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Hubbard Brook Experimental Forest: Litter and soil radiocarbon and selective metal measurements from Bear Brook, 1998–2023
Radiocarbon time series of archived litter and soil samples from Bear Brook (west of watershed 6) at lower elevation from 1998 to 2023. Additional measurements include total carbon and nitrogen for all samples, and selective dissolution metal concentrations for the Oa/A and mineral soil layers. Selective dissolution metals include pyrophosphate-extractable aluminum (Al); iron (Fe), calcium (Ca), magnesium (Mg), and manganese (Mn); oxalate-extractable Al, Fe, Ca, Mg, and Mn; and dithionite-extractable Al, Fe, Ca, Mg, and Mn. All samples are from the Microbial Biomass and Activity Sampling Effort (Groffman and Martel, 2025, https://doi.org/10.6073/pasta/aff4a2074fd56102f62f13a19ce46f2d). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the US Forest Service, Northern Research Station.
Data from: Stochastic phenotypic switching arises in response to directional selection in experimentally evolved multicellular yeast.
<p><span lang="EN">This BBC_2025__README.txt file was generated on 2025-09-24 by Beatriz Baselga Cervera</span></p> <p><span lang="EN">GENERAL INFORMATION</span></p> <ol> <li><span lang="EN">Title of Dataset and code: Data from: Stochastic phenotypic switching arises in response to directional selection in experimentally evolved multicellular yeast.</span></li> </ol> <p><span lang="EN"> </span></p> <p><span lang="EN">2. Author Information</span></p> <p><span lang="EN"> Corresponding Investigator</span></p> <p><span lang="EN"> Name: Ph.D. Beatriz Baselga-Cervera</span></p> <p><span lang="EN"> Institution: University of Minnesota Twin cities, Minnesota, US.</span></p> <p><span lang="EN"> Email: <a href="mailto:bbaselga@umn.edu"><span>bbaselga@umn.edu</span></a>; beabaselga@gmail.com</span></p> <p><span lang="EN"> Co-investigator 1</span></p> <p><span lang="EN"> Name: Ph.D. Nahui <span>Olin Medina-Chávez</span></span></p> <p><span lang="EN"> Institution: University of Minnesota Twin cities, Minnesota, US.</span></p> <p><span lang="EN"> Email: nmedinac@umn.edu</span></p> <p><span lang="EN"> Co-investigator 2</span></p> <p><span lang="EN"> Name: Ph.D. Noah Gettle</span></p> <p><span lang="EN"> Institution: Wellcome Sanger Institute, Hinxton, UK.</span></p> <p><span lang="EN"> Email: nbgettle@gmail.com </span></p> <p><span lang="EN">Co-investigator 3</span></p> <p><span lang="EN"> Name: Ph.D. Michael Travisano</span></p> <p><span lang="EN"> Institution: University of Minnesota Twin cities, Minnesota, US.</span></p> <p><span lang="EN"> Email: travisan@umn.edu</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">3. Data collectors: Ph.D. Beatriz Baselga-Cervera, Ph.D. Nahui Olin Medina-Chávez & Ph.D. Noah Gettle.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">4. Date of data collection: 2022-2024</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">5. Geographic location of data collection: Saint Paul, US</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">6. Funding sources that supported the collection of the data: Fundación Alfonso Martín Escudero, Madrid, Spain (BBC).</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">7. Recommended citation for this dataset: Baselga-Cervera et al. (2024), Data from: Stochastic phenotypic switching arises in response to directional selection in experimentally evolved multicellular yeast.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">DATA & FILE OVERVIEW</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">8. Description of dataset</span></p> <p><span lang="EN">In this study, we address whether stochastic phenotypic switching can shape biological diversity contributing to evolutionary change across the transition from singles cells to multicellular clutters in <em>Saccharomyces cerevisiae </em>multicellular yeast system. Populations characterization was conducted with a Coulter Counter multisize 4, a FlowCam 3, under the optic microscope, via ACE2 gene sequencing and RNA sequencing and mathematical modeling. The populations studied were the genetically uniform diploid wild-type <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>). </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">9. File list:</span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">Coulter Counter size distribution data: </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 1 name: File_1_Coulter_Counter_Counts_20h.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 1 description: Size distributions of <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 20-hours growth. Data for: Fig. 1A, Fig. 3A and Fig. S2, Table S2 and Table S3.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 2 name: File_2_Coulter_Counter_Counts_24h.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 2 description: Size distributions of <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 24-hours growth. Data for: Fig. 1A, Fig. 3A, Fig. S2, Table S2 and Table S3. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 3 name: File_3_Coulter_Counter_Counts_48h.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 3 description: Size distributions of <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 48-hours growth. Data for: Fig. 1, Fig. 3A, Fig. S2, Table S2 and Table S3.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File name: File_4_Coulter_Counter_Counts_Constructed_strains_diversity.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 4 description: Size distributions of the constructed ACE2 knockout and a strain containing the homozygous missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 24h growth. Size distributions were obtained from populations before (initial) and five resuspended colonies obtained from small-size particles by plating the top fraction of the population after gravitational selection from three isolates per strain. Data for: Fig. 1, Fig. S2, Table S2 and Table S3.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 5 name: File_5_Coulter_Counter_Counts_Selection_Experiment.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 5 description: Size distributions of C1W8.1 and C1W8.2 multicellular evolved strains in YPD at 24h growth. Size distributions from the selection experiment for small-size particles by plating the top fraction of the population after gravitational selection over three cycles of selection. Data for: Fig. 2B and Fig. S6.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 6 name: File_6_Coulter_Counter_Counts_12h.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 6 description: Size distributions of C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 12-hours growth. Data for: Fig. 3A and Fig. S3. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">FlowCam data:</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 7 name: File_7_Rawdata_FlowCam_all.csv </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 7 description: FlowCam data from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD at 24h growth. Data for: Fig. S4. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">Data generated statistically:</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 8 name: File_8_C1W8.2_overlapPairs_Selection_Experiment.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 8 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data of the C1W8.2 derived strain over the selection experiment. Data for: Fig. S6D.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 9 name: File_9_C1W8.1_overlapPairs_Selection_Experiment.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 9 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">of the C1W8.1 derived strain over the selection experiment. Data for: Fig. S6C.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 10 name: File_10_ overlapPairs_Constructed_strains_diversity.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 10 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data</span></p> <p><span lang="EN">of the constructed ACE2 knockout and a strain containing the homozygous missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 24h growth. Size distributions were obtained from populations before (initial) and after gravitational selection of five resuspended colonies from three isolates per strain. Data for: Fig. S7.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">Data from ImageJ:</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 11 name: File_11_ImageJ_analyses.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 11 description: ImageJ analyses of the microphotographs from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>). Cultures were grown in culture tubes with 10 ml of media, 50 mL Erlenmeyer flasks with 10 mL and 30 mL of media, in YPD under non-shaking and shaking at 250 rpm. YPD media was used across all conditions. Cultures were assessed after 24 hours growth at 30°C.<span> </span>Microphotographs of each condition and strain were obtained with a Nikon TE2000 microscope using 10x objective.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">Pictures:</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 12 name: File_12_ FlowCam_Pictures.zip</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 12 description FlowCam IMAGES from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 24h growth. Data for: Fig. 1B. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 13 name: File_13_Microphotography_controled_experimental_conditions.zip</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 13 description: Microphotographs<em> </em>from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>). Cultures were grown in culture tubes with 10 mL of media, 50 mL Erlenmeyer flasks with 10 mL and 30 mL of media, in YPD under non-shaking and shaking at 250 rpm. YPD media was used across all conditions. Cultures were assessed after 24 hours of growth at 30°C. Pictures were obtained with a Nikon TE2000 microscope using 10x objective.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">Mathematical Model</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 14 name: File_14_Mathematical_model.zip</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 14 description: Mathematical model R code and generated values. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">ARN data</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 15 name: File_15_rnaseq-final-results-Top_v_Bottom.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 15 description: RNA analyses final results Top vs Bottom phenotypic subdistributions. Top is used as control. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 16 name: File_16_Variant_Call_format_file.vcf</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 16 description: Variant Calling analyses of the sample ARN sample <em>Top 1. </em>Adhesion number: SRR32105384. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">Time-lapse videos</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 17 name: Supp. Video 1. C1W8.1 from 17 to 22 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 17 description: Supplementary Video 1. Experimentally evolved multicellular yeast video between 17 and 22 hours of growth (C1W8.1-derived strain) — time-lapse video of the formation of a single-cell propagule from a multicellular cluster<strong>. </strong></span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 18 name: Supp. Video 2. Ace2x2KO over 26 hours growth.</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 18 description: Supplementary Video 2. <em>ace2Δ knockout</em> constructed strain growth — time-lapse video of a single large multicellular cluster over 26 hours. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 19 name: Supp. Video 3. C1W8.1 over 6 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 19 description: Supplementary Video 3. Experimentally evolved multicellular yeast growth between 6 and 12 hours of growth (C1W8.1-derived strain). </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 20 name: Supp. Video 4. C1W8.1 over 24 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 20 description: Supplementary Video 4. Experimentally evolved multicellular yeast growth over 24 hours (C1W8.1-derived strain) — cell division stops in small ancestral-like phenotypes. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 21 name: Supp. Video 5. Ace2x2KO over 24 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 21 description: Supplementary Video 5. <em>ace2Δ knockout</em> constructed strain growth — time-lapse video of multiple large multicellular clusters over 24 hours. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 22 name: Supp. Video 6. Ace2x2missense from 0 to 3h45m hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 22 description: Supplementary Video 6. <em>ace2Δ missense</em> constructed strain growth — time-lapse video of multiple large multicellular clusters up to 3 hours 45 min. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">METHODOLOGICAL INFORMATION</span></p> <p><span lang="EN">Strains: ancestral wildtype (Y55 strains), C1W8.1 and C1W8.2 multicellular derived strains isolated after 60 days of selection in YPD media, constructed ACE2 gene knockouts, and strains containing the ACE2 missense mutation (ACE2 <sup>c.1934 A>T</sup>).</span></p> <p><span lang="EN">Media: Growth media used in this study were Yeast Peptone Dextrose media (YPD; 1% (v/w) yeast extract, 2% (v/w) peptone, 2% (v/w) D-glucose, pH 5.8).</span></p> <p><span lang="EN">Phenotypic characterization of the different strains was conducted in a Coulter Counter Multisizer 4 and FlowCam® 3.0 Fluid Imaging Technologies, optic microscopy and a mathematical model. Replicate populations of different individual isolates per strain were analyzed to obtain the population distributions in YPD media.</span></p> <p><span lang="EN">RNA was extracted using an Invitrogen® PureLink RNA Mini Kit. Three out of four extracted samples per treatment with the highest RNA integrity score were submitted for TrueSeq Stranded RNA-Seq. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN">10. Detailed description</span></p> <p><span lang="EN"><span>●<span> </span></span></span><span lang="EN">Coulter Counter size distribution data of all the populations: </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 1 name: File_1_Coulter_Counter_Counts_20h.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 1 description: strains naming convention; strain_Isolate_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout; C1W8.1= C1W8.1 evolved multicellular strain; C1W8.2= C1W8.2 evolved multicellular strain; Y55= ancestral strain.</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Volume (um3)</span></p> <p><span lang="EN">Column 2: Diameter (um2)</span></p> <p><span lang="EN">Columns 3 to the last column: strains counts.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 2 name: File_2_Coulter_Counter_Counts_24h.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 2 description: strains naming convention; strain_Isolate_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout; C1W8.1= C1W8.1 evolved multicellular strain; C1W8.2= C1W8.2 evolved multicellular strain; Y55= ancestral strain.</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Volume (um3)</span></p> <p><span lang="EN">Column 2: Diameter (um2)</span></p> <p><span lang="EN">Columns 3 to the last column: strains counts.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 3 name: File_3_Coulter_Counter_Counts_48h.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 3 description: strains naming convention; strain_Isolate_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout; C1W8.1= C1W8.1 evolved multicellular strain; C1W8.2= C1W8.2 evolved multicellular strain; Y55= ancestral strain.</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Volume (um3)</span></p> <p><span lang="EN">Column 2: Diameter (um2)</span></p> <p><span lang="EN">Column 3 to the last column: strains counts.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File_4_Coulter_Counter_Counts_Constructed_strains_diversity.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 4 description: strains naming convention; strain_Isolate_colony_run.pseudoreplicate. Strains: ace2x2m=strains containing the ACE2 missense mutation (ACE2 c.1934 A>T); ace2x2= ACE2 knockout.</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Volume (um3)</span></p> <p><span lang="EN">Column 2: Diameter (um2)</span></p> <p><span lang="EN">Column 3 to the last column: strains counts.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File_5_Coulter_Counter_Counts_Selection_Experiment.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 5 description: strains naming convention; strain_colony.phenotype_selection.cycle_run.pseudoreplicate. Strains: C1W8.2= C1W8.2 evolved multicellular strain and C1W8.1= C1W8.1 evolved multicellular strain.</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Volume (um3)</span></p> <p><span lang="EN">Column 2: Diameter (um2)</span></p> <p><span lang="EN">Column 3 to the last column: strains counts.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 6 name: File_6_Coulter_Counter_Counts_12h.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 6 description: Size distributions of C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockout, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 12-hours growth. Data for: Fig. 3A and Fig. S3. </span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Volume (um3)</span></p> <p><span lang="EN">Column 2: Diameter (um2)</span></p> <p><span lang="EN">Column 3: Time</span></p> <p><span lang="EN">Column 4: replicate</span></p> <p><span lang="EN">Column 5: Strain name (strain_f)</span></p> <p><span lang="EN">Column 6: Isolate (isolate_f)</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 7 name: File_3_Rawdata_Flowcam_all.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 7 description: strains naming convention; ace2_isolate= ACE2 knockout;</span></p> <p><span lang="EN">Ace2m_isolate= strain containing the ACE2 missense mutation (ACE2 <em>c.1934 A>T</em>); c1w82_isolate=C1W8.2 evolved multicellular strain; C1W81_isoalte C1W8.1 evolved multicellular strain; Y55_isolate=ancestral strain. </span></p> <p><span lang="EN">§ Page 1:</span></p> <p><span lang="EN"> Column 1: Particle ID</span></p> <p><span lang="EN"> Column 2: Area ABD</span></p> <p><span lang="EN"> Column 3: Aspect Ratio (Width/Length)</span></p> <p><span lang="EN"> Column 4: Circle Fit</span></p> <p><span lang="EN"> Column 5: Area base Diameter (ABD)</span></p> <p><span lang="EN"> Column 6: Equivalent Spherical Diameter (ESD)</span></p> <p><span lang="EN"> Column 7: Elongation</span></p> <p><span lang="EN"> Column 8: Perimeter</span></p> <p><span lang="EN"> Column 9: Roughness</span></p> <p><span lang="EN"><span> </span><span> </span>Column 10: Volume ABD-based</span></p> <p><span lang="EN"> Column 11: Volume ESD-based</span></p> <p><span lang="EN"> Column 12: Width</span></p> <p><span lang="EN"> Column 13: Source. Name of the sample.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 8 name: File_8_C1W8.2_overlapPairs_Selection_Experiment.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 8 description: C1W8.2 _lineage_selection.cycle= C1W8.2 evolved multicellular strain, lineage (A=ancestral, M1= lineage 1,<span> </span>M2= lineage 2 , M3= lineage 3) and selection cycle<span> </span>(0, 1, 2 and 3).</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Var1= strain 1</span></p> <p><span lang="EN">Column 2: Var2= strain 2</span></p> <p><span lang="EN">Column 3: overlap value of both strains compared.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 9 name: File_9_C1W8.1_overlapPairs_Selection_Experiment.csv</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 9 description: C1W8.1 _lineage_selection.cycle =C1W8.1 evolved multicellular strain, lineage (A=ancestral, M1= lineage 1,<span> </span>M2= lineage 2 , M3= lineage 3) and selection cycle<span> </span>(0, 1, 2 and 3).</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Var1= strain 1</span></p> <p><span lang="EN">Column 2: Var2= strain 2</span></p> <p><span lang="EN">Column 3: overlap value of both strains compared.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 10 name: File_10_overlapPairs_Constructed_strains_diversity.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 10 description: variables naming convention; strain _isolate_colony.number. Strains; ace2x2m=strains containing the ACE2 missense mutation (ACE2 <sup>c.1934 A>T</sup>); ace2x2= ACE2 knockout. Isolate; 1,2 and 3. Colony.number; Initial=initial population and colony number (1,2,3,4 and 5).</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Var1= strain 1</span></p> <p><span lang="EN">Column 2: Var2= strain 2</span></p> <p><span lang="EN">Column 3: overlap value of both strains compared.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 11 name: File_11_ ImageJ _analyses.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 11 description: ImageJ analyses of the microphotographs from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>). Cultures were grown in culture tubes with 10 mL of media, 50 mL Erlenmeyer flasks with 10 mL and 30 mL of media, in YPD under non-shaking and shaking at 250 rpm. YPD media was used across all conditions. Cultures were assessed after 24 hours growth at 30°C.<span> </span>Microphotographs of each condition and strain were obtained with a Nikon TE2000 microscope using 10x objective.</span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: Var1= strain 1</span></p> <p><span lang="EN">Column 2: </span><span lang="EN">Var2 =<span> strain 2</span></span></p> <p><span lang="EN">Column 3: overlap value of both strains compared.</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 12 name: File_12_ FlowCam_Pictures.zip</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 12 description: FlowCam runs, images, and raw data of <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 <sup>c.1934 A>T</sup>) in YPD at 24h growth. Data for: Fig. 1B. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 13 name: File_13_Microphotography_controled_experimental_conditions.zip</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 13 description: 149 microphotographs. </span></p> <p><span lang="EN">§ Folder 1:<span> </span>Images </span><span lang="EN">of Erlenmeyer flasks<span> with 30ml of YPD</span></span></p> <p><span lang="EN">§ Folder 2:<span> </span>Images </span><span lang="EN">of <span>Erlenmeyer’s and tubes with 10ml of YPD</span></span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 14 name: File_14_Mathematical_model.zip</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 14 description: Mathematical model, R code, and generated values. </span></p> <p><span lang="EN">§ Document 1:<span> </span>R code of the model</span></p> <p><span lang="EN">§ Document 2:<span> </span>Resulted data from </span><span lang="EN">the <span>mathematical model with different inset</span> <span>values of <em>k</em>, alpha</span>,<span> and beta. </span></span></p> <p><span lang="EN">§ Document 2:<span> </span>Resulted data from the mathematical model with different inset values of <em>k</em>, alpha, gamma, and beta. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 15 name: File_15_rnaseq-final-results-Top_v_Bottom.xlsx</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 15 description: </span></p> <p><span lang="EN">§ Page 1: </span></p> <p><span lang="EN">Column 1: number</span></p> <p><span lang="EN">Column 2: ID</span></p> <p><span lang="EN">Column 3: protID</span></p> <p><span lang="EN"><span> </span>Column 4: gene_symbol<span> </span></span></p> <p><span lang="EN"><span> </span>Column 5: chr</span></p> <p><span lang="EN"><span> </span>Column 6: chr_latin</span></p> <p><span lang="EN">Column 7: location </span></p> <p><span lang="EN">Column 8: baseMean</span></p> <p><span lang="EN"><span> </span>Column 9: log2FoldChange</span></p> <p><span lang="EN">Column 10: lfcSE</span></p> <p><span lang="EN">Column 11: stat</span></p> <p><span lang="EN"><span> </span>Column 12: pvalue<span> </span>padj</span></p> <p><span lang="EN">Column 13: test</span></p> <p><span lang="EN">Column 14: log10padj</span></p> <p><span lang="EN"><span> </span>Column 15: log10baseMean</span></p> <p><span lang="EN">Column 16: blast_pident</span></p> <p><span lang="EN">Column 17: transcript_length</span></p> <p><span lang="EN"><span> </span>Column 18: blast_evalue</span></p> <p><span lang="EN">Column 19: blast_bitscore</span></p> <p><span lang="EN">Column 20: rnaID</span></p> <p><span lang="EN"><span> </span>Column 21: feature</span></p> <p><span lang="EN">Column 22: accession</span></p> <p><span lang="EN">Column 23: strain</span></p> <p><span lang="EN"><span> </span>Column 24: gene_accession</span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 16 name: File_16_Variant_Call_format_file.vcf</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 16 description: Variant Calling analyses of the<span> </span>ARN sample <em>Top 1. </em>Adhesion number: SRR32105384. </span></p> <p><span lang="EN"> </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 17 name: Supp. Video 1. C1W8.1 from 17 to 22 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 17 description: <strong>Supplementary Video 1. Experimentally evolved multicellular yeast video between 17 and 22 hours of growth (C1W8.1-derived strain) — time-lapse video of the formation of a single-cell propagule from a multicellular cluster. </strong>The time-lapse video captures growth dynamics over this period, highlighting the formation of a single-cell propagule from a multicellular cluster on two occasions (visible in the lower left region of the frame). Images were acquired every 15 minutes using a 10x objective lens. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 18 name: Supp. Video 2. Ace2x2KO over 26 hours </span><span lang="EN">of <span>growth.</span></span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 18 description: <strong>Supplementary Video 2. <em>ace2Δ knockout</em></strong> <strong>constructed strain growth</strong> <strong>— time-lapse video of a single large multicellular cluster over 26 hours.</strong> The video captures large, multicellular clusters that produce both large, multicellular and small, ancestral-like clusters. The video shows a single large multicellular cluster fragmenting into two large multicellular clusters at ~ 13 hours of growth (from 02:09 to 02:10 minutes in the time-lapse) and generating two small ancestral-like propagules at ~19 hours of growth (from 03:07 to 03:09 minutes in the time-lapse). Microphotographs were obtained at 3-minute intervals under a 10x objective over 26 hours. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 19 name: Supp. Video 3. C1W8.1 over 6 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 19 description: <strong>Supplementary Video 3. Experimentally evolved multicellular yeast growth between 6 and 12 hours of growth (C1W8.1-derived strain). </strong>The time-lapse video captures large, multicellular clusters of the C1W8.1 strains, which produce both large, multicellular and small, ancestral-like clusters. Additionally, small ancestral-like clusters are observed undergoing cellular division <strong>—</strong>no separation is observed<strong>—</strong> during the first 2 to 3 hours, followed by a cessation of division for the remainder of the time-lapse. Images were acquired every 30 seconds using a 10x objective lens.</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 20 name: Supp. Video 4. C1W8.1 over 24 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 20 description: <strong>Supplementary Video 4. Experimentally evolved multicellular yeast growth over 24 hours (C1W8.1-derived strain) — cell division stops in small ancestral-like phenotypes. </strong>The footage captures multiple large multicellular clusters undergoing fragmentation into propagules. Additionally, a small ancestral-like cluster is observed undergoing division during the first 2 to 3 hours, followed by a cessation of division for the remainder of the time-lapse (visible in the lower left region of the frame). This early division phase is evident during the first 10 seconds of the video. Images were acquired every 5 minutes using a 10x objective lens. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 21 name: Supp. Video 5. Ace2x2KO over 24 hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 21 description: <strong>Supplementary Video 5. <em>ace2Δ knockout</em> constructed strain growth</strong> <strong>— time-lapse video of multiple large multicellular clusters over 24 hours.</strong> The video shows multiple large multicellular clusters fragmenting into large clusters and several small ancestral-like clusters being dragged by Brownian motion and evaporation of the sample. Microphotographs were obtained at fixed intervals of 3 minutes under the 10x objective over 24 hours. </span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 22 name: Supp. Video 6. Ace2x2missense from 0 to 3h45m hours growth</span></p> <p><span lang="EN"><span>o<span> </span></span></span><span lang="EN">File 22 description: <strong>Supplementary Video 6. <em>ace2Δ missense</em> constructed strain growth</strong> <strong>— time-lapse video of multiple large multicellular clusters up to 3 hours 45 min.</strong> The video shows multiple large multicellular clusters fragmenting into large clusters</span><span lang="EN">,<span> generating two small ancestral-like propagules before being dragged by Brownian motion and evaporation of the sample. Microphotographs were obtained at </span>3-minute intervals <span>under the 10x objective. </span></span></p> <p><span lang="EN"> </span></p> <p> </p>
Competitors alter selection on alpine plants exposed to experimental climate change
<p>Investigating how climate change alters selection regimes is a crucial step towards understanding the potential of populations to evolve in the face of changing conditions. Previous studies have mainly focused on understanding how changing climate directly influences selection, while the role of species' interactions has received little attention. Here, we used a transplant experiment along an elevation gradient to estimate how climate warming and competitive interactions lead to shifts in directional phenotypic selection on morphology and phenology of four alpine plants. We found that warming generally imposed novel selection, with the largest shifts in regimes acting on specific leaf area and flowering time across species. Competitors instead weakened the selection acting on traits that was imposed directly by warming. Weakened or absent selection in the presence of competitors was largely associated with the suppression of absolute means and variation of fitness. Our results suggest that although climate change can impose strong selection, competitive interactions within communities might act to limit selection and thereby stymie evolutionary responses in alpine plants facing climate change.</p>
The effect of experimental pollinator decline on pollinator-mediated selection on floral traits
<p>Human-mediated environmental change, by reducing mean fitness, is hypothesized to strengthen selection on traits that mediate interactions among species. For example, human-mediated declines in pollinator populations are hypothesized to reduce mean seed production by increasing the magnitude of pollen limitation and thus strengthen pollinator-mediated selection on floral traits that increase pollinator attraction or pollen transfer efficiency. To test this hypothesis, we measured two female fitness components and six floral traits of <em>Lobelia siphilitica</em> plants exposed to supplemental hand-pollination, ambient open-pollination, or reduced open-pollination treatments. The reduced treatment simulated pollinator decline, while the supplemental treatment was used to estimate pollen limitation and pollinator-mediated selection. We found that plants in the reduced pollination treatment were significantly pollen-limited, resulting in pollinator-mediated selection for taller inflorescences and more vibrant petals, both traits that could increase pollinator attraction. This contrasts with plants in the ambient pollination treatment, where reproduction was not pollen-limited and there was no significant pollinator-mediated selection on any floral trait. Our results support the hypothesis that human-mediated environmental change can strengthen the selection on traits of interacting species and suggest that these traits have the potential to evolve in response to changing environments.</p>
Experimental test of selection against hybridization as a driver of avian signal divergence
<p><span>Signal divergence may be pivotal in the generation and maintenance of new biodiversity by allowing closely related species to avoid some costs of co-occurrence. In birds, closely related, sympatric species are more divergent in their colour patterns than those that live apart, but the selective pressures driving this pattern remain unclear. Traditionally, signal divergence among sympatric species is thought to result from selection against hybridization, but broad evidence is lacking. Here, we conducted field experiments on na</span><span>ï</span><span>ve birds using spectrometer-matched, painted 3D-printed models to test whether selection against hybridization drives colour pattern divergence in the genus Poecile. To address selection for male colour pattern divergence without the influence of learning or the evolution of female discrimination in sympatry, we simulated secondary contact between Poecile species, and conducted mate choice experiments on naïve, allopatric females. We found that female black-capped chickadees (<em>P. atricapillus</em>) are equally likely to perform copulation solicitation displays to sympatric and allopatric heterospecific congeners when they are paired with conspecifics, but exhibit a strong preference for less divergent males when presented with paired heterospecific congeners. These results suggest that increased colour pattern divergence among sympatric species can reduce the likelihood of mixed mating in some contexts, and therefore should be favoured by selection against hybridization.</span></p>
Multilevel selection on social network traits differs between sexes in experimental populations of forked fungus beetles
<p>Both individual and group behavior can influence individual fitness, but multilevel selection is rarely quantified on social behaviors. Social networks provide a unique opportunity to study multilevel selection on social behaviors, as they describe complex social traits and patterns of interaction at both the individual and group levels. In this study, we used contextual analysis to measure the consequences of both individual network position and group network structure on individual fitness in experimental populations of forked fungus beetles (<em>Bolitotherus</em> <em>cornutus</em>) with two different resource distributions. We found that males with high individual connectivity (strength) and centrality (betweenness) had higher mating success. However, group network structure did not influence their mating success. Conversely, we found that individual network position had no effect on female reproductive success but that females in populations with many social interactions experienced lower reproductive success. The strength of individual-level selection in males and group-level selection in females intensified when resources were clumped together, showing that habitat structure influences multilevel selection. Individual and emergent group social behavior both influence variation in components of individual fitness but impact male mating success and female reproductive success differently, setting up intersexual conflicts over patterns of social interactions at multiple levels. </p>
FIGURE 2 in Prey selectivity of the invasive largemouth bass towards native and non-native prey: an experimental approach
FIGURE 2 | Relationship between the Manly-Chesson selectivity and prey availability for Micropterus salmoides. Higher values indicate preference for non-native species. Shading represents 95% confidence intervals. Note that because the index fluctuates between 0 and 1, with 2 types of prey and equal availability of prey for both types, the result of the index for one prey is exactly the opposite of the other. For this reason, the graph only shows the results of the index for the non-native species. The graph for the other type of prey would be the spectral image of this one.
FIGURE 1 in Prey selectivity of the invasive largemouth bass towards native and non-native prey: an experimental approach
FIGURE 1 | Relative consumption of non-native (Oreochromis niloticus and Coptodon rendalli) and native (Geophagus iporangensis) prey, considering different prey availability for Micropterus salmoides.
Data from: Selection on growth rate and local adaptation drive genomic adaptation during experimental range expansions in the protist Tetrahymena thermophila
<p>1. Populations that expand their range can undergo rapid evolutionary adaptation of life-history traits, dispersal behaviour, and adaptation to the local environment. Such adaptation may be aided or hindered by sexual reproduction, depending on the context.</p> <p>2. However, few empirical and experimental studies have investigated the genetic basis of adaptive evolution during range expansions. Even less attention has been given to the question how sexual reproduction may modulate such adaptive evolution during range expansions.</p> <p>3. We here studied genomic adaptation during experimental range expansions of the protist <em>Tetrahymena thermophila</em>in landscapes with a uniform environment or a pH-gradient. Specifically, we investigated two aspects of genomic adaptation during range expansion. Firstly, we investigated adaptive genetic change in terms of the underlying numbers of allele frequency changes from standing genetic variation and <em>de novo</em><span> variants. We focused on how sexual reproduction may alter this adaptive genetic change. Secondly, we identified genes subject to selection caused by the expanding range itself, and directional selection due to the presence or absence of the pH-gradient. We focused this analysis on alleles with large frequency changes that occurred in parallel in more than one population to identify the most likely candidate targets of selection. </span></p> <p><span>4. We found that sexual reproduction altered adaptive genetic change both in terms of <em>de novo</em></span><span> variants and standing genetic variation. However, sexual reproduction affected allele frequency changes in standing genetic variation only in the absence of long-distance gene flow. Adaptation to the range expansion affected genes involved in cell divisions and DNA repair, whereas adaptation to the pH-gradient additionally affected genes involved in ion balance, and oxidoreductase reactions. These genetic changes may result from selection on growth and adaptation to low pH. </span></p> <p><span>5. In the absence of gene flow, sexual reproduction may have aided genetic adaptation. Gene flow may have swamped expanding populations with maladapted alleles, thus reducing the extent of evolutionary adaptation during range expansion. Sexual reproduction also altered the genetic basis of adaptation in our evolving populations via <em>de novo </em>variants, possibly by purging deleterious mutations or by revealing fitness benefits of rare genetic variants. </span></p>
Experimental evidence for short term directional selection of epigenetic trait variation
<p>This Arabidosis data folder includes 5 folders providing the data and code associated with the publication entitled "Experimental evidence for short term directional selection of epigenetic trait variation" by Pujol et al. in Peer Community Journal:</p> <p>"in Silico 1000 resampling" folder includes two folders; one for Population 1, and one for Population 2, each including 24 files. Each file presents the 1000 lists of plant IDs that were randomly sampled to build control groups (listrand), and the corresponding lists of plant IDs selected on the basis of trait values to build the corresponding high (Top) and low (Bot) selection lines, for the strong (20) and weak (60) selection intensities.</p> <p>“phenotypic and epiril data” includes two csv files; data_pop1 and data_pop2, respectively for population 1 and 2, and a readme txt file that presents the data included in the files.</p> <p>“scripts R epigenomic selection” includes two R script files: “PCA_arabidopsis”, which includes the R code used to conduct the PCA analysis on epiRIL data and “Arabette_analysis_loop_Epigenotypes_data”, which is used to insert that epigenotype data in the analysis.</p> <p>“scripts R epigenomic validation” includes nine R scripts used to conduct tests of molecular epigenomic data validation and comparison with existing data from the literature: “Script 1 Extract 126 markers from BSMAP ouput files and apply methylkit”, “Script 2 Boxplots of the BS signal distribution for the 126 markers and their correspondence to published HMM classification”, “Script 3 Correlation between EM-seq and BS seq_published_data for the 126 markers”, “Script 4 Correlation between the BS signals of EM-seq and WGBS data for the 126markers”, “Script 5 Hierarchical clustering of the epiRILs EM-seq and published epigenomic data”, “cluster and PCA analysis of Col wt vs 24 sequenced epiRILs”, which filenames are self-explanatory. “methylkit_epiRILs_vs_Col_wt_DMCs” was used to use methylkit in order to identify Differentially Methylated Cytosines between epiRILs and in comparison to Col-0. “methylkit_tiles_script” was used to build the tiles in Methylkit. “verif DMR CHG context” was used to verify differentially methylated regions in the CHG context.</p> <p>“scripts R phenotypic selection” includes four R scripts “Arabette_analysis”, “Arabette_analysis_loop_Phenotypes_data”, “Arabette_analysis_loop_pop1_final”, “Arabette_analysis_loop_pop2_final” that were used to estimate the parameters used for comparing selection treatments, in other words, to estimate means and confidence intervals (“loop” scripts) in population 1 (“pop1” script) and population 2 (“pop2” script)</p>
The effect of experimental pollinator decline on pollinator-mediated selection on floral traits
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Multilevel selection on social network traits differs between sexes in experimental populations of forked fungus beetles
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Experimental test of selection against hybridization as a driver of avian signal divergence
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Competitors alter selection on alpine plants exposed to experimental climate change
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Data from: Selection on growth rate and local adaptation drive genomic adaptation during experimental range expansions in the protist Tetrahymena thermophila
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Leaf area for select species was measured in arctic tundra experimental sites from late June into early August,Toolik Field Sattion, Alaska, Arctic LTER 2000.
Leaf area for select species was measured in arctic tundra experimental sites from late June into early August. Measurements were made in acidic and non acidic tussock tundra and in shrub tundra in control and fertilized plots.
Experimental evolution of Dictyostelium discoideum cheating under relaxed selection
<p>Many microbes interact with one another, but the difficulty of directly observing these interactions in nature makes interpreting their adaptive value complicated. The social amoeba <em>Dictyostelium discoideum </em>forms aggregates<em> </em>wherein some cells are sacrificed for the benefit of others. Within chimeric aggregates containing multiple unrelated lineages, cheaters can gain an advantage by undercontributing, but the extent to which wild <em>D. discoideum </em>has adapted to cheat is not fully clear. In this study, we experimentally evolved <em>D. discoideum </em>in an environment where there were no selective pressures to cheat or resist cheating in chimeras. <em>D. discoideum </em>lines grown in this environment evolved reduced competitiveness within chimeric aggregates and reduced ability to migrate during the slug stage. By contrast, we did not observe a reduction in cell number, a trait for which selection was not relaxed. The observed loss of traits that our laboratory conditions had made irrelevant suggests that these traits were adaptations driven and maintained by selective pressures <em>D. discoideum </em>faces in its natural environment. Our results suggest that <em>D. discoideum</em> faces social conflict in nature, and illustrate a general approach that could be applied to searching for social or non-social adaptations in other microbes.</p>
Experimental evidence that social information affects habitat selection in Marbled Murrelets
<p>Habitat selection decisions can impact individual fitness and ultimately scale up to mediate population dynamics. Understanding how birds select habitat is thus critical for discerning the biological processes structuring populations and developing conservation strategies, particularly for species in decline. Marbled Murrelet (<i>Brachyramphus marmoratus</i>; hereafter murrelet) populations have declined in recent decades due to loss of late-successional forest nesting habitat and changing ocean conditions that impact foraging success. Most other seabirds in the family Alcidae nest colonially and evidence suggests nesting murrelets may aggregate in stands, yet no studies have examined murrelet use of social information in nest site selection. In 2016 we experimentally simulated presence of murrelets at 14 randomly chosen potential breeding sites by broadcasting murrelet calls throughout the breeding period. Between broadcasting bouts, we recorded calls of wild murrelets and compared call rates with those recorded at 14 control sites (no broadcast). One year after playbacks ceased (2017) we conducted breeding season surveys to document behaviors indicative of murrelet breeding activity. Broadcasting murrelet calls in 2016 increased daily odds of wild murrelets vocalizing during the treatment period by up to 15.4× (95% CI = 2.3, 125.4) relative to control sites. During the 2017 breeding season, the odds of occupancy were 10.0× (CI = 1.2, 81.4) greater at treatment sites than control sites. These results indicate that social information influences murrelet breeding site selection because simulated conspecific presence in potential nesting habitat appeared to attract prospectors in 2016 that continued occupying treatment sites the following year. This conspecific attraction implies murrelet nesting sites are likely to remain occupied over time and that large tracts of nesting habitat may be important for supporting murrelet populations. Murrelets may also be susceptible to information-mediated Allee effects whereby a lack of conspecific information about nesting habitat could exacerbate long-term population declines.</p>
Experimental sexual selection affects the evolution of physiological and life history traits
<p>Sexual selection and sexual conflict are expected to affect all aspects of the phenotype, not only traits that are directly involved in reproduction. Here, we show coordinated evolution of multiple physiological and life history traits in response to long-term experimental manipulation of the mating system in populations of <em>Drosophila pseudoobscura</em>. Development time was extended under polyandry relative to monogamy in both sexes, potentially due to higher investment in traits linked to sexual selection and sexual conflict. Individuals (especially males) evolving under polyandry had higher metabolic rates and locomotor activity than those evolving under monogamy. Polyandry individuals also invested more in metabolites associated with increased endurance capacity and efficient energy metabolism and regulation, namely lipid and glycogen. Finally, polyandry males were less desiccation- and starvation- resistant than monogamy males, suggesting trade-offs between resistance and sexually selected traits. Our results provide experimental evidence that mating systems can impose selection that influences the evolution of non-sexual phenotypes such as development, activity, metabolism, and nutrient homeostasis.</p>
Dataset: Selecting tree species to restore forest under climate change conditions: complementing species distribution models with field experimentation
<p>This repository contains the files associated with the following article:</p> <p>Jesús Sandoval-Martínez, Ernesto I. Badano, Francisco A. Guerra-Coss, Jorge A. Flores Cano, Joel Flores, Sandra Milena Gelviz-Gelvez, Felipe Barragán-Torres, “Selecting tree species to restore forest under climate change conditions: complementing species distribution models with field experimentation”, submitted to <em>Journal of Environmental Management</em>.</p> <p><strong>Supplementary material 01 </strong>is a compressed file that contains two Microsoft Excel files with data that support the results of the study. A file correspond to <em>Vachellia pennatula</em> and the another file correspond to <em>Prosopis laevigata</em>. In both files, the first spreadsheet shows the occurrence data (latitude and longitude) used to calibrate the distribution model (SDM) of the corresponding species, the current values of the 19 bioclimatic variables associated with these coordinates and the Spearman correlation coefficients used to select the variables included in the SDM (selected variables are indicated in green). The second spreadsheet shows the current habitat occupancy probabilities of the target species estimated with the SDM at the geographic coordinates of occurrence points, while the table on the side shows the fraction of true presences dropping at the following probability categories: (1) habitat occupancy probabilities below 0.1 = unsuitable spatial units for the species, (2) habitat occupancy probabilities between 0.1 and 0.4 = barely suitable spatial units for the species, (3) habitat occupancy probabilities between 0.4 and 0.7 = moderately suitable spatial units for the species, and (4) habitat occupancy probabilities above 0.7 = highly suitable spatial units for the species. The third spreadsheet shows the one-thousand random geographic coordinates and the corresponding current and future habitat occupancy probabilities of each species. Future habitat occupancy probabilities are provided for three time periods (2041-2060, 2061-2080 and 2081-2100) at four radiative forcing levels each (2.6, 4.5, 7.0 and 8.5 W/m<sup>2</sup>).</p> <p><strong>Supplementary material 02 </strong>is a compressed file that contains a folder for <em>Vachellia pennatula</em> and another folder for <em>Prosopis laevigata</em>. Each of these folders contains the summaries of the MaxEnt outputs that support the results of the corresponding SDM.</p> <p><strong>Supplementary material 03 </strong>is a compressed Keyhole Markup Language file (KMZ) that contains interactive maps that are optimized for the desktop version of Google Earth. To accelerate visualization of maps, we recommend installing this software in a computer meeting the following requirements: CPU Intel Core i5 9<sup>th</sup> generation or higher, CPU clock speed 1.8 GHz or higher, random-access memory (RAM) 8 GB or higher, and video random access memory (VRAM) 1 GB or higher. Otherwise, opening this file may take several minutes. These maps are organized in a folder for <em>Vachellia pennatula</em> and another folder for <em>Prosopis laevigata</em>, which must be expanded for accessing the following information (click on the arrow on the left of folders to expand them):</p> <ul> <li><strong>Current climate </strong>– Activating this folder (click the fox on the left of the folder) display the map of habitat occupancy probabilities of species across Mexico under the current climate.</li> <li><strong>Period 2041-2060, 2061-2080 and 2081-2100 </strong>– Expanding each of these folders (click on the arrow on the left of folders) shows four subfolders that correspond to different radiative forcing levels (2.6, 4.5, 7.0 and 8.5 W/m<sup>2</sup>). Activating each of these sub folders (click the fox on the left of subfolders) display the map of habitat occupancy probabilities of species across Mexico expected on the corresponding time period and radiative forcing level. These maps also show the areas classified as climatically unsuitable in the multivariate environmental similarity surface (MESS) analysis. Clicking on the names of subfolders displays a figure showing the relationship between current and future habitat occupancy probabilities of the species on the corresponding time period and radiative forcing level. In these figures, the red line is the empirical relationship between these variables and the solid blue line is the theoretical relationship with intercept = 0 and slope = 1. The statistical results that support these relationships are also shown in these figures.</li> </ul> <p><strong>Supplementary material 04 </strong>is a compressed file that contains two Microsoft Excel files with data that support the results of the study. the file labeled as “Microclimate data” contains two spreadsheets, which correspond to the temperature and rainfall values measured in controls under the current climate and climate change simulation plots located of the field experiments. The file levelled as “Seedling emergence and survival” contains a spreadsheet for <em>Vachellia pennatula</em> and another one for <em>Prosopis laevigata</em>, which contains the data used to estimate the seedling emergence and survival rates in controls and climate change simulation plots.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.