Global trait maps derived from crowd-sourced data (GBIF)

Trait maps

Here you will find global trait maps based on plant observations from GBIF and sPlotOpen, respectively, and traits found in the TRY gap-filled dataset in GeoTIFF format at a 0.2°, 0.5°, and 2° resolutions.

The methodology follows the approach described in Wolf et al. 2022, Nature Ecology and Evolution https://doi.org/10.1038/s41559-022-01904-x Additional ressources can be found on Sophie Wolf´s GitHub-repository.

Each folder also contains the sPlotOpen-based maps for all respective traits and resolutions. The folder traitmaps contains .grd files for each trait (see list below) with multiple layers: observation count, mean, median, standard deviation, 05% quantile, 95% quantile. These can be loaded as a brick of layers in R as follows:

library(raster)

test <- brick("file.grd")
plot(test)

The files in the traitmaps directory are organized as follows:

• The first subfolder categorizes trait maps by plant functional types integrated, where
  • Shrub_Tree_Grass are trait maps based on species of all plant functional types.
  • Shrub_Tree are trait maps based on shrub and tree species.
  • Grass are trait maps for grassland species only.
• Each of these subolders contains subfolders for map products at 0.2, 0.5 and 2.0 degrees (longitude, latitude)
• The file name of each trait maps contain an *X, which corresponds to the TRY ID for each trait. The names for each trait are listed below and in trait_id_and_name.csv

The trait maps can be cited using the provided citation file and the doi: .

Data & Method

Source of species observations are GBIF sampled as such:

1. GBIF download: https://doi.org/10.15468/dl.fe2kv3
2. The observations were then linked to the TRY gap-filled dataset, which resulted in a total of n= observations. 90% of the GBIF observations were matched, 70% of species in TRY, and 24% of species in GBIF (numbers based for map products using all plant functional types).
3. Matched observations were then binned into equal area hexagons (using the package size hex9, which corresponds to about 0.5 degrees at equator)
4. From each hexagon were then sampled 10,000 observations. If a hexagon contained less than 10,000 observations, all observations were kept.
5. This GBIF subsample contained approx. 35,000,000 observations

Figure 1: Global density of GBIF subsample at 2° resolution.

Traits in TRY gap-filled:

TRY trait ID	Trait name
4	Stem specific density (SSD) or wood density (stem dry mass per stem fresh volume)
6	Root rooting depth
11	Leaf area per leaf dry mass (specific leaf area, SLA or 1/LMA)
13	Leaf carbon (C) content per leaf dry mass
14	Leaf nitrogen (N) content per leaf dry mass
15	Leaf phosphorus (P) content per leaf dry mass
18	Plant height
21	Stem diameter
26	Seed dry mass
27	Seed length
46	Leaf thickness
47	Leaf dry mass per leaf fresh mass (leaf dry matter content, LDMC)
50	Leaf nitrogen (N) content per leaf area
55	Leaf dry mass (single leaf)
78	Leaf nitrogen (N) isotope signature (delta 15N)
95	Seed germination rate (germination efficiency)
138	Seed number per reproduction unit
144	Leaf length
145	Leaf width
146	Leaf carbon/nitrogen (C/N) ratio
163	Leaf fresh mass
169	Stem conduit density (vessels and tracheids)
223	Species genotype: chromosome number
224	Species genotype: chromosome cDNA content
237	Dispersal unit length
281	Stem conduit diameter (vessels, tracheids)
282	Wood vessel element length; stem conduit (vessel and tracheids) element length
289	Wood fiber lengths
1080	Root length per root dry mass (specific root length, SRL)
3112	Leaf area (in case of compound leaves: leaf, undefined if petiole in- or excluded)
3113	Leaf area (in case of compound leaves: leaflet, undefined if petiole is in- or excluded)
3114	Leaf area (in case of compound leaves: undefined if leaf or leaflet, undefined if petiole is in- or excluded)
3120	Leaf water content per leaf dry mass (not saturated)

Correlation of sPlotOpen and GBIF sample for all traits at different resolutions

The correlations (based on all plant functional types) between sPlotOpen and GBIF-based maps were calculated as in Wolf et al. 2022.