Citizen science plant observations encode global trait patterns

This repository hosts additional material accompanying the publication:

Wolf, S. and Mahecha, M. D. and Sabatini, F. M. and Wirth, C. and Bruelheide, H. and Kattge, J. and Moreno Martínez, Á. and Mora, K. and Kattenborn, T., Citizen science plant observations encode global trait patterns. Nat Ecol Evol (2022). https://doi.org/10.1038/s41559-022-01904-x

1. The entire workflow documentation: https://sojwolf.github.io/iNaturalist_traits/
2. GeoTiff trait maps based on iNaturalist observations and sPlotOpen observations, respectively
3. How to create updated trait maps

Trait maps

Versions

The maps available here were generated using iNaturalist observations downloaded on January 4, 2022 (https://doi.org/10.15468/dl.34tjre) and sPlotOpen version 52 (https://idata.idiv.de/ddm/Data/ShowData/3474?version=52).

If you would like to update the iNaturalist maps using the most recent TRY (trait) and iNaturalist data, please refer to the section Create updated trait maps below.

1. iNaturalist

    1. 0.5 degrees
   
          1. ln
          2. exp(ln)
   
    2. 2 degrees
   
          1. ln
          2. exp(ln)
   

2. sPlotOpen

    1. 0.5 degrees
   
          1. ln
          2. exp(ln)
   
    2. 2 degrees
   
          1. ln
          2. exp(ln)
   

Load in Python

Get file names:

from os import listdir
from os.path import isfile, join

path = "iNaturalist_traits-main/iNat_maps/2_deg/ln/"
files = [f for f in listdir(path) if isfile(join(path, f))]
files.sort()

Load all trait maps as xarray:

from import xarray as xr

def cubeFile(file):
    name = file.replace(".tif","")
    sr = xr.open_dataset(path + file,engine = "rasterio",chunks = 1024).sel(band = 1)
    sr = sr.assign_coords({"variable":name})
    return sr

da = xr.concat([cubeFile(x) for x in files],dim = "variable")

Select a specific band:

da.band_data.sel(variable = "iNat_Leaf.Area_2_ln")

Convert to data frame

df = da_2.band_data.to_dataset().to_dataframe().reset_index()
df_spread = df.pivot(index= ['x','y'],columns='variable',values='band_data').reset_index()

Load in R

To load one trait map:

library(raster)

plant_height <- raster("iNat_Plant.Height_05deg_expln.tif")

trait_stack <- list.files(pattern = "05deg_expln.tif")
trait_stack <- stack(all_maps)

Create updated trait maps

If you would like to use the most recent vascular plant data, instead of the maps provided here, the following instructions will guide you.

Download iNaturalist database

Open the following link and click ‘Rerun Query’:

https://doi.org/10.15468/dl.34tjre

Request the download: Click on "Download" and sign into/generate your account. For this analysis the ‘simple’ version is sufficient (roughly 10 GB). This will also generate a new DOI that you can cite. You will receive a download link via e-mail as soon as the download is ready (usually after about 15 minutes).

Download TRY data

To download data from the TRY database, create an account at https://www.try-db.org/de.

When asked which traits you would like to download, type in the following list. This filters TRY data for our traits of interest:

3113, 3117, 4, 13, 14, 15, 3106, 26, 27, 47, 50, 56, 78, 138, 163, 169, 237, 282

Continue without species list and choose open trait data only. You will receive a download link via e-mail after about one or two days.

The iNaturalist and TRY downloads will contain files that look something like this:

iNaturalist_filename: 0354963-210914110416597.csv

TRY_filename: 19287.txt

Link iNaturalist and TRY and generate trait maps

Download the make_traitmaps/ folder from this repository and execute the containing python script to generate 0.5 and 2 degree maps. Make sure the R script make_iNat_traitmaps.R is also in the directory.

python make_traitmaps.py -n iNaturalist_filename -t TRY_filename

This script does not apply fuzzy name matching, since it adds around 1% more data. If you want to add a fuzzy match or want more information, please refer to the workflow documentation at https://sojwolf.github.io/iNaturalist_traits/. After running the script, you can remove the file iNat_TRY_log.csv if you do not need information on each observation for your analysis.

Requirements

Python packages:

For handling data frames and (multidimensional) arrays:

• pandas
• numpy

For system interface:

• getopt
• sys

R packages:

For making rasters and exporting GeoTiffs:

• raster
• rgdal