Data from: Adaptational lag at high elevations depends on life stage in a California wildflower
Data files
Jun 08, 2026 version files 1.59 MB
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Processed.Data.zip
805.05 KB
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Raw.Data.zip
768.29 KB
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README.md
13.25 KB
Abstract
High elevation populations are expected to receive reduced snowpack, warmer temperatures, and more variable precipitation with climate change, potentially putting them at risk if rates of adaptation do not keep pace. Populations from climates more closely aligned with changing conditions at high elevations may prove better suited to current climate than the local populations. Thus, it is essential to assess 1) whether high elevation populations are locally adapted to current climate, or 2) whether fitness of lower elevation populations from warmer climates is higher than for local populations in high elevation conditions. We conducted a common garden study with Streptanthus tortuosus at a high elevation site. Plants from twenty-three populations from across the species range, which vary in climate and life history, were transplanted and subsequently measured for mortality and reproductive output across two growing seasons. We examined the effects of climatic distance from the site of origin on plant performance. We hypothesized that lower elevation populations would survive the first warm growth season well, but the seasonal constraint of cold temperatures and snowpack would reduce their survival into the second year, thus limiting their overall fitness. We observed evidence for adaptational lag for high elevation populations, including the local population, but only for some life stages. Lower elevation populations, with climates further from the garden, had higher survival through the first year and over winter, resulting in higher probabilities of reproducing and total fitness. However, survival to reproduction in year 2, and total reproductive output, driven largely by reproduction in the second year, were higher in high elevation populations, with climates closer to the garden. Thus, adaptational lag differed among life stages, and depended on life history (e.g., first year versus second year fitness). Our results highlight the importance of considering variation in life history and seasonal timing when evaluating climate adaptation, as well as vulnerability of high elevation populations to climate change. Further, these findings provide information for management of populations at risk, including strategic assisted gene flow that introduces beneficial alleles from warm-adapted populations but preserves some components of high elevation adaptation.
Dataset DOI: 10.5061/dryad.ncjsxktbf
Description of the data and file structure
This dataset contains all the data required to replicate analyses in Quarles-Chidyagwai et al. 2026 (Journal of Ecology). We used a high elevation common garden to assess whether high elevation populations were locally adapted to their current climate or whether low elevation populations would have higher fitness than local populations in high elevation conditions.
Files and variables
File: Raw.Data.zip
Description: This folder includes all data collected and used for analyses in this study. The contents are listed below.
Final_2023_2024_Pop_Loc_Info.csv:
File used to match unique.IDs with population and maternal family information for 2024. Missing values are indicated with blank cells or "NA".
- bed.block.order: used to organize the data sheets in the order of block.
- bed.order: used to organize the data sheets by bed.
- AB.CD.order: used to organize the data sheets in the order that censuses were conducted in.
- column.order: used to organize the data sheets by columns within beds.
- Pop.Type: identifies whether the plant is a "2023-survivor" or plant planted in 2023 that was still alive in 2024, a "2023-TM2-fruit" or location where TM2 plant reproduced in 2023 that we were watching for seedlings in, or a plant planted in 2024 identified as either a "Parent" population, "F1" or "F2" cross.
- status: identified spots that were available to plant in 2024 versus still had plants from 2023.
- block: which of the 13 blocks the plant was planted in.
- loc: the combination of the bed, bedrow, and bedcol the plant was planted in.
- bed: which of the 11 beds the plant was planted in.
- bedrow: which row within beds the plant was planted in.
- bedcol: which column within beds the plant was planted in.
- pop: which population the plant belonged to.
- mf: which maternal family the plant belonged to.
- rep: which number replicate of pop-mf the plant was.
- unique.ID: the unique identifier of the plant used on all other data sheets.
Pops_for_2023_WL2.csv:
File with information about the populations used in the study.
- parent.pop: one of the 23 populations.
- phylogroup: which phylogroup the population belonged to. Note that this information was not used in this study.
- elevation.group: which "low", "mid", "high" elevation category the population belonged to.
- seed year: which year the seeds used in the study were collected for each population.
Strep_tort_locs.csv:
File with information about 54 populations of Streptanthus tortuosus, including the 23 used in this study. Missing values are indicated by blank cells.
- Species epithet: full species name.
- Species Code: species code name.
- Site: population name.
- Site code: population code name.
- Lat: population latitude.
- Long: population longitude.
- Elevation (m): population elevation in meters.
Updated_Flint_Climate_Oct2024.csv:
File with Flint climate data downloaded for all 23 populations used in the study. The data ranges from October 1895 to August 2024. Each column corresponds to a site (see the "Clim_vars_all_sites_years_timeframes.Rmd" script for how the numbers correspond to sites). Each row represents a climate variable, year, and month. Variable names are listed in Table 1 of the manuscript.
Updated_Flint_Sept-Dec2024.csv:
File with Flint climate data for all 23 populations included in the study from September 2024 to December 2024.
WL2_2022_2023_iButton_Data_Corrected.csv:
File with ibutton soil temperature data for the garden in 2023.
- Bed: the bed the ibutton was buried in.
- Date_Time: calendar date and time of each measurement.
- column to indicate the measurements are in Celsius.
- SoilTemp: the soil temperature measurements.
WL2_2023_Bed_C_Soil_Moisture_Corrected:
File with soil moisture data for the garden in 2023.
- Date_Time: calendar date and time of each measurement.
- Port_1 through Port_5: soil volumetric water content measurements in cubic meters per cubic meter.
WL2_annual_census_20231027_corrected.csv:
File with annual census data from 2023.
- date: the date the measurements were taken.
- block through rep: the same variables as explained for "Final_2023_2024_Pop_Loc_Info.csv."
- pheno: the phenological stage of the plant. V=vegetative, B=budding, F=flowering, P=post flowering, X=dead.
- diam.mm: the stem diameter in mm.
- height.cm: height in cm.
- long.leaf.cm: length of the longest leaf in cm.
- num.flw: the number of flowers.
- num.fruit: the number of fruits.
- long.fruit.cm: length of the longest fruit in cm.
- total.branch: the total number of basal branches.
- repro.branch: the number of reproductive basal branches.
- herbiv.y.n: yes or no to wether there were signs of herbivory on the plant.
- survey.notes: any notes from the survey.
WL2_Annual_Census_20241023_corrected.csv:
File with annual census data from 2024.
- bed through unique.ID: the same variables as explained for "Final_2023_2024_Pop_Loc_Info.csv."
- phen through total.branch: the same variables as explained for "WL2_annual_census_20231027_corrected.csv."
- overhd.diam: the widest overhead diameter of the plant in cm.
- overhd.perp: the overhead diameter perpendicular to "overhd.diam."
- survey.date: the date the measurements were taken.
- collected.date: the date fruits were collected.
- survey.notes: any notes from the survey.
WL2_DNA_Collection_Size_survey_combined20230703_corrected.csv:
File with the pre-transplant size measurements.
- Pop through rep: columns explained previously.
- DNA: 1 for tissue collected, 0 for tissue not collected.
- height (cm) through Notes explained previously.
WL2_Extras_DNA_collection_size_survey_combined20230706_corrected.csv:
File with additional pre-transplant size measurements. All variables previously described.
WL2_mort_pheno_20231020_corrected.csv:
File with mortality and phenology dates for 2023.
- block through rep and survey.notes described previously.
- bud.date: first bud date.
- flower.date: first flower date.
- fruit.date: first fruiting date.
- last.flower.date: the date the plant was no longer producing flowers or buds.
- last.fruit.date: the date the plant was no longer producing flowers and all fruits had elongated.
- death.date: the date a plant was identified as dead.
WL2_mort_pheno_20241023_corrected.csv :
File with mortality and phenology dates for 2024. All variables described previously except the following:
- last.FL.date: last flower date as described above.
- lastFR.date: last fruit date as described above.
- missing.date: the date was not found in the field with no obvious signs of death. This could be caused by gopher holes for example.
WL2_status_check_20240603_corrected.csv:
File with winter survival data. All variables described previously except different symbols were used to categorize the state of plants post-winter in the death.date column.
- A - alive, “happy” leaves.
- B = “barely alive”, still has leaves but major damage.
- C = no leaves, stem not brittle.
- D = brittle, no leaves, definitely dead.
File: Processed.Data.zip
Description: This folder includes all data files output by the analysis scripts. the contents are described below.
All_Clim_Dist.csv:
File with the calculated climate distances between the 23 populations and the high elevation garden.
- parent.pop through Long: described previously.
- columns F through Q: Gower's climate distance for different time periods (recent versus historic), seasonal summaries (water year versus growth season), and years (2023, 2024, or the average of the two).
- columns R through AO: temperature and precipitation distance for different time periods, seasonal summaries, and years.
- column AP: geographic distance between each population and the garden site based on the haversine formula.
- column AQ: the elevation distance between each population and the garden site in meters.
All_Clim.csv:
File with the summarized climate for each population in the study.
- parent.pop through Long: described previously.
- timeframe: historic versus recent 30 year periods.
- year: the year the climate data has been averaged for.
- columns H through V: climate variables summarized for each year. Variable names are listed in Table 1 of the manuscript.
- Season: seasonal summary (water year versus growth season).
Prediction data frames from the fitness ~ climate distance models:
In each file listed below, ".preds" represent the predictions of each corresponding fitness metric. There are also columns for the raw climate and geographic distances and the scaled and centered ones. The file names are formatted as such "Fitness Metric_Distance Metric_Season and Time Period." "GD" is for Gower's climate distance, "GrwSsn" for growth season, "WY" for water year, "Recent" for the recent 30 years and "Hist" for the historic 30 years, "Geo" for geographic distance, "Sub" for temperature or precipitation distance as indicated at the end of the file name. These files are used to create the prediction lines in the manuscript's fitness figures.
- Establishment predictions: Est_GDPreds_GrwSsnHist.csv, Est_GDPreds_WYRecent.csv, Est_GeoDist_Preds.csv, Est_SubPreds_GrwSsnRecentTemp.csv, Est_SubPreds_WYRecentPPT.csv, Est_SubPreds_WYRecentTemp.csv
- Probability of successfully reproducing predictions: ProbRep_GDPreds_WYRecent.csv, ProbRep_SubPreds.csv
- Total fruit production of reproductive individuals predictions: RepOutput_GDPreds_GSRecent.csv, RepOutput_SubPreds_GSRecentPPT.csv, RepOutput_SubPreds_GSRecentTemp.csv, RepOutput_SubPreds_WtrYrHistPPT.csv
- Survival to budding in year 2 predictions: RepSurv2_GDPreds_GrwSsnRecent.csv, RepSurv2_GDPreds_WYRecent.csv, RepSurv2_GeoDist_Preds.csv, RepSurv2_SubPreds_WYRecentTemp.csv
- Total fitness (total fruit number including 0s) predictions: TotalFitness_GDPreds_WYRecent.csv
- Winter survival predictions: WintSurv_GDPreds_WYRecent.csv
- Year 1 survival predictions: Y1Surv_GDPreds_GrwSsnHist.csv, Y1Surv_GDPreds_WYRecent.csv, Y1Surv_SubPreds_WYRecentTemp.csv
Files with the processed fitness data:
Only new variables not previously described in this document are explained below.
- WL2_Establishment.csv: 3 week survival calculated for each individual with 0 for no survival and 1 for survival is in column BB.
- WL2_Fruits_Y1.csv: the number of flowers (y1_flowers), fruits (y1_fruits), and fruits plus flowers (FrFlN_y1) that each reproductive individual had in 2023.
- WL2_Fruits_Y2.csv: the number of flowers (y2_flowers), fruits (y2_fruits), and fruits plus flowers (FrFlN_y2) that each reproductive individual had in 2024.
- WL2_Mortality_2023.csv: all 2023 mortality and bud initiation dates.
- WL2_ProbFit.csv: the probability of making a fruit (ProbFitness) with 0 for no fruits and 1 for one or more fruits; accounts for both year 1 and 2 reproduction.
- WL2_Surv_to_Rep_Y2.csv: survival to budding in 2024 (SurvtoRep_y2) with 0 for no survival and 1 for survival. Only individuals that survived to year 2 are in this data set.
- WL2_SurvtoRep_y1.csv: survival to budding in 2023 (SurvtoRep_Y1) with 0 for no survival and 1 for survival. This is conditional on successful establishment.
- WL2_Total_Fitness.csv: the total number of fruits, accounting for year 1 and 2 reproduction.
- WL2_TotalRepOutput.csv: the total number of fruits across year 1 and 2 of reproductive individuals (Total_Fecundity).
- WL2_WinterSurv.csv: winter survival (WinterSurv) of plants that survived to the end of the first year.
- WL2_Y1Surv.csv: survival to the end of year 1 (Y1Survival) given establishment.
Code/software
To open the files in the Raw.Data and Processed.Data folders, use any software that can open .csv files, including spreadsheet software, plain text editors, and more.
To run the code in the Scripts folder of the GitHub repository linked to through Zenodo, use R version 4.5.3. To run the code, you will need packages: raster v. 3.6-32, tidyverse v. 2.0.0, ggrepel v. 0.9.8, corrplot 0.95, vegan v. 2.7-3, ggfortify v. 0.4.19, viridis v. 0.6.5, elevatr v. 0.99.1, terra v. 1.9-11, sf v. 1.1-0, giscoR v. 1.1.0, marmap v. 1.0.12, boot v. 1.3-32, broom v. 1.0.12, geosphere v. 1.6-8, lmerTest v. 3.2-1, corrplot v. 0.95, broom.mixed v. 0.2.9.7, tidymodels v. 1.4.1, multilevelmod v. 1.0.0, performance v. 0.16.0, glmmTMB v. 1.1.14, bbmle v. 1.0.25.1, ggpubr v. 0.6.3, zoo v. 1.8-15.
Use the scripts in the following order:
- Clim_vars_all_sites_years_timeframes.Rmd
- Clim_corrs_pcas.Rmd
- Clim_dist_calcs.Rmd
- Clim_dist_fitness_prep.Rmd
- Clim_dist_fitness_models.Rmd
- Total_Fitness.Rmd
- Clim_dist_fitness_figs.Rmd
- WklyClim_Mort.Rmd
Access information
Other publicly accessible locations of the data:
