DOI: 10.5061/dryad.vq83bk41b
title: The community background alters the evolution of thermal performance

description: Microbes are key drivers of global biogeochemical cycles and their
 functional roles are heavily dependent on temperature. Large population
 sizes and rapid turnover rates mean that the predominant response of
 microbes to environmental warming is likely to be evolutionary, yet our
 understanding of evolutionary responses to temperature change in microbial
 systems is rudimentary. Natural microbial communities are diverse
 assemblages of interacting taxa. However, most studies investigating the
 evolutionary response of bacteria to temperature change are focused on
 monocultures. Here we utilise high throughput experimental evolution of
 bacteria in both monoculture and community contexts along a thermal
 gradient to determine how interspecific interactions influence the thermal
 adaptation of community members. We found that community-evolved isolates
 tended towards higher maximum growth rates across the temperature gradient
 compared to their monoculture-evolved counterparts. We also saw little
 evidence of systematic evolutionary change in the shapes of bacterial
 thermal tolerance curves along the thermal gradient. However, the effect
 of community background and selection temperature on the evolution of
 thermal tolerance curves was variable and highly taxon-specific – with
 some taxa exhibiting pronounced changes in thermal tolerance, while others
 were less impacted. We also found that temperature acted as a strong
 environmental filter, resulting in the local extinction of taxa along the
 thermal gradient, implying that temperature-driven ecological change was a
 key factor shaping the community background upon which evolutionary
 selection can operate. These findings offer novel insight into how the
 community background impacts thermal adaptation.
description: Study taxa Study taxa were derived from
 biofilm samples collected in May 2016- May 2017 from rock surfaces in
 several freshwater streams in Hvergerdi Valley, Iceland (64.02, −21.18).
 These samples were frozen in a 17% glycerol solution after collection and
 were stored at -20°C. The freshwater streams from which they originated
 ranged in temperature from 7°C - 38°C, due to variation in the levels of
 geothermal warming at the site (O’Gorman et al., 2014). On return to the
 laboratory, samples were thawed at 20°C. The solution they were
 transported in was then diluted consecutively, and 10 µL of solution was
 spread onto agar plates and incubated for 10 days at 20°C. Samples were
 taken from a random selection of the resulting colonies and were placed
 into 200 µL of lysogeny broth and incubated for 48 hours. This inoculated
 lysogeny broth was then centrifuged, and the supernatant was discarded.
 The pellet of bacterial cells was then placed into a lysogeny broth
 containing 17% glycerol and was frozen at -80°C. 16S
 PCR was performed for these samples, and the resulting rRNA was sequenced
 using Sanger sequencing, and taxonomy was assigned by comparing these
 sequences with existing databases (see (García et al., 2018)). The
 specific methodology is as follows: A master-mix solution was created and
 consisted of 7.2 μl of DNA-free water, 0.4 μl of 27 forward primer, 0.4 μl
 of 1492 reverse primer and 10 μl of Taq polymerase, per sample. A template
 solution was prepared by adding 2 μl of the sample diluted 100 x in DNA
 free water, to 18 μl of master-mix solution. These samples were then
 placed in a thermal cycler (Applied Biosystems Veriti Thermal Cycler). The
 cycling protocol consisted of 1 cycle at 94°C for 4 minutes, 35 cycles at
 94, 48 and 72°C for 1 minute, 30s, and 2 minutes, respectively, and
 finally, 1 cycle at 72°C for 8 minutes. The final product of the PCR was
 cleaned using Exonuclease I and Antarctic Phosphatase. Sanger sequencing
 was conducted on high-quality samples using the 27F, 1492R primers (Core
 Genomic Facility, University of Sheffield). Geneious (version 6.1.8,
 (Kearse et al., 2012) was used to trim the sequences, removing the bp from
 the 5' end and trimming the 3' end to a maximum length of
 1000bp. Sequences longer than 974bp were then aligned to the
 Silva.Bacteria.Fasta database using Mothur version 1.39.5 (Schloss et al.,
 2009) and the RDP trainset 9 032012 was used as a reference database to
 assign taxonomy to the isolates. A total of 36 different taxa were
 identified, and from these five were chosen for use in this study. These
 five taxa were chosen as they differed in their thermal traits, and in
 their colony morphologies, the latter requirement being to facilitate
 visual identification when cultures consisting of more than one taxon were
 grown on agar. The five taxa chosen for this study and the Genbank
 accession number were: <em>Pseudomonas spp.</em> (w_Ic161A,
 MZ506751),<em> Serratia spp.</em> (h_Ic174,
 MZ506746),<em> Aeromonas spp. </em>(n_Ic167,
 MZ506748),<em> Herbaspirillum spp. </em>(j_Ic165, MZ506747),
 and<em> Janthinobacterium spp.</em> (h_Ic161A,
 MZ506745)<em>.</em> Evolution of bacteria
 in monocultures and communities Bacterial communities
 comprising all five taxa, as well as monocultures of each taxon, were
 evolved at temperatures ranging from 15°C - 42°C for ~110 generations. We
 used 110 generations as past research suggests this would be ample time
 for the communities to reach an equilibrium. In a previous community
 evolution experiment conducted at 20°C, it was observed that the majority
 of communities reached stability after approximately 50 generations
 (García et al., 2023). Earlier investigations passaging natural
 communities indicated that around 60 generations were needed for most
 communities to achieve population equilibria in various instances
 (Goldford et al., 2018). In the current study, we collected ‘initial’
 growth rate data following 2-3 transfers (~10 generations) to allow
 communities to acclimate to the temperature (mainly to avoid acute stress
 responses). We then subsequently gathered data at approximately 100
 generations later (~110 generations total). The time to reach this number
 of generations was calculated for the colder evolution temperature groups,
 to ensure all treatment groups reached a minimum of ~100
 generations. The specific methodology follows: An
 initial stock solution for each taxon was created from a single colony
 clone, using lysogeny broth, which was then incubated overnight at 20°C.
 These were then standardised to a common optical density with R2 media,
 and then a community stock solution was constructed by combining 100 µL of
 each of the five taxa. 40 µL of stock solution was then used to inoculate
 5000 µL of R2 media. Three replicates of these inoculated media were then
 incubated at each of the following temperatures: 15°C, 20°C, 23°C, 27°C,
 30°C, 33°C, 37°C, and 42°C. This was then repeated, but instead of
 inoculation with community stock solution, monoculture stock solution was
 used, ensuring the same starting biomass of each taxon for each treatment
 group. Every 48 hours during incubation, 40 µL was removed from each
 culture and was used to inoculate a fresh 5000 µL of R2 media, to prevent
 resource limitation from occurring. This was done 18 times, equating to
 ~110 generations. At the end of the experiment, serial dilutions of the
 resulting cultures were then grown on agar, and samples of individual taxa
 were isolated and frozen at -80°C in 17% glycerol. For the community
 cultures, individual taxa were identified based on colony
 morphology. Growth assay of evolved
 isolates From every evolution experiment a single
 clone was isolated. These isolates, as well as the original ancestral
 samples, were then grown at temperatures ranging from 15°C - 42°C. Maximum
 growth rates (r(h<sup>-1</sup>)) were calculated at each
 temperature. The specific methodology is as follows:
 Every evolved isolate, as well as the original ancestral taxa,
 were thawed in R2 growth media at 20°C for 24 hours. These cultures were
 then diluted with more R2 media until all cultures were at an optical
 density (OD<sub>600</sub>) of 0.05, measured using a Themo
 Scientific<sup>TM</sup> Multiskan Sky Microplate
 Spectrophotometer, at a wavelength of 600nm. 200 µL of each culture was
 then transferred into 96 well plates. Control ‘blank’ wells were filled
 with only R2 medium. The plate was then incubated at 15 °C until carrying
 capacity was reached (~54 hours), and OD<sub>600</sub>
 measurements were taken every ~4 hours. This process was repeated for all
 isolates, at incubation temperatures of 20°C, 23°C, 27°C, 30°C, 33°C,
 37°C, and 42°C. Due to handling time, there was some variation in
 measurement intervals, but in all analyses exact intervals calculated from
 timestamps are used. The mean OD<sub>600</sub> value for blank
 wells in a plate was subtracted from all OD<sub>600</sub>
 measurements. Fitting growth curves
 All modelling of growth curves was conducted in R version 4.0.2
 (R Core Team, 2021), using the nlsLoop package (Padfield, 2016/2020). The
 maximum growth rates (r (h<sup>−1</sup>), hereafter simply
 <em>r</em>, or maximum growth rate) for each incubated culture
 (see section 2.3) were calculated by fitting the logistic growth equation
 to the OD<sub>600</sub> measurements, using non-linear least
 squares regression. <em>N<sub>t</sub>
 </em>= <em>K</em>/(1
 +〖<em>Ae</em>〗^(-<em>rt</em>) ); <em>A
 </em>= (<em>K </em>-
 <em>N</em>_0)/<em>N<sub>0</sub></em>
           (1) In equation 1,
 <em>N</em><sub>t</sub> is the biomass at time, t,
 <em>K</em> is the carrying capacity,
 <em>N</em><sub>0</sub> is the starting biomass and
 <em>r</em> is the exponential population growth rate
 (r(h<sup>−1</sup>)). For some cultures, after reaching
 carrying capacity there would be a slow decline in cell density. As the
 above model cannot estimate this decline, these datapoints demonstrating a
 post-asymptote decline were removed. Statistical
 analysis All statistical analyses were conducted in R
 version 4.0.2 (R Core Team, 2021), and all plots were created
 using <em>ggplot2, </em>and other
 <em>tidyverse</em> (Wickham et al., 2019) packages were used
 for data handling. For all analyses, monoculture-evolved isolates were
 only included if their respective isolate had survived to the end of the
 community evolution experiment. For example, at an evolution temperature
 of 33°C, only <em>Aeromonas spp.</em> and
 <em>Herbaspirillum spp.</em> survived to the end of the
 evolution experiment, therefore only <em>Aeromonas spp.</em>
 and <em>Herbaspirillum spp.</em> isolates that were evolved at
 33°C in monoculture were included in the analysis, and
 <em>Pseudomonas spp.</em>, <em>Serratia spp.</em>,
 and <em>Janthinobacterium spp.</em> isolates that were evolved
 at 33°C in monoculture were excluded from the analysis. This prevented a
 survivorship bias from confounding the results. For the
 within-taxon analysis, separate generalised additive mixed-effect models
 (GAMMs) were fit for each taxon, using the function
 <em>uGamm</em> from the R package <em>MuMIn</em>
 (Bartoń, 2022). The initial full model included maximum growth rate as the
 response variable, and the following fixed effects and smoothing terms:
 evolution temperature, treatment (monoculture, ancestral, or community
 evolved), an interaction between evolution temperature and treatment, and
 a smoothing term on growth temperature, which was allowed to vary by
 treatment. A single random effect encompassing taxon and biological
 replicate (at the level of each evolved lineage) was included in all
 models. All possible sub-models were created and compared by their
 sample-corrected Akaike information criterion (AICc) using the function
 <em>AICc </em>from the package <em>MuMIn</em>,
 although models without a smoothing term on growth temperature were not
 considered. The threshold for determining a significant difference between
 models was when ΔAICc was &gt;2. Where there were one or more models
 falling within 2 ΔAICc of the lowest AICc model, the more minimal model
 was selected as the best model. The R package <em>emmeans</em>
 (Lenth, 2017/2023) was used to conduct post-hoc pairwise comparisons for
 model estimates across both evolution temperatures and treatment
 group. For the ‘all taxa combined’ analysis, model
 creation and selection was conducted in the same way as the within taxon
 analysis. Fixed and random effects were the same as in the within taxon
 analysis. However, due to issues with rank deficiency when trying to
 incorporate ancestral data into this model, a separate model for the
 effect of growth temperature on growth rate for the ancestor was
 constructed. This negates pairwise significance testing of differences
 between ancestral and evolved lineages but does allow both visual
 comparison of TPCs by superimposing the ancestral model predictions onto a
 figure displaying the predictions for the monoculture and community
 evolved isolates (Figure 1). For the community survival
 analysis, binomial Generalised linear models were fit using the base R
 <em>glm</em> function, and fixed effects taxon identify and
 evolution temperature. Fixed effects were determined to be significant if
 the ΔAICc of their removal was &gt;2.
description: # The community background alters the evolution of thermal performance ##
 GENERAL INFORMATION Corresponding author * Name: Joseph Westley *
 Institution: University of Exeter * Email:
 [jw1235@exeter.ac.uk](mailto:jw1235@exeter.ac.uk) Principal Investigator *
 Name: Prof. Gabriel Yvon-Durocher * Institution: University of Exeter *
 Email: [G.Yvon-Durocher@exeter.ac.uk](mailto:G.Yvon-Durocher@exeter.ac.uk)
 Co-author 1 * Name: Dr. Francisca C. García * Institution: King Abdullah
 University of Science and Technology (KAUST) * Email:
 [paquigrcgrc@gmail.com](mailto:paquigrcgrc@gmail.com) Co-author 2 * Name:
 Ruth Warfield * Institution: University of Exeter * Email:
 [R.Warfield@exeter.ac.uk](mailto:R.Warfield@exeter.ac.uk) Date of data
 collection: 2020 ## SHARING/ACCESS INFORMATION **Recommended citation for
 this dataset:** Westley, J., García, F. C., Warfield, R., Yvon-Durocher,
 G. (2024). Data from: The community background alters the evolution of
 thermal performance. Dryad Digital Repository.
 doi.org/10.5061/dryad.vq83bk41b **Publication associated with this
 dataset:** Westley, J., García, F. C., Warfield, R., Yvon-Durocher, G.
 (2024). The community background alters the evolution of thermal
 performance. Evolution Letters. ## Data description and file structure
 Contained within the directory "datafiles" are both the raw data
 files, partially processed data files, and a single processed data file
 used in all analyses. Raw and partially processed data files for ancestral
 and monoculture-evolved isolates are combined and are found in the
 directory "datafiles/monraw". Raw and partially processed data
 files for community-evolved isolates are located in
 "datafiles/comraw". The fully processed data file,
 "final_data.csv", used in all statistical analyses is in the
 "datafiles" directory (not within a sub-directory). ###
 Processed data * "final_data.csv" * Description: A single file
 containing all growth rate data for monoculture, community, and ancestral
 isolates * Number of columns/variables: 11 * Number of rows/observations:
 1421 * Variable List: * r: maximum growth rate per hour r(h−1) * K:
 maximum optical density reached at a wavelength of 600nm (OD600) *
 T0.biom: The OD600 at the point at which cultures were inoculated * AIC:
 The Akaike information criterion for the fit of the logistic growth curve
 * Id: A variable containing the ID of the isolate. For example, in
 "OTU2-T1-15-2", "OTU2" means the taxonomic identity is
 OTU2 (Pseudomonas spp.), "T1" means it was evolved in treatment
 1 (monoculture), "15" means it was evolved at 15°C, and
 "2" means this is biological replicate 2 * temp.c: This is the
 growth temperature in Celsius * temp: This is the growth temperature in
 kelvin * ID.a: A variable containing the ID but without distinguishing
 between biological replicates of the same experimental unit. For example,
 in "OTU2-T1-15", "OTU2" means the taxonomic identity
 is OTU2 (Pseudomonas spp.), "T1" means it was evolved in
 treatment 1 (monoculture), and "15" means it was evolved at
 15°C. * evotemp: The temperature an isolate was evolved at in Celsius *
 OTU: The taxonomic identity of the isolate * Treatment: Whether the
 isolate is ancestral, monoculture-evolved, or community evolved * Specific
 abbreviations: * T0 = ancestral isolate, T1 = monoculture evolved isolate,
 T2 = community evolved isolate * OTU2 = *Pseudomonas* spp., OTU15 =
 *Serratia* spp., OTU18 = *Aeromonas* spp., OTU20 = *Herbaspirillum* spp.,
 OTU23 = *Janthinobacterium* spp. * survival.csv * Taxon: The taxonomic
 identity of the isolate * Evolution_temperature: The temperature in
 Celsius at which the isolate was evolved * Replicates_survived: The count
 of biological replicates for which the respective isolate survived to the
 end of the community evolution experiment, out of a total of three ### Raw
 and partially processed data The following is an explanation of the
 structure of "datafiles/monraw", but the same file structure is
 used in "datafiles/comraw". Within "datafiles/monraw"
 there are the following files: * 192 Raw OD600 measurement files,
 following the naming format of "T0_15_P1.csv" * Description:
 These are raw OD600 files output by the Thermo Scientific Multiskan Sky
 Microplate Spectrophotometer recording at a wavelength of 600 nm. There is
 a file for all combinations of time points of the growth assay,
 temperature of the growth assay, and plate identity (plate 1 or plate 2).
 In the example file name, "T0_15_P1.csv", "T0" refers
 to timepoint 0 (when the culture was inoculated, not to be confused with
 the treatment factor level "T0", which denotes ancestral
 isolates), "15" denotes that the plate was grown at 15°C, and P1
 denotes that the data is for plate one. * Dataframe structure: These files
 do not follow a typical "tidy" or "long form" data
 structure. Cells are populated by values in the shape of a 96-well plate,
 where the columns are numbered 1-12, and rows contain sequential letters
 A-H. For example, the value in row A, column 1, denotes the OD600 for well
 A1 of the plate being measured. * "Data_mon.csv" * Description:
 This file contains all data from all 192 raw data files described above
 collated into an R object in a "tidy" or "long"
 format. * Number of columns/variables: 12 * Number of rows/observations:
 11520 * Variable List: * Replicate: A number designating the biological
 replicate for the respective experimental unit (note: in the analogous
 community datafile "Data_com.csv", the replicate variable is
 named community instead of Replicate) * od_cor: The OD600 measure was
 corrected to remove the absorbance of the culture media * OTU: The
 taxonomic identity of the isolate * Treatment: The treatment group that
 the isolate was evolved in. For example, in "T1-15",
 "T1" denotes that the isolate evolved in a monoculture, and
 "15" denotes that the isolates evolved at 15°C. * Timepoint: An
 integer value specifying the timepoint that the measure was taken, e.g.,
 "0" means at inoculation, "1" is the first measure
 post-inoculation, etc. * growthtemp: The temperature the plate was grown
 at in Celsius. * timestampcode: A variable that contains a combination of
 the growth temperature and the timepoint, e.g., "0-15" denotes
 timepoint 0 and a growth temperature of 15°C. * Hours: The exact time in
 hours since culture inoculation that the OD600 reading was recorded *
 Specific abbreviations: * T0 = ancestral isolate, T1 = monoculture evolved
 isolate, T2 = community evolved isolate * OTU2 = *Pseudomonas* spp., OTU15
 = *Serratia* spp., OTU18 = *Aeromonas* spp., OTU20 = *Herbaspirillum*
 spp., OTU23 = *Janthinobacterium* spp. * "mon_out.csv" *
 Description: A file containing all specific OD600 measurements to be
 removed from the "Data_mon.csv" data frame prior to construction
 of logistic growth curves (these are typically measurements occurring
 after carrying capacity has been reached) * Number of columns/variables: 3
 * Number of rows/observations: 630 * Variable List: * t: The time in hours
 that the datapoint occurs * LOG10N: The OD600 measure of the datapoint *
 pa: A variable containing the ID of the isolate. For example, in
 "OTU2-T1-15-2", "OTU2" means the taxonomic identity is
 OTU2 (Pseudomonas spp.), "T1" means it was evolved in treatment
 1 (monoculture), "15" means it was evolved at 15°C, and
 "2" means this is biological replicate 2 * Specific
 abbreviations: * T0 = ancestral isolate, T1 = monoculture evolved isolate,
 T2 = community evolved isolate * OTU2 = *Pseudomonas* spp., OTU15 =
 *Serratia* spp., OTU18 = *Aeromonas* spp., OTU20 = *Herbaspirillum* spp.,
 OTU23 = *Janthinobacterium* spp. * "mon_curves_outrem.csv" *
 Description: A file containing maximum growth rate data for all
 monoculture evolved isolates across all growth and evolution temperatures
 "mon_curves_outrem.csv". Each curve is for a single biological
 replicate and is produced by fitting a logistic growth model to the OD600
 measurements * Number of columns/variables: 6 * Number of
 rows/observations: 960 * Variable List: * pa: A variable containing the ID
 of the isolate. For example, in "OTU2-T1-15-2", "OTU2"
 means the taxonomic identity is OTU2 (Pseudomonas spp.), "T1"
 means it was evolved in treatment 1 (monoculture), "15" means it
 was evolved at 15°C, and "2" means this is biological replicate
 2 * r: maximum growth rate per hour r(h−1) * k: maximum OD600 reached *
 T0.biom: The OD600 at the point at which cultures were inoculated * AIC:
 The Akaike information criterion for the fit of the logistic growth curve
 * quasi_r2: A "quasi" or "pseudo" r squared value for
 the fit of the logistic growth curve * "Montimestamps.csv" *
 Description: A file containing the actual length of time in hours since
 inoculation (timepoint 0) that each OD600 measurement was taken, e.g.,
 OD600 measurements at timepoint 1 for plates grown at 15°C were taken 4.05
 hours after inoculation * Number of columns/variables: 3 * Number of
 rows/observations: 96 * Variable List: * Timepoint: An integer value
 specifying the timepoint that the measure was taken, e.g., "0"
 means at inoculation, "1" is the first measure post-inoculation,
 etc. * growthtemp: The temperature the plate was grown at in Celsius. *
 Hours: The exact time in hours since culture inoculation that the OD600
 reading was recorded ## Code In the scripts directory, all data processing
 steps are numbered sequentially. In summary, these scripts perform the
 following: #### Step 1: Collating raw OD600 data GRE_mon_step1.R and
 GRE_com_step1.R each collate the raw 96 well plate OD600 datafiles for all
 time points (e.g., files following the naming format of
 "T0_15_P1.csv") into an R object. #### Step 2: Converting data
 to 'tidy' format GRE_mon_step2.R and GRE_com_step2.R take the
 outputs from step 1 and convert them to a "tidy" format,
 producing the files "Data_mon.csv" and "Data_com.csv".
 #### Step 3: Creating logistic growth curves GRE_mon_logcurves_step3.R and
 GRE_com_logcurves_step3.R take the outputs from step2
 ("Data_mon.csv" and "Data_com.csv" respectively) and
 produce individual logistic growth curves for each isolate
 ("mon_curves_outrem.csv" and "com_curves_outrem.csv"
 respectively). "mon_out.csv" and "com_out.csv" are
 also produced at this stage, and include OD measurements to be removed,
 prior to growth curve estimation (for example, OD measurements showing a
 decline occurring after carrying capacity is reached would be removed).
 Plots of these growth curves are also written to the
 'plots_growth_curves' directory as mon_curves_outrem.pdf and
 com_curves_outrem.pdf (not included in this repository). #### Step 4:
 Consolidating monoculture-evolved, ancestral, and community-evolved data
 into a single .csv file GRE_data_consolidation_step4.R collates the
 monoculture and community growth curve data
 ("mon_curves_outrem.csv" and "com_curves_outrem.csv")
 into the single file, "final_data.csv". Curves where carrying
 capacity is not reached or there is no growth are removed at this stage.
 #### Step 5: Conducting growth rate analyses and creating Figures 1 and 2
 GAMM_r_analysis_step5.R takes "final_data.csv" as input and
 produces generalised additive mixed-effects models (GAMMs), conducts model
 comparison via AICc to select the best models, and produces Figure 1 and
 Figure 2 for the manuscript, based on the predictions of these best
 models. Additionally, post-hoc analysis is conducted using R package
 emmeans to get effect sizes and the significance of pairwise differences.
 See the section 'Statistical analysis' within 'Materials
 and Methods' of the manuscript for more detailed methods. #### Step
 6: Conducting survival analysis and creating Figure 3
 Survival_analysis_step6.R also takes "final_data.csv" as input
 and creates a heatmap showing the number of replicates of each community
 member that survive to the end of the community experiment, at each
 evolution temperature. Additionally, a binomial model is used here to test
 if survival to the end of the community experiment depends on taxonomic
 identity and evolution temperature.