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Culture maintenance and growthTwelve strains of Thalassiosira spp. were obtained from the Provasoli-Guillard National Centre of Marine Phytoplankton (NCMA, https://ncma.bigelow.org/), and one strain from the Australian National Culture Collection, representing 7 species in total (Supplementary Table 1). Cultures were maintained in polystyrene tissue culture flasks in artificial seawater with f/2 media [37] at 20 °C, with 60 µmolm−2s−1 of light on a 12:12 light cycle.Three strains originally identified as Thalassiosira sp. in the NCMA collection were further classified to the species level using sequencing of the ITS2 gene region (Supplementary Table 1): CCMP1055 as T. auguste-lineata (84.64% similarity; [38]) and CCMP2929 as T. weisflogii (98.37% similarity to Strain 1587 used in our study; [39]). Strain CCMP1059 was tentatively identified as Cyclotella striata (94.17% identity match to clone ZX28-3-40; [40]) also from order Thalassiosirales, but this assignment requires further investigation.Experimental set upExperimental cultures (200 mL) were grown in 250 mL polystyrene tissue culture flasks in triplicate, at a starting concentration of 2500 cells ml−1. All 13 strains were grown in a “standard” environment (identical to maintenance conditions) with 9 phenotypic traits measured to describe the initial trait-scape. Five strains (1010, 1059, 2929, 3264, and 3367) were grown in two additional environments in triplicate: a high temperature and light treatment (HT: 30 °C, 200 µmol photons m−2s−1 of light, 12:12 light:dark), and a low nutrient treatment (LN: f/400 media with an adjusted N:P ratio of 10:1 achieved by reducing the nitrate concentration from 4.4 to 1.8 µM, 60 µmol photons m−2s−1 of light, 12:12 light:dark). Cultures for the two additional treatments were inoculated with 10,000 cells ml−1 (LN) and 5,000 cells ml−1 (HT) in anticipation of limited growth.Growth was tracked daily using in vivo fluorescence as a proxy for cell density [41]. One mL aliquots of experimental cultures were measured for chlorophyll-a fluorescence using a plate reader (TECAN Infinite M1000 Pro, Männedorf, Switzerland) using 455/680 nm excitation/emission spectra. Phenotypic traits were measured at mid-late exponential phase, assessed by visually examining in vivo fluorescence growth curves. In the case of the low nutrient treatment, where growth was limited to 3–5 days, cultures were harvested in early stationary phase. Duration of growth for each experiment is summarised in Supplementary Table 2.Phenotypic trait measurement methodsPhenotypic traits were selected to capture different commonly measured base physiological functions, and to include traits that are used in biogeochemical models. We also selected traits that demonstrated independence and orthogonality (i.e., not all co-varying), based on pilot studies, in order to successfully define the multivariate trait-scape [42].Growth rateGrowth rates for each time step were calculated from the daily in vivo fluorescence measurements according to the calculation:$$mu = frac{{{{{{{{{mathrm{ln}}}}}}}}left( {F_2} right)-{{{{{{{mathrm{ln}}}}}}}}left( {F_1} right)}}{{t_2 – t_1}}$$Maximum growth rates were determined by the average growth over 2–4 consecutive steps depending on the duration of exponential growth.Flow cytometry traitsFor flow cytometry trait measures (growth rate, size, chlorophyll a content, lipid content), 1 mL aliquots of experimental culture were fixed with EM grade paraformaldehyde (0.8% final concentration, Electron Microscopy Sciences, Ft Washington, PA) in 1.6 mL cryopreservation tubes (CryoPure, Sarstedt), frozen in liquid nitrogen, then stored at −80 °C prior to analysis. All measures were performed using a Cytoflex LX (Beckman Coulter, CA, USA).Cell counts and sizeCell counts were done by gating the diatom population using chlorophyll a (488 nm excitation, 690/50 nm detector) and forward scatter channel thresholds. Cell size was estimated using forward scatter values calibrated against spherical beads (2, 4, 6, 10, 15 µM diameters; Invitrogen, CA). This resulted in a conversion equation of equivalent spherical diameter (ESD) = (FSC + 194636)/75775, which was used to assess relative changes in cell size [43].Chlorophyll a contentChlorophyll a (Chl-a) fluorescence of the gated diatom population was quantified using 488 nm excitation, 690/50 nm detection. A standard bead (Cytoflex Daily QC Fluorospheres; Beckman Coulter) was used to calibrate the performance of the instrument and ensure comparable measures across samples. Chlorophyll values were divided by ESD to account for cell size differences.Side scatter/granularitySide scatter is an indicator of the internal complexity of a cell or “granularity”. This trait is measured in tandem with other flow cytometry measures and was included as a phenotypic trait. The interpretation of this trait is not straight forward, but is independent of other flow cytometry traits measured and has been used in other flow cytometry studies of microalgae [44]. This trait was divided by ESD to account for cell size differences.Neutral lipidsRelative neutral lipid content was determined using the fluorescent stain BODIPY™ 505/515 (Thermo Fisher, MA, USA) which is commonly used to assess neutral lipid content in phytoplankton [45,46,47]. Background fluorescence (488 nm excitation, 525/40 nm detector) of PFA-fixed cells was measured in tandem with the size, chlorophyll a, and side scatter. After this, 10 µL of BODIPY stain (2 mg mL−1 in DMSO) was added to each sample, resulting in a final BODIPY concentration of 2 μg mL−1. Samples were incubated for 10 min in the dark before being read again on the flow cytometer. Neutral lipid content was defined as the difference in median fluorescence per cell between the pre- and post-stained sample. This value was then divided by the ESD size to account for size-related effects.Photophysiological traitsPhotophysiological measures were taken by conducting a rapid light curve [48] with a water PAM (Water-PAM; Walz GmbH, Effeltrich, Germany) using 1 mL of experimental culture diluted in artificial seawater. The rapid light curve protocol exposes the culture to 8 steps of increasing irradiance for 10 seconds each, measuring the photophysiological response at each step. Maximum electron transport rate (ETRmax), Ik (half saturation irradience), and alpha (the photosynthetic rate during the light-limited linear region) were calculated using the regression fit function in the PAM WinControl software. Photophysiology measurements were taken between 4–5 h after the start of the photoperiod.Reactive oxygen speciesThe development of reactive oxygen species (ROS) was measured using the fluorescent probe 2’,7’-dichlorodihydrofluorescein diacetate (H2DCFDA; Thermo Fisher, MA, USA) which has been used in a number of phytoplankton studies [49,50,51]. Two 1 mL aliquots of experimental culture were transferred to a 48 well tissue culture plate; 2 µL of stain (2.5 mg mL−1 H2DCFDA was made in DMSO) was added to one aliquot, with the other acting as a blank. The plates were sealed (Breathe-Easy, Diversified Biotech) and incubated in the dark at growth temperature (20 or 30 °C) for 2 h. Incubation was done in the dark because of the effects of light on the dye itself, therefore the effects of the excess light treatment were not captured in this trait. Fluorescence of H2DCFDA was read using a plate reader with 488 nm excitation 525 nm emission (TECAN Infinite M1000 Pro, Männedorf, Switzerland). ROS concentration was estimated as the difference in fluorescence units per cell between the stained and unstained aliquots of each culture. This metric was also divided by ESD size to account for size effects.Taxonomic confirmation of strainsDNA from stock cultures (10 mL) was extracted using a DNeasy PowerSoil kit (QIAGEN Inc., CA, USA) and checked for quality with a NanopDrop™ 2000 (ThermoFIsher Scientific, MA, USA), before amplification and sequencing at the Australian Genome Research Facility (AGRF, Sydney, Australia). PCR conditions and primers used were those developed by Chappell et al. [52] for the ITS region: forward primer: 5ʹ-RCGAAYTGCAGAACCTCG-3ʹ, reverse primer: 5ʹ-TACTYAATCTGAGATYCA-3ʹ.Bioinformatics processing was conducted using Geneious Prime (Version 2020.0.5; Biomatters Ltd.). Strain sequences were compared to GenBank using the BLAST function to confirm species identity. Nucleotide sequences were aligned using the MUSCLE alignment [53], followed by Bayesian inference analysis using MrBayes [54] to generate a phylogenetic tree. The out-group for the tree was a strain of Chaetoceros atlanticus isolate TPV2 1146 obtained from GenBank. Percentage similarity between strains according to the alignment was used as a metric of genetic relatedness.Statistical analysisWe assessed the multivariate phenotypes for the Thalassiosira strains using principal component analysis (PCA). The input variables were the 9 independent trait measurements made on each replicate culture (n = 36, 3 biological replicates per strain). Trait data was standardized (mean = 0, SD = 1) for each trait prior to PCA analysis to account for differences in the units and scale of measurements. The resulting PCA plot was defined as the ‘trait-scape’.Hierarchical clustering analysis was performed on the 9-trait dataset used to assess similarity in multivariate phenotypes between each replicate for each strain (n = 3 per strain).To compare genetic vs. phenotypic similarity, percentage similarity between strains was correlated against the distance between strain centroids (multivariate means) within the trait-scape. Distances between multivariate means (centroids) were calculated using the equation:$${{{{{{{mathrm{distance}}}}}}}} = sqrt {left( {{{{{{{{mathrm{{Delta}}}}}}}PC}}1.{{{{{{{mathrm{a}}}}}}}}} right)^2,+,left( {{{{{{{{mathrm{{Delta}}}}}}}PC}}2.{{{{{{{mathrm{b}}}}}}}}} right)^2}$$ΔPC1 is the difference in PC1 co-ordinates between the two strains, a is the % variance explained by PC1, ΔPC2 is the difference in PC2 co-ordinates between the two strains, b is the % variance explained by PC2.To assess whether a trait-scape generated using fewer input traits (4 rather than 9) was representative of the full, 9-trait plot, we conducted PCA using 4 input traits, and then assessed whether the inter-strain distances (distances between centroids) within the plot were correlated using linear regression. This provided a quantitative assessment of whether the strains were in the same relative positions to each other within the trait-scape.Covariation of traitsTo compare the pairwise relationships between traits across the strains, correlation matrices were made using data collected in the standard environment, and for the HT and LN environments.Phenotypic plasticityThe change in phenotypes in the new environments were assessed firstly by conducting PCA on the full dataset, including trait data from the 13 strains grown in the standard environment, plus the 5 strains grown in the two additional environments. This generated an “expanded trait-scape”. In addition, correlation matrices were generated for the new environments’ trait dataset to assess differences in trait-trait relationships between the ‘standard’ and “expanded” datasets.Relative changes in trait values for each trait in the new environments were calculated as follows:$$ {{{{{{{mathrm{Relative}}}}}}}},{{{{{{{mathrm{change}}}}}}}} \ = frac{{{{{{{{{mathrm{trait}}}}}}}},{{{{{{{mathrm{value}}}}}}}},{{{{{{{mathrm{new}}}}}}}},{{{{{{{mathrm{environment}}}}}}}} – overline {{{{{{{mathrm{x}}}}}}}} ,,{{{{{{{mathrm{trait}}}}}}}},{{{{{{{mathrm{value}}}}}}}},{{{{{{{mathrm{standard}}}}}}}},{{{{{{{mathrm{environment}}}}}}}}}}{{overline {{{{{{{mathrm{x}}}}}}}} ,,{{{{{{{mathrm{trait}}}}}}}},{{{{{{{mathrm{value}}}}}}}},{{{{{{{mathrm{standard}}}}}}}},{{{{{{{mathrm{environment}}}}}}}}}}$$We used PCA to assess whether the relative changes in trait values were consistent between strains in the two different environments. i.e., was the relative change in whole phenotype consistent. If the changes were consistent across strains, we expected to see clustering in the PCA based on treatment.Statistical softwareStatistical analyses were performed in R [55], Matlab, and Microsoft Excel. Hierarchical clustering analysis with multiscale bootstrap resampling (1000 replicates) on trait values from biological replicates was done with the ‘pvclust’ package in R [56] using Euclidean distance and the average (UPGMA) method. Principal component analysis was used to generate the multivariate trait-scape was done using the “vegan package” in R [57]. The contributions of each trait to the PC axes (loadings) were extracted using the “factoextra” package in R [58]. Trait correlation matrices were generated using the “corrplot” package in R [59]. More

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As stated, the primary goal of this study is to promote an objective decision support framework for water resource planning and management purposes within the context of the WEF security nexus, which takes into account the uncertainties imposed by the climate change phenomenon. Such a framework is “robust” since it takes the multi-dimensionality of water-related problems into account while addressing the uncertainties imposed by climate change projections. The basic components of this decision-making paradigm are depicted in Fig. 1. In principle, while this framework is sensitive to the uncertainties associated with the climate change projections, it can provide a dynamic water resources planning and management scheme promoted within the WEF security network. Thus, in addition to the status quo, a series of climate change projections (i.e., RCP 2.6, RCP 4.5, and RCP 8.5) are also integrated into the proposed decision support framework. In essence, the main components of the proposed framework are simulation and operation of the water resources system based on the standard operation policy (SOP), evaluating the system’s efficiency through a series of quantitative performance criteria, and finally, applying the MADM-based framework to opt for a robust system renovation setting.Figure 1Basic components of the robust decision-making paradigm for water resources planning and management.Full size imageSimulating the water resources systemSOP is a primitive, and perhaps the most-well-known real-time operation policy in water resources planning and management14. The core principle here is to minimize the prioritized water shortage at the current time step with no conservation policy (e.g., hedging rules) in place. SOP, as a standard rule curve (RC), determines how the operator should behave at any given state of a reservoir15,16. This rule curve is established as an attempt to balance various water demands including but not limited to flood control, hydropower, water supply, and recreation17. A SOP operating system attempts to release water to meet a water demand at the current time, with no regard to the future.In general, SOP can be mathematically expressed as18:$$R_{t} = left{ {begin{array}{*{20}c} {D_{t} } \ {AW_{t} } \ 0 \ end{array} – S_{min } } right.begin{array}{*{20}c} {} & {if} & {AW_{t} > S_{min } } \ {} & {if} & {AW_{t} > S_{min } } \ {} & {if} & {AW_{t} le S_{min } } \ end{array} begin{array}{*{20}c} {} & {and} & {AW_{t} – S_{min } ge D_{t} } \ {} & {and} & {AW_{t} – S_{min } < D_{t} } \ {} & {} & {} \ end{array} quad t = { 1},{ 2},{ 3}, , ... , ,T$$ (1a) where$$AW_{t} = S_{t} + Q_{t} - Loss_{t}$$ (1b) in which Rt = amount of water supplied during the tth time step; Dt = consumers’ water demand during the tth time step; AWt = amount of available water during the tth time step; St = amount of stored water during the tth time step; Smin = dead storage of the reservoir; Qt = inflow during the tth time step; Losst = net water loss (i.e., precipitation minus evaporation) of the reservoir during the tth time step; and T = total number of time steps in the operational horizon.In practice, however, a different type of water demand leads to a different interpretation of water shortage. There are cases in which the stakeholders’ needs are represented by a set of volumetric demand targets, and the decision-makers’ objective would be to minimize the water deficit based on a set of priorities for these demands. This is a typical case for agricultural, domestic, industrial, and environmental demands. For hydropower generation, however, a conventional interpretation of SOP would be to generate maximum electricity permitted by the power plant capacity (PPC) at each given time step19. For a hydropower system, the amount of water needed to reach a power plant capacity is given by19:$$R_{t} = frac{{86400 times PF times Countday_{t} times PPC}}{{gamma_{w} times g times eta times Delta H_{t} }}$$ (2) in which, γw = water specific weight; g = gravitational acceleration; η = efficiency of the hydropower system; ΔHt = height difference between the reservoir water level and the tailwater level at time step t; Countdayt = number of days within time step t; and PF = plant factor of the hydropower system.As stated earlier, applying an SOP-based plan requires a set of pre-defined priorities to advise decision-makers concerning the order, in which each of these demands is to be met. The major water demands include drinking, industry, environment, agriculture, and hydropower. Thus, according to the SOP’s principle, the decision-makers, first, allocate the available water to meet the demand of the stakeholder with the highest priority (i.e., the domestic and industrial demand). After this first water demand is fully satisfied, the available water can be used for the next demand. Such an allocation process continues until no water is available. It should be noted, however, that if the released water in each stage passes through the penstock equipped with the turbines, electricity can be generated. The amount of energy generated in previous stages must be accounted for before computing the amount of water released for hydropower purposes.Performance criteriaPerformance criteria are, in essence, quantitative measures that can provide a practical insight for the decision-makers regarding the status of a system. This definition covers a broad spectrum of mathematical representations, which can range from simple mathematical formulas such as the average of a specific output to more complex and probability-based entities20,21. The most fundamental and universal probability-based performance criteria are reliability, resiliency, and vulnerability22,23,24. In essence, reliability is the probability of successful function of a system; resiliency measures the probability of successful functioning following a system failure; and vulnerability quantifies the severity of failure during an operation horizon25. It should be noted that these three criteria assess different aspects of a water resources system, and as such, they complement one another26. For more information regarding these probabilistic performance criteria, the readers can refer to Sandoval-Solis et al.27 and Zolghadr-Asli et al.20.In this study, the concept of levelized cost of energy (LCOE) is utilized for economic evaluation. The LCOE of a given hydropower system is the ratio of lifetime costs to lifetime electricity generation, both of which are discounted back to a common year using a discount rate that reflects the average cost of capital28. The LCOE of renewable energy systems depends on the technology, geographic criteria, capital and operating costs, and the efficiency of the system. The LCOE can be mathematically expressed as follows29:$$LCOE = frac{{sumnolimits_{t = 1}^{n} {frac{{I_{t} + M_{t} + F_{t} }}{{left( {1 + r} right)^{t} }}} }}{{sumnolimits_{t = 1}^{n} {frac{{E_{t} }}{{left( {1 + r} right)^{t} }}} }}$$ (3) in which It = investment expenditures in year t; Mt = operation and maintenance expenditures in year t; Ft = fuel expenditures in year t; Et = electricity generation in year t; r = discount rate; and n = economic life expectancy of the system.MADMMADM is an umbrella term to describe a series of frameworks, which aim to help individuals or a group of individuals to prioritize a series of discretely defined alternatives with regard to a set of evaluation attributes30,31. MADM can provide the necessary means to conduct planning and management under changing circumstances such as those under climate change conditions10,32. According to one of the basic principles of MADM, the decision-maker can use the similarity of the feasible alternatives and the preferential result and/or incongruity of the undesirable alternatives. The notion mentioned above is, chiefly, the core principle of the reference-dependent theory33. Accordingly, the reference-based branch of the MADM methods can, itself, be classified into two major groups: screening methods and ranking methods. Screening methods eliminate alternatives that cannot satisfy the pre-determined conditions for the desirable solution, while ranking methods order all the alternatives from the best to the worst34.Pioneered by Hwang and Yoon35, the technique for order references by similarity to an ideal solution (TOPSIS) is a compensatory, objective MADM solving method rooted from the basic principles of the reference-dependent theory. The core idea is that the chosen alternative should have the shortest distance from the ideal solution and the farthest distance from the negative-ideal solution36. The basic computation algorithm of TOPSIS can be summarized as follows37,38:Step I: Construct the original decision matrix (X), where m feasible alternatives are to be evaluated based on n evaluation criteria:$$X = left[ {begin{array}{*{20}c} {x_{11} } & {x_{12} } & cdots & {x_{1n} } \ {x_{21} } & {x_{22} } & cdots & {x_{2n} } \ vdots & vdots & ddots & vdots \ {x_{m1} } & {x_{m2} } & cdots & {x_{mn} } \ end{array} } right]$$ (4) in which xij = the element of the ith alternative concerning the jth criterion.Step II: Defining the reference alternatives [i.e., the ideal solution (s+) and the negative-ideal solution (s−)]. To do so, first, the elements of the decision matrix that are associated with negative criteria must be redefined by using the following equation:$$x_{ij}^{ * } = frac{1}{{x_{ij} }}$$ (5a) The elements of the decision matrix that are associated with positive criteria would remain the same:$$x_{ij}^{ * } = x_{ij}$$ (5b) The ideal alternative is an arbitrarily defined vector, which describes the aspired solution to the given problem, while the inferior alternative is an arbitrarily defined solution that represents the most undesirable option for the given MADM problem. Here, the ideal and negative-ideal solutions would be represented with two separate vectors where each pair of the corresponding elements in these vectors is, respectively, the maximum and minimum values of (x_{ij}^{ * }) with regard to each of the evaluation criteria.Step III: Each element of the decision matrix should be normalized by using the following equation:$$p_{ij} = frac{{x_{ij}^{ * } }}{{sqrt {sumnolimits_{i = 1}^{m} {x_{ij}^{ * 2} } } }}$$ (6) in which pij = the normalized performance value for the ith alternative with respect to the jth criterion.Step IV: The weighted normalized preference value (zij) can be computed as follows:$$z_{ij} = p_{ij} times w_{j} quad forall i,j$$ (7) in which wj = the weight (i.e., the importance value) of the jth criterion. The weights assigned to the evolution criteria reflect their relative importance to the decision-makers. The higher the weights are, the more crucial their roles would be in the selection process. Chiefly, these weighting mechanisms are either subjective in nature or follow an objective procedure. In the subjective approaches, the weights of the attributes are assigned based on the performance information given by the decision-maker, whereas in the objective approaches, the weights of the evaluation attributes would be obtained by using the objective information extracted from the decision matrix39. Shannon’s Entropy method, used in this study as the weight assignment mechanism, is a well-known objective weighting technique40. This method tends to assign the highest weight to an evaluation attribute with the highest dispersity in its values. For more information on the computational framework of this method, the readers can refer to Lotfi and Fallahnejad41.Step V: In this step, every given alternative is compared to the reference points, namely, the ideal and inferior alternatives. The described procedure, which is known as the separation measurement in TOPSIS, can be mathematically expressed as follows35:$$D_{i}^{ + } = sqrt {sumlimits_{j = 1}^{n} {left( {z_{ij} - z_{j}^{ + } } right)^{2} } }$$ (8) And$$D_{i}^{ - } = sqrt {sumlimits_{j = 1}^{n} {left( {z_{ij} - z_{j}^{ - } } right)^{2} } }$$ (9) in which (D_{j}^{ + }) and (D_{j}^{ - }) = separation measurements of the jth criterion with respect to the ideal and inferior alternatives, respectively.Step VI: The relative closeness to the ideal solution (χi), which can be used to rank the desirability of the feasible alternative, can be computed as follows35:$$chi_{i} = frac{{D_{i}^{ - } }}{{D_{i}^{ + } + D_{i}^{ - } }}quad forall i$$ (10) The further this distance (i.e., larger values of χi), the more desirable the alternative would be.Robust multi-attribute frameworkAs stated, each climate change scenario depicts a unique future with regard to the changing climate, which in turn introduces an element of uncertainty to the projected performance of water resources systems during their operation horizon. Furthermore, downscaling methods, which link these projected changes in the global climatic pattern to a local or regional scale, can be another source of uncertainty. Naturally, for long-lasting water infrastructure such as a hydropower system, addressing these uncertainties in a proper and timely manner can be one of the key components of a robust project. Thus, this study aims to not only evaluate the system’s performance under the status quo but also assess the credibility of the system under the projected climate change conditions.The other characteristic one might expect from a robust project is its ability to take into account the multi-dimensionality nature of water-related infrastructure. Most notably, addressing the WEF security nexus must be a priority in water resources planning and management. Resultantly, any robust decision-making paradigm for water resources planning and management purposes should also account for the other pillars of the WEF nexus (i.e., energy and food sectors), as they would be consequentially affected by such decisions. It is also important to note that these sectors could be affected by the climate change phenomenon. The other crucial feature of a robust decision-making paradigm is that it should be able to account for the socio-economic, environmental, and technical factors that determine the overall quality of the project. Such a decision-making paradigm is depicted in Fig. 2. This notion in practice, however, can typically lead to a mega decision matrix composed of numerous criteria and alternatives that can be overwhelming if the subjective MADM methods are to be employed. This study, thus, employs an objective MADM framework (i.e., TOPSIS/Entropy) to help overcome the above-described problem. The basic idea is to promote a universal and practical decision support framework that enables the water resources planners and managers to account for the intricacies of the WEF security nexus while simultaneously taking the uncertainties of climate change projections into account. Figure 3 illustrates the flowchart of the proposed decision support framework.Figure 2Schematic diagram of the MADM problem.Full size imageFigure 3Flowchart of the proposed framework.Full size image More