Relative Impact of Environmental (Temperature and pH) and Biological Factors (Size and Growth) on Otolith Trace Elemental Composition

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Hurst, Jessica Adele Miller This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7761636/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Marine Biology → Version 1 posted 5 You are reading this latest preprint version Abstract Using of calcium carbonate (CaCO 3 ) structures as an ecological tool relies on the assumption that, for some elements, their composition is influenced by the water in which an organism lives. However biological processes including, growth rates, diet, ontogeny, reproductive state, or genetics can also influence their composition. It is essential we understand how intrinsic biological factors, external environmental conditions, and interactions impact the composition of CaCO 3 structures to make ecological inferences. We examined how temperature, pH, growth, and body size influenced elemental composition of staghorn sculpin ( Leptocottus armatus ) otoliths. We held animals (108–183 mm length) under three pH (7.60, 7.75, and 7.96) and two temperature (11.5°C and 14.0°C) treatments and examined relationships of three trace element:calcium ratios (Sr:Ca, Ba:Ca, B:Ca) to experimental conditions and body size. Sr:Ca ratios showed a temperature × size interaction, with smaller fish at 11.5°C having higher values than those at 14.0°C, while differences between temperatures diminished at larger sizes. Ba:Ca ratios were lower at 14.0°C across sizes, indicating consistent temperature effects. B:Ca ratios showed weak but statistically significant negative relationships with size. No consistent effects of pH or growth rate were observed. Results highlight that trace element:calcium ratios vary in sensitivity to intrinsic and extrinsic factors, with Sr:Ca influenced by both temperature and body size, Ba:Ca reflecting moderate environmental effects, and B:Ca responding primarily to biological variation. Findings reinforce the value of otolith chemistry as an ecological tool, while emphasising the importance of considering individual-level variation when interpreting elemental signatures. Otolith Chemistry Calcium Carbonate LA-ICP-MS Growth Size Strontium Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Elemental analysis of calcium carbonate (CaCO 3 ) structures such as otoliths, shells, and coral skeletons is used widely to address ecological questions. These structures have been used in several contexts including understanding larval dispersal patterns (Norrie et al. 2020 ), reconstructing migration trajectories (Miller et al. 2010 ), stock discrimination (Tanner et al. 2016 ), age estimate corroborations (e.g. Hüssy et al 2015 ), and climate reconstructions (Amekawa et al. 2016 ). While the application of these methods generally relies on the assumption that, for certain elements, the composition of CaCO 3 structures reflects the physiochemical properties of the water in which an animal resides, biological conditions such as growth rates, diet, ontogeny, reproductive state, and genetic differences can also influence their elemental composition (Sturrock et al. 2015 , Izzo et al. 2018 , Norrie et al. 2019 , Miller & Hurst 2020 ). To make ecologically useful inferences it is essential to understand how intrinsic biological factors, external environmental conditions, and their interactions impact trace elemental composition. There is particular interest in using the trace elemental composition of CaCO 3 structures to reconstruct organismal exposure to stressors associated with climate change such as increased temperatures, decreased pH, or reduced dissolved oxygen (Limburg et al. 2015 , Tanaka et al. 2015 , Reis-Santos et al. 2023 ). For these reconstructions to provide useful information, however, it is essential that there is a predictable relationship between trace element levels within the CaCO 3 matrix and the environmental variable of interest. The elemental composition of calcium carbonate structures is influenced by a range of environmental factors, including the concentration of dissolved elements, temperature, pH, and salinity. Importantly, the degree to which environmental signals are expressed varies between internally and externally calcifying organisms. In organisms that precipitate CaCO 3 in direct contact with seawater such as bivalves and corals, in addition to the concentration of elements in the surrounding seawater, the elemental composition of carbonate structures can reflect environmental variables such as pH (Levin & Frieder 2015 , Norrie et al. 2018 ), temperature (Wanamaker et al. 2008 , D’Olivo et al. 2018 ), and estuarine dynamics such as freshwater inputs or diatom blooms (Gillikin et al. 2006 , Thébault et al. 2009 ). In contrast otoliths, which are the primary focus of CaCO₃ chemistry studies in fish, form internally and are not in direct contact with seawater, making them potentially subject to stronger physiological influences in addition to environmental ones. Physiological effects may explain some of the different relationships between environmental variables and otolith composition that have been observed across studies. For example, the influence of temperature on otolith chemistry is widely reported, but its magnitude and direction can vary by species and context (e.g. Table 2 in Miller and Hurst 2020 ). In contrast, effects of pH have not been consistently detected (Hurst et al. 2012 ; Martino et al. 2017 ). The role of hypoxia is complex, potentially altering otolith chemistry through changes in metal availability, particularly for elements like Mn, while effects on Ba are less consistent (Limburg et al. 2015 ; Mohan & Walther 2014). Understanding these biological influences is essential for interpreting otolith chemical data, particularly in species or systems where environmental signals are subtle or variable. For accurate interpretation of field studies, it is essential to understand how size and growth impact trace element chemistry. These studies often involve collecting individuals from multiple locations (e.g. estuaries, bays, or coastal sites) under the assumption that the chemistry of recently deposited otolith material reflects the environmental conditions at the site of capture and that the individual has been at this location long enough for CaCO 3 material to accrete. This information can be used as a proxy for environmental conditions (Izzo et al. 2017 , Limburg & Casini 2018 ) and the elemental composition of the otolith core can be used to assign individuals to their natal origin (Williams et al. 2018 , Rogers et al. 2019 ). However, if intrinsic factors such as size or growth rate significantly influence otolith composition, these assumptions may not hold. By understanding how biological processes impact otolith microchemistry of individuals held under identical conditions, we can develop analytical and interpretative approaches that can account for such variation, thus improving the accuracy of ecological inferences. This study examined the incorporation of trace elements into otoliths of a common estuarine and marine fish, staghorn sculpin ( Leptocottus armatus ), under a combination of three pH (7.65, 7.78, and ambient (7.96)) and two temperature (11.5°C and 14.0°C) conditions. We assessed how interactions between these environmental conditions and individual size and growth impacted otolith trace elemental concentrations. Using an experimental design that simulated field studies where multiple individuals are sampled from locations under the assumption that all experience the same environmental conditions, we addressed the following questions: 1) Does the otolith trace elemental composition vary between individuals held under differing pH and temperature conditions? 2) Is otolith elemental composition related to body size or growth? 3) Is the otolith elemental composition affected by an interaction between environmental conditions and body morphometrics? METHODS Animal Husbandry Adult staghorn sculpin ( Leptocottus armatus ) were captured from Yaquina Bay in Newport, Oregon, USA (44.618350, -124.057282) with a beach seine net in late October 2020 ( n = 71. Upon capture, fish were transported to Oregon State University’s Hatfield Marine Science Center’s Coastal Ecology Lab in Newport, Oregon. In the lab, adult fish were split equally between six 150 L treatment tanks with varying combinations of environmental conditions. Each experimental tank was randomly assigned to one of six treatments, which were combinations of three pH conditions (ambient ~ 8.00, medium − 7.75, low − 7.60) and two temperature conditions (11.5°C & 14.0°C). These treatment levels were chosen based on Intergovernmental Panel on Climate Change predictions for 2100 under a high emissions scenario (Gattuso et al. 2015 , Jewett & Romanou 2017 , Wuebbles et al. 2017 ). Additionally, during upwelling events on the west coast of the USA the pH can reach 7.l6 or lower (Feely et al. 2019). We developed a pH conditioning system using a Honeywell Durafet pH monitoring and conditioning system based on Hurst et al. ( 2012 , 2019 ). Briefly, coastal oceanic water was pumped from nearby Yaquina Bay during flooding tides into a 3 million litre water reservoir. Water was pumped from the reservoir through a sand filter to three pH conditioning tanks within the fish holding facility. Honeywell Durafet III probes in each conditioning tank continuously monitored pH and regulated the injection of carbon dioxide (CO 2 ) to maintain treatment pH levels (± 0.07). Water from conditioning tanks gravity fed into the six experimental treatment tanks where fish were held. Temperature was manipulated within experimental tanks using individual aquarium heaters coupled to individual thermostats. We recorded experimental parameters (temperature, pH, dissolved oxygen) daily, using handheld probes (YSI ProQuatro Multiparameter Meter, Xylen Inc, Washington DC, USA). As the experiment was conducted indoors a timer was used to match local light/dark cycles. See below for analyses of seawater elemental and carbonate system conditions. We acclimated fish to laboratory conditions for two weeks at ambient conditions (~ 10°C, pH ~ 7.95) prior to starting the experiment. After this period, we adjusted the water temperature by 0.5°C/day and pH by 0.1 unit/day until target conditions were achieved. Staghorn sculpin were then reared under experimental treatment conditions for three months. Fish were fed a combination of Mazuri® Omnivore Aquatic Gel Diet and minced herring. Biomass in each tank was calculated at the start of the experiment, and each tank was fed ad libitum, with any uneaten food removed daily. At the start, midpoint, and conclusion of the experiment, fish were weighed (to the nearest gram) and measured (total length – TL, to the nearest mm). Initial fish sizes were 89–159 mm total length (TL; 118.29 ± 2.2 mm SEM) and 9–50 g wet mass (24.4 ± 1.2 g SEM). Although fish were not individually marked, the size variation within each tank permitted unambiguous identification of individuals throughout the experiment, with identities used in the estimation of individual growth rates (Hurst et al. 2012 , Miller & Hurst 2020 ). At the end of the experiment, fish were sacrificed by immersion in 250 ml/L of tricaine methanesulfonate (MS-222) buffered to a pH of 8.0 with sodium bicarbonate (Institutional Animal Care and Use Committee permit number − 2020 − 0129). During dissection, gonads were visually inspected; and 20 individuals had discernible gonads. Otoliths were removed, cleaned by removing all adhering tissue and rinsing in Milli Q water, and stored dry prior to analyses. Otoliths were embedded in two-part epoxy resin and transverse sections were cut using a Buehler® IsoMet Low Speed cutting machine. Sectioned otoliths were mounted on a glass slide using thermoplastic resin and polished using 3M tri-mite wet-or-dry paper (240-2,000 grit) and diamond lapping film (1–30 µm). LA-ICP-MS analysis We analysed the trace elemental composition of one randomly selected otolith from each fish using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). We collected information on the concentrations of boron ( 11 B), strontium ( 86 Sr), barium ( 137 Ba). We selected strontium and barium as they are strongly linked to ambient water chemistry and are among the most widely used elements in otolith studies of salinity, temperature, and diadromy (Elsdon & Gillanders 2003 ; Kraus & Secor 2004 ; Walther & Thorrold 2006 ). Boron was included because it has shown potential as a proxy for exposure to ocean acidification in biogenic calcium carbonate structures (Levin & Frieder 2015 ; Norrie et al. 2018 ; Limburg et al. 2023 ). We also quantified Co, Mn, and Mg, but although all three elements were detectable, none showed consistent differences among treatments or with biological covariates, so they were not included in further analyses. We used a Photon Machines Analyte G2 laser coupled to a Thermo Scientific X-Series II quadruple ICP-MS. We collected data along a 100-µm transect parallel to the otolith dorsal edge. We used counts of daily otolith increments to ensure that sampled otolith material was deposited while animals were held under experimental conditions. The laser operated at 7 Hz with an output of 75% and target energy of 7 J cm 2 with a spot size of 40 µm that travelled at 2 µm s − 1 across the sample. Mean daily otolith increment width was 1.6 µm (± 0.5 SEM), thus the 40 µm spot ablated material deposited over a 25-day period. To remove any surface contamination each otolith was pre-ablated along the same transect used for sample analyses. The pre-ablation scan ran with at 2 Hz with a spot size of 50 µm and a speed of 100 µm s − 1 . National Institute of Standards and Technology (NIST) 610 and 612 standards were analysed at the start and end of each day and after every 10 otoliths throughout for standardisation purposes and estimates of precision. USGS calcium carbonate standards MACS-1 and MACS-3 were also analysed at the start and end of each run for calculations of accuracy. Otolith analyte count data were normalised by 43 Ca and trace element:calcium ratios (TE:Ca) are presented in µmol:mol (B:Ca, Ba:Ca) or mmol:mol (Sr:Ca). Data was converted to elemental ratios based on measurements of the NIST 612 standard. Precision (% RSD) was calculated from multiple analyses of NIST612 standard (B:Ca, 4.4%, Sr:Ca = 1.5%, Ba:Ca = 0.53%). Accuracy was determined through repeated analysis of MACS-1 and MACS-3 standards (B:Ca = 98.9%, Sr:Ca = 88.0%, Ba:Ca = 96.2%). To describe elemental concentrations in each otolith TE:Ca values across each transect were averaged for each otolith. Water carbonate and trace element analysis To determine if there were any tank differences in trace element composition of water, we measured the Ca, Sr, Ba, and B levels in water samples collected from each experimental tank each month (total n = 3 from each tank) using a Spectros Arcos inductively coupled optical emissions spectrometer (ICP-OES) (Ca at 317.9 nm, B at 249.7 nm, Ba at 233.5 nm, and Sr at 460 nm). Prior to analysis, samples were filtered (0.25 µm) and acidified (< 2 pH) with ultrapure HNO 3 . Matrix matched standards were created using SPEX Certiprep Group® certified reference materials and a NaCl solution. Accuracy was assessed based on repeated measurements of SLRS-6 river water certified reference material. Measured B, Ba, Ca, and Sr concentrations were within 0.5%, 2.2%, 1.9%, and 0.8% of the certified values, respectively ( n = 3). Precision was estimated with repeated measurements of the same sample and varied by < 2% for each element. We compared the concentration of elements between experimental tanks using a one-way analysis of variance (ANOVA). To parameterise the carbonate system, we collected 200 mL samples of seawater once a month which were preserved by the addition of 200 µL mercuric chloride (HgCl 2 ). Dissolved inorganic carbon (DIC) and total alkalinity (TA) were quantified at the Ocean Acidification Research Center at the University of Alaska at Fairbanks. An AIRICA (Automated InfraRed Inorganic Carbon Analyzer) was used to measure DIC and a VINDTA 3C (Versatile Instrument for the Determination of Total dissolved inorganic carbon and Alkalinity) was used to measure TA. The AIRICA and VINDTA 3C instruments were calibrated using Certified Reference Materials (CRMs) from the Dickson Laboratory at the Scripps Institute of Oceanography and a daily correction was applied. Mean deviation of measurements from CRM values was ± 1.40 µmol kg − 1 for DIC and ± 2.00 µmol kg − 1 for TA. These measurements were used to calculate the pH and CO 2 conditions during the experiment based on the dissociation constants of Dickson and Millero (1987). Data analysis Metrics of growth and condition We calculated mass specific (MSGR) and length specific (LSGR) growth rates (percentage increase per day) over the course of the experiment using the formula [ln(final size) -ln(initial size) / experimental duration (in days)]*100. We also used total change in the length-weight residuals (∆LWR) over the course of this experiment to quantify changes in fish condition. Impact of treatment conditions on body size, growth, and condition All statistical analyses were conducted in R (v 4.01 – R Core Team). We first examined if temperature and pH treatments impacted size (final mass and TL), growth (LSGR & MSGR), and condition (∆LWR). We used linear models where temperature, pH, and their interaction were included as fixed effects. Where appropriate, response variables were log-transformed to meet assumptions of homogeneity of variance and normality. Relationships between individual size, growth, and otolith TE:Ca ratios We used linear models with categorical predictors (temperature, pH) and continuous predictors (body size, growth, and condition) to evaluate their effects and interactions on TE:Ca ratios (Sr:Ca, Ba:Ca, and B:Ca). To avoid issues of collinearity among biological traits, we ran separate models in which temperature, pH, and their interaction were assessed alongside a single biological predictor (length, mass, growth, or condition). TE:Ca ratios were log-transformed where necessary to meet assumptions of homogeneity of variance and normality of residuals. We then conducted post hoc pairwise tests using the emtrends function in the ‘emmeans’ package (Lenth et al. 2024). This approach allowed us to evaluate whether individual-level biological variation contributed to differences in TE:Ca ratios within shared environmental conditions. It also allowed us to test whether the effects of biological covariates were consistent across temperature and pH treatments. In field studies, where individual variation is often uncontrolled, such effects could be misattributed to environmental differences among sites. RESULTS Water properties In each tank, mean temperatures were within 0.4°C of target temperatures over the course of the experiment (Table 1). Mean pH in the ambient treatment tanks was 7.96 and the other treatments were within 0.02 pH units of our targets. The trace elemental composition of seawater did not vary among treatments (Table 1) as one-way ANOVAs showed no significant differences existed in water levels of B:Ca ( F 5,18 =0.643, p = 0.67), Sr:Ca ( F 5,18 = 0.652, p = 0.664) or Ba:Ca ( F 5,18 = 1.469, p = 0.248) ratios among tanks/treatments. Size, growth and condition Of 71 animals in the experiment, there were 9 mortalities (Table 2) but there was no clear pattern across treatments or obvious cause of the mortalities. Temperature and pH differences among tanks did not impact fish size, growth, or condition across treatment tanks as indicated by the lack of significant temperature, pH, or interaction effects for TL, mass, LSGR, MSGR, final LWR or ∆LWR (all F 1,56 0.250). Final TL ranged from 108 to 183 mm with a mean of 137.2 mm (± 2.5 SEM) and mass ranged from 14 to 79 g with a mean of 34.7 g (± 2.1 SEM). No significant differences in final size or growth existed between individuals with discernible gonads and those without (final mass: t (52) = -0.85, p = 0.40; final length: t (52) = -1.05, p = 0.297; LSGR : t( 52 ) = 0.04, p = 0.80; MSGR: t( 52 ) = 0.89, p = 0.38) and there were no patterns in maturity across tanks. Table 2 Mean biological attributes (± SEM) of fish from each tank at the conclusion of this study. TL = total length (mm), LSGR = Length specific growth rate (% total length day− 1), Mass (grams), MSG = mass specific growth rate (% body weight day− 1), LWR = Length weight residuals, ∆ LWR = change in length weight residuals, n (mort.) = sample size - mortalities that occurred throughout the experiment shown in brackets. Temp pH TL LSGR Mass MSGR LWR ∆ LWR n (mort.) 11.5 Ambient 142.9 (± 5) 0.18 (± 0.02) 35.8 (± 3.9) 3.65 (± 0.18) -2.0 (± 2.5) -4.3 (± 2.3) 12 (0) 11.5 Medium 139.8 (± 5.2) 0.15 (± 0.02) 37.9 (± 4.9) 3.87 (± 0.18) 2.7 (± 1.5) 4.1 (± 2.4) 10 (2) 11.5 Low 135.5 (± 6.4) 0.20 (± 0.01) 33.0 (± 5.0) 3.80 (± 0.12) 0.8 (± 1.2) -1.9 (± 2.4) 10 (2) 14 Ambient 136.4 (± 7.0) 0.17 (± 0.02) 34.6 (± 6.0) 3.71 (± 0.16) 1.8 (± 2.0) 2.9 (± 2.7) 10 (2) 14 Medium 137.1 (± 11.0) 0.14 (± 0.03) 39.1 (± 9.7) 3.61 (± 0.30) 5.8 (± 2.2) 2.3 (± 3.6) 8 (3) 14 Low 133.9 (± 5.2) 0.17 (± 0.01) 31.7 (± 4.4) 3.60 (± 0.17) 0.5 (± 1.6) -1.3 (± 1.5) 12 (0) Otolith trace elemental composition Overall, models that included body size metrics (length or mass) as covariates explained more variation in otolith trace element ratios than models including other biological covariates such as growth rate or condition as shown by the higher model r 2 values (Table 3; Table 4). These models typically explained between ~ 12–36% of the variation depending on the element examined. In some cases, body size also interacted with environmental variables. Strontium Otolith strontium levels were influenced by an interaction between temperature and body size. Individuals from the 11.5°C temperature treatment exhibited a significant negative relationship between size and Sr:Ca ratios, while at 14.0°C this relationship was weaker (Fig. 4; Table 3; Table 4). The slope between Sr:Ca and final length was − 1.04 at 11.5°C and − 0.34 at 14.0°C, while for final mass the slope was − 0.35 at 11.5°C and − 0.10 at 14.0°C. This indicated that Sr:Ca declined approximately three times more steeply with increasing size in colder water. This resulted in Sr:Ca ratios in smaller individuals being approximately 20% higher than larger fish the 11.5°C treatment. There were no significant effects of pH, LSGR, MSGR, or ΔLWR on Sr:Ca ratios ( p > 0.2). Table 3 Mean otolith trace element:calcium (± SE) ratios of staghorn sculpin held under a combination of varying pH and temperature conditions. Temp pH Sr:Ca (mmol:mol) B:Ca (µmol:mol) Ba:Ca (µmol:mol) 11.5 Ambient 1.96 (± 0.11) 26.4 (± 5.75) 1.34 (± 0.1) 11.5 Medium 2.02 (± 0.13) 25.43 (± 4.28) 1.36 (± 0.18) 11.5 Low 2.23 (± 0.15) 33.95 (± 5.65) 1.41 (± 0.12) 14 Ambient 1.96 (± 0.12) 22.26 (± 3.46) 1.24 (± 0.14) 14 Medium 1.86 (± 0.11) 33.2 (± 11.45) 1.32 (± 0.13) 14 Low 1.82 (± 0.04) 19.43 (± 2.3) 0.99 (± 0.04) Table 4 p values, adjusted r2, and AIC values from linear models examining covariates of final length, final mass, length specific and mass specific (MSGR) growth rates, and length weight residuals (LWR) for each trace element calcium ratio (TE:Ca) investigated in this work. Bold values indicate p < 0.05. For full results including F values and DF see supplementary Table S1. p values TE:Ca Covariate Temp x pH x covariate pH x covariate Temp x covariate Temp x pH Covariate pH Temp Model adjusted r 2 AIC Sr:Ca Final Length 0.93 0.39 0.02 0.13 < 0.001 0.69 0.02 0.3629 -52.984 Final Mass 0.84 0.59 0.02 0.08 < 0.001 0.69 0.03 0.3305 -48.642 LSGR 0.72 0.49 0.56 0.28 0.21 0.79 0.07 -0.0062 -28.301 MSGR 0.38 0.18 0.35 0.13 0.22 0.76 0.07 0.0952 -32.675 LWR 0.25 0.16 0.73 0.12 0.35 0.73 0.07 0.09297 -34.384 B:Ca Final Length 0.23 0.48 0.79 0.11 0.01 0.71 0.21 0.1273 85.439 Final Mass 0.09 0.47 0.85 0.05 0.05 0.43 0.31 0.1491 76.932 LSGR 0.99 0.85 0.6 0.24 0.12 0.75 0.26 -0.05889 95.88 MSGR 0.67 0.11 0.33 0.1 0.13 0.45 0.33 0.09354 82.306 LWR 0.92 0.06 0.68 0.13 0.65 0.59 0.04 0.06913 84.187 Ba:Ca Final Length 0.03 0.13 0.49 0.1 0.02 0.44 0.01 0.284 11.049 Final Mass 0.08 0.17 0.56 0.08 0.03 0.5 0.02 0.2341 17.934 LSGR 0.45 0.56 0.77 0.2 0.4 0.54 0.03 0.0409 26.859 MSGR 0.95 0.33 0.51 0.2 0.22 0.57 0.04 0.04313 25.7 LWR 0.06 0.78 0.92 0.07 0.81 0.46 0.31 0.09491 21.967 Boron Body size weakly, but significantly impacted B:Ca ratios, with negative relationships observed for both total length and mass (Table 4; Fig. 5). Boron levels were not affected by interactions between biological and environmental variables. The slope of the relationship between mass and B:Ca ratios was − 0.04, whereas the relationship between total length and B:Ca was − 0.02. LSGR, MSGR, and ΔLWR were not significantly related to B:Ca ratios (Table 4). Barium There was a consistent negative effect of temperature on Ba:Ca ratios with significant temperature effects in all but one of the models (Table 4; Fig. 6). In the model that included total length as a covariate we observed a significant total length*pH*temperature interaction effect (Table 4). There was also a weak, but significant, negative effect of mass on Ba:Ca ratios (slope = -0.004). No impacts of growth or condition on Ba:Ca ratios were observed. DISCUSSION Spatial variation in the concentration of certain trace elements in otolith is often interpreted as being due to environmental differences between locations. Based on this interpretation otolith chemistry can serve as a useful marker in studies of distribution, movement, mixing, and, possibly, serve as a proxy for environmental conditions. The potential for biological processes to confound these interpretations has become clearer over recent decades (e.g. Sturrock et al. 2015 , Stanley et al. 2015 , Miller & Hurst 2020 ). Few studies, however, explicitly determine how individual size influences otolith chemistry or if size interacts with environmental conditions in determining elemental composition under controlled experimental conditions. Here we used an approach that mimicked field studies that examine individuals from distinct geographic locations (e.g. Grammer et al. 2017 , Pease et al. 2023 ) and demonstrated that biological (i.e. size) and environmental variables (i.e. pH/temperature) can impact trace element levels, both independently and interactively. Of the elements we analysed, Sr, Ba, and B were influenced by biological and/or environmental factors, whereas Co, Mn, and Mg did not vary across treatments, suggesting limited value of these elements as proxies under the conditions tested here. Our results emphasise previous work showing that physiological attributes such as size and growth should be accounted for when making ecological inferences using otolith trace elements to avoid confounding environmental interpretations (Sturrock et al. 2017, Grammer et al. 2017 , Izzo et al. 2018 ). Strontium Strontium is one of the most frequently used elements in otolith trace elemental studies as it reflects environmental conditions (Doubleday et al. 2014 , Reis-Santos et al 2018 , Norrie et al 2022 ). Otolith strontium levels track ambient concentrations (Elsdon & Gillanders 2003 , Walther & Thorrold 2006 ) because Sr²⁺ has a similar ionic radius to Ca²⁺ and can easily substitute for calcium in the CaCO₃ lattice (Doubleday et al 2014 ; Thomas et al. 2017 ). Thus, Sr:Ca ratios are commonly used to reconstruct salinity due to the higher levels of strontium in seawater than in most freshwater systems (Kraus & Secor 2004 , Tian et al. 2021 ). Sr:Ca ratios have also been shown to vary with temperature, although the direction and magnitude of this relationship is inconsistent (Sturrock et al. 2015 , Miller and Hurst 2020 ), and it is often included in multi-elemental suites used for stock delineation and dispersal studies (e.g. Tanner et al 2016 , Rogers et al. 2019 , Teichert et al. 2024 ). Our results, which show an interactive effect of body size and temperature on otolith strontium levels, add to the growing body of evidence that, in addition to environmental influences, organismal physiology can impact otolith strontium levels (Sturrock et al. 2015 , Grammer et al. 2017 , Hussey et al. 2021) and highlight the need to explicitly account for these effects in field studies. To understand how our results could impact the interpretation of field studies, it is important to compare the scale of variation we observed with those documented in situ. Previous work on this same species (Miller 2007 ) reported mean Sr:Ca differences of ~ 10% between estuaries located 200–400 km apart along the Oregon coast. Similarly, studies of other species have detected mean differences in Sr:Ca ratios of 10–20% between sites separated by less than 50 km (e.g. Reis-Santos et al. 2018 ; Aschenbrenner et al. 2016 ). Based on our model predictions, the smallest individual from the 11.5°C treatment in our study (111 mm TL) would have a Sr:Ca ratio 35% higher than that of the largest individual (178 mm TL) from the same treatment. This inter-individual effect is larger than the mean inter-site differences reported at smaller spatial scales, but smaller than the maximum inter-estuary difference of 114% reported in Miller ( 2007 ). At these smaller scales environmental gradients are likely to be narrower which suggests that differences in size or physiology may contribute more substantially to otolith variation than spatial environmental differences. In such cases, differences in Sr:Ca may be misinterpreted as environmental in origin (e.g., salinity differences between habitats) when they may instead reflect biological effects. Indeed, these biological effects may explain some of the variation in strontium levels observed over relatively small scales where environmental conditions may be consistent (Reis Santos et al. 2018, Williams et al 2018 ). Maturity can alter energy allocation and ion regulation, which in turn could affect element incorporation into calcified structures (Sturrock et al. 2015 ). However, no differences in size or growth were detected between fish with and without gonads, and maturity showed no consistent patterns across tanks, suggesting that reproductive state was not likely the driver of the size-related Sr:Ca patterns observed here. Together, these results reinforce that body size rather than maturity status is the key biological factor underlying Sr variation in our study. When using trace elements to assign individuals to a collection or natal location, strontium is generally used as part of a multi-elemental suite. The relative contribution of strontium to the site classification can vary. Thus, carefully balancing the site level discrimination provided by including strontium with potential risks associated with inter individual variation should be carefully considered when using this trace element. This could be achieved by restricting comparisons to individuals of similar life stages or by explicitly including a size covariate in classification models. While size-related variation in Sr:Ca may affect fine-scale spatial assignments, it is unlikely to influence migratory reconstructions in anadromous fish, which rely on detecting large intra-individual shifts between freshwater and marine environments of up to 400% as the concentration as environmental strontium changes between freshwater and marine habitats (Austin et al. 2019 ; Norrie et al. 2022 ). Understanding the mechanisms underlying the interactive effects of size and temperature on strontium incorporation into otoliths is challenging. However, size-related physiological differences likely influence how strontium is processed and partitioned during otolith formation. Elements present as free ions in the endolymph are more likely to substitute for Ca²⁺ in the CaCO₃ fraction and thus reflect ambient strontium concentrations, while those bound to proteins are more likely to be incorporated into the organic matrix and influenced by physiological processes. Because strontium occurs in both fractions (Thomas et al. 2017 ), this dual incorporation pathway complicates the interpretation of Sr signals in otoliths. While previous work has shown negative relationships between growth and Sr:Ca ratios (Stanley et al. 2015 ; Miller & Hurst 2020 ) we did not observe any impact of growth rates on strontium levels in our work. This suggests that size-related variation in Sr:Ca may arise independently of growth rate, highlighting the importance of considering physiological traits beyond growth alone when interpreting otolith chemistry. Barium Although water Ba concentrations are the dominant driver of otolith Ba:Ca ratios (Elsdon & Gillanders 2003 , Walther & Thorrold 2006 , Reis-Santos et al. 2013 ), negative relationships have also been found between barium levels and growth rates (Miller 2011 , Sturrock et al. 2015 ), salinity (Reis-Santos et al. 2013 , Barnes and Gillanders 2013, Stanley et al. 2015 ), and temperature (Elsdon and Gillanders 2002 , 2004 ; Miller 2009 ; Barnes and Gillanders 2013; Reis-Santos et al. 2013 ; Stanley et al. 2015 ). Ba:Ca can also be impacted by diet (Webb et al. 2012 , Izzo et al. 2015 ). The three-way interaction between body size, pH, and temperature, together with the main effects of temperature and body size, highlight the impact of environmental and biological factors on otolith Ba:Ca ratios. Along with strontium, barium is often frequently included in multi-elemental suites used to reconstruct dispersal pathways (e.g. Matta et al. 2019 , Beer et al 2011) and has been used to reconstruct salinity histories of individuals (e.g. Elsdon & Gillanders 2005, Miller 2011 , Shima & Swearer 2016 ). Although there was a significant negative effect of body size on Ba:Ca ratios, this effect was relatively weak, so it is unlikely to obscure spatial differences in otolith chemistry. For example, the largest pairwise difference in mean Ba:Ca ratios between tanks in our study was approximately 140% (0.99 µmol:mol vs 1.41 µmol:mol), whereas differences of 150–200% have been reported to exist between different sites within estuaries or between closely located (10s of km) estuaries (Miller 2007 , Barnes and Gillanders 2013). This suggests that, over broad spatial scales, the size-related variation observed in our study is unlikely to compromise the utility of Ba:Ca for ecological classification. Like strontium, barium can substitute for Ca²⁺ in the CaCO₃ matrix and be incorporated into the proteinaceous fraction of the otolith (Thomas et al. 2017 ). This dual incorporation pathway suggests that both environmental and physiological processes may influence Ba:Ca ratios. For example, free Ba²⁺ ions are more likely to be incorporated into the carbonate matrix and thus reflect ambient concentrations, while protein-bound barium may be more sensitive to individual physiology. Processes such as ion transport, protein synthesis, and metabolic activity could therefore contribute to residual variation in Ba:Ca ratios even under stable environmental conditions (Loewen et al. 2016 ; Sturrock et al. 2015 , Hüssy et al. 2021 ). While the effects observed here were small, considering individual characteristics such as body size may improve the interpretation of otolith Ba:Ca patterns in ecological studies. Boron Although recent studies suggest links between boron incorporation into carbonate structures and dissolved oxygen and pH variability (Norrie et al. 2018 ; Cavole et al. 2023 ; Limburg et al. 2023 ), the environmental drivers of B:Ca ratios in fish otoliths remain poorly defined. In our study, body size had a weak but statistically significant effect on B:Ca ratios, while no consistent effects of pH were observed. B:Ca only varied with temperature in one model, and this was dependent on the inclusion of a condition index as a covariate. Our results are consistent with Matta et al. ( 2019 ), who also reported a negative relationship between body size and B:Ca ratios in juvenile fish. However, the magnitude of biologically driven variation in our study was small: for example, the predicted ~ 50% difference in B:Ca between the smallest and largest individuals was less than the 70–300% variation reported across spatial or environmental gradients in field studies (e.g., Limburg et al. 2023 ; Matta et al. 2019 ; Cavole et al. 2023 ). This suggests that, in most field applications, size-related variation is unlikely to obscure ecologically meaningful spatial differences in B:Ca. Our findings are also consistent with previous experimental studies in fish that found no detectable effect of short-term pH variation on otolith elemental incorporation (e.g., Hurst et al. 2012 ; Martino et al. 2017 ; Cavole et al. 2023 ). In contrast, studies on invertebrates that precipitate CaCO₃ in direct contact with seawater have shown strong pH effects on boron incorporation. In these organisms, reduced pH increases the availability of the borate ion B(OH) 4 ⁻, which are more readily incorporated into the carbonate matrix (Levin & Frieder 2015 ; Norrie et al. 2018 ; Gagnon et al. 2021 ). Fish otoliths, however, are internally formed structures. Ions must cross epithelial barriers in the gill or gut and move through the bloodstream to reach the endolymph, where biomineralization occurs (Thomas et al. 2017 , Hussey et al. 2021). This physiological separation from ambient seawater, combined with ion–protein interactions and active homeostatic regulation, likely limits the impact of environmental pH on otolith boron levels (Sturrock et al. 2015 ; Thomas et al. 2017 ; Hüssy et al. 2021 ). Thus, unlike invertebrate structures, fish otoliths may not reliably record ambient pH variation. While boron may not serve as a sensitive proxy for pH in otoliths, it could still contribute to multi-metric approaches aimed at reconstructing environmental histories. For example, when combined with other elemental or isotopic markers, boron has the potential to strengthen overall interpretations. Integrating multiple proxies may help disentangle the complex interplay of environmental stressors, particularly in systems experiencing simultaneous changes. Caveats and future directions Our analyses allowed us to examine how individual variation in otolith chemistry arises when fish are held under identical environmental conditions. While temperature differences between tanks were relatively small (< 3°C), these differences fall within the range that might be encountered between estuaries at regional spatial scales. This is particularly relevant for estuarine or coastal field applications, where small environmental gradients may influence classification accuracy. Expanding the range of temperature conditions in future experiments would allow for clearer assessment of how environmental variation interacts with individual physiology to shape otolith composition. A multi-elemental approach is generally used to reconstruct habitat movements or assign individuals to specific locations (Shima & Swearer 2016 ; Rogers et al. 2019 ; Teichert et al. 2024 ), as multiple environmental variables jointly influence otolith composition (Williams et al. 2018 ; Reis-Santos et al. 2018 ). While our study focused on understanding size and temperature effects on individual elements, our findings highlight the value of integrating otolith chemistry with other proxies to reconstruct exposure histories more comprehensively. For example, strontium concentrations may help reconstruct salinity after controlling for size effects, while pH and temperature exposure may be better inferred using isotopic approaches (Høie et al. 2003 ; Gagnon et al. 2021 ). The observed stability of otolith elemental chemistry under different pH conditions also supports its use in multi-metric approaches for reconstructing long-term exposure histories. This is critical, as marine organisms are increasingly exposed to multiple stressors that may interact in complex and biologically meaningful ways. Future work that connects otolith-based exposure records with measures of individual performance will sharpen predictions of climate change impacts on marine populations and ecosystems (Levin & Frieder 2015 , Limberg et al. 2023, Reis-Santos et al. 2023 ). Conclusions Overall, this work provides additional evidence that otolith trace elemental chemistry is impacted by both environmental and biological factors and that it is unlikely to be impacted by the pH. Our results highlight the need to explicitly consider individual body size when interpreting the results of otolith microchemistry studies in an ecological context. The stability of otolith microchemistry across pH treatments suggests that this technique remains valuable for reconstructing individual environmental exposure, especially when integrated with other calcium carbonate-based approaches to assess responses to multiple interacting stressors. Declarations COMPLIANCE WITH ETHICAL STANDARDS The authors declare no financial or non-financial competing interests that are directly or indirectly related to the work submitted for publication. The use of animals in this study was carried out in accordance with all applicable institutional and national guidelines at the time that the study was conducted; all work followed American Fisheries Society policies on the Guidelines for Use of Fishes in Research ( https://fisheries.org/docs/policy_useoffishes.pdf ) and AVMA (American Veterinary Medical Association) Guidelines on Euthanasia ( https://olaw.nih.gov/sites/default/files/Euthanasia2007.pdf ). Fish collections and use took place under OSU Institutional Animal Care and Use Committee (IACUC) protocols (IACUC-2020-0129). DATA AVAILBALITY Data will be made available upon request. ACKNOWLEDGEMENTS The authors would like to thank Thomas Murphy for assistance with animal husbandry and in the field, Anna Bolm for field assistance, Chris Magel for assistance with setup of the CO 2 regulation system, and Jessica Andrade for assistance with laboratory processing of animals. This research was supported by funding from Oregon Sea Grant (R/ECO-48-PD) to JAM and CN and NOAA Ocean Acidification Program funding (award 20903) to TPH. CN was also supported by Bonneville Power Administration (Project 1998-014-00). References Amekawa S, Kubota K, Miyairi Y, Seki A, Kawakubo Y, Sakai S, Ajithprasad P, Maemoku H, Osada T, Yokoyama Y (2016) Fossil otoliths, from the Gulf of Kutch, Western India, as a paleo-archive for the mid- to late-Holocene environment. Quatern Int 397:281–288 Aschenbrenner A, Ferreira BP, Rooker JR (2016) Spatial and temporal variability in the otolith chemistry of the Brazilian snapper Lutjanus alexandrei from estuarine and coastal environments. 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13:24:17","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":44420,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7761636/v1/630dac27e42cf383d04effcd.png"},{"id":94372274,"identity":"6c3bbab0-381b-456b-bc04-b0b937821819","added_by":"auto","created_at":"2025-10-27 13:24:39","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41325,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7761636/v1/b0adabc9c44431bdf869be81.png"},{"id":94372282,"identity":"a803c492-3d89-4f98-be88-b218af80bf28","added_by":"auto","created_at":"2025-10-27 13:24:40","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":40334,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7761636/v1/b68b97aed077fda78640f3c6.png"},{"id":94372286,"identity":"cce3ccac-8b1c-4d78-8ac6-56a2d11670b0","added_by":"auto","created_at":"2025-10-27 13:24:41","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":208061,"visible":true,"origin":"","legend":"","description":"","filename":"MABID25005320structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7761636/v1/d6e7b0b00379530271e38271.xml"},{"id":94372387,"identity":"34ba7bc1-d308-4ece-9767-d1a70ebf205c","added_by":"auto","created_at":"2025-10-27 13:24:49","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":212748,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7761636/v1/04ae0eed14f5086a0300c725.html"},{"id":94372236,"identity":"2425bce3-2c88-431c-8460-4a0e26e93924","added_by":"auto","created_at":"2025-10-27 13:24:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":245118,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4. Relationship between individual A) total length and B) individual mass and Sr:Ca ratios for individuals pooled across pH treatments. Dotted lines show 95 % confidence intervals and numbers show slopes of the relationship between body size and Sr:Ca and size for each temperature treatment.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7761636/v1/7d4fe8749156b2bd7cbf60a8.png"},{"id":94372832,"identity":"b2143940-e2cf-4c01-b6b1-416b62adf099","added_by":"auto","created_at":"2025-10-27 13:25:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230260,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5. Relationship between individual A) total length and B) individual mass and B:Ca ratios. Dotted lines show 95 % confidence intervals and numbers show the slope of the relationship between body size and B:Ca ratios.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7761636/v1/810f2cdf04d5761ad1c017ff.png"},{"id":94372388,"identity":"73fddf88-2915-49f2-b2aa-72f2be9eb904","added_by":"auto","created_at":"2025-10-27 13:24:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":223652,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 6. Relationship between a) total length b) mass and Ba:Ca ratios in otolith of staghorn sculpin held at 11.5 °C and 14 °C. Dotted lines show 95 % confidence intervals. Numbers show the slope of the relationship between body size and Ba:Ca ratios where a significant main effect existed.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7761636/v1/19cc196cf890d17938c9a143.png"},{"id":104739605,"identity":"c0f56f96-aa4d-47bc-b332-f390c572b2d5","added_by":"auto","created_at":"2026-03-16 16:09:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1653282,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7761636/v1/ad82c6ae-9934-4482-93ee-72bedb08dd27.pdf"},{"id":94372498,"identity":"ad815b28-3d0e-46e5-9f30-af04de1db6ae","added_by":"auto","created_at":"2025-10-27 13:24:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18079,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7761636/v1/934271d192d05cd1069e0bc4.docx"},{"id":94372273,"identity":"05ca33fc-e24b-4e06-9516-3507f485fbfd","added_by":"auto","created_at":"2025-10-27 13:24:39","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19953,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYTABLES.docx","url":"https://assets-eu.researchsquare.com/files/rs-7761636/v1/f1ae97ee13c0587a2296c572.docx"}],"financialInterests":"","formattedTitle":"Relative Impact of Environmental (Temperature and pH) and Biological Factors (Size and Growth) on Otolith Trace Elemental Composition","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eElemental analysis of calcium carbonate (CaCO\u003csub\u003e3\u003c/sub\u003e) structures such as otoliths, shells, and coral skeletons is used widely to address ecological questions. These structures have been used in several contexts including understanding larval dispersal patterns (Norrie et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), reconstructing migration trajectories (Miller et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), stock discrimination (Tanner et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), age estimate corroborations (e.g. H\u0026uuml;ssy et al \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and climate reconstructions (Amekawa et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). While the application of these methods generally relies on the assumption that, for certain elements, the composition of CaCO\u003csub\u003e3\u003c/sub\u003e structures reflects the physiochemical properties of the water in which an animal resides, biological conditions such as growth rates, diet, ontogeny, reproductive state, and genetic differences can also influence their elemental composition (Sturrock et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Izzo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Norrie et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Miller \u0026amp; Hurst \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To make ecologically useful inferences it is essential to understand how intrinsic biological factors, external environmental conditions, and their interactions impact trace elemental composition.\u003c/p\u003e\u003cp\u003eThere is particular interest in using the trace elemental composition of CaCO\u003csub\u003e3\u003c/sub\u003e structures to reconstruct organismal exposure to stressors associated with climate change such as increased temperatures, decreased pH, or reduced dissolved oxygen (Limburg et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Tanaka et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Reis-Santos et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For these reconstructions to provide useful information, however, it is essential that there is a predictable relationship between trace element levels within the CaCO\u003csub\u003e3\u003c/sub\u003e matrix and the environmental variable of interest. The elemental composition of calcium carbonate structures is influenced by a range of environmental factors, including the concentration of dissolved elements, temperature, pH, and salinity. Importantly, the degree to which environmental signals are expressed varies between internally and externally calcifying organisms. In organisms that precipitate CaCO\u003csub\u003e3\u003c/sub\u003e in direct contact with seawater such as bivalves and corals, in addition to the concentration of elements in the surrounding seawater, the elemental composition of carbonate structures can reflect environmental variables such as pH (Levin \u0026amp; Frieder \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Norrie et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), temperature (Wanamaker et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, D\u0026rsquo;Olivo et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and estuarine dynamics such as freshwater inputs or diatom blooms (Gillikin et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Th\u0026eacute;bault et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In contrast otoliths, which are the primary focus of CaCO₃ chemistry studies in fish, form internally and are not in direct contact with seawater, making them potentially subject to stronger physiological influences in addition to environmental ones.\u003c/p\u003e\u003cp\u003ePhysiological effects may explain some of the different relationships between environmental variables and otolith composition that have been observed across studies. For example, the influence of temperature on otolith chemistry is widely reported, but its magnitude and direction can vary by species and context (e.g. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e in Miller and Hurst \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, effects of pH have not been consistently detected (Hurst et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Martino et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The role of hypoxia is complex, potentially altering otolith chemistry through changes in metal availability, particularly for elements like Mn, while effects on Ba are less consistent (Limburg et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mohan \u0026amp; Walther 2014). Understanding these biological influences is essential for interpreting otolith chemical data, particularly in species or systems where environmental signals are subtle or variable.\u003c/p\u003e\u003cp\u003eFor accurate interpretation of field studies, it is essential to understand how size and growth impact trace element chemistry. These studies often involve collecting individuals from multiple locations (e.g. estuaries, bays, or coastal sites) under the assumption that the chemistry of recently deposited otolith material reflects the environmental conditions at the site of capture and that the individual has been at this location long enough for CaCO\u003csub\u003e3\u003c/sub\u003e material to accrete. This information can be used as a proxy for environmental conditions (Izzo et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Limburg \u0026amp; Casini \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the elemental composition of the otolith core can be used to assign individuals to their natal origin (Williams et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Rogers et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, if intrinsic factors such as size or growth rate significantly influence otolith composition, these assumptions may not hold. By understanding how biological processes impact otolith microchemistry of individuals held under identical conditions, we can develop analytical and interpretative approaches that can account for such variation, thus improving the accuracy of ecological inferences.\u003c/p\u003e\u003cp\u003eThis study examined the incorporation of trace elements into otoliths of a common estuarine and marine fish, staghorn sculpin (\u003cem\u003eLeptocottus armatus\u003c/em\u003e), under a combination of three pH (7.65, 7.78, and ambient (7.96)) and two temperature (11.5\u0026deg;C and 14.0\u0026deg;C) conditions. We assessed how interactions between these environmental conditions and individual size and growth impacted otolith trace elemental concentrations. Using an experimental design that simulated field studies where multiple individuals are sampled from locations under the assumption that all experience the same environmental conditions, we addressed the following questions: 1) Does the otolith trace elemental composition vary between individuals held under differing pH and temperature conditions? 2) Is otolith elemental composition related to body size or growth? 3) Is the otolith elemental composition affected by an interaction between environmental conditions and body morphometrics?\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eAnimal Husbandry\u003c/h2\u003e\u003cp\u003eAdult staghorn sculpin (\u003cem\u003eLeptocottus armatus\u003c/em\u003e) were captured from Yaquina Bay in Newport, Oregon, USA (44.618350, -124.057282) with a beach seine net in late October 2020 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;71. Upon capture, fish were transported to Oregon State University\u0026rsquo;s Hatfield Marine Science Center\u0026rsquo;s Coastal Ecology Lab in Newport, Oregon. In the lab, adult fish were split equally between six 150 L treatment tanks with varying combinations of environmental conditions. Each experimental tank was randomly assigned to one of six treatments, which were combinations of three pH conditions (ambient\u0026thinsp;~\u0026thinsp;8.00, medium \u0026minus;\u0026thinsp;7.75, low \u0026minus;\u0026thinsp;7.60) and two temperature conditions (11.5\u0026deg;C \u0026amp; 14.0\u0026deg;C). These treatment levels were chosen based on Intergovernmental Panel on Climate Change predictions for 2100 under a high emissions scenario (Gattuso et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Jewett \u0026amp; Romanou \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Wuebbles et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, during upwelling events on the west coast of the USA the pH can reach 7.l6 or lower (Feely et al. 2019).\u003c/p\u003e\u003cp\u003eWe developed a pH conditioning system using a Honeywell Durafet pH monitoring and conditioning system based on Hurst et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Briefly, coastal oceanic water was pumped from nearby Yaquina Bay during flooding tides into a 3\u0026nbsp;million litre water reservoir. Water was pumped from the reservoir through a sand filter to three pH conditioning tanks within the fish holding facility. Honeywell Durafet III probes in each conditioning tank continuously monitored pH and regulated the injection of carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) to maintain treatment pH levels (\u0026plusmn;\u0026thinsp;0.07). Water from conditioning tanks gravity fed into the six experimental treatment tanks where fish were held. Temperature was manipulated within experimental tanks using individual aquarium heaters coupled to individual thermostats. We recorded experimental parameters (temperature, pH, dissolved oxygen) daily, using handheld probes (YSI ProQuatro Multiparameter Meter, Xylen Inc, Washington DC, USA). As the experiment was conducted indoors a timer was used to match local light/dark cycles. See below for analyses of seawater elemental and carbonate system conditions.\u003c/p\u003e\u003cp\u003eWe acclimated fish to laboratory conditions for two weeks at ambient conditions (~\u0026thinsp;10\u0026deg;C, pH\u0026thinsp;~\u0026thinsp;7.95) prior to starting the experiment. After this period, we adjusted the water temperature by 0.5\u0026deg;C/day and pH by 0.1 unit/day until target conditions were achieved. Staghorn sculpin were then reared under experimental treatment conditions for three months. Fish were fed a combination of Mazuri\u0026reg; Omnivore Aquatic Gel Diet and minced herring. Biomass in each tank was calculated at the start of the experiment, and each tank was fed ad libitum, with any uneaten food removed daily. At the start, midpoint, and conclusion of the experiment, fish were weighed (to the nearest gram) and measured (total length \u0026ndash; TL, to the nearest mm). Initial fish sizes were 89\u0026ndash;159 mm total length (TL; 118.29\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2 mm SEM) and 9\u0026ndash;50 g wet mass (24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 g SEM). Although fish were not individually marked, the size variation within each tank permitted unambiguous identification of individuals throughout the experiment, with identities used in the estimation of individual growth rates (Hurst et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Miller \u0026amp; Hurst \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAt the end of the experiment, fish were sacrificed by immersion in 250 ml/L of tricaine methanesulfonate (MS-222) buffered to a pH of 8.0 with sodium bicarbonate (Institutional Animal Care and Use Committee permit number \u0026minus;\u0026thinsp;2020\u0026thinsp;\u0026minus;\u0026thinsp;0129). During dissection, gonads were visually inspected; and 20 individuals had discernible gonads. Otoliths were removed, cleaned by removing all adhering tissue and rinsing in Milli Q water, and stored dry prior to analyses. Otoliths were embedded in two-part epoxy resin and transverse sections were cut using a Buehler\u0026reg; IsoMet Low Speed cutting machine. Sectioned otoliths were mounted on a glass slide using thermoplastic resin and polished using 3M tri-mite wet-or-dry paper (240-2,000 grit) and diamond lapping film (1\u0026ndash;30 \u0026micro;m).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLA-ICP-MS analysis\u003c/h3\u003e\n\u003cp\u003eWe analysed the trace elemental composition of one randomly selected otolith from each fish using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). We collected information on the concentrations of boron (\u003csup\u003e11\u003c/sup\u003eB), strontium (\u003csup\u003e86\u003c/sup\u003eSr), barium (\u003csup\u003e137\u003c/sup\u003eBa). We selected strontium and barium as they are strongly linked to ambient water chemistry and are among the most widely used elements in otolith studies of salinity, temperature, and diadromy (Elsdon \u0026amp; Gillanders \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Kraus \u0026amp; Secor \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Walther \u0026amp; Thorrold \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Boron was included because it has shown potential as a proxy for exposure to ocean acidification in biogenic calcium carbonate structures (Levin \u0026amp; Frieder \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Norrie et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Limburg et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We also quantified Co, Mn, and Mg, but although all three elements were detectable, none showed consistent differences among treatments or with biological covariates, so they were not included in further analyses. We used a Photon Machines Analyte G2 laser coupled to a Thermo Scientific X-Series II quadruple ICP-MS. We collected data along a 100-\u0026micro;m transect parallel to the otolith dorsal edge. We used counts of daily otolith increments to ensure that sampled otolith material was deposited while animals were held under experimental conditions. The laser operated at 7 Hz with an output of 75% and target energy of 7 J cm\u003csup\u003e2\u003c/sup\u003e with a spot size of 40 \u0026micro;m that travelled at 2 \u0026micro;m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e across the sample. Mean daily otolith increment width was 1.6 \u0026micro;m (\u0026plusmn;\u0026thinsp;0.5 SEM), thus the 40 \u0026micro;m spot ablated material deposited over a 25-day period. To remove any surface contamination each otolith was pre-ablated along the same transect used for sample analyses. The pre-ablation scan ran with at 2 Hz with a spot size of 50 \u0026micro;m and a speed of 100 \u0026micro;m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. National Institute of Standards and Technology (NIST) 610 and 612 standards were analysed at the start and end of each day and after every 10 otoliths throughout for standardisation purposes and estimates of precision. USGS calcium carbonate standards MACS-1 and MACS-3 were also analysed at the start and end of each run for calculations of accuracy.\u003c/p\u003e\u003cp\u003eOtolith analyte count data were normalised by \u003csup\u003e43\u003c/sup\u003eCa and trace element:calcium ratios (TE:Ca) are presented in \u0026micro;mol:mol (B:Ca, Ba:Ca) or mmol:mol (Sr:Ca). Data was converted to elemental ratios based on measurements of the NIST 612 standard. Precision (% RSD) was calculated from multiple analyses of NIST612 standard (B:Ca, 4.4%, Sr:Ca\u0026thinsp;=\u0026thinsp;1.5%, Ba:Ca\u0026thinsp;=\u0026thinsp;0.53%). Accuracy was determined through repeated analysis of MACS-1 and MACS-3 standards (B:Ca\u0026thinsp;=\u0026thinsp;98.9%, Sr:Ca\u0026thinsp;=\u0026thinsp;88.0%, Ba:Ca\u0026thinsp;=\u0026thinsp;96.2%). To describe elemental concentrations in each otolith TE:Ca values across each transect were averaged for each otolith.\u003c/p\u003e\n\u003ch3\u003eWater carbonate and trace element analysis\u003c/h3\u003e\n\u003cp\u003eTo determine if there were any tank differences in trace element composition of water, we measured the Ca, Sr, Ba, and B levels in water samples collected from each experimental tank each month (total \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 from each tank) using a Spectros Arcos inductively coupled optical emissions spectrometer (ICP-OES) (Ca at 317.9 nm, B at 249.7 nm, Ba at 233.5 nm, and Sr at 460 nm). Prior to analysis, samples were filtered (0.25 \u0026micro;m) and acidified (\u0026lt;\u0026thinsp;2 pH) with ultrapure HNO\u003csub\u003e3\u003c/sub\u003e. Matrix matched standards were created using SPEX Certiprep Group\u0026reg; certified reference materials and a NaCl solution. Accuracy was assessed based on repeated measurements of SLRS-6 river water certified reference material. Measured B, Ba, Ca, and Sr concentrations were within 0.5%, 2.2%, 1.9%, and 0.8% of the certified values, respectively (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3). Precision was estimated with repeated measurements of the same sample and varied by \u0026lt;\u0026thinsp;2% for each element. We compared the concentration of elements between experimental tanks using a one-way analysis of variance (ANOVA).\u003c/p\u003e\u003cp\u003eTo parameterise the carbonate system, we collected 200 mL samples of seawater once a month which were preserved by the addition of 200 \u0026micro;L mercuric chloride (HgCl\u003csub\u003e2\u003c/sub\u003e). Dissolved inorganic carbon (DIC) and total alkalinity (TA) were quantified at the Ocean Acidification Research Center at the University of Alaska at Fairbanks. An AIRICA (Automated InfraRed Inorganic Carbon Analyzer) was used to measure DIC and a VINDTA 3C (Versatile Instrument for the Determination of Total dissolved inorganic carbon and Alkalinity) was used to measure TA. The AIRICA and VINDTA 3C instruments were calibrated using Certified Reference Materials (CRMs) from the Dickson Laboratory at the Scripps Institute of Oceanography and a daily correction was applied. Mean deviation of measurements from CRM values was \u0026plusmn;\u0026thinsp;1.40 \u0026micro;mol kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for DIC and \u0026plusmn;\u0026thinsp;2.00 \u0026micro;mol kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for TA. These measurements were used to calculate the pH and CO\u003csub\u003e2\u003c/sub\u003e conditions during the experiment based on the dissociation constants of Dickson and Millero (1987).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003eMetrics of growth and condition\u003c/h2\u003e\u003cp\u003eWe calculated mass specific (MSGR) and length specific (LSGR) growth rates (percentage increase per day) over the course of the experiment using the formula [ln(final size) -ln(initial size) / experimental duration (in days)]*100. We also used total change in the length-weight residuals (∆LWR) over the course of this experiment to quantify changes in fish condition.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eImpact of treatment conditions on body size, growth, and condition\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted in R (v 4.01 \u0026ndash; R Core Team). We first examined if temperature and pH treatments impacted size (final mass and TL), growth (LSGR \u0026amp; MSGR), and condition (∆LWR). We used linear models where temperature, pH, and their interaction were included as fixed effects. Where appropriate, response variables were log-transformed to meet assumptions of homogeneity of variance and normality.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRelationships between individual size, growth, and otolith TE:Ca ratios\u003c/h3\u003e\n\u003cp\u003eWe used linear models with categorical predictors (temperature, pH) and continuous predictors (body size, growth, and condition) to evaluate their effects and interactions on TE:Ca ratios (Sr:Ca, Ba:Ca, and B:Ca). To avoid issues of collinearity among biological traits, we ran separate models in which temperature, pH, and their interaction were assessed alongside a single biological predictor (length, mass, growth, or condition). TE:Ca ratios were log-transformed where necessary to meet assumptions of homogeneity of variance and normality of residuals. We then conducted post hoc pairwise tests using the emtrends function in the \u0026lsquo;emmeans\u0026rsquo; package (Lenth et al. 2024).\u003c/p\u003e\u003cp\u003eThis approach allowed us to evaluate whether individual-level biological variation contributed to differences in TE:Ca ratios within shared environmental conditions. It also allowed us to test whether the effects of biological covariates were consistent across temperature and pH treatments. In field studies, where individual variation is often uncontrolled, such effects could be misattributed to environmental differences among sites.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eWater properties\u003c/h2\u003e\n \u003cp\u003eIn each tank, mean temperatures were within 0.4°C of target temperatures over the course of the experiment (Table\u0026nbsp;1). Mean pH in the ambient treatment tanks was 7.96 and the other treatments were within 0.02 pH units of our targets. The trace elemental composition of seawater did not vary among treatments (Table\u0026nbsp;1) as one-way ANOVAs showed no significant differences existed in water levels of B:Ca (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e5,18\u003c/sub\u003e=0.643, \u003cem\u003ep\u003c/em\u003e = 0.67), Sr:Ca (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e5,18\u003c/sub\u003e = 0.652, \u003cem\u003ep\u003c/em\u003e = 0.664) or Ba:Ca (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e5,18\u003c/sub\u003e = 1.469, \u003cem\u003ep\u003c/em\u003e = 0.248) ratios among tanks/treatments.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eSize, growth and condition\u003c/h2\u003e\n \u003cp\u003eOf 71 animals in the experiment, there were 9 mortalities (Table\u0026nbsp;2) but there was no clear pattern across treatments or obvious cause of the mortalities. Temperature and pH differences among tanks did not impact fish size, growth, or condition across treatment tanks as indicated by the lack of significant temperature, pH, or interaction effects for TL, mass, LSGR, MSGR, final LWR or ∆LWR (all \u003cem\u003eF\u003c/em\u003e\u003csub\u003e1,56\u003c/sub\u003e \u0026lt; 1.25, p \u0026gt; 0.250). Final TL ranged from 108 to 183 mm with a mean of 137.2 mm (± 2.5 SEM) and mass ranged from 14 to 79 g with a mean of 34.7 g (± 2.1 SEM). No significant differences in final size or growth existed between individuals with discernible gonads and those without (final mass: t\u003csub\u003e(52)\u003c/sub\u003e = -0.85, p = 0.40; final length: t\u003csub\u003e(52)\u003c/sub\u003e = -1.05, p = 0.297; LSGR : t(\u003csub\u003e52\u003c/sub\u003e) = 0.04, p = 0.80; MSGR: t(\u003csub\u003e52\u003c/sub\u003e) = 0.89, p = 0.38) and there were no patterns in maturity across tanks.\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMean biological attributes (± SEM) of fish from each tank at the conclusion of this study. TL = total length (mm), LSGR = Length specific growth rate (% total length day− 1), Mass (grams), MSG = mass specific growth rate (% body weight day− 1), LWR = Length weight residuals, ∆ LWR = change in length weight residuals, n (mort.) = sample size - mortalities that occurred throughout the experiment shown in brackets.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemp\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLSGR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMass\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMSGR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLWR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e∆ LWR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e (mort.)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmbient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142.9 (± 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18 (± 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.8 (± 3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.65 (± 0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.0 (± 2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.3 (± 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139.8 (± 5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15 (± 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.9 (± 4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.87 (± 0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.7 (± 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.1 (± 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135.5 (± 6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20 (± 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.0 (± 5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.80 (± 0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8 (± 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.9 (± 2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmbient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136.4 (± 7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17 (± 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.6 (± 6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.71 (± 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8 (± 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.9 (± 2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026nbsp;(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137.1 (± 11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14 (± 0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.1 (± 9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.61 (± 0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.8 (± 2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3 (± 3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133.9 (± 5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17 (± 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.7 (± 4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.60 (± 0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5 (± 1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.3 (± 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eOtolith trace elemental composition\u003c/h2\u003e\n \u003cp\u003eOverall, models that included body size metrics (length or mass) as covariates explained more variation in otolith trace element ratios than models including other biological covariates such as growth rate or condition as shown by the higher model r\u003csup\u003e2\u003c/sup\u003e values (Table\u0026nbsp;3; Table\u0026nbsp;4). These models typically explained between ~ 12–36% of the variation depending on the element examined. In some cases, body size also interacted with environmental variables.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eStrontium\u003c/h2\u003e\n \u003cp\u003eOtolith strontium levels were influenced by an interaction between temperature and body size. Individuals from the 11.5°C temperature treatment exhibited a significant negative relationship between size and Sr:Ca ratios, while at 14.0°C this relationship was weaker (Fig.\u0026nbsp;4; Table\u0026nbsp;3; Table\u0026nbsp;4). The slope between Sr:Ca and final length was − 1.04 at 11.5°C and − 0.34 at 14.0°C, while for final mass the slope was − 0.35 at 11.5°C and − 0.10 at 14.0°C. This indicated that Sr:Ca declined approximately three times more steeply with increasing size in colder water. This resulted in Sr:Ca ratios in smaller individuals being approximately 20% higher than larger fish the 11.5°C treatment. There were no significant effects of pH, LSGR, MSGR, or ΔLWR on Sr:Ca ratios (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.2).\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMean otolith trace element:calcium (± SE) ratios of staghorn sculpin held under a combination of varying pH and temperature conditions.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemp\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSr:Ca (mmol:mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB:Ca (µmol:mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBa:Ca (µmol:mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmbient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.96 (± 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.4 (± 5.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.34 (± 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.02 (± 0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.43 (± 4.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36 (± 0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.23 (± 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.95 (± 5.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.41 (± 0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmbient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.96 (± 0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.26 (± 3.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.24 (± 0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.86 (± 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.2 (± 11.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.32 (± 0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.82 (± 0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.43 (± 2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99 (± 0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ep values, adjusted r2, and AIC values from linear models examining covariates of final length, final mass, length specific and mass specific (MSGR) growth rates, and length weight residuals (LWR) for each trace element calcium ratio (TE:Ca) investigated in this work. Bold values indicate p \u0026lt; 0.05. For full results including F values and DF see supplementary Table S1.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e values\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTE:Ca\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCovariate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemp x pH x covariate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH x covariate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemp x covariate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemp x pH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCovariate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemp\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel adjusted r\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSr:Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFinal Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-52.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFinal Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-48.642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-28.301\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-32.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLWR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-34.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB:Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFinal Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFinal Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLWR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBa:Ca\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFinal Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFinal Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLWR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eBoron\u003c/h2\u003e\n \u003cp\u003eBody size weakly, but significantly impacted B:Ca ratios, with negative relationships observed for both total length and mass (Table\u0026nbsp;4; Fig.\u0026nbsp;5). Boron levels were not affected by interactions between biological and environmental variables. The slope of the relationship between mass and B:Ca ratios was − 0.04, whereas the relationship between total length and B:Ca was − 0.02. LSGR, MSGR, and ΔLWR were not significantly related to B:Ca ratios (Table\u0026nbsp;4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eBarium\u003c/h2\u003e\n \u003cp\u003eThere was a consistent negative effect of temperature on Ba:Ca ratios with significant temperature effects in all but one of the models (Table\u0026nbsp;4; Fig.\u0026nbsp;6). In the model that included total length as a covariate we observed a significant total length*pH*temperature interaction effect (Table\u0026nbsp;4). There was also a weak, but significant, negative effect of mass on Ba:Ca ratios (slope = -0.004). No impacts of growth or condition on Ba:Ca ratios were observed.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eSpatial variation in the concentration of certain trace elements in otolith is often interpreted as being due to environmental differences between locations. Based on this interpretation otolith chemistry can serve as a useful marker in studies of distribution, movement, mixing, and, possibly, serve as a proxy for environmental conditions. The potential for biological processes to confound these interpretations has become clearer over recent decades (e.g. Sturrock et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Stanley et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Miller \u0026amp; Hurst \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Few studies, however, explicitly determine how individual size influences otolith chemistry or if size interacts with environmental conditions in determining elemental composition under controlled experimental conditions. Here we used an approach that mimicked field studies that examine individuals from distinct geographic locations (e.g. Grammer et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Pease et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and demonstrated that biological (i.e. size) and environmental variables (i.e. pH/temperature) can impact trace element levels, both independently and interactively. Of the elements we analysed, Sr, Ba, and B were influenced by biological and/or environmental factors, whereas Co, Mn, and Mg did not vary across treatments, suggesting limited value of these elements as proxies under the conditions tested here. Our results emphasise previous work showing that physiological attributes such as size and growth should be accounted for when making ecological inferences using otolith trace elements to avoid confounding environmental interpretations (Sturrock et al. 2017, Grammer et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Izzo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eStrontium\u003c/h2\u003e\u003cp\u003eStrontium is one of the most frequently used elements in otolith trace elemental studies as it reflects environmental conditions (Doubleday et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Reis-Santos et al \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Norrie et al \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Otolith strontium levels track ambient concentrations (Elsdon \u0026amp; Gillanders \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Walther \u0026amp; Thorrold \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) because Sr\u0026sup2;⁺ has a similar ionic radius to Ca\u0026sup2;⁺ and can easily substitute for calcium in the CaCO₃ lattice (Doubleday et al \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Thomas et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Thus, Sr:Ca ratios are commonly used to reconstruct salinity due to the higher levels of strontium in seawater than in most freshwater systems (Kraus \u0026amp; Secor \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Tian et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Sr:Ca ratios have also been shown to vary with temperature, although the direction and magnitude of this relationship is inconsistent (Sturrock et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Miller and Hurst \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and it is often included in multi-elemental suites used for stock delineation and dispersal studies (e.g. Tanner et al \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Rogers et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Teichert et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our results, which show an interactive effect of body size and temperature on otolith strontium levels, add to the growing body of evidence that, in addition to environmental influences, organismal physiology can impact otolith strontium levels (Sturrock et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Grammer et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Hussey et al. 2021) and highlight the need to explicitly account for these effects in field studies.\u003c/p\u003e\u003cp\u003eTo understand how our results could impact the interpretation of field studies, it is important to compare the scale of variation we observed with those documented in situ. Previous work on this same species (Miller \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) reported mean Sr:Ca differences of ~\u0026thinsp;10% between estuaries located 200\u0026ndash;400 km apart along the Oregon coast. Similarly, studies of other species have detected mean differences in Sr:Ca ratios of 10\u0026ndash;20% between sites separated by less than 50 km (e.g. Reis-Santos et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Aschenbrenner et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Based on our model predictions, the smallest individual from the 11.5\u0026deg;C treatment in our study (111 mm TL) would have a Sr:Ca ratio 35% higher than that of the largest individual (178 mm TL) from the same treatment. This inter-individual effect is larger than the mean inter-site differences reported at smaller spatial scales, but smaller than the maximum inter-estuary difference of 114% reported in Miller (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). At these smaller scales environmental gradients are likely to be narrower which suggests that differences in size or physiology may contribute more substantially to otolith variation than spatial environmental differences. In such cases, differences in Sr:Ca may be misinterpreted as environmental in origin (e.g., salinity differences between habitats) when they may instead reflect biological effects. Indeed, these biological effects may explain some of the variation in strontium levels observed over relatively small scales where environmental conditions may be consistent (Reis Santos et al. 2018, Williams et al \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Maturity can alter energy allocation and ion regulation, which in turn could affect element incorporation into calcified structures (Sturrock et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, no differences in size or growth were detected between fish with and without gonads, and maturity showed no consistent patterns across tanks, suggesting that reproductive state was not likely the driver of the size-related Sr:Ca patterns observed here. Together, these results reinforce that body size rather than maturity status is the key biological factor underlying Sr variation in our study.\u003c/p\u003e\u003cp\u003eWhen using trace elements to assign individuals to a collection or natal location, strontium is generally used as part of a multi-elemental suite. The relative contribution of strontium to the site classification can vary. Thus, carefully balancing the site level discrimination provided by including strontium with potential risks associated with inter individual variation should be carefully considered when using this trace element. This could be achieved by restricting comparisons to individuals of similar life stages or by explicitly including a size covariate in classification models. While size-related variation in Sr:Ca may affect fine-scale spatial assignments, it is unlikely to influence migratory reconstructions in anadromous fish, which rely on detecting large intra-individual shifts between freshwater and marine environments of up to 400% as the concentration as environmental strontium changes between freshwater and marine habitats (Austin et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Norrie et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnderstanding the mechanisms underlying the interactive effects of size and temperature on strontium incorporation into otoliths is challenging. However, size-related physiological differences likely influence how strontium is processed and partitioned during otolith formation. Elements present as free ions in the endolymph are more likely to substitute for Ca\u0026sup2;⁺ in the CaCO₃ fraction and thus reflect ambient strontium concentrations, while those bound to proteins are more likely to be incorporated into the organic matrix and influenced by physiological processes. Because strontium occurs in both fractions (Thomas et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), this dual incorporation pathway complicates the interpretation of Sr signals in otoliths. While previous work has shown negative relationships between growth and Sr:Ca ratios (Stanley et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Miller \u0026amp; Hurst \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) we did not observe any impact of growth rates on strontium levels in our work. This suggests that size-related variation in Sr:Ca may arise independently of growth rate, highlighting the importance of considering physiological traits beyond growth alone when interpreting otolith chemistry.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eBarium\u003c/h2\u003e\u003cp\u003eAlthough water Ba concentrations are the dominant driver of otolith Ba:Ca ratios (Elsdon \u0026amp; Gillanders \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Walther \u0026amp; Thorrold \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Reis-Santos et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), negative relationships have also been found between barium levels and growth rates (Miller \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Sturrock et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), salinity (Reis-Santos et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Barnes and Gillanders 2013, Stanley et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and temperature (Elsdon and Gillanders \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Miller \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Barnes and Gillanders 2013; Reis-Santos et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Stanley et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Ba:Ca can also be impacted by diet (Webb et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Izzo et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The three-way interaction between body size, pH, and temperature, together with the main effects of temperature and body size, highlight the impact of environmental and biological factors on otolith Ba:Ca ratios.\u003c/p\u003e\u003cp\u003eAlong with strontium, barium is often frequently included in multi-elemental suites used to reconstruct dispersal pathways (e.g. Matta et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Beer et al 2011) and has been used to reconstruct salinity histories of individuals (e.g. Elsdon \u0026amp; Gillanders 2005, Miller \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Shima \u0026amp; Swearer \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although there was a significant negative effect of body size on Ba:Ca ratios, this effect was relatively weak, so it is unlikely to obscure spatial differences in otolith chemistry. For example, the largest pairwise difference in mean Ba:Ca ratios between tanks in our study was approximately 140% (0.99 \u0026micro;mol:mol vs 1.41 \u0026micro;mol:mol), whereas differences of 150\u0026ndash;200% have been reported to exist between different sites within estuaries or between closely located (10s of km) estuaries (Miller \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Barnes and Gillanders 2013). This suggests that, over broad spatial scales, the size-related variation observed in our study is unlikely to compromise the utility of Ba:Ca for ecological classification.\u003c/p\u003e\u003cp\u003eLike strontium, barium can substitute for Ca\u0026sup2;⁺ in the CaCO₃ matrix and be incorporated into the proteinaceous fraction of the otolith (Thomas et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This dual incorporation pathway suggests that both environmental and physiological processes may influence Ba:Ca ratios. For example, free Ba\u0026sup2;⁺ ions are more likely to be incorporated into the carbonate matrix and thus reflect ambient concentrations, while protein-bound barium may be more sensitive to individual physiology. Processes such as ion transport, protein synthesis, and metabolic activity could therefore contribute to residual variation in Ba:Ca ratios even under stable environmental conditions (Loewen et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sturrock et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, H\u0026uuml;ssy et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While the effects observed here were small, considering individual characteristics such as body size may improve the interpretation of otolith Ba:Ca patterns in ecological studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eBoron\u003c/h2\u003e\u003cp\u003eAlthough recent studies suggest links between boron incorporation into carbonate structures and dissolved oxygen and pH variability (Norrie et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cavole et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Limburg et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the environmental drivers of B:Ca ratios in fish otoliths remain poorly defined. In our study, body size had a weak but statistically significant effect on B:Ca ratios, while no consistent effects of pH were observed. B:Ca only varied with temperature in one model, and this was dependent on the inclusion of a condition index as a covariate. Our results are consistent with Matta et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who also reported a negative relationship between body size and B:Ca ratios in juvenile fish. However, the magnitude of biologically driven variation in our study was small: for example, the predicted\u0026thinsp;~\u0026thinsp;50% difference in B:Ca between the smallest and largest individuals was less than the 70\u0026ndash;300% variation reported across spatial or environmental gradients in field studies (e.g., Limburg et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Matta et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cavole et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This suggests that, in most field applications, size-related variation is unlikely to obscure ecologically meaningful spatial differences in B:Ca.\u003c/p\u003e\u003cp\u003eOur findings are also consistent with previous experimental studies in fish that found no detectable effect of short-term pH variation on otolith elemental incorporation (e.g., Hurst et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Martino et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cavole et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, studies on invertebrates that precipitate CaCO₃ in direct contact with seawater have shown strong pH effects on boron incorporation. In these organisms, reduced pH increases the availability of the borate ion B(OH)\u003csub\u003e4\u003c/sub\u003e⁻, which are more readily incorporated into the carbonate matrix (Levin \u0026amp; Frieder \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Norrie et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gagnon et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fish otoliths, however, are internally formed structures. Ions must cross epithelial barriers in the gill or gut and move through the bloodstream to reach the endolymph, where biomineralization occurs (Thomas et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Hussey et al. 2021). This physiological separation from ambient seawater, combined with ion\u0026ndash;protein interactions and active homeostatic regulation, likely limits the impact of environmental pH on otolith boron levels (Sturrock et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Thomas et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; H\u0026uuml;ssy et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, unlike invertebrate structures, fish otoliths may not reliably record ambient pH variation.\u003c/p\u003e\u003cp\u003eWhile boron may not serve as a sensitive proxy for pH in otoliths, it could still contribute to multi-metric approaches aimed at reconstructing environmental histories. For example, when combined with other elemental or isotopic markers, boron has the potential to strengthen overall interpretations. Integrating multiple proxies may help disentangle the complex interplay of environmental stressors, particularly in systems experiencing simultaneous changes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eCaveats and future directions\u003c/h2\u003e\u003cp\u003eOur analyses allowed us to examine how individual variation in otolith chemistry arises when fish are held under identical environmental conditions. While temperature differences between tanks were relatively small (\u0026lt;\u0026thinsp;3\u0026deg;C), these differences fall within the range that might be encountered between estuaries at regional spatial scales. This is particularly relevant for estuarine or coastal field applications, where small environmental gradients may influence classification accuracy. Expanding the range of temperature conditions in future experiments would allow for clearer assessment of how environmental variation interacts with individual physiology to shape otolith composition.\u003c/p\u003e\u003cp\u003eA multi-elemental approach is generally used to reconstruct habitat movements or assign individuals to specific locations (Shima \u0026amp; Swearer \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rogers et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Teichert et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), as multiple environmental variables jointly influence otolith composition (Williams et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Reis-Santos et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While our study focused on understanding size and temperature effects on individual elements, our findings highlight the value of integrating otolith chemistry with other proxies to reconstruct exposure histories more comprehensively. For example, strontium concentrations may help reconstruct salinity after controlling for size effects, while pH and temperature exposure may be better inferred using isotopic approaches (H\u0026oslash;ie et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Gagnon et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe observed stability of otolith elemental chemistry under different pH conditions also supports its use in multi-metric approaches for reconstructing long-term exposure histories. This is critical, as marine organisms are increasingly exposed to multiple stressors that may interact in complex and biologically meaningful ways. Future work that connects otolith-based exposure records with measures of individual performance will sharpen predictions of climate change impacts on marine populations and ecosystems (Levin \u0026amp; Frieder \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Limberg et al. 2023, Reis-Santos et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOverall, this work provides additional evidence that otolith trace elemental chemistry is impacted by both environmental and biological factors and that it is unlikely to be impacted by the pH. Our results highlight the need to explicitly consider individual body size when interpreting the results of otolith microchemistry studies in an ecological context. The stability of otolith microchemistry across pH treatments suggests that this technique remains valuable for reconstructing individual environmental exposure, especially when integrated with other calcium carbonate-based approaches to assess responses to multiple interacting stressors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCOMPLIANCE WITH ETHICAL STANDARDS\u003c/h2\u003e\u003cp\u003eThe authors declare no financial or non-financial competing interests that are directly or indirectly related to the work submitted for publication. The use of animals in this study was carried out in accordance with all applicable institutional and national guidelines at the time that the study was conducted; all work followed American Fisheries Society policies on the Guidelines for Use of Fishes in Research (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fisheries.org/docs/policy_useoffishes.pdf\u003c/span\u003e\u003cspan address=\"https://fisheries.org/docs/policy_useoffishes.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and AVMA (American Veterinary Medical Association) Guidelines on Euthanasia (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://olaw.nih.gov/sites/default/files/Euthanasia2007.pdf\u003c/span\u003e\u003cspan address=\"https://olaw.nih.gov/sites/default/files/Euthanasia2007.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Fish collections and use took place under OSU Institutional Animal Care and Use Committee (IACUC) protocols (IACUC-2020-0129).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eDATA AVAILBALITY\u003c/h2\u003e\u003cp\u003eData will be made available upon request.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e\u003cp\u003eThe authors would like to thank Thomas Murphy for assistance with animal husbandry and in the field, Anna Bolm for field assistance, Chris Magel for assistance with setup of the CO\u003csub\u003e2\u003c/sub\u003e regulation system, and Jessica Andrade for assistance with laboratory processing of animals. This research was supported by funding from Oregon Sea Grant (R/ECO-48-PD) to JAM and CN and NOAA Ocean Acidification Program funding (award 20903) to TPH. CN was also supported by Bonneville Power Administration (Project 1998-014-00).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmekawa S, Kubota K, Miyairi Y, Seki A, Kawakubo Y, Sakai S, Ajithprasad P, Maemoku H, Osada T, Yokoyama Y (2016) Fossil otoliths, from the Gulf of Kutch, Western India, as a paleo-archive for the mid- to late-Holocene environment. Quatern Int 397:281\u0026ndash;288\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAschenbrenner A, Ferreira BP, Rooker JR (2016) Spatial and temporal variability in the otolith chemistry of the Brazilian snapper \u003cem\u003eLutjanus alexandrei\u003c/em\u003e from estuarine and coastal environments. 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Mar Ecol Prog Ser 453:189\u0026ndash;199\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilliams J, Jenkins GP, Hindell JS, Swearer SE (2018) Fine-scale variability in elemental composition of estuarine water and otoliths: Developing environmental markers for determining larval fish dispersal histories within estuaries. Limnol Oceanogr 63:262\u0026ndash;277\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWuebbles DJ, Fahey DW, Hibbard KA, Dokken DJ, Stewart BC, Maycock TK (2017) Climate Science Special Report: Fourth National Climate Assessment, Volume I. U.S. Global Change Research Program\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\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":"marine-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mabi","sideBox":"Learn more about [Marine Biology](https://www.springer.com/journal/227)","snPcode":"227","submissionUrl":"https://submission.nature.com/new-submission/227/3","title":"Marine Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Otolith Chemistry, Calcium Carbonate, LA-ICP-MS, Growth, Size, Strontium","lastPublishedDoi":"10.21203/rs.3.rs-7761636/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7761636/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUsing of calcium carbonate (CaCO\u003csub\u003e3\u003c/sub\u003e) structures as an ecological tool relies on the assumption that, for some elements, their composition is influenced by the water in which an organism lives. However biological processes including, growth rates, diet, ontogeny, reproductive state, or genetics can also influence their composition. It is essential we understand how intrinsic biological factors, external environmental conditions, and interactions impact the composition of CaCO\u003csub\u003e3\u003c/sub\u003e structures to make ecological inferences. We examined how temperature, pH, growth, and body size influenced elemental composition of staghorn sculpin (\u003cem\u003eLeptocottus armatus\u003c/em\u003e) otoliths. We held animals (108\u0026ndash;183 mm length) under three pH (7.60, 7.75, and 7.96) and two temperature (11.5\u0026deg;C and 14.0\u0026deg;C) treatments and examined relationships of three trace element:calcium ratios (Sr:Ca, Ba:Ca, B:Ca) to experimental conditions and body size. Sr:Ca ratios showed a temperature \u0026times; size interaction, with smaller fish at 11.5\u0026deg;C having higher values than those at 14.0\u0026deg;C, while differences between temperatures diminished at larger sizes. Ba:Ca ratios were lower at 14.0\u0026deg;C across sizes, indicating consistent temperature effects. B:Ca ratios showed weak but statistically significant negative relationships with size. No consistent effects of pH or growth rate were observed. Results highlight that trace element:calcium ratios vary in sensitivity to intrinsic and extrinsic factors, with Sr:Ca influenced by both temperature and body size, Ba:Ca reflecting moderate environmental effects, and B:Ca responding primarily to biological variation. Findings reinforce the value of otolith chemistry as an ecological tool, while emphasising the importance of considering individual-level variation when interpreting elemental signatures.\u003c/p\u003e","manuscriptTitle":"Relative Impact of Environmental (Temperature and pH) and Biological Factors (Size and Growth) on Otolith Trace Elemental Composition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-25 01:55:12","doi":"10.21203/rs.3.rs-7761636/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revise and Resubmit","date":"2025-11-24T04:48:47+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-10-11T22:49:53+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-11T22:29:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-04T11:16:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Marine Biology","date":"2025-10-01T13:42:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"marine-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mabi","sideBox":"Learn more about [Marine Biology](https://www.springer.com/journal/227)","snPcode":"227","submissionUrl":"https://submission.nature.com/new-submission/227/3","title":"Marine Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5bd1e2b2-889d-479a-9b75-0427fb1cf3a6","owner":[],"postedDate":"October 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:05:48+00:00","versionOfRecord":{"articleIdentity":"rs-7761636","link":"https://doi.org/10.1007/s00227-026-04811-y","journal":{"identity":"marine-biology","isVorOnly":false,"title":"Marine Biology"},"publishedOn":"2026-03-13 15:58:28","publishedOnDateReadable":"March 13th, 2026"},"versionCreatedAt":"2025-10-25 01:55:12","video":"","vorDoi":"10.1007/s00227-026-04811-y","vorDoiUrl":"https://doi.org/10.1007/s00227-026-04811-y","workflowStages":[]},"version":"v1","identity":"rs-7761636","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7761636","identity":"rs-7761636","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}