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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

DataWe tested our models on ten publicly available datasets. In Fig. 4 we show examples of images from each of the datasets. When applicable, the training and test splits were kept the same as in the original dataset. For example, the ZooScan, Kaggle, EILAT, and RSMAS datasets lack a specific training and test set; in these cases, benchmarks come from k-fold cross-validation51,52, and we followed the exact same procedures in order to allow for a fair comparison.Figure 4Examples of images from each of the datasets.(a) RSMAS (b) EILAT (c) ZooLake (d) WHOI (e) Kaggle (f) ZooScan (g) NA-Birds (h) Stanford dogs (i) SriLankan Beetles (j) Florida Wildtrap.Full size imageRSMAS This is a small coral dataset of 766 RGB image patches with a size of (256times 256) pixels each53. The patches were cropped out of bigger images obtained by the University of Miami’s Rosenstiel School of Marine and Atmospheric Sciences. These images were captured using various cameras in various locations. The data is separated into 14 unbalanced groups and whose labels correspond to the names of the coral species in Latin. The current SOTA for the classification of this dataset is by52. They use the ensemble of best performing 11 CNN models. The best models were chosen based on sequential forward feature selection (SFFS) approach. Since an independent test is not available, they make use of 5-fold cross-validation for benchmarking the performances.EILAT This is a coral dataset of 1123 64-pixel RGB image patches53 that were created from larger images that were taken from coral reefs near Eilat in the Red sea. The image dataset is partitioned into eight classes, with an unequal distribution of data. The names of the classes correspond to the shorter version of the scientific names of the coral species. The current SOTA52 for the classification of this dataset uses the ensemble of best performing 11 CNN models similar to RSMAS dataset and 5-fold cross-validation for benchmarking the performances.ZooLake This dataset consists of 17943 images of lake plankton from 35 classes, acquired using a Dual-magnification Scripps Plankton Camera (DSPC) in Lake Greifensee (Switzerland) between 2018 and 2020 14,54. The images are colored, with a black background and an uneven class distribution. The current SOTA22 on this dataset is based on a stacking ensemble of 6 CNN models on an independent test set.WHOI This dataset 55 contains images of marine plankton acquired by Image FlowCytobot56, from Woods Hole Harbor water. The sampling was done between late fall and early spring in 2004 and 2005. It contains 6600 greyscale images of different sizes, from 22 manually categorized plankton classes with an equal number of samples for each class. The majority of the classes belonging to phytoplankton at genus level. This dataset was later extended to include 3.4M images and 103 classes. The WHOI subset that we use was previously used for benchmarking plankton classification models51,52. The current SOTA22 on this dataset is based on average ensemble of 6 CNN models on an independent test set.Kaggle-plankton The original Kaggle-plankton dataset consists of plankton images that were acquired by In-situ Ichthyoplankton Imaging System (ISIIS) technology from May to June 2014 in the Straits of Florida. The dataset was published on Kaggle (https://www.kaggle.com/c/datasciencebowl) with images originating from the Hatfield Marine Science Center at Oregon State University. A subset of the original Kaggle-plankton dataset was published by51 to benchmark the plankton classification tasks. This subset comprises of 14,374 greyscale images from 38 classes, and the distribution among classes is not uniform, but each class has at least 100 samples. The current SOTA22 uses average ensemble of 6 CNN models and benchmarks the performance using 5-fold cross-validation.ZooScan The ZooScan dataset consists of 3771 greyscale plankton images acquired using the Zooscan technology from the Bay of Villefranche-sur-mer57. This dataset was used for benchmarking the classification models in previous plankton recognition papers51,52. The dataset consists of 20 classes with a variable number of samples for each class ranging from 28 to 427. The current SOTA22 uses average ensemble of 6 CNN models and benchmarks the performance using 2-fold cross-validation.NA-Birds NA-Birds58 is a collection of 48,000 captioned pictures of North America’s 400 most often seen bird species. For each species, there are over 100 images accessible, with distinct annotations for males, females, and juveniles, totaling 555 visual categories. The current SOTA59 called TransFG modifies the pure ViT model by adding contrastive feature learning and part selection module that replaces the original input sequence to the transformer layer with tokens corresponding to informative regions such that the distance of representations between confusing subcategories can be enlarged. They make use of an independent test set for benchmarking the model performances.Stanford Dogs The Stanford Dogs dataset comprises 20,580 color images of 120 different dog breeds from all around the globe, separated into 12,000 training images and 8,580 testing images60. The current SOTA59 makes use of modified ViT model called TransFG as explained above in NA-Birds dataset. They make use of an independent test set for benchmarking the model performances.Sri Lankan Beetles The arboreal tiger beetle data61 consists of 380 images that were taken between August 2017 and September 2020 from 22 places in Sri Lanka, including all climatic zones and provinces, as well as 14 districts. Tricondyla (3 species), Derocrania (5 species), and Neocollyris (1 species) were among the nine species discovered, with six of them being endemic . The current SOTA61 makes use of CNN-based SqueezeNet architecture and was trained using pre-trained weights of ImageNet. The benchmarking of the model performances was done on an independent test set.Florida Wild Traps The wildlife camera trap62 classification dataset comprises 104,495 images with visually similar species, varied lighting conditions, skewed class distribution, and samples of endangered species, such as Florida panthers. These were collected from two locations in Southwestern Florida. These images are categorized in to 22 classes. The current SOTA62 makes use of CNN-based ResNet-50 architecture and the performance of the model was benchmarked on an independent test set.ModelsVision transformers (ViTs)31 are an adaptation to computer vision of the Transformers, which were originally developed for natural language processing30. Their distinguishing feature is that, instead of exploiting translational symmetry, as CNNs do, they have an attention mechanism which identifies the most relevant part of an image. ViTs have recently outperformed CNNs in image classification tasks where vast amounts of training data and processing resources are available30,63. However, for the vast majority of use cases and consumers, where data and/or computational resources are limiting, ViTs are essentially untrainable, even when the network architecture is defined and no architectural optimization is required. To settle this issue, Data-efficient Image Transformers (DeiTs) were proposed32. These are transformer models that are designed to be trained with much less data and with far less computing resources32. In DeiTs, the transformer architecture has been modified to allow native distillation64, in which a student neural network learns from the results of a teacher model. Here, a CNN is used as the teacher model, and the pure vision transformer is used as the student network. All the DeiT models we report on here are DeiT-Base models32. The ViTs are ViT-B16, ViT-B32, and ViT-L32 models31.ImplementationTo train our models, we used transfer learning65: we took a model that was already pre-trained on the ImageNet43 dataset, changed the last layers depending on the number of classes, and then fine-tuned the whole network with a very low learning rate. All the models were trained with two Nvidia GTX 2080Ti GPUs.DeiTs We used DeiT-Base32 architecture, using the Python package TIMM66, which includes many of the well-known deep learning architectures, along with their pre-trained weights computed from the ImageNet dataset43. We resized the input images to 224 x 224 pixels and then, to prevent the model from overfitting at the pixel level and help it generalize better, we employed typical image augmentations during training such as horizontal and vertical flips, rotations up to 180 degrees, small zoom up’s to 20%, a small Gaussian blur, and shearing up to 10%. To handle class imbalance, we used class reweighting, which reweights errors on each example by how present that class is in the dataset67. We used sklearn utilities68 to calculate the class weights which we employed during the training phase.The training phase started with a default pytorch69 initial conditions (Kaiming uniform initializer), an AdamW optimizer with cosine annealing70, with a base learning rate of (10^{-4}), and a weight decay value of 0.03, batch size of 32 and was supervised using cross-entropy loss. We trained with early stopping, interrupting training if the validation F1-score did not improve for 5 epochs. The learning rate was then dropped by a factor of 10. We iterated until the learning rate reached its final value of (10^{-6}). This procedure amounted to around 100 epochs in total, independent of the dataset. The training time varied depending on the size of the datasets. It ranged between 20min (SriLankan Beetles) to 9h (Florida Wildtrap). We used the same procedure for all the datasets: no extra time was needed for hyperparameter tuning.ViTs We implemented the ViT-B16, ViT-B32 and ViT-L32 models using the Python package vit-keras (https://github.com/faustomorales/vit-keras), which includes pre-trained weights computed from the ImageNet43 dataset and the Tensorflow library71.First, we resized input images to 128 × 128 and employed typical image augmentations during training such as horizontal and vertical flips, rotations up to 180 degrees, small zooms up to 20%, small Gaussian blur, and shearing up to 10%. To handle class imbalance, we calculated the class weights and use them during the training phase.Using transfer learning, we imported the pre-trained model and froze all of the layers to train the model. We removed the last layer, and in its place we added a dense layer with (n_c) outputs (being (n_c) the number of classes), was preceded and followed by a dropout layer. We used the Keras-tuner72 with Bayesian optimization search73 to determine the best set of hyperparameters, which included the dropout rate, learning-rate, and dense layer parameters (10 trials and 100 epochs). After that, the model with the best hyperparameters was trained with a default tensorflow71 initial condition (Glorot uniform initializer) for 150 epochs using early stopping, which involved halting the training if the validation loss did not decrease after 50 epochs and retaining the model parameters that had the lowest validation loss.CNNs CNNs included DenseNet38, MobileNet39, EfficientNet-B240, EfficientNet-B540, EfficientNet-B640, and EfficientNet-B740 architectures. We followed the training procedure described in Ref.22, and carried out the training in tensorflow.Ensemble learningWe adopted average ensembling, which takes the confidence vectors of different learners, and produces a prediction based on the average among the confidence vectors. With this procedure, all the individual models contribute equally to the final prediction, irrespective of their validation performance. Ensembling usually results in superior overall classification metrics and model robustness74,75.Given a set of n models, with prediction vectors (vec c_i~(i=1,ldots ,n)), these are typically aggregated through an arithmetic average. The components of the ensembled confidence vector (vec c_{AA}), related to each class (alpha ) are then$$begin{aligned} c_{AA,alpha } = frac{1}{n}sum _{i=1}^n c_{i,alpha },. end{aligned}$$
(2)
Another option is to use a geometric average,$$begin{aligned} c_{GA,alpha } = root n of {prod _{i=1}^n c_{i,alpha }},. end{aligned}$$
(3)
We can normalize the vector (vec c_g), but this is not relevant, since we are interested in its largest component, (displaystyle max _alpha (c_{GA,alpha })), and normalization affects all the components in the same way. As a matter of fact, also the nth root does not change the relative magnitude of the components, so instead of (vec c_{GA}) we can use a product rule: (displaystyle max _alpha (c_{GA,alpha })=max _alpha (c_{PROD,alpha })), with (displaystyle c_{PROD,alpha } = prod _{i=1}^n c_{i,alpha }).While these two kinds of averaging are equivalent in the case of two models and two classes, they are generally different in any other case33. For example, it can easily be seen that the geometric average penalizes more strongly the classes for which at least one learner has a very low confidence value, a property that was termed veto mechanism36 (note that, while in Ref.36 the term veto is used when the confidence value is exactly zero, here we use this term in a slightly looser way). More

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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

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Birds and housing62 males and 8 females of house sparrows were caught with mist nets in September and October 2019 in several sites in Kraków, Poland. Before releasing them to the outdoor aviary located on the campus of the Jagiellonian University, Kraków, Poland, each bird was weighed and banded with a metal band. The aviary measured 3.5 m in width, 10.0 m in length, 2.5 m in height, and was outfitted with trees, bushes, perches, wooden shelters, a water source, and food dishes. Initially, birds were maintained with water and a mixture of seeds: wheat, barley, millet, and sunflower seeds, provided ad libitum. Additionally, they had access to sand with shells and sepia.Experimental designAfter a few weeks of acclimation to captivity, the aviary was divided into two separate parts (3.5 × 5 m): aviary no. 1 (A1) and aviary no. 2 (A2). At the same time male individuals were assigned to two crossed experimental treatments, ensuring that in each aviary birds originated from all sampled populations. The experiment comprised of two different treatments conducted simultaneously—one designed to simulate a deficiency in an environmental factor influencing colouration (the quality of available food), the other—to introduce physiological stress and facilitate trade-offs in the allocation of resources limited by the first treatment (an immune response induced by a bacteria-derived compound, S1).The dietary manipulation was achieved by feeding one group of birds with a low-quality protein food (diet reduced in exogenous amino acids, namely phenylalanine and tyrosine content, which are precursors essential for melanin synthesis; PT-reduced diet), and the other one with a wholesome diet (control diet). At the same time, two levels of immune challenge were achieved within each dietary group, by injecting half of the birds with either lipopolysaccharide (LPS) from the cell wall of Escherichia coli, or a 0.9% saline vehicle (as a control). Four females were placed in each group of males to alleviate interspecific conflicts occurring in all-male sparrow flocks, but they did not take part in the experiments. After three weeks of experiment, birds housed in A1 were moved to A2, whereas birds from A2 were moved to A1.Immune challengeBefore receiving injections, birds were first weighed and then transferred from the outdoor aviary to the laboratory. 31 house sparrows (from both dietary groups) were injected intraperitoneally with 0.026 mg LPS (serotype O55:B5, Sigma-Aldrich) diluted in 0.1 mL of 0.9% saline vehicle, so that each bird received a dose of ca. 1 mg/kg body mass, which had previously been shown to induce sickness behaviour in another passerine, the white-crowned sparrow, Zonotrichia leucophrys55. 31 control males were injected with the same volume (0.1 mL) of 0.9% saline vehicle. All individuals were injected twice throughout the experiment with an interval of three weeks between the injections. Birds were always injected at the same time in the morning and early afternoon (between 9:00 am and 12:30 pm).Diet manipulationDuring the six weeks of the experiment (S1), birds received synthetic diet ad libitum, which constituted of a mixture of protein (WPC80, free amino acids and whey protein isolate BiPRO GMP 9000 (Agropur Inc., Appleton, USA)), fats, carbohydrates, and fiber30. The ingredients were thoroughly mixed to produce small pellets (6 mm in diameter) that the sparrows consumed readily. The experimental diet had phenylalanine and tyrosine at 42% (N = 32) of their level in the control diet (N = 30)30. The food pellets were prepared by ZooLab (zoolab.pl/en/home, Sędziszów, Poland). Each bird was weighed before and after the experiment to monitor potential effects of diet on body mass of each animal. Following the experiment, during the next three consecutive days, the amount of food consumed by passerines within every 24 h (starting from 10 am each day to 10 am next day) was noted for both compartments of the aviary. Because of different numbers of individuals per aviary, an overall weight of food consumed in A1 and A2 was calculated per individual, respectively.Feathers samplingMoult of the black bib feathers was stimulated at the end of the moulting period occurring in natural conditions in early November. At day 1 of the dietary/immunological experiment (S1) a small area of the bib (around 25 mm2) was plucked from each male sparrow held in A1. At day 2 the same procedure was performed on individuals from A2. The time difference is orders of magnitude smaller than the timescale of feather growth and hence it would not affect the results in any way.Because the feather growth rate may differ during melanogenesis, with consequences for final colouration (if feathers grow at a faster rate, pigments may be deposited over a larger surface and therefore result in less intense colouration56, we measured the rate of feather development during the course of the experiment. After three weeks of the experiment, three feathers from the upper, central, and lower region of the previously plucked bib were plucked once again. The mass of the collected feathers was determined to the nearest 0.01 mg (XP26 Micro Balance, Mettler-Toledo, Greinfensee, Switzerland). The experiment was completed after six weeks after fully regrown and developed feathers from the bib and PC2 were sampled the second time (S1). Three feathers from the central part of previously plucked bib region were collected to perform transmission electron microscopy (TEM) imaging, whereas the feathers obtained from the rest of the regrown bib area were subjected to electron paramagnetic resonance (EPR) spectroscopy and feather microstructure analyses (greater spatial density of melanized barbs or barbules may affect colouration17.Feathers measurementsReflectance measurementsAn USB4000 spectrophotometer (range 300–700 nm) with the PX-2 Pulsed Xenon Lamp (Ocean Optics, Dunedin, FL, USA) and a bifurcated probe with 7 × 400 μm optical fibres, equipped with a permanently attached 3 mm long black collar, was used to quantify the brightness of the bib feathers collected at the end of the experiment. The measurements were taken with 90 ms integration time and the probe held at 90° to a feather’s surface. Calibration measurements of a Spectralon white standard (Ocean Optics. Largo, FL, USA) were taken every 15 min during measurements. The order in which the samples were measured was randomized in terms of belonging to the experimental group. From each sample (N = 62), seven feathers were chosen and stacked in one pile on a piece of black paper. Ten reflectance measurements were taken on each pile, avoiding distal, brighter parts of the feathers. The obtained spectra were averaged and smoothed in the package ‘pavo’57. Brightness was calculated as a sum of the reflectance values over all wavelengths of a spectrum, and its lower values were interpreted as those indicative of a more melanin-rich feathers (i.e., absorbing more light).Feather developmentEach feather (3 per individual; N = 62 individuals) was laid on a white card and covered by a microscope slide to flatten the naturally curved feathers. Digital photographs were taken using camera (Canon EOS 7D) and imported to ImageJ v1.52a Software (National Institutes of Health, USA). The lengths of fully developed and undeveloped (still in sheath) parts of each feather were measured. To estimate the degree of a feather’s development, the length of the developed part of the vane was divided by its total length (quill with rachis plus the developed vane, Fig. 4A).Figure 4House sparrow feathers sampled from bib after three weeks of the experiment. Feathers during development (A), a TEM cross-sections of feather sampled from bib after the experiment (B).Full size imageFeather densityBarb density measurements were performed on the sampled regrown black bib feathers (N = 2–3 for each individual; N = 62 individuals), but because of their sparser structure we calculated the number of non-down (i.e., rigid) barbs on both sides of the vane, and divided this number by two (to obtain an average single-sided number of barbs) and then by the length of the rachis.Melanosome density (TEM)Feathers sampled from the bib of male sparrows (N = 62) were fixed for transmission electron microscopy (TEM) analysis in a mixture of 0.25 M sodium hydroxide and 0.1% Tween for 20 to 30 min on a bench-top shaker. Next, the feathers were treated with formic acid and ethanol in the ratio of 2:3 for 2.5 h and dehydrated twice for 20 min in 100% ethanol. Samples were embedded in a mixture of the PolyBed 812 resin (20 ml), DDSA (9 ml), NMA (12 ml) and DMP-30 (0.82 ml). Resin infiltration was gradual from 15% resin content in ethanol through 50%, 70% to 100% without alcohol. Each step lasted for 24 h. Then, the feathers were placed in silicone embedding moulds (Agar Scientific) and transferred to an oven. The polymerization proceeded at the temperature of 60 °C for 16 h. The epoxy resin blocks were then trimmed to get rid of excess resin. The surface of each block was prepared by its trimming, starting from the end of the feather, to approximately 5 mm using a glass knife. Next, ultrathin sections (70 nm) were cut with a diamond knife (DIATOME A. G., Berno, Switzerland) on a microtome (UC7, Leica, Wetzlar, Germany) and collected on single slot grids coated with a formvar film. The sections were then contrasted in uranyl acetate and lead citrate for 3 min. They were viewed and photographed with a transmission electron microscope (TEM) JEOL 2100HT (Jeol Ltd, Tokyo, Japan) for the purpose of investigating the number and density of the embedded pigment granules. For each individual three photographs of the cross-sections from a similar feather region were selected. Melanosome density was measured as the number of melanin granules observed in the barb cross-section divided by its area. Images were analysed using Adobe Photoshop (cross-sections area) and ImageJ (number of melanosomes, Fig. 4B).Melanin content: electron paramagnetic resonance (EPR) spectroscopyQuality and quantity of melanin pigments58 in individual feather samples obtained from the bib of house sparrows (N = 57) were characterized using a Varian E3 spectrometer (Varian, Sunnyvale, LA, USA) equipped with a rectangular resonance (TE 102) cavity. Five milligrams of feathers per individual were placed inside the Wilmad finger quartz dewar WG-816-Q (Rototec-Spintec GmbH, Griesheim, Germany). Prior to inserting the vessel into the resonance cavity of the EPR spectrometer, feathers were pressed down the quartz finger to a height of approximately 0.5 cm to ensure comparable volumes of each sample. Measurements were performed at room temperature, at X-band (9.26–9.27 GHz frequency), using the following parameters: magnetic field range 3240–3340 Gs, microwave power 1 mW, modulation frequency 100 kHz, modulation amplitude and time constant—5 Gs and 0.3 s for quantitative analysis, 1 Gs and 0.1 s for qualitative analysis. An EPR signal was recorded as its first derivative, averaged from three consecutive scans, lasting 160 s each (giving a total of 480 s of scan time per EPR spectrum). Then, the following parameters were measured: peak-to-peak amplitude, area under the microwave absorption curve (the integral intensity of the recorded signal) and linewidth of the EPR absorption curve (ΔH;59).Statistical analysesStatistical analysis was performed in R (version 4.0.2,60) using a two-way ANOVA test, with bird’s diet (control vs. PT-reduced) and applied immune challenges (LPS vs. saline-injections) as the independent variables. The following parameters were used as the dependent variables: feathers reflectance (brightness), feather growth rate, feather density (number of barbs per mm), and melanisation level (expressed as the EPR spectrum amplitude measured in arbitrary units [a.u.]). The density of melanosomes was analysed by fitting a linear mixed-effects model. In this model, melanosome density was used as the dependent variable, with diet, immunological challenge, and slice ID as independent variables, and individual ID as a random-effect term. Additionally, to assess the reliability of measurements, the intraclass correlation coefficient (i.e., technical repeatability) was calculated. The models’ residuals were checked for normality and homoscedasticity. Mean food consumption per individual was analysed by the Friedman test. Body mass before and after the experiment was analysed by fitting a linear mixed-effect model. Body mass was used as the dependent variable, whereas diet, immunological challenge, and time as the independent variables, and individual ID as a random-effect term. The model included the following interaction terms: time × diet, time × injection, and diet × injection, and was reduced by removing the non-significant interactions. Results are reported with appropriate statistical tests and estimates (accompanied by standard errors) signifying relevant factor contrasts (relative to the reference group, which in all analyses was diet: control; injection: LPS, body mass: before experiment).
Ethical noteAll applicable national and institutional guidelines for the care and use of animals were followed. The research was performed under permit no. 25/2019 (with a supplementary permit no. 78/2020) from the 2nd Local Institutional Animal Care and Use Committee in Kraków. More