10.5061/DRYAD.59ZW3R266
Kollar, Leslie
0000-0001-8726-9085
University of Florida
Moss growth, development, morphology, and physiology dataset and code
Dryad
dataset
2021
National Science Foundation
https://ror.org/021nxhr62
1701915
National Science Foundation
https://ror.org/021nxhr62
1541005
National Science Foundation
https://ror.org/021nxhr62
1542609
MicroMorph Cross Disciplinary Training Grant*
Evo-Devo-Eco Network Training Grant*
University of Florida's Biology Departmental Grant*
Swedish Research Council*
2018-06775
MicroMorph Cross Disciplinary Training Grant
Evo-Devo-Eco Network Training Grant
University of Florida's Biology Departmental Grant
Swedish Research Council
https://ror.org/03zttf063
2018-06775
2021-02-11T00:00:00Z
2021-02-11T00:00:00Z
en
12243005 bytes
3
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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. PTR Data: 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. Leaf traits:
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. Reproductive output: 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. Growth and developmental data: 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] <repeat this section for each dataset, folder or file,
as appropriate> 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