Bigger genomes provide environment-dependent growth benefits in grasses
Data files
Sep 26, 2024 version files 129.10 KB
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grass.niche.data.csv
49.72 KB
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grass.phylogeny.tre
28.11 KB
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grass.RGR.csv
44.14 KB
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MCMCglmm_example_script.R
4.02 KB
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README.md
3.10 KB
Abstract
Increasing genome size (GS) is associated with slower rates of DNA replication and greater cellular nitrogen and phosphorus demands. Nevertheless, plant species vary enormously in GS. Using data on grass species’ climatic niches and growth rates under different environmental conditions, we tested for growth costs or benefits associated with GS. We found that evolutionary history, photosynthetic pathway and life-history all influence the distribution of grass species’ GS. GS was constrained in annual and C4 species, the latter allowing for small cells necessary for C4 leaf anatomy. We found that larger GS were advantageous under high nitrogen availability and, for perennial species, low growth-season temperature. Increased GS benefits are likely due to associated larger cell sizes, allowing rapid biomass production where soil fertility meets nitrogen demands or when growth occurs via temperature-independent cell expansion. Our findings reveal that GS is a globally important predictor of grass performance dependent on environmental conditions.
README: Bigger genomes provide environment-dependent growth benefits in grasses
Description of the data and file structure
Dataset descriptions
grass.niche.data.csv = Species-level data on mean grass environmental niches (relating to temperature, precipitation and soil nitrogen), life history and photosynthetic pathway
grass.RGR.csv = Species-level data on grass relative growth rate under different environmental conditions, as well as life history and photosynthetic pathway
grass.phylogeny.tre = Maximum clade credibility phylogeny of 660 grass species (subset for the study species for the niche analyses; from Forrestel 2015)
N.B. Data on genome size (1C values) for the study species can be accessed at the Plant DNA C-values database https://cvalues.science.kew.org/
Explanation of abbreviations/code names used in column headings
grass.niche.data.csv columns:
species – Species name
phylo.name – species name that matches the tip.labels of the phylogeny (‘grass.phylogeny.tre’)
PP – photosynthetic pathway (C3 or C4)
LH – life history (Annual or Perennial)
bioclim10.mean – mean values of temperature of warmest quarter (Bioclim 10; degrees celcius)
bioclim15.mean - mean values of precipitation seasonality (Bioclim15; coefficient of variation)
soilN.med – median values of soil N density (g/m2); NA indicates missing values
grass.RGR.csv columns:
phylo.name
rgr – relative growth rate (g g-1 day-1)
experiment – The experimental treatment with four possible levels: Control, D (‘reduced watering’ treatment), N (‘reduced nitrogen’ treatment), T (’reduced temperature’ treatment). Descriptions of control and treatment conditions can be found in the manuscript
pp – photosynthetic pathway (C3 or C4)
lh– life history (Annual or Perennial)
tribe – Grass tribe the species belongs to
Sharing/Access information
Links to other publicly accessible locations of the data:
https://www.worldclim.org/data/bioclim.html - WorldClim data website (where temperature and precipitation values were acquired)
https://cvalues.science.kew.org/ - Plant DNA C-values database (where published grass 1C values were acquired)
https://daac.ornl.gov/ISLSCP_II/guides/islscp2_soils_1deg.html - SLSCP II Global Gridded Soil Characteristics (where soil N density data was acquired)
Code
The R script included (‘MCMCglmm example script.R’) runs through importing data and phylogeny, running a Markov chain Monte Carlo generalised linear mixed effects (MCMCglmm) model, plotting the data and results from the model, and the generation of R2 values for the model too. This code uses the data for soil N density, and determines the effect of soil N density, grass life history and photosynthetic pathway on grass genome size.