DOI: 10.5061/dryad.xwdbrv1ps
title: Greater adaptation potential to climate change in populations of <em>Quercus macrocarpa</em> closer to edges of latitudinal gradient than those from mid-latitudes

description: With climate change ongoing, tree populations will encounter variable
 selection pressures that may lead to local extinction if they are unable
 to adapt or respond appropriately. We use a reciprocal transplant
 experiment with the widespread tree species, Quercus macrocarpa L., across
 a latitudinal gradient to predict responses to selection expected with
 climate change. We asked 1) Is there genetic variation within populations
 for traits relevant to climate change? 2) Are there differences in
 direction and strength of selection for these traits in each garden? and
 3) Do northern populations have evolutionary potential to adapt to
 selection in warmer, southern gardens? To address these questions, we
 estimated genetic variance for three sets of three traits grouped by
 physiology, spectral wavelengths, and morphology. We then conducted
 selection analysis to estimate directional and stabilizing selection on
 each of the traits in each garden and for each population. We used the
 Breeder’s equation to estimate the response to selection for each trait to
 assess their evolutionary potential. Our results indicate that traits
 related to morphology and growth are under strong directional selection in
 warmer gardens. We also found that populations closer to the edges of the
 species’ range have a high potential to adapt to climate change, due to
 their responses to selection in the warmest garden. We found low genetic
 variance in the population in the middle of the range, which may be a
 result of lower environmental stress. These results can help inform
 strategies to improve species resilience in the face of climate change as
 they tell us which populations are likely to be able to adapt to climate
 change, so managers planning assisted migration can source seeds from
 populations likely to adapt to climate change.
description: # Title of Dataset
 [https://doi.org/10.5061/dryad.xwdbrv1ps](https://doi.org/10.5061/dryad.xwdbrv1ps) ## Description of the data and file structure We measured physiology and morphology data in 3 bur oak common gardens in the growing season in 2023. We estimated genetic variance for three sets of three traits grouped by physiology, spectral wavelengths, and morphology. We then conducted selection analysis to estimate directional and stabilizing selection on each of the traits in each garden and for each population. We used the Breeder’s equation to estimate the response to selection for each trait to assess their evolutionary potential. ### Files and variables #### File: heritability.zip **Description:** Data and code necessary to estimate heritability using QUERCUS software. Description of files in heritability.zip File name: h2 results.csv Description: Genetic variance results of QUERCUS analysis extracted from QUERCUS program output Column headers: garden: common garden location (MN = Minnesota, IL=Illinois, OK = Oklahoma) population: individual's population based on state of origin trait: trait that data corresponds to (LMA = leaf mass per area, RGR = relative growth rate, thickness = leaf thickness, WBI = water band index, ANT = anthocyanin content, CCI = chlorophyll:carotenoid index, 700 = reflectance at 700 nm, 900 = reflectance at 900 nm, 1400 = reflectance at 1400 nm analysis: grouping that traits were analysed in Va: genetic variance Ve: environmental variance Log-like_Va:log likelihood value for genetic variance Log-like_Va constrained: log likelihood of genetic variance with components constrained to 0 notes: notes on what QUERCUS Analysis file output the data was from h2_full: broad sense heritability LRT:likelihood ratio test value df: degrees of freedom Chi_sq dist: chi squared distribution Significant?: Yes indicates significant at the p&lt;0.05 level based on likelihood ratio test Va_varcov: variance covariance for genetic variance Va_SE: standard error for genetic variance Ve_varcov: variance covariance for environmental variance Ve_SE: standard error for environmental variance File name: adaptation potential.csv Description: Results of Breeder’s equation and data used to calculate Column headers: garden: common garden location (MN = Minnesota, IL=Illinois, OK = Oklahoma) population: individual's population based on state of origin trait: trait that data corresponds to (LMA = leaf mass per area, RGR = relative growth rate, thickness = leaf thickness, WBI = water band index, ANT = anthocyanin content, CCI = chlorophyll:carotenoid index, 700 = reflectance at 700 nm, 900 = reflectance at 900 nm, 1400 = reflectance at 1400 nm S: Selection differential h2_full: broad sense heritability R(breeder's eq): Breeder's equation calculated from S and h2_full File name: Heritability_post quercus analysis_clean.R Description: R script to analyze data in h2 results.csv and adaptation potential.csv Folder: quercus_program_files Description: This folder contains folders for each population in each garden labeled as “garden.population” (for example il.ok contains data for the Oklahoma population in the Illinois garden). Each of the “garden.population” folders contains a .pas file that is run with a Pascal compiler in command line of your operating system using the command “fpc” followed by your file path. The .pas file can be edited in a text editing software. In addition, there are three folders (physio, spectra, and morpho) which contain sibships.txt files with data for each of the three traits as well as the parental relationships for running with the QUERCUS program, using the code in the .pas files. At the top of each sibship file is encoded information to give instructions to the QUERCUS program and tell it which components to constrain based on the covariance matrix. (More information about running QUERCUS can be found in the QUERCUS documentation here: [https://cbs.umn.edu/eeb/about-eeb/helpful-links/quercus-quantitative-genetics-software](https://cbs.umn.edu/eeb/about-eeb/helpful-links/quercus-quantitative-genetics-software)). The folders also contain .csv sibships files with the data used to create the sibships.txt files. The column names for the sibship csv files contain the data for garden individuals IDs (individual_ID), maternal IDs (maternal_ID), paternal_IDs, the block the individual is located in in the garden (block), and measurements for 3 traits (which varies depending on the physio, spectral, or morpho analysis). Data that is missing for an individual is encoded with -99, as specified in the QUERCUS program. I have also filed the output files (which start with Analysis) into their respective folders. 
 
 #### File: selection.zip **Description:** Data and code necessary to
 calculate selection gradients for physiological traits and morphological
 traits using R software. Description of files in selection.zip File name:
 spec 2023_resamp 100.csv File description: resampled spectral reflectance
 data Column headers: garden_ID: unique identifier for individuals as
 garden.block.row.column block: block in common garden garden: garden
 location mother_id: identifier for maternal tree that seed was collected
 from 700: 700 nm 900: 900 nm 1400: 1400 nm File name:
 spectra_indices2023.csv File description: spectral indices calculated from
 spectral reflectance data garden_ID: unique identifier for individuals as
 garden.block.row.column CCI: chlorophyll:carotenoid index WBI: Water band
 index File name: leaf thickness.csv File description: leaf thickness data
 measured using calipers garden_ID: unique identifier for individuals as
 garden.block.row.column leaf_thickness_mm: leaf thickness in millimeters.
 Blank values indicate where individuals were not measured due to lack of
 sufficient leaf material. File name: prospect_traits.csv File description:
 traits modeled from spectral reflectance data using the PROSPECT models
 garden_ID: unique identifier for individuals as garden.block.row.column
 block: block in common garden garden: garden location population:
 population that individual originated from mother_id: identifier for
 maternal tree that seed was collected from ANT: anthocyanins LMA: leaf
 mass per area File name: growth calcs 2023.1.csv File description:
 relative growth rate from 2021-2023 garden_ID: unique identifier for
 individuals as garden.block.row.column mother_id: identifier for maternal
 tree that seed was collected from garden: garden location population:
 population that individual originated from block: block in common garden
 garden_block: identifier for block in garden RGR_Vol: relative growth rate
 for stem volume, in mm^3/day. NA indicates dead individuals that do not
 have growth status. deadmissing_fall23: survival status of individuals
 when surveyed in 2023, with 1 indicating alive and 0 indicating dead
 individuals. 
 
 ## Code/software QUERCUS
 software: [https://cbs.umn.edu/eeb/about-eeb/helpful-links/quercus-quantitative-genetics-software](https://cbs.umn.edu/eeb/about-eeb/helpful-links/quercus-quantitative-genetics-software) R software v.4.4.1 using packages: aster, gdata, MASS, car, lme4, lattice, grDevices, scatterplot3d, tidyverse