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

Citation

Pan, Yingji et al. (2020), Data from: Drivers of plant traits that allow survival in wetlands, Dryad, Dataset, https://doi.org/10.5061/dryad.7h44j0zqx

Abstract

  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.

Methods

We compiled a dataset of wetland plant adaptive traits, defining wetlands and wetland plants according to the Ramsar Convention (Ramsar Convention Secretariat, 2013), which includes plant species inhabiting aquatic systems (e.g. rivers and lakes) as well as those non-wetland terrestrial plants that inhabit temporarily/permanently flooded areas. The wetland plant adaptive trait dataset was compiled from a systematic search in Web of Science and Google Scholar (last updated on the 5th June 2018). The literature search included permutations of the following keywords: wetland plants, marsh plant, bog plant, isoetid, aquatic plants, macrophytes, submerged plants, floating-leaved plants, emergent plants, root porosity, root/shoot ratio and underwater photosynthesis. We also drew on references presented in several important reviews that focused on the eco-physiological studies of how wetland plants adapt to flooding conditions published in the past 15 years  (e.g. Voesenek et al., 2006; Bailey-Serres & Voesenek, 2008; Voesenek & Bailey-Serres, 2015). Finally, we added several of our own unpublished data sources, along with others within our network.

For the current analysis, we selected those studies that i) measured plants occurring in wetlands with sufficient information for us to consistently classify the habitat types and the hydrological regime(s) (drained, waterlogged or submerged); ii) were measured using field-collected specimens, thus we did not include data on plants from greenhouse experiments; and iii) provided accurate location information (with coordinates). We then compiled data from the selected studies that included quantitative measurements of three intensively studied wetland plant adaptive traits (root porosity (%), root/shoot ratio and the rate of underwater photosynthesis (mol m-2 s-1)). We are aware that there are many other important wetland adaptive traits, such as root radial oxygen loss (ROL), ethanol metabolism, and tolerance of reduced metal ions. However, the data available for these traits either were measurements in greenhouse/laboratory settings or were available only in a qualitative form, which was not suitable for this quantitative analysis. In total, 598 trait records from 21 studies at 38 different study sites were analysed. For root porosity, the data comprised 198 measurements of 103 unique species in 13 studies at 25 different sites; root/shoot ratio data contained 321 measurements on 12 unique species, described in 6 studies at 7 different sites; the 79 underwater photosynthetic rate measurements on 27 unique species were contained in 3 studies at 8 different sites. Location of the sampling sites in a global map were shown in Appendix B Figure S1.

For our analyses, we classified hydrological regime as drained, waterlogged or submerged (as defined by Sasidharan et al., 2017), as provided in the original study. While this provides baseline information on local (hydrological and fertility) wetland conditions, additional insights can be obtained from a classification into specific wetland habitat types. Based on the guidance of the Ramsar Convention (Ramsar Convention Secretariat, 2013) and the definitions by the Environmental Protection Agency (EPA, https://www.epa.gov/wetlands/classification-and-types-wetlands#marshes), we grouped wetland habitats into eleven categories (Appendix A). Studies selected for the current paper encompassed eight habitat types (Table 1). We grouped the life form of plants into seven categories (Table 1). We acquired bioclimatic variables at the global scale with an accuracy of 2.5 minutes (WorldClim Version 2.0, http://www.worldclim.org/) (Fick & Hijmans, 2017). These bioclimatic variables represent 19 climate attributes of ecological importance, in terms of annual means, seasonality and extreme or limiting climate factors. To determine the major axes of variation in all bioclimatic variables and to minimize the effect of inter-correlations, we ran a principal component analysis (PCA), and took the scores of the first two axes of the PCA to represent the climatic conditions. The PCA surface and axis scores reveal that the first and second axes (explained 51.8% and 25.8% of total variance, respectively) are mainly related to temperature and precipitation, respectively (Appendix B Figure S2). The code file for obtaining and analyzing the WorldClim data can be found in ReadMe file, and be run under R software. The result is also available by request from the correspondence author.

Table 1. The explanatory variables in the model as driving factors for wetland adaptation traits

Explanatory variables

Continuous/Categories

Bioclimatic variables

temperature; precipitation

Hydrological regime

drained; waterlogged; submerged

Habitat type

fens; permanent forested wetlands; mangrove swamps; marshes; permanent brackish/saline non-forested wetlands; rivers and lakes; temporary brackish/saline non-forested wetlands; temporary non-forested wetlands

Plant life form

emergent; floating-leaved; grass; isoetid; sedge; shrub/tree; submerged

Usage 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

China Scholarship Council, Award: Grant No. 201606140037