DOI: 10.5061/dryad.83bk3jb58
title: Evidence of local adaptation in a freshwater diatom indicates higher sensitivity to nutrient limitation as water temperature rises

description: Climate change is reshaping freshwater ecosystems by warming waters and
 modifying nutrient dynamics. These combined environmental changes exert
 novel gradients of selection on phytoplankton populations and communities.
 Temperature and phosphorus availability are individually critical
 determinants of growth in phytoplankton, and can have interactive impacts
 on population and community dynamics. While we understand how
 interspecific variation in thermal and resource-use traits of
 phytoplankton can affect community composition in response to changing
 environments, the extent of local adaptation in these responses among
 strains of the same species remains poorly understood. In this study, we
 examined the local adaptation in the temperature- and
 phosphorus-dependences of growth in the freshwater diatom Fragilaria
 crotonensis. We predict that the growth of this diatom is locally adapted
 to the environmental conditions of the lakes of origin. To test this, we
 isolated strains from eight Swiss lakes (one strain per lake) with
 distinct historical temperatures and nutrient status. We estimated the
 growth rate of each strain under combined gradients of temperature and
 phosphorus availability. We fitted Monod curves to the growth rate data,
 and we quantified the minimal phosphorus requirements (P*),
 half-saturation constants (Ks), and maximum growth rates (μmax) for each
 strain as a function of temperature. We also fitted thermal performance
 curves and quantified activation energies (Ea) and cumulative performance
 across the thermal gradient as a function of phosphorus availability. We
 observed large variation among strains in the dependency of P* on
 experimental temperature, with strains from phosphorus-rich lakes showing
 stronger increases in phosphorus requirements with warming. These patterns
 imply local adaptation to phosphorus availability. Our findings highlight
 a potentially critical role for local adaptation in shaping phytoplankton
 responses to global change and call for a greater recognition of this
 trait variation in making predictions of community-level responses to
 future climate.
description: # Evidence of local adaptation in a freshwater diatom indicates higher
 sensitivity to nutrient limitation as water temperature rises Dataset DOI:
 [10.5061/dryad.83bk3jb58](10.5061/dryad.83bk3jb58) ## Authors Name: Li
 Zhao Institution: Department of Aquatic Ecology, Eawag, Dübendorf,
 Switzerland Email: [mdzhaoli@outlook.com](mailto:mdzhaoli@outlook.com)
 Name: Divina Ryf Institution: Department of Aquatic Ecology, Eawag,
 Dübendorf, Switzerland Email:
 [divina.ryf@bluewin.ch](mailto:divina.ryf@bluewin.ch) Name: Sarah
 Levasseur Institution: Department of Aquatic Ecology, Eawag, Dübendorf,
 Switzerland Name: Raphaël Bossart Institution: Department of Aquatic
 Ecology, Eawag, Dübendorf, Switzerland Name: Marta Reyes Institution:
 Department of Aquatic Ecology, Eawag, Dübendorf, Switzerland Name: Frank
 Pennekamp Institution: Department of Evolutionary Biology and
 Environmental Studies, University of Zurich, Zurich, Switzerland Name:
 Jukka Jokela Institution: Department of Aquatic Ecology, Eawag, Dübendorf,
 Switzerland; Institute of Integrative Biology, D-USYS, ETH Zurich, Zurich,
 Switzerland Name: Anita Narwani Institution: Department of Aquatic
 Ecology, Eawag, Dübendorf, Switzerland Email:
 [anita.narwani@eawag.ch](mailto:anita.narwani@eawag.ch) Name: Vanessa
 Weber de Melo Institution: Department of Aquatic Ecology, Eawag,
 Dübendorf, Switzerland Email:
 [vanessa.weberdemelo@eawag.ch](mailto:vanessa.weberdemelo@eawag.ch) ##
 Content This is the supporting information for the manuscript. It consists
 of four ZIPs: 1.Experiment_data.zip: measurements from the experiment
 2.Codes: R codes for analyses 3.Outputs: results and models from analyses,
 generated from the R codes 4.longPCR: 18S amplicon sequences
 5.Matrix_plots: time series of growth of each strain ###
 Experiment_data.zip This folder contains 11 CSV files named *“dayxx.csv”*,
 representing measurements collected over seven experimental days. Files
 labeled with letters “a” and “b” indicate two measurements taken on the
 same day. For example, *day2a.csv* corresponds to the morning measurement
 on Day 2, while *day2b.csv* corresponds to the afternoon measurement on
 the same day. Within each file: | Column Name | Description | |
 :--------------------------------- |
 :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Plate Number | Plate identifier. Each plate corresponds to one treatment. Plates 1–6 = Temperature level 1; Plates 7–12 = Temperature level 2; … Plates 31–36 = Temperature level 6. Within each temperature level, phosphorus levels increase from 1 to 6 (see Table 2 in the article). | | Well | Well identifier within the 96-well plate. See Strain\_Numbers.csv for replicate arrangement. | | Mean RFU Chlorophyll\_A:445,685 | Mean chlorophyll-a fluorescence (Relative Fluorescence Unit) measured at excitation 445 nm and emission 685 nm. Each well was measured five times (center, up, down, left, right), and the mean is reported. | | Std Dev RFU Chlorophyll\_A:445,685 | Standard deviation of the five fluorescence measurements. | | Reading Date/Time | Timestamp of measurement for each well. | The file "Strains_Numbers.csv" shows how each plate was randomized. The numbers indicate the locations in the 96-well plate of the replicates. Wells A1, A2 ... H12 are numbered as 1, 2 ... 96. ### Codes.zip \[1_experimental_data_processing]: organize measurements from the experiment and calculate growth rates. Input: files in "experimental_data". Output: 1_all_RFU.csv; 1a_growth_rate.csv. \[2_TPC_thermal_traits]: Model thermal performance curves from growth rates. Input: 1a_growth_rate.csv. Output: 2_TPC_model_parameters.csv; 2a_TPC_bootrapped.Rdata; 2b_TPC_predictions.Rdata; 3a_AUC.Rdata; 3b_ea.Rdata. \[3_Monod_resource_traits]: Model thermal performance curves from growth rates. Input: 1a_growth_rate.csv. Output: 4a_Monod_fits.rds; 4b_Monod_predict.rds; 4c_Monod_fits_from_boot.rds; 4d_Monod_predict_from_boot.rds; 5_R_traits.RData. \[4_GAMs]: Generalized additive models for traits Input: 5_R_traits.RData; 3a_AUC.RData; 3b_ea.RData. Output: n/a (model parameters available directly from the code). \[5_Correlation]: Correlation of resource-use and thermal traits to lake environmental data Input: 5_R_traits.Rdata; 6_lakes_TP_Table_S5.Rda; 3a_AUC.Rdata; 3b_ea.Rdata. Output: n/a (model parameters available directly from the code). ### Output.zip This folder contains results and models from analyses, generated from the R codes: \[1_all_RFU.csv]: all fluorescence measurements organised together. | Column Name | Description | | :----------------------------------- | :------------------------------------------------------------------------------------------------------- | | "Well" | Position of replicate within the plate. See Strains\_Numbers.csv for mapping. | | "WellNumber" | Numerical identifier of the well (1–96). | | "Strain" | Numbers 1–8 represent the eight strains listed in Table 1 (from top to bottom). | | "Replicate" | Replicate identifier; each strain has four replicates under each experimental condition. | | "Plate.Number" | Plate identifier. See explanation under *Experiment\_data.zip*. | | "Mean RFU Chlorophyll\_A:445,685" | Mean chlorophyll-a fluorescence (RFU) measured at excitation 445 nm and emission 685 nm. | | "Std Dev RFU Chlorophyll\_A:445,685" | Standard deviation of the five fluorescence measurements per well. | | "Reading.Date" | Date of measurement. | | "Reading Date/Time" | Timestamp of measurement; same meaning as in *Experiment\_data.zip*. | | "time" | Duplicate of “Reading Date/Time”, used for time-series processing. | | "day" | Time converted to days (may be non-integer) since the beginning of the experiment. | | "lnRFU" | Natural-logarithm (ln) transformation of measured RFU. | | "Plate\_Number" | Plate number in numeric format. | | "Temp\_level" | Temperature treatment level (1–6). See *Plate Number* definition under *Experiment\_data.zip*. | | "Resource\_Level" | Phosphorus (resource) treatment level (1–6). See *Plate Number* definition under *Experiment\_data.zip*. | \[1a_growth_rate.csv]: calculated growth rates. Columns "strain", "level_of_temperature", "level_of_resource", and "replicate": same as above. Column "best_model": the model selected by the R package "growthTools" (see Methods). Column "best_slope": the fitted slope of the selected model. [2_TPC_model_parameters.csv]: TPC parameters, see methods for details. \[2a_TPC_bootrapped.Rdata]: bootstrapped TPC models. \[2b_TPC_predictions.Rdata]: predictions of TPC models. \[3a_AUC.Rdata]: thermal traits-area under TPC. \[3b_ea.Rdata]: thermal traits-activation energy. \[4a_Monod_fits.rds]: fitted models of Monod curves. \[4b_Monod_predict.rds]: predictions of models of Monod curves. \[4c_Monod_fits_from_boot.rds]: bootstrapped models of Monod curves. \[4d_Monod_predict_from_boot.rds]: predictions of models of Monod curves. \[5_R_traits.RData]: resource-use traits: minimum phosphorus requirement, maximum growth rate, and half-saturation constant. \[6_lakes_TP_Table_S5]: temperatures and phosphorus availabilities of lakes, as in table S5. ### longPCR.zip 18S amplicon sequences Lake and file name: Lake Aegeri: 18S1132F-AGG2-ITS055R.mapping.tsv Lake Constance: 18S1132F-CO2G2-ITS055R.mapping.tsv Lake Lucerne: 18S1132F-LUG15-ITS055R.mapping.tsv Lake Maggiore: DiaITS12F1-MAG5-DiaITS12R1.mapping.tsv Lake Seealp: DiaITS12F1-SEG3-DiaITS12R1.mapping.tsv Lake Walen: 18S1132F-WAG1-ITS055R.mapping.tsv Lake Zurich: DiaITS12F1-ZHG5-DiaITS12R1.mapping.tsv Lake Zug: 18S1132F-ZUG9-ITS055R.mapping.tsv Strain numbering: `1` = "Lake Aegeri", `2` = "Lake Constance", `3` = "Lake Lucerne", `4` = "Lake Maggiore", `5` = "Lake Seealp", `6` = "Lake Walen", `7` = "Lake Zurich", `8` = "Lake Zug" ### Matrix_plots.zip Time series of the growth of each strain. Each strain has 6 temperature × 6 resource levels × 4 replicates = 144 time series. ## Access information Other publicly accessible locations of the data: * Sequencing data has been deposited on GenBank and can be accessed with the following accession numbers: PV932063-PV932070 (18S sequences) and PV941022-PV941029 (rbcL sequences)