DOI: 10.5061/dryad.7h44j0zqx
title: Data from: Drivers of plant traits that allow survival in wetlands

description: 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. 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. 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.
description: 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 5<sup>th</sup> 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 <i>et
 al.</i>, 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<sup>-2</sup>
 s<sup>-1</sup>)). 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 <i>et al.</i>, 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 <i>temperature; precipitation</i> Hydrological regime <i>drained; waterlogged; submerged</i> Habitat type <i>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</i> Plant life form <i>emergent; floating-leaved; grass; isoetid; sedge; shrub/tree; submerged</i>
description: ###Setup###<br> ###installing of glwdr package###<br>
 devtools::install_github("jsta/glwdr")
 ############################<br> library(glwdr)<br>
 library(raster)<br> library(vegan) ###extract
 BioClim at the resolution=2.5<br> wc_bio2.5 &lt;-
 getData("worldclim", var="bio", res=2.5)<br>
 ###turning the raster file into spatial points<br> wc2.5&lt;-
 rasterToPoints(wc_bio2.5, spatial=FALSE)<br> ###omit the NA
 points<br> wc2.5na=na.omit(as.data.frame(wc2.5))
 ###using vegan package to do the PCA<br>
 pcaBio2.5=rda(wc2.5na[,-c(1,2)],scale = T) ###plot the
 result<br> biplot(pcaBio2.5,display = "sp")<br>
 biplot(pcaBio2.5,display = "si")
 PCAsites2.5=scores(pcaBio2.5, choices = 1:2, display =
 "si")<br> 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<br> r &lt;-
 raster(ncols=2160, nrows=900)<br> n &lt;-1944000<br>
 r2_pca1 &lt;- rasterize(PCA_results2.5[, 1:2], r, PCA_results2.5[, 3],
 fun=mean)<br> r2_pca2 &lt;- 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<br> DB_PCA1_2.5=extract(r2_pca1,DB_cor)###match the PCA result to corresponding DB coordinates<br> DB_PCA2_2.5=extract(r2_pca2,DB_cor)<br> 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