README.Rmd

---
output: github_document
fig_caption: yes
---

<style>
p.caption {
  font-size: 0.8em;
}
</style>

````{r InitialSettings, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  message = FALSE,
  warning = FALSE
)
library("tidyverse")
library("lubridate")
library("readxl")
library("tibble")
library("dataDownloader")
#library("googlesheet4")
library("vegan")
#library("ggvegan")
library("patchwork")

# Source/read stuff (download data, figures, analysis)
# source("community/start_here.R")
# knitr::read_chunk("DataPaper_Vandviketal_2020/1_Download_Data.R")


theme_set(theme_bw(base_size = 12))


```


# PFTCOURSES, ELEVATIONAL GRADIENT, BIRD CLIFF AND ITEX EXPERIMENT, LONGYEARBYEN, SVALBARD

This is the git repository for the paper:
XXX et al. (not written yet). Plant traits, vegetation and carbon flux data from an elevation gradient, a bird cliff and a warming experiment in Longyearbyen, Svalbard.

## PROJECT AND SITE INFORMATION
This project reports on plant functional traits, vegetation, ecophysiology, ecosystem, climate and remote sensing data. The data was collected across 7 sites along a 250 m elevational gradient and across 5 sites along a 160 m elevational gradient providing a strong nutrient gradient (bird cliff) near Longyearbyen, Svalbard. Data was also collected in a ITEX warming experiment with Open Top chamber (OTC). The ITEX experiment was set up in 2001 in three habitats (Dryas heath, Cassiope heath and snowbed).
The data were collected between 2003 and 2018 as part of Norwegian research projects and the international Plant Functional Traits Courses 4 (PFTC4).


```{r echo=FALSE, out.width='60%', fig.cap= "Fig caption text here"}
#knitr::include_graphics('./map/TransplantMap.png')
```



## The experimental set-up
#### Elevational and nutrient (Bird Cliff) gradients
For the elevational gradient, we established seven transects (Table 1) at different elevations along the slope of Lindholmhøgda, near Isdammen.  Transects were established in herb-like vegetation, in between snow beds (Cassiope heath) and ridge communities (rocky with very scarce vegetation).

The nutrient deposition gradient was established under a little auk (Alle alle, but also a few black guillemots, Cepphus grylle) colony near Bjørndalen, up the slope of Platåfjellet. The birds breeding on the cliffs above the site deposit nutrients in the form of guano. Nutrient deposition will therefore be highest closest to the bird nests as the number of bird droppings per surface area will increase.

In both gradients we established 75 x 75 cm plots arranged in transects (running perpendicular to the slope) containing 7 plots each. In the bird cliff gradient, we established in total 5 transects, in the elevational gradient we established 7 transects.

#### ITEX warming experiment
The experiment was carried out in the high Arctic, in Endalen (78°11‘N, 15°45‘E), a valley situated approximately four kilometers east of Longyearbyen, Svalbard, at 80-90 m elevation. Average annual temperature is –5.2°C (1981–2010) and the average annual precipitation is 191 mm (Førland et al., 2011). The prevailing wind direction is from the east. The soils are typical Cryosols with thin organic layer on top of inorganic sediments (Jones et al., 2010). The experiment was first established in 2002 in three different habitats which are located in the south–southeast–facing hillside of the valley. The habitats differ in vegetation composition and the time of snowmelt (and hence the length of the growing season). These habitats include a relatively dry Dryas heath with thin snow cover (ca. 10 cm) and early snowmelt, a mesic Cassiope heath habitat with intermediate snow depth and melting date, and a moist snowbed community with deep snow (> 100 cm) and late snowmelt.

In 2001 30 plots (75x75 cm), were selected, ten in each of the three habitats (Dryas heath, Cassiope heath and snowbed) based on a priory criteria. Half of the plots were randomly assigned to the warming treatment in 2002 and the other half to control.

#### Saxifraga oppositifolia polyploidy sites
Saxifraga oppositifolia data was collected in the Endalen Valley. The Endalen site consists of three three plots representing three different habitats with 48 marked plants in each (144 total). The first habitat type (H1; River) has the lowest elevation and is located on the west side of the river in Quaternary alluvium gravels (Table 1). The second habitat type (H2; Heath) has the middle elevation and is located to further to the west from the river site. Cassiope tetragona is the dominant vascular plant, indicating snow bed conditions (concave landscape). The third site (Scree) is located at the highest elevation above a break in the slope. The site’s substrate is composed of sandstone and siltstone scree. 

#### Snowfence?


## DATASETS, CODE AND ANALYSES
The raw and cleaned datasets are stored on OSF PFTCourses, Elevational Gradient, Bird Cliff and ITEX Experiment, Longyearbyen, Svalbard: https://osf.io/smbqh/

The data was processed and analysed using R. All code is stored on github: https://github.com/EnquistLab/PFTC4_Svalbard

### Download data
All raw data is available on the OSF repo and the code used to clean the data is in the folder cleaningRawData in the git repository. The cleaned data are also available on OSF, and we strongly suggest to use these files.

To download the data, the following function can be used:

```{r InstallPackages, eval=TRUE}
#install.packages("remotes")
#remotes::install_github("Plant-Functional-Trait-Course/PFTCFunctions")
library("PFTCFunctions")
```

```{r DownloadExample1, eval=TRUE}
#Download files from OSF
download_PFTC_data(country = "Svalbard", 
                   datatype = "community", 
                   path = "community/cleaned_data")
```

It is also possible to download several files at the same time:

```{r DownloadExample2, eval=TRUE}
#Download files from OSF
download_PFTC_data(country = "Svalbard", 
                   datatype = c("community", "trait"), 
                   path = "cleaned_data")
```

Use the following specifications to download the data:
```{r showCode, echo = TRUE, eval=FALSE}
data("location", package = "PFTCFunctions")
```

```{r showTable, echo = FALSE}
data("location", package = "PFTCFunctions")
location %>% 
  select(Country, DataType, Remark) %>% 
  filter(Country == "Svalbard") %>% 
  knitr::kable()
```


## Data collection
```{r showDataCollection, echo = FALSE}
tibble(ResponseVariable = c("PlantCommunity", "PlantFunctionalTraits", "BryophyteTrais", "ClimateData", "LeafPhysiology", "C-Fluxes", "SaxifragaPoliploidy", "RemoteSensing-SpectralReflectance"),
       Elevation_N_Gradient = c(1,1,1,1,1,1,NA,1),
       ITEX_Warming = c(1,1,NA,1,NA,1,NA,1),
       Polyploidy = c(NA,1,NA,NA,NA,NA,1,NA),
       FluxTower = c(NA,NA,NA,NA,NA,NA,NA,1),
       SnowFence = c(NA,NA,NA,NA,NA,NA,NA,1)) %>% 
  knitr::kable()
```



### Community Dataset
#### Elevational and nutrient gradients
In each plot, we identified and estimated cover percentage of all forb and graminoid species, and if they are flowering. Median vegetation height was estimated in the field by measuring the height of plants that appeared to be around the average height for the plot. In addition, we estimated cover percentage of moss/bryophytes, biocrust, lichens, litter, rocks and bare ground, and marked presence and identification of scat, and weather conditions.

#### ITEX warming experiment
To estimate the abundance of different plant species in the plots, the point intercept method (PIM) was used in the summer of 2003, 2009 and 2015 following the protocols from the ITEX manual (Molau & Mølgaard, 1996). A 75 × 75 cm frame with 100 equally distributed points was used. All hits within the canopy were recorded until the pin hit the cryptogam layer (composed of bryophytes and lichens), bare ground, or litter. Species of vascular plants, bryophytes and lichens were recorded (some bryophytes and lichens only at the genus level). Dead plant tissue on the ground was recorded as litter and unvegetated surface was recorded as rock or soil (bare ground). 


To download the clean database use: `XXX.R`
To load the community data start with the R script: `XXX.R`


#### Data cleaning steps
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.


#### Diversity along elevational gradient
 Add some plots here...
 
 

### Plant functional leaf traits
The dataset contains eleven functional traits related to potential physiological rates and environmental tolerance of plants. These include: 

- leaf area (LA, cm^2^ ) 
- leaf thickness (LT, mm)
- leaf dry matter content (LDMC, g/g)
- specific leaf area (SLA, cm^2^ /g)
- carbon (C, %)
- nitrogen (N, %)
- phosphorus (P, %)
- carbon:nitrogen ratio (C:N)
- nitrogen:phosphorus (N:P)
- carbon13 isotope (δ ^13^C, ‰)
- nitrogen15 isotope (δ ^15^N, ‰)


The traits were measured according to Pérez-Harguindeguy et al. (2012) as well as the Enquist Macrosystems protocol with the following modifications: 

#### Sampling
For each of the gradient and ITEX experimental plots, we collected three individuals of each species covering more than 1 % of each plot for leaf functional trait measurements. Plant material for trait measurements was collected outside but within the close surroundings of each plot because the Carbon flux group still had to perform measurements, we could not sample destructively from within the plots. Some additional samples collected from the Saxifraga and reflectance studies (see below) were also measured for traits following this protocol.  

#### Sample storage
The samples were transported from the field to the lab as whole plants, including roots and soil, to keep them fresh and fit as long as possible. We used ziplock plastic bags with plot numbers, date, height, species ID and sample ID on them. Samples were stored outdoors (temperature around +6 degrees Celcius) to ensure they stay fresh and water saturated until the measurements started. 

#### Sample preparation
The first step was to check the species identification and taxonomy of plant samples. Then, plant height was measured (in cm) from the bottom of each individual plant to the highest tip of a photosynthetic leaf, excluding florescences but including stem leaves for graminoids. Then, leaves were separated from the plants using tweezers and sorted into paper envelopes with a standardized labelling system. Because leaf sizes and shapes vary significantly between species, some standardized rules were applied (see Table below). For Equisetum sp., which lacks obvious leaves, an 8 cm section was cut off including side shoots. For every plant sample, we collected at least three leaves. If the leaves were very small, we collected an amount of leaves equivalent to approx 3 cm2. Small leaves were rolled into a small white paper envelope to ensure no leaves were lost during the measurements. At all times, the plant material was kept in moist conditions (sprayed with water, stored outside and processed within 24h). 

```{r Rules, echo = FALSE}
tibble(Nr = seq(1,13, by = 1),
       Rule = c("Obey the rules and keep the leaves wet",
                "Sample 3 leaves per ind or fill up 3cm^2^",
                "Write # of leaves; if > 10 write bulk",
                "Sample leaf to where it joins the stem (Inlcude leaflets for Dryas; Exclude sheaths/stipules)",
                "Plant height: from ground to highest photosynthetic unit. Exclude florescenses",
                "Equisetum length should be width of envelope, and include branches if needed",
                "For folding graminoids tape the leaves to the scanner",
                "Scanning: avoid overlap, stay away from edges, face down",
                "Wear slipper in the lab",
                "Check species list regularly (Dupontia)",
                "NO MINING! = trading for better plants",
                "For small leaves use folded paper envelopes",
                "Do not unroll Festuca"
                )) %>% 
  knitr::kable()
```


#### Fresh mass in g
We weighed the fresh, moist leaves in the lab (in grams, rounded to four decimals). A weighing boat was used for small leaves. Blances used were Mettler AE200, Mettler TOLEDO, and AG204 DeltaRange (0.1 mg precision).

#### Leaf surface area in cm^2^ 
To measure leaf surface area, leaves were placed on a scanner (Canon Lide 220). Leaves were placed so that the upper sides of the leaves faces downwards. In case of folded leaves, we used transparent Scotch tape to flatten them out and fix them on the scanner. A measurement tape was attached on each scanner as reference scale. Each scan was named according to its ID number (standardized tag system, written on the paper bag). The leaf area was calculated using R package “LeafArea” (Katabuchi 2017). 

#### Leaf thickness in mm
Then, leaf thickness was measured three times with electronic micrometers (Mitutoyo 293-348) on each leaf (i.e. three) per sample. If fewer leaves were available, several measurements were taken on the same leaf. We avoided measuring on veins, but that was not possible for all species, e.g. for very small leaves or in the case of Equisetum species.

#### Leaf thermal constants
We calculated thermal time constants from the measured functional traits. The thermal time constant is a critical trait underlying leaf carbon economics (Michaletz et al. 2015 TREE, 2016 Nature Plants). It is a composite leaf trait that comprises several additional traits, including dry matter mass, water mass, specific heat capacity, total (two-sided) surface area, width, geometry (e.g., broadleaf or needle-leaf), and stomatal conductance. These traits are then combined into a single ratio tau that quantifies the ability of the leaf to store heat versus its ability to exchange heat with the environment. Thermal time constants were calculated from wet and dry mass. 

#### Dry mass in g and sample storage
Samples were stored in their envelopes and dried in a drying cupboard (Thermo Scientific Heraeus®, USA) at 60°C for approximately 24 hours to prepare them for shipment to the University of Arizona (UoA). At UoA, the samples were dried at 60°C for 72 hours, weighed, and sent for C and N analyses. 

#### Specific leaf area
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 (cm^2^)/dry mass (g).

#### Leaf Dry Matter Content 
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: `XXX.R`



#### Traits Distributions and Values
Add some plots here...



#### Data processing and cleaning 
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 number, project) were added or corrected if possible. Wrong and missing site, plotID and individual number were imputed if possible. For some leaves it was more a guess to which plot the leaf belongs. These leaves are flagged. Leaves where this was not possible were excluded. Duplicated entries were removed.

The taxonomy was checked according to TNRS (http://tnrs.iplantcollaborative.org/).

The data was then checked visually to detect apparent measurement errors. Unrealistic values were removed. For the trait data this included leaves where leaf dry matter values higher than 1 g/g and leaves with specific leaf area values greater than 500 cm^2^ /g.

This still needs to be checked!!!
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.


### Bryophyte traits
Bryophyte samples (tussocks) were collected at different elevational gradient from two sites, bird cliff (B) and elevational gradient (C). Site B had five elevational transects and site C had seven elevational transects. At each transect, a survey of dominant moss species determined which species to collect. Three tussocks of the (maximum) three most dominant species were sampled at both ends (plot A and G) and the middle (plot D) of each transect, for a maximum of nine samples per transect. To avoid destructive sampling, we samples were collected from the surrounding of each plot, adjacent to the transects. 

The following taxa were sampled: Aulacomnium turgidum, Dicranum sp., Hylocomium splendens, Polytrichum sp., Polytrichum piliferum, Racomitrium sp., Racomitrium canescens, Sanionia uncinata, Syntrichia ruralis, and Tomentypnum nitens. 

Aulacomnium turgidum, Hylocomium splendens, and Sanionia uncinata were sampled at both sites at multiple elevational gradients. Polytrichum sp. (assuming this is P. piliferum?) and P. piliferum were sampled at both sites at one elevational transect. Racomitrium sp. and R. canescens were sampled at both sites, however R. sp. was sampled only from one transect at site B while R. canescens was sampled across multiple elevational transects at site C. Dicranum sp. and Tomentypnum nitens were sampled only at site C, however T. nitens was sampled at multiple elevational transects. Syntrichia ruralis was only sampled at site B at the highest elevational transect. 

#### Bryophyte trait measurements
Due to the inherent differences between bryophytes and vascular plants, bryophyte samples did not enter the trait wheel (see 3.2) but followed a separate methodology described below. 

#### Sample collection, transport, and storage
For each sample, we collected tussock of about 25cm2 in the field. These were stored in plastic bags, transported to UNIS and stored outside (at +6 degrees Celsius) away from direct sunlight until further processing. 

#### Sorting and preparation of samples
From each tussock, we collected at least five living (i.e. green) shoots (considering approximate biomass needed for chemical analysis) including any dead lower parts. These shoots we carefully cleaned from soil and debris under a stereo microscope using tweezers if necessary. Samples were then labelled as described for vascular plants in section 1.7. 

#### Shoot length in cm
Next, both the length of the living part (green) and complete shoot was measured on three shoots per sample (in cm). In case of a “split tip” with multiple green shoots, the longest was measured (usually the main shoot). 

#### Wet mass in g
Before measuring the wet weight of all the shoots in each sample, samples were soaked in petri dishes with demineralized water for 30 minutes. Then, the water was removed and samples were kept in sealed petri dishes lined with moist tissue paper over night. Before weighing (d=0.1 mg), samples were blotted dry with tissue paper. 

#### Sample storage and further measurements
Samples were oven dried (60 ºC) and stored before shipment to the University of Arizona for further analysis.



### Climate data
Climate data was recorded along both elevational gradients and in the ITEX warming experiment.

#### Elevational gradient

##### Temperature and moisture (TomsT logger)
Info needs to be added

##### Short term microclimate data
Along both gradients, we collected soil micro-climatic data. Soil moisture and temperature were measured on 19.7.2018 from all plots in the elevation (n = 46)  and nutrient gradient (n = 35). We used hand-held time-domain reflectometry sensors to measure volumetric water content (VWC%) up to a depth of 7,5 cm (FieldScout TDR 300; Spectrum Technologies, Plainfield, IL, USA). Each plot was measured from three points, for covering possible moisture variation within the plot. Average of the three points was used. We used high-accuracy digital thermometers measure soil temperature (°C) up to a depth of 7,5 cm (TD 11 Thermometer; VWR International bcba; Leuven, Belgium). Each plot was measured from the centre once. In addition, miniature temperature loggers (ThermoChron iButtons, San Jose, CA, USA) were buried c. 7,5 cm below the soil surface, measuring at 4-hour intervals throughout 19.7.-10.8.2018. On the elevation gradient, the loggers were buried 10 cm to the left of the upper-left corner (seen from the bottom of the slope) of the plot. On the nutrient gradient, the loggers were buried in the middle of the plot. Loggers were buried in the following plots (n = 67):
Elevation gradient (n = 36): 
		Transect 1: A, B, C, D, F, G
		Transect 2: A, B, C, D, F, G
		Transect 3: A, B, D, F, G
		Transect 4: A, B, D, F, G
		Transect 5: A, B, D, F, G
		Transect 6: A, B, D, F, G
		Transect 7: A, B, C, D

Nutrient gradient (n = 31):
		Transect 1: A, B, C, D, F, G
    Transect 2: A, B, C, E, F, G 
		Transect 3: A, B, C, E, F, G
		Transect 4: A, B, C, D, F, G
		Transect 5: A, B, C, D, E, F, G


#### ITEX warming experiment
* There is a climate station at the ITEX site recording air temperature at 2m (°C Tair). Data are recorded (usually) every 10 minutes and there are data from 2015-2018.
* Soil temperature (-5 cm) from iButtions recorded between 2004-2005 and 2015-2018. This data was recorded inside the OTC and in the control plots in all 3 habitats (X plots per habitat). 

To download the clean data use: `XXX.R`


#### Data processing
The data was provided in excel or csv files. The data was checked visually for outliers. Unrealistic values were removed.


```{r ClimatePlot, echo=FALSE, eval = TRUE, fig.cap= "Climate and soil temperature data in the ITEX warming experiment."}
```


### Leaf Photosynthesis Temperature Response
...

### CO2 Efflux
...

### Polyploidy in Saxifraga oppositifolia
...

### Remote sensing and spectral reflectance
...