This is the git repository for the paper: Vandvik et al. (under review in DataScience). Plant traits and vegetation data from climate warming experiments along an 1100 m elevation gradient in Gongga Mountains, China.
This project reports on plant functional traits, vegetation, ecosystem, and climate data in response to climate change experiments and along a 1000 m elevational gradient in the Gongga Mountains, Sichuan Province, China. Across four sites along the elevational gradient, we conducted whole-community TransPlant warming and cooling experiments and passive Open Top chamber (OTC) warming experiments. The data were collected between 2012 and 2020 as part of several Chinese and Norwegian research projects and the international Plant Functional Traits Courses 1 and 2 (PFTC1 and PFTC2).
| Site | Elevation | Latitude | Longitude |
|---|---|---|---|
| High Elevation | 4100 | 29.90742 | 102.0118 |
| Alpine | 3850 | 29.88911 | 102.0173 |
| Middle | 3500 | 29.86192 | 102.0360 |
| Lowland | 3000 | 29.84347 | 102.0343 |
Location of the four study sites along an elevational gradient. Inset indicates the locations of the study sites in China.
Experimental set up for the different warming treatments at each site. Shown are control, local control, OTC, and transplant and arrows indicate direction of transplant. For each site elevations are shown in meters a.s.l. and summer temperature in °C.
Each of four study sites contains an in situ warming experiments using OTCs and reciprocal community transplantation experiments (i.e., warming and cooling) between all pairs of neighbouring sites along the gradient. Within each of the seven blocks at each site, plots were randomly designated to six different experimental treatments:
- passive warming with an open top chamber (OTC; all sites)
- transplanting to a site one step warmer along the gradient (warming; from sites High alpine, Alpine, Middle alpine
- transplanting to a site one step colder along the gradient (cooling; from sites Alpine, Middle alpine, Lowland
- transplanting down the entire gradient (extreme warming; from site High alpine)
- up the entire gradient (extreme cooling, from site Lowland)
- transplanting within blocks (to control for the transplanting itself)(Local transplant; all sites)
- an untouched control plot (Control; all sites)
Thus, each OTC has a local unmanipulated control, and each transplanted turf has an “origin” site and a “destination” site, with two types of controls, local transplant and untouched plots.
The raw and cleaned datasets are stored on OSF PFTCourses Elevational Gradient and TransPlant Experiment, Gongga Mountain, China: https://osf.io/f3knq/
The data was processed and analysed using R. All code is stored on github: https://github.com/Plant-Functional-Trait-Course/PFTC_1_2_China
To download the data, the following function can be used:
#install.packages("remotes")
#remotes::install_github("Plant-Functional-Trait-Course/PFTCFunctions")
library("PFTCFunctions")#Download files from OSF
download_PFTC_data(country = "China",
datatype = "biomass",
path = "data")
#> # A tibble: 1 x 3
#> node file remote_path
#> <chr> <chr> <chr>
#> 1 f3knq China_2016_Biomass_cleaned.csv BiomassUse the following specifications to download the data:
data("location", package = "PFTCFunctions")| Country | DataType | Remark |
|---|---|---|
| China | community | |
| China | trait | Full leaf trait data set from PFTC1 and 2 |
| China | trait | Full chemical trait data set from PFTC1 and 2 |
| China | trait_Henn | Trait data set used for Henn et al. 2018 |
| China | biomass | |
| China | flux | |
| China | meta | |
| China | climate | Climate data from iButtons |
| China | climate | Monthly climate data from iButtons |
| China | climate | Climate data from Tomst Logger |
| China | climate | Climate data from weather station |
Leaves from the most common species in the plant community were collected at each of the four sites in August 2015 and 2016. Plants were collected outside of the experimental plots and we aimed to collect one healthy, fully expanded leaf from up to five individuals for each species at each site where they occurred. This was not possible for all species at all sites. To avoid repeated sampling from a single clone, we selected individuals that were visibly separated from other stems of that species.
Ten of the most common species along the gradient were selected for sampling in the experimental turfs during August 2016. These include Artemisia flaccida, Epilobium fangii, Geranium pylzowianum, Hypericum wrightianum, Pedicularis davidii, Persicaria vivipara, Plantago asiatica, Potentilla leuconota, Veronica szechuanica, and Viola biflora var. rockiana.
The dataset contains eleven functional traits related to potential physiological rates and environmental tolerance of plants. These include:
- leaf area (LA, cm2 )
- leaf thickness (LT, mm)
- leaf dry matter content (LDMC, g/g)
- specific leaf area (SLA, cm2 /g)
- carbon (C, %)
- nitrogen (N, %)
- phosphorus (P, %)
- carbon:nitrogen ratio (C:N)
- nitrogen:phosphorus (N:P)
- carbon13 isotope (δ 13C, ‰)
- nitrogen15 isotope (δ 15N, ‰)
The traits were measured according to Pérez-Harguindeguy et al. (2012) as well as the Enquist Macrosystems protocol with the following modifications:
Each leaf (including petiole) was cut from the stem. Leaf area was
measured with a flatbed scanner, set on 300dpi, and used with colours to
provide maximal information. The leaves were carefully flattened and
laid down in the position that gave the largest area before measurement
to avoid squashing, overlapping or curled leaves. To determine the leaf
area in squared centimeters ImageJ was used with the Ben Bolder’s macro
method.
Allium prattii was harder to make flat so it was flattened as much as
possible and the once still overlapping was manually taken care of by
doubling the leaf area.
Simple leaves - Leaf lamina and petioles are measured independently for 5 leaves per individual, when possible. The petiole and lamina are then scanned and area is measured. Compound leaves - The rachis and petiole was removed. The fresh area was measured for the rachis and petiole of 5 leaves including all leaflets and petiolules per individual. The area of the rachis plus petiole was measured separately from the lamina plus petiolules.
Wet leaf mass was measured in grams. Leaves was then dried at 60-65°C for 72 hours and weighed for dry mass in grams.
The leaf thickness was measured carefully with a digital micrometer. Three random spots were selected for each leaf that includes midrib, lamina with veins and lamina without veins. The average value of these three spots was used to obtain leaf thickness. Some leaves were so small that three spots measurement was impossible. On this small leaves only one or two spots were measured and then this was used to get the average value.
Whole leaf SLA. Specific leaf area is calculated from the leaf area for the whole leaf (measured with the scanner), divided by the dry mass (measured after 72 hours drying) for the whole leaf. SLA = leaf area (cm2)/dry mass (g).
LDMC was measured with the leaf dry mass divided by the leaf wet mass. LDMC = Leaf dry mass (g)/ leaf wet mass (g).
To download the clean data use:
DataPaper_Vandviketal_2020/Download_Data.R
All data was manually entered from into digital worksheets, and manually proofread.
All data cleaning and checking was done using code. The data was checked and corrected for spelling mistakes and mislabelling. Missing or mislabeled information (e.g. elevation, site, taxon, individual and leaf numbers, location, project) were added or corrected if possible. Duplicated entries were removed. The taxonomy was checked according to the Plant List (http://www.theplantlist.org/). The data was then checked visually to detect apparent measurement errors. Unrealistic values were removed. For the trait data this included leaves with very small wet mass (< 0.0003 g), leaf dry matter values higher than 1 g/g, leaves with specific leaf area values less than 5 cm2 /g or greater than 500 cm2 /g and leaf nitrogen values higher than 6.4%. The nitrogen cutoff value was chosen based on the highest published leaf nitrogen values found in the Botanical Information and Ecology Network (Enquist et al., 2009) for the genera in our study.
The 2015 data had some unhealthy leaves which led to unrealistic trait values that needed to be flagged.
- The dry mass was once weighed in 2015 and reweighed in 2016, because the first time many leaves had the wrong unit (g instead of mg). 2016 dry mass is used, but when it is missing the 2015 dry mass is used. These leaves have been flagged (GeneralFlag).
- Leaf Area has been recalculated for the 2015 leaves (see below)
- The 2015 scans were often very dirty included black lines, the leaves were folded and cut. Each scan has been checked and comments about the scan added (allComments)
- obviously wrong areas have been removed (empty, double, dirt)
- For a few leaves the scanning did not work (e.g. Swertias) and these areas are missing
- Leaves with black lines or part of the leaf too white, were scanned again with different options (cropping, etc), improving the calculation of the leaf area
- 62 duplicate leaves that only had one scan have been matched according to size and mass. Some of the leaves (intermediate size and mass) might have been wrongly matched. (GeneralFlag)
- Each measured variable has a separate flag: Area (AreaFlag), Wet mass (WetFlag), Dry mass (DryFlag) and Leaf Thickness (ThickFlag) and one for general comments (GeneralFlag)
- Flags that include #zap should be removed before analysing
- all other comments are warnings
- brown and yellow leaves can affect dry mass (Wet and DryFlag)
- eaten, folded, cut white parts, black line can affect area (AreaFlag)
- Wrong and missing individual number, leaf number have been flagged. Does not affect analysis, unless these variables are included (GeneralFlag)
- Impossible combinations of location, site and project have been assigned the right combination if possible. If not, the leaves have been excluded
All vascular plant species in each plot were surveyed in 2012 (before treatment), and annually between 2013 and 2016. At each survey, vegetation was surveyed during the peak of the growing season using a 25 cm × 25 cm. We registered presence-absence of all species in 25 subplots per plot and estimated the percentage coverage of each species in the whole plot. Note that the total coverage in each plot can exceed 100 due to layering of the vegetation. Mean vegetation height for each plot was measured in 2013, at five points per plot.
The community data is stored in a database.
To download the clean database use:
DataPaper_Vandviketal_2020/Download_Data.R To load the community data
start with the R script: community/start_here.R
All data was manually entered from into digital worksheets, and manually proofread.
All data cleaning and checking was done using code. The data was checked and corrected for spelling mistakes and mislabeled. Missing information (e.g. PlotID, Site) were added if possible. The data was then checked visually to detect apparent measurement errors.
Over the years of collecting this extensive data set, different people
were involved, which increases the risk of observation errors. In
particular, species can be misidentified (i.e., sterile graminoids) or
might be overlooked in one of the observations. These errors will result
in pseudo-turnover in the plant community data. To detect such errors,
we compared each recorded species in each subplot over the 5 years
(subturf_maps.Rmd). We used the subplots to assign unidentified or
missing species if there was a record of the species in the previous and
following year. Further, we re-estimated species cover in cases where
cover was either too low or high to be real when comparing with other
years, and replaced these values with the mean cover from the previous
and following year. We did such re-estimates in totally 48 occasions (c.
1%) of the whole 5 years dataset.
Vegetation height and structure data for each plot was recorded each year. Mean vegetation height and bryophyte depth was measured at five evenly spaced points per plot using a ruler. The percent coverage of vascular plant vegetation was also recorded. Obvious outliers in the data were removed.
Vegetation height increases in the first 3 years and then decreases, because biomass accumulates inside the fence over time (no grazing). After 2014 the vegetation inside the fence was clipped at the end of the field season to remove this fence effect.
We measured standing biomass at each of the sites in 2015 (Table 1). As the above-ground biomass dies back each winter, standing biomass can be considered an approximation of above-ground net productivity . We harvested all above ground biomass from 13 0.5 m × 0.5 m plots at the High alpine site and 20 plots from the other sites. The biomass was oven-dried it at 60°C for 72 h before weighing.
To download the clean data use: biomass/R/1_Download_Cleaned_Biomass.R
- There are still duplicate plants in the data set that should be merged
- it is unclear how the height was measured. How were the 1 random leaves selected. Needs clarification from China
- How good is the cover estimate?
- Can we trust the graminoid species or should these be merged?
- There are 2 plots that contain > 20% seedlings (impossible!)
- a couple of seedlings are huge
- There is a climate station at each transplant site along the gradients recording air temperature at 2m (°C Tair). Data are recorded (usually) every 10 minutes and there are data from 2013-2016 in the database.
- Air (30 cm), ground (0 cm) and soil temperature (-5 cm) from iButtions in summer 2017. This data was recorded inside the OTC and outside.
- Air (15 cm), ground (0 cm) and soil temperature (-6 cm) and volumetirc soil moisture from Tomst loggers from September 2019 onwards.
To download the clean data use:
DataPaper_Vandviketal_2020/Download_Data.R
The data was provided in excel or csv files. The data was checked visually for outliers. Outliers and unrealistic values were removed.
Data Documentation and explanations for the following paper:
Henn, J.J., Vanessa Buzzard, V., Enquist, B.J, Halbritter, A.H., Klanderud, K., Maitner, B.S., Michaletz, S.T., Patterson, L., Pötsch, C., Telford, R.J., Yang, Y., Zhang, L. and Vandvik, V. (2018). Intraspecific trait variation and phenotypic plasticity mediate alpine plant species response to climate change. Frontiers in Plant Science. 9:1548 https://doi.org/10.3389/fpls.2018.01548
This dataset contains two data files.
The first is called “trait.rdata” and it contains all trait measurements made on all leaves sampled during 2015 and 2016 from this study. These traits include wet mass, dry mass, leaf area, leaf thickness, and leaf nutrients/isotopes. The leaves come from within the experimental transplanted plots, along with leaves from each site but outside the experimental plots.
The second is called “China_moments_site_level_for_Jon_9_13_2018.csv” and contains bootstrapped mean and 95%CI moments of trait distributions for each site based on the traits measured for eahd species present at each site and their abundance based on biomass harvested from up to 20 plots at each site.
The data files are located in the OFS repo associated with this project under the traits component (see above).
To load data and run the analysis done for the paper “Intraspecific trait variation and phenotypic plasticity mediate alpine plant species response to climate change” there are three R scripts located in the transplant github repository linked in the OSF repo. These scripts are located in the “traits” folder then in the “trait_plasticity_paper” folder.
The first R script opens the “traits.Rdata” file and cleans unrealistic trait measurements and selects only data from experimental plots to examine the plasticity related to transplantation. It also calculates trait distributions for each species at each site for use in assessing significance of plastic responses to transplantation.
The second R script runs variance partitioning analysis and produces the plot for that analysis. This starts with the “traits.Rdata” file but it selects only leaves that are not within the experimental plots to examine the traits of all species present at each site without the effect on transplantation.
The third R script runs plasticity analysis and produces relevant plots for that analysis. The analysis is based on comparing trait values of each species in a transplanted plot to the trait values of the community in the new location of that species and to the traits of that species in it’s original home location.
These scripts will reproduce all analyses and figures reported in “Intraspecific trait variation and phenotypic plasticity mediate alpine plant species response to climate change” manuscript.