DOI: 10.5061/dryad.18931zd7g
title: Data from: Evolutionary responses to historic drought across the range of scarlet monkeyflower

description: Adaptive evolution is a key means for populations to persist under
 environmental change, yet whether populations across a species’ range can
 adapt quickly enough to keep pace with climate change remains unknown. The
 breeder’s equation predicts the evolutionary change in a trait from one
 generation to the next as the product of the selection differential and
 the narrow-sense heritability in that trait. Incorporating these aspects
 of the breeder’s equation, we performed a resurrection study with the
 scarlet monkeyflower (Mimulus cardinalis) to evaluate whether traits
 associated with drought adaptation have evolved in populations across a
 species’ range in response to extreme drought. We compared trait and
 fitness differences of pre-drought ancestors and post-drought descendants
 from six populations transplanted into three latitudinally-arrayed common
 gardens and quantified phenotypic selection and trait heritabilities. The
 strength, direction, and mode of selection varied among traits and
 gardens. Trait heritabilities were relatively low and did not differ
 dramatically among populations or gardens. Overall, instances of
 evolutionary responses between ancestors and descendants were few and
 small in magnitude, but the magnitude of these evolutionary differences
 varied among gardens. Together, these results suggest that the expression
 of genetic variation, and thus traits, depends on the environment, and
 that environmental variability in field settings may mask the genetic
 variation that is often detected in greenhouse environments. 
description: # Data from: Evolutionary responses to historic drought across the range
 of scarlet monkeyflower
 [https://doi.org/10.5061/dryad.18931zd7g](https://doi.org/10.5061/dryad.18931zd7g) ## Description of the data and file structure These data are associated with a common garden study of scarlet monkeyflower (*Mimulus cardinalis*). In 2023, we transplanted pre-drought 2010 ancestors alongside post-drought 2017 descendants from two northern-edge, two central, and two southern-edge populations into three experimental gardens near the northern range edge, latitudinal range center, and southern range edge in the western United States. We collected data on several physiological and leaf traits associated with adaptation to drought, along with proxies for fitness, including survival and reproductive output.   ### Files and variables 1\. **PERSIST_2023_data.csv**: 2023 trait data for all populations and cohorts in all gardens * garden: experimental garden (north, center, south) - block: experimental randomized block in garden (1 - 10 in north, 1 - 11 in center, 1 - 10 in south) * garden_block: variable that combines garden and block - row: row (y-coordinate) of experimental garden; with 4 rows per block; rows 101 - 104 are in block 1; rows 1101 - 1104 are in block 11, etc. * position: position (x-coordinate) of experimental garden; corresponds to a unique plant ID (Cross_ID_Rep), or has no plant (NA) - rowPosition: variable that combines row and position * Cross_ID_Rep: variable that combines unique ID for each full-sibling family and replicate of that family within a particular garden - Cross_ID: unique ID for each full-sibling family; each Cross_ID has a unique mom and dad * Sire_ID: unique ID for sire (father); plants with the same Sire_ID and different Dam_IDs are half-sibs - Dam_ID: unique ID for sire (mother); dams are nested within sires to yield a nested half-sib/full-sib design * Population: Population of scarlet monkeyflower (N1 and N2: northern populations; C1 and C2: central populations; S1 and S2: southern populations) - Year: Year that seeds were collected in the field (2010 ancestors and 2017 descendants) * Year1: Alternate coding for year corresponding to "ancestor" and "descendant" - Date_early: Date of early-season li-600 data collection * Time_early: Time of early-season li-600 data collection - VPDleaf_early: Leaf vapor pressure deficit at the time of early-season li-600 data collection * gsw_early: Early-season stomatal conductance to water vapor, measured at the leaf level with a li-600 porometer in units of mmol/m²/s - freshMass_g: Fresh leaf mass in grams (CDM please add something here about leaf selection) * dryMass_g: Oven-dried leaf mass in grams (CDM please add something here about leaf selection) - leafArea_cm2: Leaf area in square centimeters, derived from leaf scans (CDM please clarify) * lma_g_per_m2: Dry leaf mass in grams per area in meters squared (CDM please clarify) - ldmc: Leaf dry matter content, measured as dry leaf mass in grams divided by fresh leaf mass in grams * sla_cm2_per_g: specific leaf area, measured as leaf area in squared centimeters divided by dry leaf mass in grams - L1: Length of the primary or longest stem at first flower in centimeters * L2: Length of the second longest stem at first flower in centimeters - L3: Length of the third longest stem at first flower in centimeters * totalStemLen: Sum of the lengths of the three longest stems at first flower in centimeters - first_flower_date: Date of first flower * first_flower_doy: Day of year of first flower - last_flower_date: Date of last flower; note this is not reliable because we did not continue collecting data after a certain point in the growing season * last_flower_doy: Day of year of last flower; note this is not reliable because we did not continue collecting data after a certain point in the growing season - flowering_duration: Duration of flowering expressed as the difference between the date of last flower and the date of first flower; note this is not reliable because we did not continue collecting data after a certain point in the growing season * Date_late: Date of late-season li-600 data collection - Time_late: Time of late-season li-600 data collection * VPDleaf_late: Leaf vapor pressure deficit at the time of late-season li-600 data collection - gsw_late: Late-season stomatal conductance to water vapor, measured at the leaf level with a li-600 porometer in units of mmol/m²/s * maxHeight: Maximum stem height in centimeters at the end of the growing season - repBranchN: Number of major reproductive branches at the end of the growing season * RScount1: Number of reproductive structures (flowers, fruits, buds, and pedicels) counted on the stem with the most reproductive structures at the end of the growing season - RScount2: Number of reproductive structures (flowers, fruits, buds, and pedicels) counted on a representative major reproductive branch at the end of the growing season * RScount3: Number of reproductive structures (flowers, fruits, buds, and pedicels) counted on a representative major reproductive branch at the end of the growing season - totalRS: An estimate of the total number of reproductive structures (flowers, fruits, buds, and pedicels) on a plant, calculated as described in Supplementary Methods and Results 2\. **PERSIST_populations_gardens_1901-2021SY.csv**: annual climate data (1951-2021) for focal populations and experimental gardens. Downloaded from climateNA v. 7.30 on 2022-09-14 (Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. [https://doi.org/10.1371/journal.pone.0156720](https://doi.org/10.1371/journal.pone.0156720)) * Year: year to which climate data corresponds - ID1: identifier corresponding to population (N1 and N2: northern populations; C1 and C2: central populations; S1 and S2: southern populations) or experimental garden (N_garden: northern garden; C_garden: central garden; S_garden: southern garden) * ID2: identifier that ranks population from northernmost (1) to southernmost (6) - Latitude: y-position of each population or garden in decimal degrees * Longitude: x-position of each population or garden in decimal degrees - Elevation: meters above sea level of each population or garden All other columns are climate variables with units and definitions defined here: [https://climatena.ca/Help2](https://climatena.ca/Help2) 3\. **subset_correlations.csv**: 2023 fitness data collected on a subset of individuals from each garden * rowPos: Variable that combines row and position (unique plant ID within each garden) - garden: Experimental garden (north, central, south) * population: Population of scarlet monkeyflower (N1 and N2: northern populations; C1 and C2: central populations; S1 and S2: southern populations) - cohort: Year that seeds were collected in the field (2010 ancestors and 2017 descendants) * repBranchN: The number of reproductive branches on an individual (used to calculate total number of reproductive structures/successful fruits) - biomass: mass of the whole plant in grams * L1: Length of the primary (usually longest) stem at first flower in centimeters - L2: Length of the second longest stem at first flower in centimeters * L3: Length of the third longest stem at first flower in centimeters - SC1: Successful fruit count for stem 1 * SC2: Successful fruit count for stem 2 - SC3: Successful fruit count for stem 3 * TC1: Total reproductive structure count for stem 1 - TC2: Total reproductive structure count for stem 2 * TC3: Total reproductive structure count for stem 3 Missing data code: NA ## Code/software #### Code and objects associated with "Evolutionary responses to historic drought across the range of scarlet monkeyflower" Manuscript is in review at The American Naturalist #### STEPS #### A. Download entire repository to desired location B. Open PERSIST-general.Rproj file in R Studio C. Install associated R packages listed at the beginning of each script. D. Create a new subdirectory with the structure "figures/2024_AmNat/manuscript" #### DIRECTORY DESCRIPTIONS data/2024_AmNat: raw data files used in analyses and figures r/2024_AmNat: script files to reproduce analyses in manuscript, numbered sequentially objects/2024_AmNat: output files created by R scripts PERSIST.Rproj: R Studio project file README.txt: text file that contains descriptions of each data file and R script #### SCRIPTS 01a_anomalies_climateNA.R: Calculate winter precipitation anomalies, make Fig. 2b and c 01b_Cardinalis_map.R: Make Fig. 2 (map of Mimulus cardinals populations and experimental gardens combined with panels from script 01a) 02_R_analyses.R: Run models with each trait as response variable to estimate trait medians, evolutionary change between ancestors and descendants and quantitative genetic parameters for each population and cohort in each garden 03_selection_analyses.R: Run models with fitness as response variable and each trait as a predictor to estimate phenotypic selection in each garden 04a_summary_R_h2_NCS.R: Summarize trait models from script 02 for traits measured in all gardens 04b_summary_R_h2_NS.R: Summarize trait models from script 02 for traits only measured in northern and southern gardens 05_model_selection_Va.R: Compare different models of additive genetic variance and make Table S9 06_plot_R_h2.R: Make figures and tables of trait medians (Fig. 3, Table S6), evolutionary change between ancestors and descendants (Fig. 6, Table S10) and quantitative genetic parameters for each population and cohort in each garden (Fig. 5, Table S8) 07a_summary_S_NCS.R: Summarize selection models from script 03 for traits measured in all gardens 07b_summary_S_NS.R: Summarize selection models from script 03 for traits only measured in northern and southern gardens 08_plot_S.R: Make figures and tables of phenotypic selection (Fig. 5, Table S7) 09_fitness-subset-correlations.R: Perform simple correlation tests among various fitness proxies measured on a subset of plants in each garden and make Fig. S1 and S2 and Table S3. 10_sample_sizes_sires_dams.R: Extract sample sizes reported in Tables S2 and S4. 11_brms_vs_mcmcglmm.R: Compare global brms model including data from all populations, cohorts, and gardens, to sub-models in brms and MCMCglmm built from each ancestral cohort of each population x garden combination (Table S5, Figure S3). 12_gxe_plot.R: Visualize genotype-by-environment interactions by plotting breeding values of each population across each pair of gardens (Figure S4). #### OBJECTS The scripts produce several intermediate objects. These are included in the repository but are not individually listed and described here. ## Access information Climate data were downloaded from climateNA v. 7.30 on 2022-09-14 (Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. [https://doi.org/10.1371/journal.pone.0156720](https://doi.org/10.1371/journal.pone.0156720))