Dataset Open Access
The main dataset consists of ecoregion-level data on five plant functional traits (wood density, leaf size, stem spines, leaf spines and latex production), as well as ecoregion-level data on extinct megafauna historical patterns, fire, climate, soil, hurricanes and geografical variables (first spreadsheet) for the Neotropical biogeographic realm (Table 1). It also includes species-level plant functional trait data, and the abundance (presence-absence for leaf size) of these species, and the occurrences extinct megafauna and extant mammal herbivore species per Neotropical ecoregion, as well as diet data compiled for megafauna species. The species-level functional trait data was compiled from the literature and the names of the species in these data was used to search for occurrence data for these species in the Global Biodiversity Information Facility (Data available from GBIF using the following doi: WD: 10.15468/dl.3vua3x; Stem spines: 10.15468/dl.ar5ddj; Latex: 10.15468/dl.m8dzjd; Leaf spines: 10.15468/dl.vv8gw4; Leaf size: 10.15468/dl.k98nxc). During the process, species level were corrected and updated using tools from the "rgbif" package for R. We then croped only the Neotropical region, and calculate ecoregion level trait means for continuous traits (Wood Density and Leaf Size) and maximum por binary traits (Stem and Leaf Spines, Latex), using the ecoregion shapefile provided in https://storage.googleapis.com/teow2016/Ecoregions2017.zip. We obtained data on historical distribution of megafauna species and extant mammal species from the MegaPast2Future/PHYLACINE_1.2 dataset, and obtained diet information from literature sources. Climate data was obtained from WorldClim 2.1 (10 minute spatial resolution) and was based on climate data from 1970 and 2000. Soil data were obtained from SoilGrids (5 km of spatial resolution), and consisted of mean values for two depths, 0.05 and 2 m. We obtained the number (a proxy for frequency) and intensity of wildfires per ecoregion area using the MODIS active fire location product (MCD14ML). We only considered fires with detection confidence of 95% or higher occurring from November 2000 to December 2019 (both included). To ensure that only wildfires were considered, we associated each fire pixel with a land cover type (300 m of spatial resolution) from for a buffer area of 1000 m surrounding the fire pixel centroid. We excluded all of the fires occurring in areas in which more than 10% of the surrounding land cover pixels corresponded to agricultural, urban and water classes. We calculated the number of wildfires per ecoregion area by dividing the fire count of each Ecoregion by the ecoregion area, and multiplying the resulting value by the proportion of vegetated land cover pixels (same classes used to exclude fires in anthropogenic areas and water bodies above). Fire intensity was estimated as the average fire radiative power across all detected wildfires in the ecoregion. We also classified ecoregions into insular (1), when most of the ecoregion area was located in islands, vs. continental (0), otherwise. We also compiled data on hurricane activity, as woody density was suggested to confer resistance against this disturbance. We used data from 1990 to 2019 from the HURDAT2 dataset, containing six-hourly information about the location of all of the known tropical and subtropical cyclones (0.1° latitude/longitude). We used the sum of hurricane occurrences per ecoregions divided by ecoregion area as an indicator of hurricane activity.
Three .txt files containing the custom codes developed for building the Ecoregion-level dataset (predictors and traits) and for data analyses used in the article are also included.
Codes for Data Analyses and Figures.txt
Codes for environment data, fire, megafauna and hurricane datasets.txt
Codes for traits per ecoregion.txt