DOI: 10.5061/dryad.59zw3r266
title: Moss growth, development, morphology, and physiology dataset and code

description: A central problem in evolutionary biology is to identify the forces that
 maintain genetic variation for fitness in natural populations. Sexual
 antagonism, in which selection favors different variants in males and
 females, can slow the transit of a polymorphism through a population or
 can actively maintain fitness variation. The amount of sexually
 antagonistic variation to be expected depends in part on the genetic
 architecture of sexual dimorphism, about which we know relatively little.
 Here, we used a multivariate quantitative genetic approach to examine the
 genetic architecture of sexual dimorphism in a scent-based fertilization
 syndrome of the moss  Ceratodon purpureus. We found sexual
 dimorphism in numerous traits, consistent with a history of sexually
 antagonistic selection. The cross-sex genetic correlations (rmf) were
 generally heterogeneous with many values indistinguishable from zero,
 which typically suggests that genetic constraints do not limit the
 response to sexually antagonistic selection. However, we detected no
 differentiation between the female- and male-specific trait (co)variance
 matrices (Gf and Gm, respectively), meaning the evolution of sexual
 dimorphism may be constrained. The cross-sex cross-trait covariance matrix
 B contained both symmetric and asymmetric elements, indicating that the
 response to sexually antagonistic or sexually concordant selection, and
 the constraint to sexual dimorphism, is highly dependent on the traits
 experiencing selection. The patterns of genetic variances and covariances
 among these fitness components is consistent with partly sex-specific
 genetic architectures having evolved in order to partially resolve
 multivariate genetic constraints (i.e. sexual conflict), enabling the
 sexes to evolve toward their sex-specific multivariate trait optima.
description: This MossQuantGenreadme.txt
 file was generated on 2021-02-05 by Leslie M.
 Kollar
   GENERAL
 INFORMATION
   1. Moss growth,
 development, morphology, and physiology dataset and code:
 
   2. Author
 Information
                A. Principal
 Investigator Contact
 Information
                                Name: Leslie M. Kollar                                Institution: Michigan State University                                Email: lesliemkollar@gmail.com                  B. Associate or Co-investigator Contact Information                                Name: Karl Grieshop                         Institution: Stockholm University                         Email: karlgrieshop@gmail.com     3. Date of data collection (single date, range, approximate date): Spring 2016   4. Geographic location of data collection: Portland, OR (Portland State University)   5. Information about funding sources that supported the collection of the data: This work was supported by the National Science Foundation Doctoral Dissertation Improvement Grant (NSF DEB 1701915) to LMK and SFM, NSF grants to SFM (DEB 1541005 and 1542609); EDEN: Evo-Devo-Eco Network Training Grant to LMK, MicroMorph Cross-Disciplinary Training Grant to LMK, the University of Florida’s Biology Department grants to LMK, and by the Swedish Research Council (2018-06775 to KG).   SHARING/ACCESS INFORMATION   1. Licenses/restrictions placed on the data: N/A   2. Links to publications that cite or use the data:   3. Links to other publicly accessible locations of the data:   https://doi.org/10.5061/dryad.59zw3r266   4. Links/relationships to ancillary data sets: N/A   5. Was data derived from another source? No   6. Recommended citation for this dataset:     DATA & FILE OVERVIEW   File List:   LH.traits.data.NONA.csv Data.homemade.traits.csv Multivariate_analyses_Kollar.L.R Univariate_analyses_Kollar.L.R scaled.threeLHtraits.parexp.obj Final.VOC.4.repro.obj   Relationship between files, if important:   LH.traits.data.NONA.csv contains traits categorized as “growth and development” and Data.homemade.traits.csv contains traits categorized and “morphology and physiology”. We fit models for each category of traits and included both of the models. Scaled.threeLHtraits.parexp.obj is the model fit for the growth and development traits while Final.VOC.4.repro.obj is the model for the morphology and physiology traits. The majority of analyses can be found in Multivariate_analyses_Kollar.L.R while testing for significant genetic variation can be found in the file Univariate_analyses_Kollar.L.R.   3. Additional related data collected that was not included in the current data package: NA   4. Are there multiple versions of the dataset? Not yet   METHODOLOGICAL INFORMATION   1. Description of methods used for collection/generation of data:   Please see manuscript and supplemental material.   2.Methods for processing the data:   Data collection and processing is discussed in detail in the manuscript and the supplemental methods. We include some brief points here to clarify.   <b>PTR Data:</b> We identified 75 different masses using the PTR-TOF-MS in mature sex expressing gametophytes. We represented the masses as total volatile output and as number of compounds. The raw PTR data files were analyzed using the PTR viewer and background/blank cuvette air was subtracted from the sample readings to account for noise in the signal.   <b>Leaf traits: </b>After leaves were mounted flat on a slide and images were taken, leaf traits including (length, area, and perimeter) were calculated using a custom script in ImageJ.   <b>Reproductive output: </b>For female reproduction we counted the number of archegonia (eggs) in each female sex structure. To account for possible differences in placement of the sex structure along the stem, we sampled sex structures from three different locations on the stem (top, middle, and bottom) per sample. For male reproduction, we counted the total number of male sex structures per 10 stems per sample. To make the male and female reproductive units comparable we combined male and female reproductive units into a single column and mean centered and variance standardized reproductive units. Standardization methods are in the R scripts.   <b>Growth and developmental data: </b>We collected many traits encompassing growth and development in juvenile moss tissue. These samples were grown in a growth chamber in 12 well plates. Images were taken every 7 days and analyzed using ImageJ software. Some measurements such as total number of gametophores were measured simply by counting the presence of mature gametophytes.     3. Instrument- or software-specific information needed to interpret the data:   Proton Transfer Reaction Time of Flight Mass Spectrometer (PTR-TOF-MS 1000, Ionicon) PTR-MS Viewer 3.1 (Ionicon) R (version 4.0.2; R Development Core Team 2020) MCMCglmm’ (v. 2.29)   4. Standards and calibration information, if appropriate: See supplemental material   5. Environmental/experimental conditions: See supplemental material   6. Describe any quality-assurance procedures performed on the data: See supplemental material   DATA-SPECIFIC INFORMATION FOR: [FILENAME] &lt;repeat this section for each dataset, folder or file, as appropriate&gt;   Number of variables:    LH.traits.data.NONA.csv: 10                Data.homemade.traits.csv: 18   Number of cases/rows:    LH.traits.data.NONA.csv: 1070                Data.homemade.traits.csv: 580   3. Variable List:   LH.traits.data.NONA.csv: "sampid", "famid", "ssex", "Plate", "Tray", "area_wk3", "perim_wk3", "circ_wk3",  "Days_gam", "Total_gam"   Data.homemade.traits.csv: "famid", "sampid", "Date.PTR", "ssex", "Total.conc", "Total.masses", "Avg.conc.raw", "Total.conc.stand.mean", "Avg.conc.divmean", "low.end.sum.Total.conc", "high.end.sum.Total.conc", "low.end.Total.masses", "high.end.Total.masses", "high.avg.conc”, "low.avg.conc", "data.Leaf_Length_Average", "data.repro"   4. Missing data codes: We removed samples with missing data.   5. Specialized formats or other abbreviations used: NA