10.5061/DRYAD.7H44J0ZQX
Pan, Yingji
0000-0002-8203-3943
Leiden University
Cieraad, Ellen
Leiden University
Clarkson, Bev
Landcare Research
Colmer, Tim
University of Western Australia
Pedersen, Ole
0000-0002-0827-946X
University of Copenhagen
Visser, Eric
Radboud University Nijmegen
Voesenek, Laurentius A.C.J.
Utrecht University
van Bodegom, Peter
0000-0003-0771-4500
Leiden University
Data from: Drivers of plant traits that allow survival in wetlands
Dryad
dataset
2020
adaptive strategy
bioclimatic variables
Driving factors
root porosity
root/shoot ratio
underwater photosynthetic rate
wetland plant functional traits
wetland plant eco-physiological adaptive traits
China Scholarship Council
https://ror.org/04atp4p48
Grant No. 201606140037
2020-01-28T00:00:00Z
2020-01-28T00:00:00Z
en
https://doi.org/10.1111/1365-2435.13541
https://doi.org/10.1111/1365-2435.13541
47299 bytes
6
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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
###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