DOI: 10.5281/zenodo.15231415
title: Species distribution models associtaed with journal article The utility of dynamic forest structure from GEDI lidar fusion in tropical mammal species distribution models

description: The dataset includes species distribution models (SDMs) associtaed with published journal article The utility of dynamic forest structure from GEDI lidar fusion in tropical mammal species distribution models (https://doi.org/10.3389/frsen.2025.1563430). We modeled the distribution of 47 mammal species found in Borneo and/or Sumatra. Not all species are naturally occurring on both islands. Furthermore, some species, which may be naturally occuring on an island, did not have sufficient observations for distribution modeling on one island or the other. SDM prediction maps for critically endangered species (Bornean orangutan, Sunda pangolin, and Sumatran tiger) will be curated by the relevant IUCN taxonomic authority (i.e., specialist group) and may be made available upon request. See Table 1 in the journal article for details regarding species observations and modeling domains. 

For analytical purposes we provide cloud optimized GeoTIFF files corresponding to SDM predictions from the Hindcast with GEDI scenario (see journal article for details). Predictions corresponding to the probability of the presence class (1) from both Random Forest (RF) and Generalized Linear Model (GLM) algorithms are included in each file archive (.tar.gz). There are separate files for Borneo and Sumatra predictions since we fit separate models for these regions. For each species and region we made 10 predictions based on a shuffling of the model training/testing data (i.e. bootstrapping). The file archives contain GeoTIFF files which are aggregates of the (up to) 10 predictions per species. We used the mean and standard deviation to aggregate the bootstrap model predictions per pixel. The band order in each file isband1: rf_p1_mn - RF per-pixel meanband2: rf_p1_sd - RF per-pixel standard deviationband3: glm_p1_mn - GLM per-pixel meanband4: glm_p1_sd - GLM per-pixel standard deviation

The spatial reference system is EPSG:4326. The spatial resolution (i.e. pixel size) is 0.00080848 decimal degrees which is equivalent to ~90 m.

The prediction year (either 2001 or 2021) is included in the file name.

All pixel values are scaled by 10000 and stored as Int16 to decrease file size. A value of 0 corresponds to 0.0 probability of occurrence while a value of 10000 corresponds to 1.0 probability of occurrence. Lakes were set to 0 probability of occurrence using the HydroLAKES lake polygons shapefile (https://www.hydrosheds.org/products/hydrolakes).

The nodata value is -9999.

SDM performance was assessed using the area under the receiver operator characteristic curve (AUCROC) metric and is summarized in Table1.xlsx of the journal article Supplementary Material. 

 

For quick visualization we also provide lower resolution, non-georeferenced PNG maps in the file archive sdm_maps_png.tar.gz. These maps show the mean probability of the presence class, as well as the coefficient of variation (standard deviation divided by the mean). There are separate files corresponding to SDM predictions for each region, modeling algorithm (RF and GLM), and year (2001 and 2021).