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Isotope labelling and phylogenetic analysis of a natural marine bacterioplankton population at seaMediterranean seawater was collected during August 2017 (station N1200, 32.45° N, 34.37 °E) from 11 depths by Niskin bottles and divided into triplicate 250 ml polycarbonate bottles. Two bottles from each depth were labelled with 1 mM sodium bicarbonate-13C and 1 mM ammonium-15N chloride (Sigma-Aldrich), and all three bottles (two labelled and one control) were incubated at the original depth and station at sea for 3.5 h around mid-day. The stable isotopes were chosen to enable direct comparison of C and N uptake in single cells, and the short incubation time was chosen to minimize isotope dilution and potential recycling and transfer of 13C and 15N between community members25. After incubation, bottles were brought back on board and the incubations were stopped by fixing with 2× electron-microscopy-grade glutaraldehyde (2.5% final concentration) and stored at 4 °C until sorting analysis. Cell sorting, NanoSIMS analyses and the calculation of uptake rates were performed as described in Roth-Rosenberg et al.26.DNA collection and extraction from seawaterSamples for DNA were collected on 0.22 µm Sterivex filters (Millipore). Excess water was removed using a syringe, 1 ml lysis buffer (40 mM EDTA, 50 mM Tris pH 8.3, and 0.75 M sucrose) was added and both ends of the filter were closed with parafilm. Samples were kept at −80 °C until extraction. DNA was extracted by using a semi-automated protocol including manual chemical cell lysis before automated steps using the QIAamp DNA Mini Protocol: DNA Purification from Blood or Body Fluids (Spin Protocol, starting from step 6, at the BioRap unit, Faculty of Medicine, Technion). The manual protocol began with thawing the samples, then the storage buffer was removed using a syringe and 170 µl lysis buffer added to the filters. Thirty microlitres of Lysozyme (20 mg ml−1) were added to the filters and incubated at 37 °C for 30 min. After incubation, 20 µl proteinase K and 200 µl buffer AL (from the Qiagen kit) were added to the tube for 1 h at 56 °C (with agitation). The supernatant was transferred to a new tube, and DNA was extracted using the QIAcube automated system. All DNA samples were eluted in 100 μl DNA-free distilled water.ITS PCR amplificationPCR amplification of the ITS was carried out with specific primers for Prochlorococcus CS1_16S_1247F (5′-ACACTGACGACATGGTTCTACACGTACTACAATGCTACGG) and Cs2_ITS_Ar (5′-TACGGTAGCAGAGACTTGGTCTGGACCTCACCCTTATCAGGG)21,22. The first PCR was performed in triplicate in a total volume of 25 μl containing 0.5 ng of template, 12.5 μl of MyTaq Red Mix (Bioline) and 0.5 μl of 10 μM of each primer. The amplification conditions comprised steps at 95 °C for 5 min, 28/25 (16 S/ITS) cycles at 95 °C for 30 s, 50 °C for 30 s and 72 °C for 1 min followed by one step of 5 min at 72 °C. All PCR products were validated on a 1% agarose gel, and triplicates were pooled. Subsequently, a second PCR amplification was performed to prepare libraries. These were pooled and after a quality control sequenced (2 × 250 paired-end reads) using an Illumina MiSeq sequencer. Library preparation and pooling were performed at the DNA Services facility, Research Resources Center, University of Illinois at Chicago. MiSeq sequencing was performed at the W.M. Keck Center for Comparative and Functional Genomics at the University of Illinois at Urbana-Champaign.ITS sequence processingPaired-end reads were analysed using the Dada2 pipeline46. The quality of the sequences per sample was examined using the Dada2 ‘plotQualityProfile’ command. Quality filtering was performed using the Dada2 ‘filterAndTrim’ command with parameters for quality filtering truncLen=c(290,260), maxN=0, maxEE=c(2,2), truncQ=2, rm.phix=TRUE, trimLeft=c(20,20). Following error estimation and dereplication, the Dada2 algorithm was used to correct sequences. Merging of the forward and reverse reads was done with minimum overlap of 4 bp. Detection and removal of suspected chimaeras was done with command ‘removeBimeraDenovo’. In total, 388,417 sequences in 484 amplicon sequence variants were counted. The amplicon sequence variants were aligned in MEGA6 (ref. 47), and the first ~295 nucleotides, corresponding to the 16S gene, were trimmed. The ITS sequences were then classified using BLASTn against a custom database of ITS sequences from cultured Prochlorococcus and Synechococcus strains as well as from uncultured HL and LL clades.Individual-based modelPlanktonIndividuals.jl (v0.1.9) was used to run the individual-based simulations48. Briefly, the cells fix inorganic carbon through photosynthesis and nitrogen, phosphorus and DOC from the water column into intracellular quotas and grow until division or grazing. Cell division is modelled as a probabilistic function of cell size. Grazing is represented by a quadratic probabilistic function of cell population. Cells consume nutrient resources, which are represented as Eulerian, density-based tracers. A full documentation of state variables and model equations are available online at https://juliaocean.github.io/PlanktonIndividuals.jl/dev/. Equations related to mixotrophy are shown below as an addition to the online documentation.$$V_{{mathrm{DOC}}} = V_{{mathrm{DOC}}}^{{mathrm{max}}} cdot {{mathrm{max}}}left( {0.0,{{mathrm{min}}}left( {1.0,,frac{{q_{mathrm{C}}^{{mathrm{max}}} – q_{mathrm{C}}}}{{q_{mathrm{C}}^{{mathrm{max}}} – q_{mathrm{C}}^{{mathrm{min}}}}}} right)} right) cdot frac{{{mathrm{DOC}}}}{{{mathrm{DOC}} + K_{{mathrm{DOC}}}^{{mathrm{sat}}}}}$$
(1)
$$f_{{mathrm{PS}}} = frac{{P_{mathrm{S}}}}{{P_{mathrm{S}} + V_{{mathrm{DOC}}}}}$$
(2)
$$V_{{mathrm{DOC}}} = 0,,{mathrm{if}},f_{{mathrm{PS}}} < f_{{mathrm{PS}}}^{{mathrm{min}}}$$ (3) where VDOC is the cell-specific DOC uptake rate (mol C cell−1 s−1), (V_{{mathrm{DOC}}}^{{mathrm{max}}}) is the maximum cell-specific DOC uptake rate (mol C cell−1 s−1), (q_{mathrm{C}}^{{mathrm{max}}}) is the maximum cell carbon quota (mol C cell−1), (q_{mathrm{C}}^{{mathrm{min}}}) is the minimum cell carbon quota (mol C cell−1). The maximum and minimum functions here is used to keep qC between (q_{mathrm{C}}^{{mathrm{min}}}) and (q_{mathrm{C}}^{{mathrm{max}}}). (K_{{mathrm{DOC}}}^{{mathrm{sat}}}) is the half-saturation constant for DOC uptake (mol C m−3). fPS is the fraction of fixed C originating from photosynthesis (PS, mol C cell−1 s−1). DOC uptake stops when fPS is smaller than (f_{{mathrm{PS}}}^{{mathrm{min}}})(minimum fraction of fixed C originating form photosynthesis, 1% by default) according to laboratory studies of Prochlorococcus that showed that they cannot survive long exposure to darkness (beyond several days) even when supplied with organic carbon sources13. (1 − fPS) is also shown in Fig. 3 as the contribution of DOC uptake.We set up two separate simulations; each of them has a population of either an obligate photo-autotroph or a mixotroph that also consumes DOC. The initial conditions and parameters (Supplementary Table 3) are the same for the two simulations except the ability of mixotrophy. The simulations were run with a timestep of 1 min for 360 simulated days to achieve a steady state. We run the two simulations for multiple times in order to get the range of the stochastic processes.Evaluation of autotrophic growth ratesWe evaluated the carbon-specific, daily-averaged carbon fixation rate, ℙ as a function of light intensity (I, µE), following Platt et al.33:$${Bbb P} = frac{1}{{Delta t}}{int}_0^{Delta t} {frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}} P_{mathrm{S}}^{{mathrm{Chl}}}left( {1 - e^{ - alpha _{{mathrm{Chl}}}I/P_{mathrm{S}}^{{mathrm{Chl}}}}} right)e^{ - beta _{{mathrm{Chl}}}I/P_{mathrm{S}}^{{mathrm{Chl}}}}Delta t$$ (4) Here, (P_{mathrm{S}}^{{mathrm{Chl}}}), αChl and βChl are empirically determined coefficients representing the chlorophyll-a-specific carbon fixation rate (mol C (mol Chl)−1 s−1), the initial slope of the photosynthesis–light relationship and photo-inhibition effects at high photon fluxes, respectively. We impose empirically determined values for (P_{mathrm{S}}^{{mathrm{Chl}}}), αChl and βChl from the published study of Moore and Chisholm24. The natural Prochlorococcus community comprises HL and LL ecotypes, which have different values of (P_{mathrm{S}}^{{mathrm{Chl}}}), αChl and βChl, and the community growth rate is expected to be between that of HL extremes and LL extremes. Therefore, we use photo-physiological parameters for an HL-adapted ecotype (MIT9215), acclimated at 70 µmol photons m−2 s−1 and an LL-adapted ecotype (MIT9211), acclimated 9 µmol photons m−2 s−1. The models with these values are shown as the different lines in Fig. 2b,d. I is the hourly PAR, estimated by scaling the observed noon value at each depth with a diurnal variation evaluated from astronomical formulae based on geographic location and time of year37,38.(frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}) is the molar chlorophyll-a to carbon ratio, which is modelled as a function of growth rate and light intensity using the Inomura34 model (equation 17 therein) where parameters were calibrated with laboratory data from Healey49. In addition, the maximum growth rate ((mu _{{mathrm{max}}}^I)) based on macromolecular allocation is also estimated using the Inomura model (equation 30 therein). An initial guess of the growth rate and the empirically informed light intensity are used to estimate (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}), which is then used to evaluate the light-limited, photoautotrophic growth rate$${Bbb V}_{mathrm{C}}^{{mathrm{auto}}} = min left( {{Bbb P} - K_{mathrm{R}},mu _{{mathrm{max}}}^I} right)$$ (5) from which the (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}) is again updated. The light-limited growth rate is used to re-evaluate the (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}). Repeating this sequence until the values converge, ({Bbb V}_{mathrm{C}}^{{mathrm{auto}}}) and (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}) are solved iteratively.The nitrogen-specific uptake rate of fixed nitrogen (day−1) is modelled as$${Bbb V}_{{{mathrm{N}}}} = {Bbb V}_{mathrm{N}}^{{mathrm{max}}}frac{1}{{Q_{mathrm{N}}}}frac{N}{{N + K_{{{mathrm{N}}}}}}$$ (6) where values of the maximum uptake rate, ({Bbb V}_{mathrm{N}}^{{mathrm{max}}}), and half-saturation, KN, are determined from empirical allometric scalings35, along with a nitrogen cell quota QN from Bertilsson et al.39.The P-limited growth rate, or the phosphorus-specific uptake rate of phosphate (day−1), is modelled as$${Bbb V}_{mathrm{P}} = {Bbb V}_{mathrm{P}}^{{mathrm{max}}}frac{1}{{Q_{mathrm{P}}}}frac{{{mathrm{PO}_{4}}^{3 - }}}{{{mathrm{PO}_{4}}^{3 - } + K_{mathrm{P}}}}$$ (7) where values of the maximum uptake rate, ({Bbb V}_{mathrm{P}}^{{mathrm{max}}}). and half-saturation, KP, are determined from empirical allometric scalings35, along with a nitrogen cell quota QP from Bertilsson et al.39.Iron uptake is modelled as a linear function of cell surface area (SA), with rate constant ((k_{{mathrm{Fe}}}^{{mathrm{SA}}})) following Lis et al.36.$${Bbb V}_{{mathrm{Fe}}} = k_{{mathrm{Fe}}}^{{mathrm{SA}}} cdot {mathrm{SA}}frac{1}{{Q_{{mathrm{Fe}}}}}{mathrm{Fe}}$$ (8) The potential light-, nitrogen-, phosphorus- and iron-limited growth rates (({Bbb V}_{mathrm{C}},{Bbb V}_{mathrm{N}},{Bbb V}_{mathrm{P}},{Bbb V}_{{mathrm{Fe}}})) were evaluated at each depth in the water column and the minimum is the local modelled photo-autotrophic growth rate estimate, assuming no mixotrophy (Fig. 2b,d, blue lines). Parameters used in this evaluation are listed in Supplementary Table 2.An important premise of this study is that heterotrophy is providing for the shortfall in carbon under very low light conditions, but not nitrogen. It is known that Prochlorococcus can assimilate amino acids9 and therefore the stoichiometry of the heterotrophic contribution might alter the interpretations. However, it is also known that Prochlorococcus can exude amino acids40, which might cancel out the effects on the stoichiometry of Prochlorococcus.For the estimates of phototrophic growth rate from local environmental conditions (Fig. 2) we employed photo-physiological parameters from laboratory cultures of Prochlorococcus24. For the purposes of this study, we have assumed that the photosynthetic rates predicted are net primary production, which means that autotrophic respiration has been accounted for in the measurement. However, the incubations in that study were of relatively short timescale (45 min), which might suggest they are perhaps more representative of gross primary production. If this is the case, our estimates of photo-autotrophic would be even lower after accounting for autotrophic respiration, and thus would demand a higher contribution from heterotrophic carbon uptake. In this regard, our estimates might be considered a lower bound for organic carbon assimilation.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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Cohort demographicsA total of 206 female participants were enrolled in the study and passed our quality control standards. All participants were required to be between the ages of 18–26 years old (22.5 ± 2.1) and to be born and at the time living in one of four geographically distinct regions of the world: Barbados; Santiago, Chile; Pretoria, S. Africa; and Bangkok, Thailand. The regions do, however, differ by an order of magnitude in their geographic spread as the intra-distance separating the residence neighborhood of participants ranged from 34 (Barbados) to 681 km (Pretoria, S. Africa) (Fig. S2). The Chilean and the South African datasets are further divided into two contiguous sub-regions, or neighborhoods, to allow for a micro-geographic analysis. The study population is largely dominated by individuals with self-identified Thai heritage (33%), followed by Black African (16%), Afro-Caribbean (14%) and white (14%) descent, although 19% of the Chilean population did not report ethnicity.Study participants, despite the divergent geographies, mostly have similar dietary and lifestyle habits (Table S1). Over half the study population (62%) have a normal BMI, with the mean BMI in this range (22.6 ± 5.5). The diets of the different cohorts are also similar as of the total cohort, 78% consume a starch heavy diet (≥ 4 days a week) of rice, bread and pasta, followed by 66% who frequently consume (≥ 4 days a week) vegetables and fruit and 49% who frequently consume dairy products. The study population is split by level of tobacco exposure, with 51% of the population having never smoked, and 43% being exposed to second-hand smoke through living with a smoker. Over half (56%) of the study population own one or more pets.Stool microbiomeThe OTUs identified using the UPARSE pipeline17 were used to compute the alpha diversity of the microbial communities using the Chao1 (species richness) and Shannon (species evenness) indices. The mean Shannon indices reveal that the microbiota diversity is only significant between Thailand-Chile with FDR  More

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Our results show that GoM BFT larval survey samples can provide the crucial mark events for eventual CKMR estimates of adult abundance. The adult parents marked by larval samples can be directly recaptured in the fishery many years later as POPs, and indirectly through their progeny in future samples of larvae, as evidenced by the two cross-cohort HSPs (XHSPs) recovered in this study, which imply that a parent survived and spawned in the GoM in consecutive years. As more cohorts are sampled in future, the growing number of XHSPs could be used to estimate average adult survival rates, in addition to helping with the estimation of adult abundance31, as is now done for southern blue tuna40.There is a modest level of sibship within our 2016 samples, and a high level (involving over half the samples) in 2017, but it turns out not to be high enough to cause serious problems for POP-based CKMR. High sibship per se does not lead to bias in CKMR by virtue of the statistical construction of the estimate, but it does increase variance, which can be summarized through a reduction in effective sample size. In a POP-based CKMR model, our effective sample size would be about 75% of nominal for the two years combined, or 66% of nominal for the targeted sampling of 2017. Since it is actually the product of adult and juvenile sample sizes which drives precision in CKMR14, one way to think about the 75% is that we will need about 33% more adult samples to achieve a given precision on abundance estimates than if we had somehow been able to collect the same number of “independent” juvenile samples (i.e. without oversampling siblings). That increase is appreciable but entirely achievable; for WBFT, it is logistically much easier to collect more feeding-ground adult samples than to collect more larvae, and at present there is no known practical way to collect large numbers of older, more dispersed, and thus more independent, juvenile western origin bluefin tuna (WBFT).This study was motivated by the concern that sibship might be a serious impediment to use of WBFT larvae for CKMR. High levels of sibship have been found in larval collections for other taxa despite a pelagic larval phase, suggesting that abiotic factors can impede random mixing of larvae after a spawning event41. Our larval samples were only a few days old (4–11) and thus had little time to disperse since fertilization; our concern beforehand was that each tow might sample the offspring of a very small number of adults (one spawning group in one night), and in 2017 that repeatedly towing the same water mass might simply be resampling the same “family”. In practice, though, the cumulative effect was limited. Samples were not dominated by progeny from just a few adults; the maximum DPG size (i.e., number of offspring from any one adult) was 5, which is under 2% of the larval sample size. There are several possible reasons for this finding. First, plankton sample tows are typically standardized to a ten-minute duration, covering on average about 0.3 nautical miles. Based on continuous plankton cameras42, each tow is likely to tow through multiple patches of zooplankton, and therefore potentially multiple patches of BFT larvae. Second, spawning aggregations of BFT may contain many adults. For example, on the spawning grounds near the Balearic Islands in the Mediterranean, purse seine fisheries target spawning fish and individual net sets routinely capture upwards of 500 mature individuals43. These numbers suggest that BFT spawner aggregations can be quite large, although the number of individuals that contribute gametes to a single spawning event may be lower. The results of this study pose intriguing scenarios for understanding BFT larval ecology and spawning behavior, which could be explored with larger sample sizes paired with data on oceanographic conditions, direct observation of spawning aggregations, and modeling to compare observed and predicted dispersal. The results of this study are based on just two years of sampling, and numerous practical and theoretical challenges remain to fully understand BFT reproduction in the GoM.Our sibship impact calculations assume use of an unmodified adult-size-based CKMR POP model, where each juvenile is compared to each adult taking into account the latter’s size (e.g.,14). That will give unbiased estimates, which we regard as essential in a CKMR model. However, for WBFT the estimates are not fully statistically efficient, in that some adults receive more statistical weight than others because they are marked more often (by having a large DPG), and thus variance might not be the lowest achievable. Modifying the model to fix that would be simple in a “cartoon” CKMR setting where all adults are identical (e.g., Fig. 1 of14), simply by first condensing each DPG to a single representative, then only using those representatives (rather than all the larvae) in POP comparisons. Each marked parent then receives the same weight, giving maximum efficiency. For the cartoon, this condensed-DPG model still gives an unbiased estimate of abundance, because each DPG has one parent of given sex, and the chance of any sampled cartoon adult of that sex being that parent is 1/N. The DPG-condensed effective sample size is simply half the number of distinct parents, which would be a little larger than the effective sample sizes for the unmodified model shown in Table 3; e.g., in 2017, 504/2 = 252 versus 209. However, no such straightforward improvement is available for an adult-size-based CKMR model such as is needed for WABFT. Using condensed DPGs directly would bias the juvenile sampling against larger more-fecund adults, whose DPGs will tend on average to be larger and thus to experience disproportionate condensation. Those adults would be marked less often by the DPG-condensed juveniles than the model assumes, violating the basic requirements for unbiased CKMR in14. A more sophisticated model might be able to combine unbiasedness with higher efficiency but, since the unmodified adult-size-based POP model that we expect to use is unbiased and only mildly inefficient (at worst 209/252 = 83% efficient, in 2017) there seems no particular need for extra complications at present. However, that may not hold true if we eventually move to a POP + XHSP model, where the impact on unmodified CKMR variance is worse (though there is still no bias, for the same reason as with POPs). Intuitively, the biggest impact that a DPG of size 5 can have in a POP model is to suddenly raise the number of POPs by 5 if its parent happens to be sampled; within a useful total of, say, 75 POPs, the influence is not that large. But if two DPGs both of size 5 in different cohorts happen to share a parent, then the total of XHSPs suddenly jumps by 25— likely a substantial proportion of total XHSPs. Supplementary Material B also includes effective sample size formulae for a simplified XHSP-only model, which demonstrate the increased impact of within-cohort sibship; for our WBFT samples, it turns out that the XHSP-effective size is slightly lower for the targeted 2017 samples (110) than for the 2016 samples (130), unlike the POP-only effective size. Dropping from a maximum theoretical effective sample size of 252 (half the number of DPGs) down to 110 would be rather inefficient and would increase the number of years of sampling required to yield a useful XHSP dataset. This motivates developing a modified POP + XHSP model that retains unbiasedness without sacrificing too much efficiency. In principle, that can be done by condensing each DPG but then conditioning its comparison probabilities on the DPG’s original size, in accordance with the framework in14. This is a topic for subsequent research, and the results will inform future sampling strategy decisions for WBFT.One potential difficulty for western BFT CKMR might occur if a substantial proportion of animals reaching maturity are the offspring of “Western” (in genetic terms) adults who persistently spawn in the western North Atlantic but outside the GoM. However, as long as the adults marked by GoM larvae are well mixed at the time of sampling with any western adults that do spawn outside of the GoM, the total POP-based population estimate of genetically-western BFT from CKMR will remain unbiased. Given evidence from tagging of widespread adult movements within the western North Atlantic2, good mixing in the sampled feeding grounds seems likely; so, even if successful non-GoM western BFT spawning really is commonplace, there should not be a problem with relying on GoM larvae for at least the POP component of CKMR14.Studies of fish early life history have long been considered to have great potential to provide novel insight into the unique population dynamics of fishes44,45,46. Sampling efforts aimed at estimating fish recruitment dynamics have spawned a diversity of larval survey programs. Examples of these long-term programs include the California Cooperative Oceanic Fisheries Investigations, International Council for the Exploration of the Sea (ICES) surveys in the North Atlantic and adjacent areas, Southeast Monitoring and Assessment Program (SEAMAP) in the GoM, Ecosystem Monitoring (EcoMon) in the Northeast U.S., and numerous others, many of which provide indices of larval abundance widely used in fisheries and ecosystem assessments. Yet, as a result of the inherent patchiness of larvae42, sampling variability, and highly variable density dependent mortality45, fisheries scientists have often struggled to determine how larval surveys relate to the adult fish populations. Inclusion of estimates of sibship among larvae collected in surveys could refine estimates of adult spawning stock biomass estimated from these surveys.The results of this study also represent products of decades of work and coordination in obtaining high-quality DNA from larval specimens. Key steps to successful genotyping of larvae include ensuring that larvae are preserved, sorted, and handled in 95% non-denatured ethanol. In addition, strict instrument cleaning protocols must be followed, and stomachs should be removed or avoided (this study used larval tails and, when possible, eyes to avoid cross contamination of prey contents, including possible congeners and other BFT individuals). Exposure to hot lamps during the sorting and dissection processes should also be minimized to ensure that DNA quality is sufficiently high for genotyping-by-sequencing. Although the tissues available for genetic analysis were limited by the needs of other experiments that required BFT tissues, otoliths, gut contents, and other information from the same larvae, we were able to successfully genotype most larvae greater than 6 mm SL and identify thousands of informative SNPs. The lower size limit of larvae could likely be decreased if whole specimens were available for genotyping, although the use of younger larvae could increase the incidence of sibship.In summary, while we observed both FSPs and HSPs in larval collections, with elevated sibship overall and with siblings being more prevalent within tows and in nearby tows, the level of sibship was sufficiently low that collections of GoM BFT larvae can still provide the critical genetic mark of parental genotypes required for CKMR. Our results demonstrate a crucial proof of concept and are the first step towards an operational CKMR modelling estimate of spawning stock abundance for western BFT. More

Animal ethicsAll experiments were conducted under the UK Home Office licence P88687E07 and with approval from the University of Exeter Ethics Committee.Fish husbandryJuvenile rainbow trout (Oncorhynchus mykiss) (n = 42; body mass: 159.9 ± 5.2 g), were obtained from Houghton Spring Fish Farm (Dorset, UK) and housed in the Aquatic Research Centre at the University of Exeter (UK). Before transfer to individual experimental chambers, all fish were housed across two 400 L tanks (n = 21 per tank) supplied with recirculated fresh water for 14 days. During this 14 day acclimation period, fish were maintained at 15 °C and fed on a 1% ration of commercial trout feed (Aller platinum 4.5 mm (Aller AQUA ©) three times a week. Prior to experimentation, fish were fasted for seven days.Acid buffering dietsDiets were prepared by adding one of three calcium-based salts, CaCO3, Ca3(PO4)2 or CaCl2 (as non-buffering control) with isomolar quantities of calcium to a commercial trout pelleted diet (Skretting 4.5 mm Horizon, Skretting, UK). The quantities of these salts used were designed to mimic the calcium content of the skeletal component of crustacean or bony fish prey.Cameron (1985)50 estimated that the bone of teleost fish represents 16.3% of whole-body mass (and therefore soft tissue represents 83.7%). However, bone is not just calcium phosphate, but includes numerous organic components as well as water content. By comparing titrations of pure calcium phosphate salt and samples of ground-up teleost (rainbow trout) bone, we established that it required 10.25 times less calcium phosphate salt to achieve the same acid-buffering capacity as that of an equal mass of bone. We therefore created a diet that was supplemented with 1.9 g calcium phosphate for every 100 g of trout pellets (i.e. [16.3 g ÷ 10.25] x [100 ÷ 83.7 g] = 1.9 g), in order to match the bone content of calcium phosphate typically found in fish prey as a proportion of the soft tissue mass. This amounted to 18.4 mmoles of calcium phosphate salt (Ca3(PO4)2; M.W. = 310.2) per 100 g of trout pellets. For the two other diets we aimed to maintain the same molar amount of calcium cation added whilst varying the anionic component of the salt added. So, for the unbuffered version of the diet 2.7 g of calcium chloride (CaCl2.2H2O; M.W. = 147.0) was added, whilst for the calcium carbonate (CaCO3; M.W. = 100.0) buffered diet 1.84 g was added, per 100 g of trout pellets.To form each diet, 100 g of Skretting 4.5 mm Horizon trout pellets were ground to a fine powder using a pestle and mortar. Following grinding, 1.9, 1.84 and 2.7 g of Ca3(PO4)2, CaCO3 and CaCl2 were added to the ground pellet and mixed. Then, 70 ml of ultrapure water was added to the dry material to form a paste. This paste was pressed into commercial 4 mm moulds, removed and dried at 70 °C for 24 h. An acid titration test was conducted to ensure that diets remained representative of the buffer capacity of prey and each calcium salt. For this test, 60 ml of ultrapure water were added to 1 g of each experimental diet and titrated down to pH 3.5 using 0.05 mol L−1 HCl. The CaCl2 diet treatment required 4.56 ml of the acid which was only slightly less than the 6.4 ml required to titrate the Ca3(PO4)2 diet. In contrast it took almost double the amount of acid (11 ml) to titrate the CaCO3 diet. In molar terms it took 228, 320 and 550 µmoles of HCl to titrate 1 g of the CaCl2, Ca3(PO4)2 and CaCO3 feeds to pH 3.5, respectively. To calculate the total acid-buffering consumed, the buffer capacity (per g of food) was multiplied by the actual ration ingested for each individual. Based on manufacturer details each diet had a gross energy of 23 kJ per gram of feed.Acid secretion in the stomach and the blood alkaline tideTo investigate the effect of dietary buffer capacity on the blood acid–base chemistry (alkaline tide) and gut secretions, blood and gut samples were taken from fish to determine blood gas and acid–base balance and haematology variables of fish fed each experimental diet. Fish were fasted for 7 days and then fed a 2.5% ration of one of three experimental feeds. Diet was randomly allocated to each individual (n = 6 per diet). At 24 and 48 h following meal ingestion fish were anesthetised using benzocaine (100 mg l−1). Once fish had lost equilibrium and were un-responsive to a tail pinch, fish were transferred to a gill irrigation system dosed with a lower concentration of benzocaine (75 mg l−1). Fish were placed upside down within the irrigation chamber so that the head was fully submerged, and the entire gill basket covered. A micro pump was used to artificially ventilate the gills via a tube placed into the fish mouth. This allowed for the continuous ventilation of fish gills and ensured there was no build-up of CO2 or lactic acid during blood sampling that could unintentionally affect blood acid–base status. Blood was then drawn into a sodium-heparinised syringe via caudal puncture. Fish were then euthanased via pithing and dissected to collect stomach and intestinal contents. Gut samples were centrifuged to isolate gastric and intestinal juices.Blood and gastric pH were measured using an Accumet CP-620-96 MicroProbe (Accumet Engineering Corporation, USA) connected to a Hanna HI 8424 m (Hanna Instruments, Woonsocket, Rhode Island, USA). Whole blood PO2 was measured using a Strathkelvin 1302 electrode, housed within a thermostatted glass chamber (Strathkelvin), and connected to Strathkelvin 781 m (Strathkelvin Instruments Ltd., Scotland)51. Blood was drawn into three micro-haematocrit tubes (Hawksley) via capillary action and anaerobically sealed using Hawksley Critaseal Wax Sealant, then centrifuged (Hawksley microhaematocrit centrifuge, 10,000 rpm for 2 min) and then used to record haematocrit and held on ice before using the plasma. Plasma and intestinal total CO2 was then measured using a Mettler Toledo 965 carbon dioxide analyser and together with blood and intestinal pH measurements was used to calculate plasma and intestinal HCO3− and PCO2 by rearranging the Henderson–Hasselbalch equation and using values for solubility and pKapp from Boutilier et al. (1985)52.Net acid–base fluxes to the external waterThe effect of diet on the net flux of acid–base relevant ions to the external water was measured in a separate subset of juvenile rainbow trout (n = 10, 161.8 ± 6.9 g). Prior to measurements fish were weighed and transferred to individual 25 L chambers supplied with recirculated freshwater maintained at 15 °C. Following a 3-week acclimation period, fish were fed weekly on a 2.5% ration of one of three experimental feeds, with diet order randomised to each individual (See Supplementary Table 4). Initial and final water samples were taken from each chamber over six flux periods each week for three weeks (−23 to 1 (fasted), 0–6, 7–23, 24–47, 48–71 and 72–96 h post feed). Water inflow to each chamber was turned off during each flux period whilst aeration was maintained. Following the final measurement from each flux period, tanks were flushed with dechlorinated freshwater for 60 min so to ensure solid faeces and dissolved waste products (e.g., ammonia) were removed.Total ammonia was measured in triplicate on 200 µL water samples using the colourimetric salicylate-based method adapted from Cooper and Wilson (2008)19 and Verdouw et al. (1978)53 and the Infinite 200 PRO microplate reader (Tecan Trading AG Switzerland ©). Titratable alkalinity was measured in 20 ml water samples using an auto-titrator with autosampler (Metrohm 907 Titrando with 815 Robotic USB Autosampler XL) running double titrations with 0.02 mol l−1 of HCl and 0.005 mol l−1 NaOH. The double titration method calculates titratable alkalinity based on the difference in HCl required to titrate each water sample down to pH 3.9 and the amount of NaOH required to bring the sample back to the starting pH. During the titration, the sample is continuously bubbled or ‘purged’ with the inert gas N2 to remove any CO2. The net fluxes of titratable alkalinity (JTalk) and total ammonia (JTamm) were calculated using the following equation from Cooper and Wilson 2008:$${J}_{mathrm{net}}mathrm{X}=frac{[left(left[{mathrm{X}]}_{i}-{left[mathrm{X}right]}_{mathrm{f}}right) times Vright]}{(M times t)}$$
(1)

where Xi and Xf are the initial and final ion concentration in each tank (μmol l−1) from each flux period, V is the tank volume (L), M is the animal mass (kg) and t is the flux duration (h).The net acid–base flux was calculated as the difference between the flux of titratable alkalinity (JTalk) and the flux of total ammonia (JTamm).Measuring the SDAIntermittent flow-through respirometry was used to determine the rate of oxygen consumption (MO2) by juvenile rainbow trout fed voluntarily on a 2.5% ration of three experimental feeds. Prior to measurements, juvenile rainbow trout (n = 8, 162.2 ± 7.5 g) were weighed and transferred to individual 25 L chambers supplied with recirculated freshwater at 15 °C for 3 weeks. During this acclimation period, fish were fed weekly on a 2.5% ration of Skretting 4.5 mm Horizon trout pellets (Skretting UK). Following this acclimation period, measurements were conducted after 7 days of fasting. Each fish was fed once per week on all three diets over a 3-week period, with diet order randomised for each individual.During experimentation, fresh water was supplied continuously to two aerated 160 L sumps each fitted with a ballcock valve and overflow. Aerated freshwater was then pumped from the sump to the eight respirometry chambers in a loop for the duration of the testing period. Water within each fish chamber was continuously mixed using a submerged mini-pump (WP300; Tetra Werke, Melle, Germany). During measurements, water inflow to each chamber was shut off and the decline in O2 was recorded by PO2 OxyGuard Mini Probe (OxyGuard ® International, Denmark) connected directly to the mini-pump. Oxygen partial pressure values were logged continuously by Pyro Oxygen Logger software (Pyroscience GmBH, Germany) which interfaced with a respirometry software package (AquaResp3: aquaresp.com, see Svendsen et al. 2016 54) to instantaneously convert PO2 into O2 content and calculate the rate of oxygen consumption (MO2, mg O2 kg−1 body mass h−1) based on the fish body mass in kg (m), chamber water volume in L after discounting the fish body volume (Vresp), and the slope (s) of the decline in oxygen concentration (kPa O2 h−1) versus time using the following equation from Svendsen et al. (2016)54:$${MO}_{2}= {sV}_{Resp}{alpha m}^{-1}$$where:$$s= frac{{O}_{2}, initial- {O}_{2}, final}{time, initial-time, final}$$Following each closed measurement period, the chamber was automatically flushed with freshwater from the aerated sumps by two AquaMedic Ocean Runner pumps (Aqua Medic, Ocean Runner 6500). The length of the flush and measurement periods was controlled by two USB- 4 Cleware switches (Cleware GmbH, Germany) which were also interfaced with the AquaResp software to ensure that the partial pressure of oxygen (PO2) within the respirometry chambers never fell below 90% of the starting value. This meant that the measurement period of 15 min was followed by a flushing period of 2 min and a wait time of 60 s.Prior to feeding a baseline 24 h period of standard metabolic rate (SMR) was recorded. The mean SMR of each individual was calculated using the R package ‘fishMO2’ and the ‘calcSMR’ function. Following Chabot et al. (2016)55, the coefficient of variation (CVmlnd) was used to determine whether the mean of the lowest normal distribution (MLND) or the quantile method (P = 0.2) was used to estimate SMR for each individual. Following the SMR measurement, fish voluntarily fed on a 2.5% ration of experimental feed and MO2 recorded continuously for six days. This procedure was repeated for two more consecutive weeks to measure MO2 in fish fed all three experimental diets. Background oxygen consumption was recorded overnight (18 h) in blank (no fish) chambers. Oxygen consumption was not corrected for background respiration as it was considered negligible ( More