Dryad
Published July 31, 2020 | Version v1
Dataset Open

Data from: Drivers of plant traits that allow survival in wetlands

Creators

  • 1. Leiden University
  • 2. Landcare Research
  • 3. University of Western Australia
  • 4. University of Copenhagen
  • 5. Radboud University Nijmegen
  • 6. Utrecht University

Description

  1. Plants have developed a suite of traits to survive the anaerobic and anoxic soil conditions in wetlands. Previous studies on wetland plant adaptive traits have focused mainly on physiological aspects under experimental conditions, or compared the trait expression of the local species pool. Thus, a comprehensive analysis of potential factors driving wetland plant adaptive traits under natural environmental conditions is still missing.
  2. In this study, we analysed three important wetland adaptive traits, i.e. root porosity, root/shoot ratio and underwater photosynthetic rate, to explore driving factors using a newly compiled dataset of wetland plants. Based on 21 studies at 38 sites across different biomes, we found that root porosity was affected by an interaction of temperature and hydrological regime; root:shoot ratio was affected by temperature, precipitation and habitat type; and underwater photosynthetic rate was affected by precipitation and life form. This suggests that a variety of driving mechanisms affect the expression of different adaptive traits.
  3. The quantitative relationships we observed between the adaptive traits and their driving factors will be a useful reference for future global methane and denitrification modelling studies. Our results also stress that besides the traditionally emphasized hydrological driving factors, other factors at several spatial scales should also be taken into consideration in the context of future functional wetland ecology.

Notes

###Setup###
###installing of glwdr package###
devtools::install_github("jsta/glwdr")

############################
library(glwdr)
library(raster)
library(vegan)

###extract BioClim at the resolution=2.5
wc_bio2.5 <- getData("worldclim", var="bio", res=2.5)
###turning the raster file into spatial points
wc2.5<- rasterToPoints(wc_bio2.5, spatial=FALSE)
###omit the NA points
wc2.5na=na.omit(as.data.frame(wc2.5))

###using vegan package to do the PCA
pcaBio2.5=rda(wc2.5na[,-c(1,2)],scale = T)

###plot the result
biplot(pcaBio2.5,display = "sp")
biplot(pcaBio2.5,display = "si")

PCAsites2.5=scores(pcaBio2.5, choices = 1:2, display = "si")
PCA19va=scores(pcaBio2.5, choices = 1:2, display = "species")

PCA_results2.5=cbind(wc2.5na[,1:2],PCAsites2.5)###combine results with x-y coordinates

########################rasterize the PCA result, with each cell=2.5 minutes
r <- raster(ncols=2160, nrows=900)
n <-1944000
r2_pca1 <- rasterize(PCA_results2.5[, 1:2], r, PCA_results2.5[, 3], fun=mean)
r2_pca2 <- rasterize(PCA_results2.5[, 1:2], r, PCA_results2.5[, 4], fun=mean)

DB_cor=read.csv("database_coordinates.csv",sep=",")###read the coordinates of wetland trait database
DB_PCA1_2.5=extract(r2_pca1,DB_cor)###match the PCA result to corresponding DB coordinates
DB_PCA2_2.5=extract(r2_pca2,DB_cor)
DB_PCA_2.5=cbind(DB_PCA1_2.5,DB_PCA2_2.5)###produce the data sheet contains PCA1 & PCA2

write.csv(DB_PCA_2.5,"DB2.5_PCA1&PCA2 Results.csv")###save the result

Funding provided by: China Scholarship Council
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100004543
Award Number: Grant No. 201606140037

Files

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Additional details

Related works

Is cited by
10.1111/1365-2435.13541 (DOI)
10.1111/1365-2435.13541 (DOI)