DOI: 10.5061/dryad.sn02v6xdd
title: Data from: Mapping threatened Thai bovids provides opportunities for improved conservation outcomes in Asia

description: Wild bovids provide important ecosystem functions throughout their ranges.
 Five wild bovids remain in Thailand: gaur (Bos gaurus), banteng (Bos
 javanicus), wild water buffalo (Bubalus arnee), mainland serow
 (Capricornis sumatraensis) and Chinese goral (Naemorhedus griseus).
 However, their populations and habitats have declined substantially and
 become fragmented. Here, we identify potentially suitable habitat for
 these threatened bovids using ecological niche models and quantify how
 much suitable area remains within protected areas. We combined species
 occurrence data with environmental variables and used spatially-restricted
 Biotic-Abiotic-Mobility frameworks with species-specific and single large
 accessible areas. We used ensembles from eight algorithms for generating
 maps and out-of-sample predictions to validate model performance against
 new data. Gaur, banteng, and buffalo models performed well throughout the
 entire distribution (≥62%) and in Thailand (≥80%). Mainland serow and
 Chinese goral performed poorly for the entire distribution and in
 Thailand, though a 5 km movement buffer markedly improved model
 performance for serow. Particularly large suitable areas were in Thailand
 and India for gaur, Cambodia and Thailand for banteng, and India for
 buffalo. Over 50% of overall suitable habitat is located outside protected
 areas, with just 9% for buffalo in Thai protected areas, highlighting area
 for potential habitat management and conflict mitigation.
description: We collected the raw species occurrence data from several
 different sources. The data were collected using various field study
 methodologies, including observation of animal signs (e.g. footprint and
 dung) during forest patrols, direct observation during wildlife surveys,
 camera trapping, and radio-collar signals. The data coverage by country
 can be found in Table 1 in the main text, and for details of the data
 collection sites, see the supplementary materials Table S2 and
 S10. We built ecological niche models (using ENMTML
 package in R) to identify habitat suitability and calculate how much
 potential suitable habitat for these species remains across their species
 distribution. The R code and map results are provided.
description: # Data from: Mapping threatened Thai bovids provides opportunities for
 improved conservation outcomes in Asia ## Code for creating Ecological
 Niche Models (ENMs) for 5 wild Bovidae species. Reference: Horpiencharoen
 et al. *submitted*. Mapping threatened bovids provides opportunities for
 improved conservation outcomes in Asia. * *Bos gaurus* (Gaur) * *Bos
 javanicus* (Banteng) * *Bubalus arnee* (Wild water buffalo) * *Capricornis
 sumatraensis* (Mainland serow) * *Naemorhedus griseus* (Chinese goral) We
 used the package [ENMTML](https://github.com/andrefaa/ENMTML) for model
 building. ## How to use these scripts: * Download the dataset. **Warning:
 These are large files (~10 GB) and download can be slow.** * folders and
 subfolder structure: * 1\. data_preparation: the dataset use for
 modelling. * 1.1 `acc`: the polygons (.shp) of accessible areas, it
 contains 6 subfolders large accessible areas (named: acc_la), and the
 others for species-specific accessible areas (accbanteng, accbuffalo,
 accgaur, accserow, accgoral). Selecting the accessible areas using
 [ecoregions.](https://www.worldwildlife.org/publications/terrestrial-ecoregions-of-the-world) * 1.2 `adm_border`: the polygons (.shp) of admistrative border using for mapping and creating figures. * 1.3 `env`: contains all environmental variables, cropped as two accessible areas 1) species-specific accessible areas (envgaur, envbanteng, eenvbuffalo, envserow, envgoral) and 2) large accessible area (env_la). * 1.4 `PA_and_country`: contains the data of countries and protected areas boundary for each species (.tif). * 1.5 `model_area.csv`: the total suitable areas from the best model of species-specific accessible areas (SSA) and large accessible areas (LA), classified by country. * 2\. result_sample: Some of the results (habitat suitability maps, richness maps (.tif) from modelling, contains 3 subfolders. * 2.1 `binary`: The best binary model of the habitat suitability (.tif). * 2.2 `ensembles`: The best ensemble models of the habitat suitability (.tif). * 2.3 `dataset_combine`: The dataset extracting and combining from rasters results contains habitat suitability values (binary and probability), protected areas category, environmental variables and observation data. * Unzip the downloaded folder. * Set up a working directory in R before running the code. * Run the scripts according to the numerical order. * Note: Some scripts may need to download external data, which can be downloaded within the provided scripts or from the following links: species occurrence data ([GBIF](https://www.gbif.org/)), [protected areas](https://www.protectedplanet.net/region/AS), and [IUCN spatial data](https://www.iucnredlist.org/resources/spatial-data-download).