offspring, females migrate closer to the surface, likely to take advantage of greater nutrient availability, before returning to more sheltered subterranean areas (Ariani & Wittmann, 2010 ). Collection site S. bottazzii specimens used in this study have been collected from a cave in the locality of Sant’Isidoro, (Nardò Municipality - Apulia, Italy) (40.222 N − 17.929 E), part of coastal karstic systems extensively connected to the Ionian Sea (Onorato et al., 2017 ; Liso & Parise, 2023 ). The environmental conditions at the collection site are substantially constant throughout the year, with a water temperature around 17°C, salinity around 2 and a pH between 7.00 and 7.65 (Denitto et al., 2006 ). A comprehensive description of the collection site and sampling procedure is provided in Appendix 1 – Collection site. Experimental Procedures Respirometry Experimental Design To evaluate standard aerobic metabolic rates (that is, the minimum metabolic rate required to sustain basic physiological functions in a post-absorptive, inactive state) under varying temperature and salinity conditions, constant volume respirometry (CVR) assays were conducted on three groups of S. bottazzii , each consisting of 18 individuals with a similar size distribution (total: 54 specimens randomly selected from the field sample). Following acclimatization to laboratory conditions, a group was kept at 17°C, matching the water temperature at the collection site, while the other two were exposed to 21 ° C and 25 ° C for two weeks at a salinity of 2 (the natural salinity at the collection site). The first CVR assay was conducted at this initial salinity level, after which the specimens were sequentially exposed to salinities of 4 and 6, each for 24 hours, with oxygen consumption measured at each step. This design allowed for three repeated CVR measurements per specimen at different salinities, but no repeated measurements across temperatures (Fig. 2 ). The specimens used in these assays were the only ones sacrificed in this study to determine dry mass and sex. A detailed description of the acclimation protocols and respirometry procedures is provided in Appendix 2 – Methodology. The complete data set is provided as Supplementary Material ( Respirometry_Dataset.csv ) with this paper. Data Analysis The relationship between the response variable Standard Metabolic Rate (SMR, J day -1 ) and the fixed explanatory variables Body Mass (BM, mg) and Temperature (C) across different salinities (categorical variable with three salinity levels, 2, 4 and 6) and sexes (categorical variable with two levels, male and female) was analyzed by linear mixed ANCOVA. Both SMR and body mass were logarithmically transformed to account for the power relationship between these two variables, while the negative inverse of temperature (1 / kT, where k is the Boltzmann constant, 8.617 × 10⁻⁵ eV K -1 and T is the absolute temperature) was used to model the thermal dependence of metabolic rates according to a Boltzmann-Arrhenius exponential equation (Brown et al., 2004 ). To test the effects of repeated measures on individuals of both sexes at three different salinity levels, we specified individual identity (54 individuals, each of whom was measured three times at different salinity levels) as a grouping factor for random variation in scale coefficient and response to salinity variation. Following Barr ( 2013 ), the model was originally (over)fitted up to second-degree interactions between fixed terms and simplified to the most appropriate parsimonious model via backward stepwise elimination, using the Bayesian Information Criterion (BIC) to test fixed and random terms for elimination (Bates et al., 2015 ). For model fitting and automated simplification, we used the R function buildmer (Voeten, 2023 ). The full version of the model can be found in Appendix 3 - Data Analysis. To determine the relative importance of the predictors in the reduced model, we partitioned the explained variance by averaging over orders (Lindemann et al., 1980) using the R function calc.relimp (Groemping, 2006). Growth Rate Set up To measure growth rates, six of the smallest sampled individuals were isolated in separate microcosms to allow individual recognition. These specimens were kept in darkness, in natural water at 17 ° C and salinity of 2, reflecting the conditions at the collection site, and fed ad libitum with dry fish food (Sera © Nature Micron). Body length was measured regularly for 262 days. Due to the exceptionally slow growth rate of the individuals, a prolonged observation period was required to gather sufficient data to approximate a growth pattern. This unexpected constraint, along with the limited availability of small individuals, prevented replication of the observations under different temperatures and salinity conditions. A detailed description of the growth rate measurement procedures is provided in Appendix 2 – Methodology. The complete dataset is provided as Supplementary Material ( GrowthRate_Dataset.csv ) accompanying this paper. Data Analysis To estimate individual growth trajectories and time to reach adult size, we fitted the von Bertalanffy Growth Model, which describes growth as: Eq. 2 L(t) = L ∞ (1 − e −k(t−t0) ) where L(t) is the length (mm) at age t (year), L ∞ is the asymptotic length (mm), k is the growth rate coefficient (year -1 ), and t 0 ​ (year) represents the theoretical age at which L = 0 . L ∞ was fixed at 13.5 mm, based on the maximum observed sizes in the population. To capture interindividual growth variation, we used nonlinear mixed-effects modeling specifying individual identity as a grouping factor for random variation in k and t 0 . The model was fitted using the nlmer R function (Bates et al., 2015 ). Mortality in Laboratory Conditions The 46 remaining sampled specimens not used for respirometry or growth rate assessment are being maintained long-term in the lab under dark conditions, in natural water at 17 ° C and salinity of 2, mimicking the conditions of their collection site, and fed ad libitum with dry fish food (Sera © Nature Micron). These individuals are regularly counted to track population variations. As the experiment is still ongoing, it has not yet been possible to determine their sex or mass. A detailed description of the maintenance conditions is provided in Appendix 2 – Methodology. Declarations Author names *Sarah Boulamail 1 , Lara Marastella Fumarola 2,3 , Michele Onorato 4,5 Stefano Piraino 2,3 Sara Ventruti 2 , Isabella Serena Liso 4 , Mario Parise 4 , *Francesco Cozzoli 1,2 Author Contribution SB, LMF and FC conceived the ideas and designed methodology; SB and LMF collected the data; SB and FC analysed the data; SB, FC, LMF,MO,SV, SP, ISL and MP led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication. Acknowledgement We sincerely thank Salvatore Inguscio, Il Gruppo Speleologico Leccese ‘Ndronico, Il Gruppo Speleologico Tricase (GST) for their invaluable knowledge and essential support for the specimen collection, and Efrain M. Chávez Solís for sharing his expertise on stygofauna respirometry. Research funded by the project PRIN 2022 STIGE-CLIMAQUIFERI DTA.PN010.014 2022MM8P88_LS8_PRIN2022 - CUP:B53D23012170006. We thank Martina Pulieri, Cristina Di Muri and the LifeWatch Italy Research Infrastructure for the support with data management and publication. SP acknowledges the support of the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union-NextGenerationEU. Project code CN_00000033, Concession Decree No. 1034. On 14 June 2022 adopted by the Italian Ministry of University and research, CUP D33C22000960007, Project title “National Biodiversity Future Center- NBFC” Data Availability Data available on LifeWatch Data Portal: https://data.lifewatchitaly.eu/handle/123456789/129683”. When a DOI will be available for the data, the full data citation will be also given in the reference list. References Ariani, A. P., & Wittmann, K. J. (2010). 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Supplementary Files APPENDIX1COLLECTIONSITE.pdf APPENDIX2METHODOLOGY.pdf APPENDIX3DATAANALYSIS.pdf Cite Share Download PDF Status: Published Journal Publication published 13 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 30 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviews received at journal 07 Jul, 2025 Reviewers agreed at journal 07 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers invited by journal 11 Jun, 2025 Editor assigned by journal 06 Jun, 2025 Editor invited by journal 22 Apr, 2025 Submission checks completed at journal 22 Apr, 2025 First submitted to journal 14 Apr, 2025 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. 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The brown fat deposit is also visible in the abdomen. Image obtained with a stereoscope connected to a computer and using the NIS Elements AR Analysis 4.60.00 64-bit software.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6447063/v1/6c8b8d9c361a5f09021c2cc3.png"},{"id":84791532,"identity":"f6c3ce41-7275-44fc-b316-00307d29ea5b","added_by":"auto","created_at":"2025-06-17 11:26:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113859,"visible":true,"origin":"","legend":"\u003cp\u003egraphical representation of the experimental design. Each group I, II, III was composed of 18 individuals. They were maintained at different temperature levels, corresponding to 17 ° C in blue, 21 ° C in orange, and 25 ° C in red. Each group at constant temperature experienced salinities of 2, 4 and 6, after a 24 hour exposure time.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6447063/v1/7b50220a390416ed23bb109e.png"},{"id":84791533,"identity":"2269f716-7e20-45a1-aa24-fa30acf73200","added_by":"auto","created_at":"2025-06-17 11:26:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30657,"visible":true,"origin":"","legend":"\u003cp\u003eThe bar graph illustrates the body mass distribution of the individuals involved in the respirometry measurements. The bins represent the number of specimens within each size class based on their body mass. Colors indicate sex: turquoise represents males, while coral red represents females.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6447063/v1/ddb3854e5b6230890ccc5ca8.png"},{"id":84791536,"identity":"c70af3b2-0e8e-4ae7-8e26-bfe1c475cf45","added_by":"auto","created_at":"2025-06-17 11:26:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":79703,"visible":true,"origin":"","legend":"\u003cp\u003erelationship between body mass (mg) and Standard Metabolic Rate (J day\u003csup\u003e-1\u003c/sup\u003e) at 17°C (blue), 21°C (orange) and 25 ° C (red), as predicted by Eq. 2 (Table 1). The colored area indicates 95% confidence intervals around the estimates.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6447063/v1/ba9961c7fba663d7b9c2f052.png"},{"id":84791550,"identity":"1f9e3f90-bbb2-4e99-904a-e730f462b454","added_by":"auto","created_at":"2025-06-17 11:26:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":37586,"visible":true,"origin":"","legend":"\u003cp\u003eThe variation in length of the five specimens maintained for 262 days under laboratory conditions was incorporated into the von Bertalanffy mixed Growth Model, as summarized in Table 2. Different individuals are represented in distinct colors.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6447063/v1/ad8f9261ee3c0b5cb4c1d197.png"},{"id":96105083,"identity":"42679709-61a0-4615-b535-4bd168a67e6d","added_by":"auto","created_at":"2025-11-17 16:08:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1209345,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6447063/v1/41fc9340-b46e-4158-aa8a-ee04b3b60e53.pdf"},{"id":84791556,"identity":"17380300-1433-4b20-a257-9012ef2c7854","added_by":"auto","created_at":"2025-06-17 11:26:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":7025594,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIX1COLLECTIONSITE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6447063/v1/873639f5ccb6478f1a3359c1.pdf"},{"id":84792662,"identity":"88fce89a-3c04-4f4f-8434-a280d6a7abda","added_by":"auto","created_at":"2025-06-17 11:42:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":156455,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIX2METHODOLOGY.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6447063/v1/4134e308ac427e1c76955f3e.pdf"},{"id":84791537,"identity":"b8b5561c-6165-437f-b490-cc46a0355eeb","added_by":"auto","created_at":"2025-06-17 11:26:15","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":118385,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIX3DATAANALYSIS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6447063/v1/e1f36d7fe7264974f25b5578.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Breathing in the dark interactive influence of intrinsic and extrinsic factors on stygofauna metabolic rate","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe rate at which an organism expends energy (i.e., the individual metabolic rate) is one of its most fundamental traits, shaping life histories, reproductive strategies, and behavior (Brown et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Brandl et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite the central role of metabolism in ecology, the drivers of its variability in biological diversity remain debated. Classical models like the Metabolic Theory of Ecology (MTE) (Brown et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) propose universal scaling laws, predicting that metabolic rate increases allometrically with body mass and exponentially with temperature. However, newer frameworks challenge these generalizations (Glazier, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), instead viewing metabolism as a dynamic \"hub trait\" (sensu Glazier \u0026amp; Gjoni, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) that interacts with physiological, ecological, and evolutionary factors, including Consistent Individual Differences (CIDs) (sensu Dingemanse et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Resolving these theoretical tensions requires broadening the empirical basis of metabolic ecology, moving beyond traditional model species (Jeltsch et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Stark et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In particular, integrating data from species with inherently low metabolic rates (such as subterranean fauna, deep-sea organisms, or polar invertebrates) can offer critical insights to refine metabolic scaling models and improve their applicability across ecosystems with contrasting energy constraints (Glazier, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGroundwater ecosystems provide a unique opportunity to investigate the effects of extreme environmental adaptation on individual metabolism (Sacc\u0026ograve;, et al., 2024). These environments are characterized by distinct conditions, including perpetual darkness, low oxygen levels, resource scarcity, and relatively stable temperatures. Such conditions have driven the evolution of a set of convergent traits among groundwater inhabitants, collectively known as stygofauna. These traits include low metabolic, reproductive, and growth rates, high longevity, high foraging plasticity, decreased pigmentation, and vision, and reduced antipredator behavior (White \u0026amp; Culver, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch on stygofauna metabolism has historically been constrained by the inherent inaccessibility of groundwater habitats, which pose logistical and methodological challenges for in situ sampling and experimental studies (Sacc\u0026ograve;, et al., 2024). For example, a review by Di Lorenzo et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003ea) found only 23 studies on aerobic metabolism of the stygofauna published by December 2023, in stark contrast to the 1,700 studies on surface-dwelling aquatic organisms that live on the surface available in PubMed.\u003c/p\u003e \u003cp\u003eExisting studies generally suggest lower resting metabolic rates in stygofauna than in surface species (Wilhelm et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Di Lorenzo et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003ea), although some findings (Simčič and Sket, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) report no significant differences. Stygofauna metabolic rates also tend to be less responsive to warming and decline more rapidly at higher temperatures, suggesting vulnerability and lack of adaptability to warming (Di Lorenzo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003ea). At the interspecific level, metabolic rates scale allometrically with body mass, as predicted by the Metabolic Theory of Ecology (MTE). On the contrary, intraspecific studies report a lack of expected mass scaling (Wilhelm et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Di Lorenzo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Di Lorenzo \u0026amp; Reboleira, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which has been interpreted as a physiological adaptation to resource-limited environments specific to adult stygobites (Di Lorenzo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hose et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although these pioneer studies provide important information, limited research still hinders a comprehensive understanding of metabolic patterns in the stygofauna and their responses to intrinsic and extrinsic factors (Casta\u0026ntilde;o-Sanchez et al., 2020).\u003c/p\u003e \u003cp\u003eThe knowledge gap on stygofauna metabolic scaling patterns is particularly critical because these organisms not only serve as valuable models for understanding metabolic scaling across biological diversity, but also inhabit groundwater ecosystems increasingly threatened by rising temperature (Benz, et al., 2024), aridification and seawater intrusion (Richardson, et al., 2024) and other anthropogenic impacts (Becher et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Environmental changes could endanger these highly specialized organisms, potentially disrupting their ecological stability and long-term survival (Vaccarelli et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To better predict and mitigate these impacts, it is crucial to investigate how the metabolic rates of the stygofauna respond to multiple interacting environmental stressors (Hose et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study investigates the resting aerobic metabolic responses of the stygofauna to variations in temperature and salinity, as projected for coastal aquifers under climate change (Richardson et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To this end, we used \u003cem\u003eSpelaeomysis bottazzii\u003c/em\u003e Caroli 1924, a hypogean phreatic Stygiomysida endemic to Apulia (SE Italy), as a model organism. Constant volume respirometry (CVR) was employed to measure resting metabolic rates across factorial combinations of temperature and salinity. To assess metabolic rate as a \"hub-trait\" influenced by interacting intrinsic and extrinsic drivers, individuals of different body mass, a driver of metabolic rate and a proxy for age, and both sexes, were examined. Three repeated measurements per individual at varying salinity levels allowed us to investigate CIDs in metabolic rates. Furthermore, data on population demography, sexual dimorphism, somatic growth, and mortality rates were collected to explore covariation between metabolism and other traits.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eMass and Sex Distribution\u003c/p\u003e \u003cp\u003eIn total, 107 individuals were collected in a single sampling event using three traps with a 24-hour deployment period. Incubating females were not observed in the sample. The subset of specimens used for the respirometry analysis (the only ones that were sex and weighed) included 31 women and 21 males. Body mass of these individuals ranged from 0.58 mg to 6.67 mg, with an average of 2.93 mg\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33 s.d. The four specimens that exceeded 4.5 mg were all females. There were no significant differences in average body mass (F\u003csub\u003e1,156\u003c/sub\u003e = 2.44, p-value\u0026thinsp;=\u0026thinsp;0.12) between the sexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Since these specimens were randomly selected and represent the majority of individuals sampled in the field (excluding the six smaller individuals assigned to growth rate measurements), these statistics can be considered representative of the species' demographic characteristics at the sampling site.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStandard Metabolic Rate\u003c/p\u003e \u003cp\u003eOf the 162 planned respirometry measurements, three observations were missing due to accidental loss of a specimen during handling, and one was discarded due to a failure in the measurement system for a total of 158 observations. The SMR of the analyzed individuals ranged from 0.02 J day\u003csup\u003e-1\u003c/sup\u003e to 6.07 J day\u003csup\u003e-1\u003c/sup\u003e, averaging 0.69 J day\u003csup\u003e-1\u003c/sup\u003e \u0026plusmn; 0.92 s.d. The SMR exhibited an exponential increase with temperature following a Boltzmann-Arrhenius model (activation energy E\u0026thinsp;=\u0026thinsp;0.85 eV\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43 95% CI, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, explaining 10% of the observed variance in the SMR) and an allometric scaling with body mass (scaling exponent b\u0026thinsp;=\u0026thinsp;0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35 95% CI, p\u0026thinsp;\u0026lt;\u0026thinsp;0.016, explaining 5% of the observed variance in the SMR). No significant effects of salinity variation, sex, or interactions between fixed terms on SMR were detected, leading to their exclusion from the model. Additionally, we found no evidence of random consistent individual differences in SMR, either in baseline metabolic rates or in responses to salinity variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the regression model that describes the scaling of the standard metabolic rate (SMR J day\u003csup\u003e-1\u003c/sup\u003e) with body mass (BM, mg) and temperature (T, K). The model was simplified through stepwise procedure using the Bayesian Information Criterion (BIC), which eliminated nonsignificant predictors (sex, salinity, interaction terms, random variation at individual level) to retain only those with a significant effect on SMR. The full version of the model can be found in Appendix 3 - Data Analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003elog(SMRJ)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e95% CI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.90\u0026ndash;49.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBM [log]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u0026ndash;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-1/(kT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u0026ndash;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e / R\u003csup\u003e2\u003c/sup\u003e adjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.153 / 0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGrowth Rate\u003c/p\u003e \u003cp\u003eThe Von Bertalanffy growth model was fitted to five of the six individuals observed over a one-year period for growth rate measurements. One individual was excluded due to its initial size being too large, which prevented the nonlinear model from converging. The five individuals included in the analysis had an average length of 4.78 mm\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82 s.d. on day 0 of measurements. After 260 days of observations, their average length reached 6.32 mm\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45 s.d., corresponding to an average growth increase of 32%. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Molting occurred, but was observed in only a few cases, as the exuviae were quickly consumed.\u003c/p\u003e \u003cp\u003eThe Von Bertalanffy growth model estimated a growth coefficient (\u003cem\u003ek\u003c/em\u003e) of 0.25 years\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e [95% CI: 0.17\u0026ndash;0.33]. At the start of the measurement period, the estimated age based on the theoretical length at zero (\u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e) was 1.99 years [95% CI: 1.27\u0026ndash;2.71]. Considerable random variation was observed among individuals, with the estimated time to reach 90% of the maximum length recorded in the field (13.5 mm) ranging from 5 to 13 years. The model predicted a strong covariance between \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ek\u003c/em\u003e at the individual level, likely an artifact arising from the unknown initial age of the specimens. Consequently, higher growth rates were estimated for the smaller individuals (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the non-linear mixed regression model describing the individual growth in body length (mm) over time (year) observed over 5 individuals kept in laboratory as a von Bertalanffy Growth Function, specifying individual identity as a grouping factor for random variation in \u003cem\u003ek\u003c/em\u003e and \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eLength (mm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eFixed Effects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEstimates\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e95% CI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ek\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u0026ndash;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003et\u003c/b\u003e\u003csub\u003e\u003cb\u003e0\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.71 \u0026ndash; -1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRandom Effects\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eStdDev\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCorr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ek\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003et\u003c/b\u003e\u003csub\u003e\u003cb\u003e0\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidual\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndividuals\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObservations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMortality in Laboratory Conditions\u003c/p\u003e \u003cp\u003eOver more than a year of laboratory monitoring, only one of the 107 \u003cem\u003eS. bottazzii\u003c/em\u003e individuals died of unknown causes, in addition to one accidental death and those sacrificed for measurements. No mortality occurred during experimental treatments or among 46 undisturbed individuals kept under constant natural conditions. As also observed by Ariani and Wittmann (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), these latter individuals developed a visible brown fat deposit beneath their cuticle, and five females produced eggs visible in the egg pouch, although no reproduction events were recorded.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study provides new information on the metabolic physiology of \u003cem\u003eSpelaeomysis bottazzii\u003c/em\u003e. As expected for the stygofauna (White \u0026amp; Culver, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), the species showed slow metabolism, low mortality, and high longevity under laboratory conditions. SMR increased moderately with temperature and showed weak but positive scaling with body mass, a novel result for the stygofauna. \u003cem\u003eS. bottazzii\u003c/em\u003e also exhibited high tolerance to environmental stressors: no mortality or stress signs were observed, and SMR remained unaffected by changes in salinity. No sexual dimorphism or consistent interindividual differences in SMR were detected.\u003c/p\u003e \u003cp\u003eThe measured SMR of \u003cem\u003eS. bottazzii\u003c/em\u003e falls within the range reported for other stygobitic species (Wilhelm et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), suggesting that low metabolic rates may represent a common physiological feature among obligate groundwater fauna. It is approximately five times lower than what was reported for surface-dwelling sympatric Peracarida of similar size tested across a comparable temperature range (Shokri et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and two times lower than taxonomically related surface-dwelling Mysisda at similar temperature (Chapina et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, our study supports the interpretation that stygofauna exhibit extreme adaptations to the subterranean environment, minimizing energy expenditure in resting conditions (White \u0026amp; Culvier, 2012).\u003c/p\u003e \u003cp\u003eConsistent with traditional expectations, we observed an exponential increase in \u003cem\u003eS. bottazzii\u003c/em\u003e SMR with temperature and an independent allometric increase with body mass. However, the variance cumulatively explained by these two predictors (15% over 158 observations) is considerably lower than that commonly reported for surface-dwelling Peracarida (e.g., 78% over 75 observations, Shokri et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNo significant interaction between body mass and temperature in shaping \u003cem\u003eS. bottazzii\u003c/em\u003e SMR was detected, despite expectations of the metabolic boundary hypothesis, which predicts such an effect due to constraints on oxygen supply and diffusion at higher metabolic demands (Glazier, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This interaction has been observed in the surface-dwelling Peracarida (Shokri et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A possible explanation is that the inherently low metabolic rates of the stygofauna do not approach the limitations imposed by internal oxygen diffusion, at least within the temperature range tested in this study.\u003c/p\u003e \u003cp\u003eOur findings indicate that the SMR of the stygofauna, while generally conforming to the mass and temperature scaling principles, is also shaped by additional mechanisms likely linked to adaptations to the subterranean environment (Hose et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, subterranean organisms often exhibit long-term anaerobic metabolism capacity (Pop et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) which may further reduce the explanatory power of body mass and temperature in our scaling analyses based on oxygen consumption.\u003c/p\u003e \u003cp\u003eThe estimated activation energy (E) for metabolism in this study (0.85 eV\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22, 95% CI) is higher than the average 0.48 eV reported by J\u0026oslash;rgensen et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for 314 ectothermic species within their permissive temperature range. Along with the relatively low variance in SMR explained by temperature scaling (approximately 10%), this supports the findings of Di Lorenzo et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003ea), which suggest that stygofauna exhibit comparatively lower sensitivity to temperature increases within their physiological limits.\u003c/p\u003e \u003cp\u003e \u003cem\u003eS. bottazzii\u003c/em\u003e showed no metabolic decline or mortality between 17\u0026ndash;25\u0026deg;C, indicating a greater thermal tolerance than typical for the stygofauna (Hose, et al., 2022; Di Lorenzo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003ea). This is consistent with observations that \u003cem\u003eS. bottazzii\u003c/em\u003e migrates from deep, thermally insulated groundwater, where it reproduces, to thermally fluctuating surface waters to feed (Ariani \u0026amp; Wittmann, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). As a result, individuals experience substantial temperature variation throughout their lifespan and daily activities, which may have selected greater thermal plasticity compared to other, more strictly stygobitic species (MacLean et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e Fusi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur observation of positive scaling of SMR with body mass contrasts with previous studies reporting a lack of intraspecific scaling in adult stygofauna (Di Lorenzo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Di Lorenzo \u0026amp; Reboleira, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wilhelm et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), despite detecting positive scaling trends among developing individuals (Di Lorenzo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The previously observed \"ametric\" scaling pattern (Di Lorenzo et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) has been hypothesized as a physiological adaptation to food-limited environments, potentially unique to the stygofauna (Hose et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReconciling our findings with this interpretation requires considering two key aspects: (a) our dataset likely includes a mix of adult and developing individuals, and (b) while the observed mass-scaling trend is statistically significant, it remains weak, accounting for only 5% of the variance. Furthermore, the estimated scaling exponent (0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18, 95% CI) is lower than the 0.75 predicted by the Metabolic Theory of Ecology (MTE).\u003c/p\u003e \u003cp\u003eOur observations suggest that while general SMR mass scaling principles apply to stygofauna, larger individuals may further suppress metabolic rates as an adaptation to the resource and oxygen constraints of subterranean environments. For example, our well-fed specimens developed brown fat deposits (probably an adaptation to sporadic resource availability in subterranean habitats, as suggested by Ariani \u0026amp; Wittmann, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which contribute to body mass, but have low specific metabolic activity (Simčič and Sket, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), potentially influencing the observed scaling relationship.\u003c/p\u003e \u003cp\u003eThe stable metabolic response across salinity levels supports the view that \u003cem\u003eS. bottazzii\u003c/em\u003e is euryhaline (Ariani, 1982; Pesce, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Its coastal groundwater origins likely favored the evolution of physiological adaptations to salinity fluctuations, reducing osmoregulatory costs. This is consistent with Stock\u0026rsquo;s (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) Regression Model, which posits that the 'talassoid' stygofauna evolved from isolated littoral ancestors during marine regressions, retaining their ancestral tolerance to salinity stress.\u003c/p\u003e \u003cp\u003eSimilar metabolic stability across salinity gradients has been observed in other cave-dwelling crustaceans, such as \u003cem\u003eTyphlatya pearsei\u003c/em\u003e, which showed no significant variation in energy use across different salinities (Ch\u0026aacute;vez-Solis et al., 2024). This suggests that metabolic resilience to osmotic stress may be a common trait among certain anchialine subterranean crustaceans, allowing them to thrive in dynamic groundwater environments where salinity and resource availability fluctuate over time. However, metabolic responses of the stygofauna can be highly variable, as studies have reported heterogeneous energy use patterns even among closely related syntopic species in anchialine caves (Shapouri et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ch\u0026aacute;vez-Solis et al., 2024).\u003c/p\u003e \u003cp\u003eFuture experiments testing a broader salinity range beyond the limited conditions evaluated in this study are necessary to determine the full sensitivity and physiological limits of \u003cem\u003eS. bottazzii\u003c/em\u003e to salinity fluctuations. Expanding these tests will provide a clearer understanding of its tolerance thresholds and adaptive capacity in response to changing groundwater salinity.\u003c/p\u003e \u003cp\u003eThe absence of pronounced sexual dimorphism in the SMR of \u003cem\u003eS. bottazzii\u003c/em\u003e mirrors the patterns observed in other cave-dwelling species, such as \u003cem\u003eNiphargus\u003c/em\u003e spp., where reduced metabolic and behavioral differences between sexes have been documented (Premate et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This contrasts with surface dwelling species, where metabolic rates often show greater individual repeatability and sex-related differentiation (Killen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Auer et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), suggesting that the energetic constraints of subterranean environments can limit sexual dimorphism in metabolic rates.\u003c/p\u003e \u003cp\u003eConsistently, the absence of individual repeatability in SMR in \u003cem\u003eS. bottazzii\u003c/em\u003e points to a plastic metabolic phenotype, rather than a fixed intrinsic trait. One possible explanation is that, in resource-limited groundwater habitats, selection favors energy efficiency and convergence towards similar metabolic optima, homogenizing metabolic rates within populations.\u003c/p\u003e \u003cp\u003eBased on observations from five individuals raised in the lab at natural levels of salinity and temperature, \u003cem\u003eS. bottazzii\u003c/em\u003e should take on average more than 8 years (\u0026plusmn;\u0026thinsp;3 SD) to approximate the maximum field observed body length (13.5 mm). The slow growth rate observed in this study is in the lower range than that observed for other groundwater or cold water organisms (Gottstein et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It must be considered that our estimates may be influenced by a combination of factors, including model assumptions (notably, the assumption of an asymptotic growth pattern and a uniform maximum size between individuals), the unknown age of the specimens, and the limited duration of the growth cycle covered in the study. Additionally, experimental conditions, such as restricted space (which, while artificial, mirrors the confined nature of groundwater habitats), a stable microclimate and diet, and the absence of interactions with conspecifics or other species, may have further shaped the observed growth patterns. However, they indicate that the growth rate of \u003cem\u003eS. bottazzii\u003c/em\u003e is several times lower than most Pericarida species, which typically reach their maximum size within a few months (Fenwick, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). For example, under controlled laboratory conditions, the surface-dwelling mysid \u003cem\u003eGastrosaccus roscoffensis\u003c/em\u003e can grow nearly 7 mm in length over six weeks (Esc\u0026aacute;nez et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur observation of disparity in the life history metrics among subterranean and surface-dwelling species (see Lunghi \u0026amp; Bilandžija, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) is further reinforced by the near-complete absence of mortality occurring both during the experimental trials and among larger, presumably older individuals that have been maintained in the laboratory, so far, for nearly a year. Given the very low mortality observed, our laboratory survival observations could probably extend beyond the 16 months reported by Ariani and Wittmann (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For comparison, similar experiments conducted on surface-dwelling Peracarida, such as Gammaridae, frequently report higher mortality rates due to stress from handling and susceptibility to infections (pers. obs.). The observed slow growth rate, low mortality, and high longevity indicate that the larger specimens collected in the field, which exceed 13 mm in length, could potentially be more than a decade old. Likewise, Mysida species inhabiting surface waters typically exhibit a maximal lifespan of just 1 to 2 years (Fenwick, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur observation of co-occurring low metabolic, growth, and mortality rates in \u003cem\u003eS. bottazzii\u003c/em\u003e is consistent with theories of energy use and life history evolution, including the Pace of Life Syndrome (R\u0026eacute;ale et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which links low metabolic rates to slow and prolonged life histories. It also aligns with the visual interactions hypothesis (Siebel \u0026amp; Drazen, 2007) and the Mortality Theory of Ecology (Glazier, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which predict reduced metabolic rates and slow pace of life in species experiencing limited predation risk and, thus, reduced need for costly antipredator tissues (e.g. muscles), organs (e.g. eyes), and behaviors (e.g. movement). A low SMR also reflects reduced maintenance costs, allowing energy to be allocated to survival rather than rapid growth (Glazier, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This creates a reinforcing feedback loop: slow metabolism restricts growth, while slow growth further reduces energy demands, sustaining long-term metabolic efficiency (Burton et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The link between slow metabolism, longevity, and shallow metabolic scaling can be reinforced by reduced oxidative stress: maintaining consistently low and relatively size-independent metabolic activity likely minimizes free radical production, especially in larger and, likely, older individuals, reducing cellular damage and extending lifespan without requiring costly repair mechanisms (Paital et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results highlight the remarkable tolerance of \u003cem\u003eS. bottazzii\u003c/em\u003e to rising temperatures and salinity changes, as no mortality or signs of physiological stress were observed during the experimental treatments. The absence of sex differences in SMR across variations in temperature and salinity suggests that environmental changes are unlikely to differentially impact the demographic balance between sexes. Additionally, the rigid metabolic response to temperature, the weak response to body mass, and the lack of interaction between mass, temperature, and salinity indicate that climate change is unlikely to drive significant shifts in the species' size distribution.\u003c/p\u003e \u003cp\u003eThis physiological resilience, combined with the ancient origin of the Stygiomysida order, which has persisted through multiple climate fluctuations and marine transgressions since its emergence (Pesce, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), suggests that \u003cem\u003eS. bottazzii\u003c/em\u003e is a warming and salt-tolerant species. From this perspective, the limited metabolic response to temperature and the absence of a response to salinity variations observed in our study may be related to the \"buffer effect\" of subterranean habitats against global warming (Kaandorp et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This relative stability may have contributed to the evolution of a rigid metabolic strategy in \u003cem\u003eS. bottazzii\u003c/em\u003e, allowing it to persist in fluctuating but generally buffered conditions of coastal aquifers over geological timescales.\u003c/p\u003e \u003cp\u003eHowever, an alternative interpretation must be considered: the lack of metabolic plasticity could imply a lack of adaptability to a particularly rapidly changing environment, as could happen to groundwater under the current regime of climate change (Benz, et al., 2024). This could have significant ecological consequences. Even if environmental changes remain within the physiological tolerance range of the species, a fixed metabolic strategy can prevent individuals from optimizing energy use under new conditions (Norin \u0026amp; Metcalfe, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For instance, rising temperatures and increased salinity due to groundwater depletion or marine intrusions can alter nutrient availability, oxygen solubility (Liso et al., 2020), and the pace of ecological interactions in subterranean ecosystems (Vaccarelli et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Species with greater metabolic flexibility may gain a selective advantage, while those with constrained metabolic responses, like \u003cem\u003eS. bottazzii\u003c/em\u003e, could face reduced fitness and population declines over time. Additionally, if metabolic rigidity extends to other physiological processes such as growth, reproduction, and behavioral responses, \u003cem\u003eS. bottazzii\u003c/em\u003e may struggle to cope with long-term environmental instability. This is particularly concerning as groundwater buffering capacity against global warming can be weakened by additional anthropogenic pressures, especially in areas with impact on land use, including urban centers, industrial zones, and agricultural districts (Becher et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond \u003cem\u003eS. bottazzii\u003c/em\u003e, our findings underscore the value of stygofauna as models for studying metabolic adaptation in extreme environments. Groundwater habitats offer a natural laboratory to explore how chronic resource scarcity, stability, and hypoxia shape metabolic strategies. With traits like low metabolic rates and long lifespans, stygofauna help test scaling laws and life history trade-offs in oligotrophic systems, including deep-sea analogs.\u003c/p\u003e \u003cp\u003eHowever, our results show that the standard metabolic rate (SMR) alone is insufficient to assess energy strategies. To capture complete physiological plasticity, future research should measure active metabolic rate, metabolic scope, and anaerobic pathways (Ch\u0026aacute;vez-Solis et al., 2024) and consider variation across life stages and timescales.\u003c/p\u003e \u003cp\u003eIn the face of growing threats such as climate change and groundwater depletion, understanding the metabolic limits of the stygofauna is vital. Integrating metabolic, ecological, and evolutionary perspectives will enhance both ecological theory and conservation efforts for these vulnerable ecosystems.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eModel organism\u003c/p\u003e \u003cp\u003e \u003cem\u003eSpelaeomysis bottazzii\u003c/em\u003e Caroli, 1924 is an anophthalmic stygobiont endemic to Apulia (Pesce, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1982\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It belongs to the Stygiomysida order within Peracarida, a group exclusively composed of highly specialized species for groundwater habitats. Stygiomysida is closely related to Mysida, a predominantly surface dwelling order that is more widely distributed (H\u0026ouml;pel et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eS. bottazzii\u003c/em\u003e is widely distributed across the coastal aquifers of the Apulia region (Boulamail et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It is an omnivorous detritivore, primarily grazing on diatoms and microbial films while opportunistically scavenging for additional food sources (Ariani \u0026amp; Wittmann, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). To release offspring, females migrate closer to the surface, likely to take advantage of greater nutrient availability, before returning to more sheltered subterranean areas (Ariani \u0026amp; Wittmann, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCollection site\u003c/p\u003e \u003cp\u003e \u003cem\u003eS. bottazzii\u003c/em\u003e specimens used in this study have been collected from a cave in the locality of Sant\u0026rsquo;Isidoro, (Nard\u0026ograve; Municipality - Apulia, Italy) (40.222 N \u0026minus;\u0026thinsp;17.929 E), part of coastal karstic systems extensively connected to the Ionian Sea (Onorato et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liso \u0026amp; Parise, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The environmental conditions at the collection site are substantially constant throughout the year, with a water temperature around 17\u0026deg;C, salinity around 2 and a pH between 7.00 and 7.65 (Denitto et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). A comprehensive description of the collection site and sampling procedure is provided in Appendix 1 \u0026ndash; Collection site.\u003c/p\u003e \u003cp\u003eExperimental Procedures\u003c/p\u003e \u003cp\u003eRespirometry\u003c/p\u003e \u003cp\u003eExperimental Design\u003c/p\u003e \u003cp\u003eTo evaluate standard aerobic metabolic rates (that is, the minimum metabolic rate required to sustain basic physiological functions in a post-absorptive, inactive state) under varying temperature and salinity conditions, constant volume respirometry (CVR) assays were conducted on three groups of \u003cem\u003eS. bottazzii\u003c/em\u003e, each consisting of 18 individuals with a similar size distribution (total: 54 specimens randomly selected from the field sample). Following acclimatization to laboratory conditions, a group was kept at 17\u0026deg;C, matching the water temperature at the collection site, while the other two were exposed to 21 \u0026deg; C and 25 \u0026deg; C for two weeks at a salinity of 2 (the natural salinity at the collection site). The first CVR assay was conducted at this initial salinity level, after which the specimens were sequentially exposed to salinities of 4 and 6, each for 24 hours, with oxygen consumption measured at each step. This design allowed for three repeated CVR measurements per specimen at different salinities, but no repeated measurements across temperatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The specimens used in these assays were the only ones sacrificed in this study to determine dry mass and sex. A detailed description of the acclimation protocols and respirometry procedures is provided in Appendix 2 \u0026ndash; Methodology. The complete data set is provided as Supplementary Material (\u003cem\u003eRespirometry_Dataset.csv\u003c/em\u003e) with this paper.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThe relationship between the response variable Standard Metabolic Rate (SMR, J day\u003csup\u003e-1\u003c/sup\u003e) and the fixed explanatory variables Body Mass (BM, mg) and Temperature (C) across different salinities (categorical variable with three salinity levels, 2, 4 and 6) and sexes (categorical variable with two levels, male and female) was analyzed by linear mixed ANCOVA. Both SMR and body mass were logarithmically transformed to account for the power relationship between these two variables, while the negative inverse of temperature (1 / kT, where \u003cem\u003ek\u003c/em\u003e is the Boltzmann constant, 8.617 \u0026times; 10⁻⁵ eV K\u003csup\u003e-1\u003c/sup\u003e and T is the absolute temperature) was used to model the thermal dependence of metabolic rates according to a Boltzmann-Arrhenius exponential equation (Brown et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). To test the effects of repeated measures on individuals of both sexes at three different salinity levels, we specified individual identity (54 individuals, each of whom was measured three times at different salinity levels) as a grouping factor for random variation in scale coefficient and response to salinity variation. Following Barr (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the model was originally (over)fitted up to second-degree interactions between fixed terms and simplified to the most appropriate parsimonious model via backward stepwise elimination, using the Bayesian Information Criterion (BIC) to test fixed and random terms for elimination (Bates et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For model fitting and automated simplification, we used the R function buildmer (Voeten, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The full version of the model can be found in Appendix 3 - Data Analysis. To determine the relative importance of the predictors in the reduced model, we partitioned the explained variance by averaging over orders (Lindemann et al., 1980) using the R function calc.relimp (Groemping, 2006).\u003c/p\u003e \u003cp\u003eGrowth Rate\u003c/p\u003e \u003cp\u003eSet up\u003c/p\u003e \u003cp\u003eTo measure growth rates, six of the smallest sampled individuals were isolated in separate microcosms to allow individual recognition. These specimens were kept in darkness, in natural water at 17 \u0026deg; C and salinity of 2, reflecting the conditions at the collection site, and fed \u003cem\u003ead libitum\u003c/em\u003e with dry fish food (Sera\u003csup\u003e\u003cb\u003e\u0026copy;\u003c/b\u003e\u003c/sup\u003e Nature Micron). Body length was measured regularly for 262 days. Due to the exceptionally slow growth rate of the individuals, a prolonged observation period was required to gather sufficient data to approximate a growth pattern. This unexpected constraint, along with the limited availability of small individuals, prevented replication of the observations under different temperatures and salinity conditions. A detailed description of the growth rate measurement procedures is provided in Appendix 2 \u0026ndash; Methodology. The complete dataset is provided as Supplementary Material (\u003cem\u003eGrowthRate_Dataset.csv\u003c/em\u003e) accompanying this paper.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eTo estimate individual growth trajectories and time to reach adult size, we fitted the von Bertalanffy Growth Model, which describes growth as:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEq.\u0026nbsp;2\u003c/strong\u003e \u003cp\u003e \u003cem\u003eL(t)\u0026thinsp;=\u0026thinsp;L\u003c/em\u003e \u003csub\u003e \u003cem\u003e\u0026infin;\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e(1\u0026thinsp;\u0026minus;\u0026thinsp;e\u003c/em\u003e \u003csup\u003e \u003cem\u003e\u0026minus;k(t\u0026minus;t0)\u003c/em\u003e \u003c/sup\u003e \u003cem\u003e)\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eL(t)\u003c/em\u003e is the length (mm) at age \u003cem\u003et\u003c/em\u003e (year), \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u0026infin;\u003c/em\u003e\u003c/sub\u003e is the asymptotic length (mm), \u003cem\u003ek\u003c/em\u003e is the growth rate coefficient (year\u003csup\u003e-1\u003c/sup\u003e), and \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e​ (year) represents the theoretical age at which \u003cem\u003eL\u0026thinsp;=\u0026thinsp;0\u003c/em\u003e. \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u0026infin;\u003c/em\u003e\u003c/sub\u003e was fixed at 13.5 mm, based on the maximum observed sizes in the population. To capture interindividual growth variation, we used nonlinear mixed-effects modeling specifying individual identity as a grouping factor for random variation in \u003cem\u003ek\u003c/em\u003e and \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e. The model was fitted using the nlmer R function (Bates et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMortality in Laboratory Conditions\u003c/p\u003e \u003cp\u003eThe 46 remaining sampled specimens not used for respirometry or growth rate assessment are being maintained long-term in the lab under dark conditions, in natural water at 17 \u0026deg; C and salinity of 2, mimicking the conditions of their collection site, and fed \u003cem\u003ead libitum\u003c/em\u003e with dry fish food (Sera\u003csup\u003e\u003cb\u003e\u0026copy;\u003c/b\u003e\u003c/sup\u003e Nature Micron). These individuals are regularly counted to track population variations. As the experiment is still ongoing, it has not yet been possible to determine their sex or mass. A detailed description of the maintenance conditions is provided in Appendix 2 \u0026ndash; Methodology.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eAuthor names\u003c/h2\u003e \u003cp\u003e*Sarah Boulamail\u003csup\u003e1\u003c/sup\u003e, Lara Marastella Fumarola \u003csup\u003e2,3\u003c/sup\u003e, Michele Onorato \u003csup\u003e4,5\u003c/sup\u003e Stefano Piraino \u003csup\u003e2,3\u003c/sup\u003e Sara Ventruti \u003csup\u003e2\u003c/sup\u003e, Isabella Serena Liso \u003csup\u003e4\u003c/sup\u003e, Mario Parise \u003csup\u003e4\u003c/sup\u003e, *Francesco Cozzoli \u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSB, LMF and FC conceived the ideas and designed methodology; SB and LMF collected the data; SB and FC analysed the data; SB, FC, LMF,MO,SV, SP, ISL and MP led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe sincerely thank Salvatore Inguscio, Il Gruppo Speleologico Leccese \u0026lsquo;Ndronico, Il Gruppo Speleologico Tricase (GST) for their invaluable knowledge and essential support for the specimen collection, and Efrain M. Ch\u0026aacute;vez Sol\u0026iacute;s for sharing his expertise on stygofauna respirometry. Research funded by the project PRIN 2022 STIGE-CLIMAQUIFERI DTA.PN010.014 2022MM8P88_LS8_PRIN2022 - CUP:B53D23012170006. We thank Martina Pulieri, Cristina Di Muri and the LifeWatch Italy Research Infrastructure for the support with data management and publication. SP acknowledges the support of the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union-NextGenerationEU. Project code CN_00000033, Concession Decree No. 1034. On 14 June 2022 adopted by the Italian Ministry of University and research, CUP D33C22000960007, Project title \u0026ldquo;National Biodiversity Future Center- NBFC\u0026rdquo;\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData available on LifeWatch Data Portal: https://data.lifewatchitaly.eu/handle/123456789/129683\u0026rdquo;. When a DOI will be available for the data, the full data citation will be also given in the reference list.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAriani, A. P., \u0026amp; Wittmann, K. J. (2010). 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A global meta-analysis reveals multilevel and context-dependent effects of climate change on subterranean ecosystems. \u003cem\u003eOne Earth\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(11), 1510\u0026ndash;1522. https://doi.org/10.1016\u003c/li\u003e\n\u003cli\u003eVoeten, C.C., 2023. buildmer: Stepwise elimination and term reordering for mixed-effects regression (Version 2.8)[Computer software] [online]\u003c/li\u003e\n\u003cli\u003eWhite, W. B., \u0026amp; Culver D.C. (2012). Encyclopedia of caves.\u003c/li\u003e\n\u003cli\u003eWilhelm, F. M., Taylor, S. J., Adams, G. L. (2006). Comparison of routine metabolic rates of the stygobite, \u003cem\u003eGammarus acherondytes \u003c/em\u003e(Amphipoda: Gammaridae) and the stygophile, \u003cem\u003eGammarus troglophilus\u003c/em\u003e. \u003cem\u003eFrBio\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(6), 1162\u0026ndash;1174. https://doi.org/10.1111/J.1365-2427.2006.01564.X\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"climate change, metabolism, SMR, stygofauna","lastPublishedDoi":"10.21203/rs.3.rs-6447063/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6447063/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetabolic rate is a key physiological trait shaping ecological and evolutionary processes, yet research on subterranean organisms remains scarce. With climate change increasingly impacting groundwater ecosystems, understanding how stygofauna respond to abiotic stressors is vital. We investigated the standard metabolic rate (SMR) of \u003cem\u003eSpelaeomysis bottazzii\u003c/em\u003e Caroli, 1924, an endemic groundwater crustacean, under varying temperature and salinity conditions. Using constant volume respirometry, we measured oxygen consumption in 54 individuals across three temperatures (17, 21, and 25\u0026deg;C) and salinities (2, 4, and 6). We also assessed the effects of body mass, sex, and potential Consistent Individual Differences (CIDs) in SMR. \u003cem\u003eS. bottazzii\u003c/em\u003e exhibited a notably low SMR compared to epigean related crustaceans, supporting the hypothesis of metabolic suppression in resource-limited environments. SMR scaled with temperature (E\u0026thinsp;=\u0026thinsp;0.85 eV) and body mass (b\u0026thinsp;=\u0026thinsp;0.44), though with lower explanatory power than in epigean species. Salinity, sex, and mass-temperature interactions had no significant effect, and no CIDs were detected. Long-term observations revealed slow growth, low mortality, and extended lifespan, indicating a slow-paced life history. These findings enhance our understanding of subterranean metabolic scaling and underscore the importance of studying stygofauna resilience under environmental change.\u003c/p\u003e","manuscriptTitle":"Breathing in the dark interactive influence of intrinsic and extrinsic factors on stygofauna metabolic rate","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 11:26:10","doi":"10.21203/rs.3.rs-6447063/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-30T09:01:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-10T09:11:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-07T20:41:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321448159885845433875055527135663022111","date":"2025-07-07T18:05:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264155154470940150965567062907090539636","date":"2025-07-02T17:28:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193998832067706348956807045024401050249","date":"2025-07-02T14:40:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-11T16:56:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-06T08:41:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-22T13:17:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-22T06:20:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-14T14:30:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b8dd5dd-d79e-47ab-8965-b5777f78311c","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50025558,"name":"Biological sciences/Ecology/Ecophysiology"},{"id":50025559,"name":"Earth and environmental sciences/Ecology"},{"id":50025560,"name":"Earth and environmental sciences/Ecology/Theoretical ecology"}],"tags":[],"updatedAt":"2025-11-17T16:02:45+00:00","versionOfRecord":{"articleIdentity":"rs-6447063","link":"https://doi.org/10.1038/s41598-025-23493-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-13 15:57:20","publishedOnDateReadable":"November 13th, 2025"},"versionCreatedAt":"2025-06-17 11:26:10","video":"","vorDoi":"10.1038/s41598-025-23493-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-23493-y","workflowStages":[]},"version":"v1","identity":"rs-6447063","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6447063","identity":"rs-6447063","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}