Data from: Tree functional traits across Caribbean island dry forests are remarkably similar
Data files
Dec 25, 2023 version files 1.02 MB
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chelsa_dllist.txt
3.27 KB
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potential_dryforest.tif
941.95 KB
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potential_dryforest.tif.aux.xml
158 B
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README.md
3.75 KB
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traits_by_sites.csv
49.21 KB
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WestIndiesSDTF_locality.csv
21.62 KB
Abstract
Delineation of potential dry forest and estimated actual dry forest on Caribbean islands. Potential dry forest is delineated based on CHELSA climate data (www.chelsa-climate.org) and the FAO definition of dry forest. Estimated actual dry forest is corrected for land cover using data from Hansen et al. (2022) https://doi.org/10.1088/1748-9326/ac46ec. Areas of potential dry forest, estimated actual dry forest, and area of built-up land covers are summarized by islands and joined to CHELSA bioclimatic variables for selected islands where data on functional traits are available. Trait values by sites are also included. The package consists of data outputs and R scripts to reproduce the data outputs from identified publicly available data sources.
README
README
Delineation of potential dry forest and estimated actual dry forest on Caribbean islands.
Potential dry forest is delineated based on CHELSA climate data and the FAO definition of dry forest.
Estimated actual dry forest is corrected for land cover using data from Hansen et al. (2022) https://doi.org/10.1088/1748-9326/ac46ec.
Areas of potential dry forest, estimated actual dry forest, and area of built-up land covers are summarized by islands and joined to CHELSA bioclimatic variables for selected islands where data on functional traits are available.
The package consists of data outputs and R scripts to reproduce the data outputs from identified publicly available data sources.
Description of the data and file structure
The data include raster image (GeoTIFF) files of potential and estimated actual dry forest, and a spreadsheet (.csv) of island and land cover areas joined to bioclimatic variables for a selection of islands where plant functional trait data were available.
A spreadsheet (.csv) provides the locations where plant functional trait data were available. An additional spreadsheet (.csv) provides the species names and trait values used for calculations.
A text file (.txt) of urls of CHELSA files to download supports an R script for downloading those data.
potential_dryforest.tif: raster image in GeoTIFF format of potential dry forest. Delineated based on climate normals data from CHELSA using the FAO definition of total annual precipitation 500 to 1500 mm, 5-8 mo < 100 mm precipitation.
potential_dryforest.tif.aux,xml: helper file for file 1.
WestIndiesSDTF_locality.csv: spreadsheet of locations where functional trait data are available. Columns are as follows:
country
island
archipelag
long: longitude
lat: latitude
chelsa_dllist.txt: a text file of urls of files to download. Used by downloadCHELSA.R.
traits_by_sites.csv: a spreadsheet of individual sites and their calculated functional richness (Fric), functional dispersion (FDis), wood density, specific leaf area, and seed mass. 'NA' indicates missing values. Columns are as follows:
site
FRic (functional richness)
FDis (functional dispersion)
wd (wood density, g cm^-2)
sla (specific leaf area, cm^2 g^-1)
sm (seed mass, mg)
Sharing/Access information
Data was derived from the following sources:
CHELSA climate project: https://chelsa-climate.org.
Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122.
Brun, P., Zimmermann, N.E., Hari, C., Pellissier, L., Karger, D. (2022): Data from: CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution. *EnviDat. *https://doi.org/10.16904/envidat.332
Global land cover and land use 2019: https://glad.umd.edu/dataset/global-land-cover-land-use-v1, https://doi.org/10.1088/1748-9326/ac46ec.
Hansen, M. C., Potapov, P. V., Pickens, A. H., Tyukavina, A., Hernandez-Serna, A., Zalles, V., ... & Kommareddy, A. (2022). Global land use extent and dispersion within natural land cover using Landsat data. Environmental Research Letters, 17(3), 034050.
Code/Software
There are two R scripts provided that reproduce the data products in this repository:
- downloadCHELSA.R: downloads selected precipitation normals and bioclimate variables from CHELSA.
- delineateDryForest.R: performs data management, subsetting, summarization, and spatial analysis fuctions to produce the data outputs.
These scripts were produced in R version 4.2.1 in RStudio using the raster (v3.5.21), rgdal (v1.5.32), rgeos (v0.5.9), sf (v1.0.8), and fields (v13.3) packages.
Methods
Areas potentially supporting tropical dry forest in the Caribbean were delineated based on precipitation normals data from CHELSA and a climatically-based definition of tropical dry forest established by the Food and Agriculture Association of the United Nations (FAO): total annual precipitation 500 to 1500 mm, 5-8 mo < 100 mm precipitation. Land cover data (2019 conditions) from Hansen et al. were used to constrain estimated actual dry forest based on the intersection of climate-based potential dry forest with forested land cover. To facilitate analysis with functional trait data obtained from forests on a subset of islands, data on the area of potential and estimated actual dry forest and forest and built land covers were summarized by island and associated with a standard suite of 19 bioclimate variables. R scripts showing and reproducing our detailed methods are provided with the repository.
Usage notes
Data processing and analysis was conducted in R version 4.2.1 in RStudio using the raster (v3.5.21), rgdal (v1.5.32), rgeos (v0.5.9), sf (v1.0.8), and fields (v13.3) packages.