A compilation of spatial covariates data of landscape, topographic, hydrologic, and anthropogenic metrics for the entire AF at fine spatial resolution (30-m) for the year 2020.
ATLANTIC SPATIAL dataset is part of the ATLANTIC series, on which research teams are compiling biodiversity information of Atlantic Forest. This paper follows previous published data papers in Ecology.
Vancine, M. H., B. B. Niebuhr, R. L. Muylaert, J. E. F. Oshima, V. Tonetti, R. Bernardo, R. S. C. Alves, E. M. Zanette, V. C. Souza, J. G. R. Giovanelli, J. W. Ribeiro, C. de Angelo, C. H. Grohmann, M. Galetti, M. C. Ribeiro. ATLANTIC SPATIAL: a dataset of landscape, topographic, hydrologic and anthropogenic metrics for the Atlantic Forests. Ecology (in review). Preprint: EcoEvoRxiv. DOI: https://doi.org/10.32942/X26P58.
Space is one of the main drivers of biodiversity, once it regulates the underlying processes affecting the distribution and dynamics of species and communities. It is a fundamental factor in face of the rapid climate and land use and land cover changes at local and global scales, which are linked to habitat loss and fragmentation and their impacts on various organisms. The Atlantic Forest of South America (AF) is among the global biodiversity hotspots because of its high species richness and endemism. Most of the threats to the AF biodiversity are due to the expansion of urbanization and industry, extensive agricultural and livestock production, and mining. Here, we make available integrated and fine-scale spatial information (resolution = 30 m) for the entire AF extent for the year 2020. The metrics consider different vegetation classes (forest and forest plus other natural vegetation), effects of linear structure (roads and railways) and multiple scales (radius buffer from 50 m to 2,500 m and up to 10 km for some metrics). The entire data set consists of over 500 rasters and the AF delimitation vector, available through the R package atlanticr, which we developed to facilitate the organization and acquisition of the data. The metrics consists of land cover (31 classes), distance to grouped land cover classes (forest vegetation, natural vegetation, pasture, temporary crop, perennial crop, forest plantation, urban areas, mining, and water), a set of landscape, topographic and hydrological metrics, and anthropogenic infrastructure. The landscape metrics include landscape morphology (classification as matrix, core, edge, corridor, stepping stone, branch, and perforation), fragment area and proportion, area and number of patches, edge and core areas and proportion, structural and functional connectivity (for different organisms’ gap-crossing capabilities), distance to fragment edges, fragment perimeter and perimeter-area ratio, and landscape diversity. Topographic metrics include elevation, slope, aspect, curvatures, and landform elements (peak, ridge, shoulder, spur, slope, hollow, footslope, valley, pit and flat). Hydrological metrics comprise potential springs and its kernel density and potential streams and the respective distances, and anthropogenic infrastructure maps contain roads, railways, protected areas, indigenous territories, and quilombola territories, and the respective distance to each of them. This data set will allow efficient integration of biodiversity and environmental data for the AF in future ecological studies, and we might be an important reference and data source for landscape planning, biodiversity conservation and forest restoration programs.
Figure 1. Data framework used to summarize land use land cover (LULC) classes into Atlantic Forest habitat types. (a) The LULC classes refer to MapBiomas in the AF (Brazil, Argentina, and Paraguay). (b) Grouped land cover classes. (c) Two vegetation classes were considered as habitat to calculate the landscape metrics.
The data sets are archived in a series of Zenodo repositores, where one might see the metadata and download the layers manually. The repositories are organized thematically according to types of metrics, as described below:
We also created the R package atlanticr which, in addition to facilitating access to other data papers, provides a table with all metrics and their information "atlantic_spatial" and a function to download rasters "atlantic_spatial_download()".
# install package
install.packages("remotes")
remotes::install_github("mauriciovancine/atlanticr")
# load package
library(atlanticr)
# list files
head(atlanticr::atlantic_spatial)
# file download
atlanticr::atlantic_spatial_download(id = 1, path = ".")
This allows for automated and sequential access to the data, complementing the direct access to individual files from the Zenodo repositores shown above.
This repository provides code to guarantee the transparency and reproducibility of the metrics computed for the Atlantic Forest. For access and use of the resulting layers, use the atlanticr R package mentioned above.
The material of this repository is organized in the following folders, as described below.
All analyses were performed in R language and GRASS GIS through rgrass R package. We gather in this foler the scripts to make the preparation of data and the computation of all the metrics reproducible. This folder contains R code files used to:
01_01_download_limits.R: download Atlantic Forest limit01_02_download_landscape.R: download Land Use and Land Cover from MapBiomas Brazil v07 and MapBiomas Bosque Atlántico v0201_03_download_topographic.R: download topographic data from FABDEM v1.201_04_download_hydrologic.R: download hydrologic data from HydroBASINS and HydroLAKES, and official databases from Brazil (IBGE), Argentina (IGN) and Paraguay (INE),01_05_download_anthropogenic_roads_railways.R: download roads and railways from official databases from Brazil (IBGE), Argentina (IGN) and Paraguay (INE)01_06_download_anthropogenic_protected_areas.R: download protected areas from Protected Planet01_07_download_anthropogenic_indigenous_territories.R: download indigenous territories from official databases from Brazil (FUNAI) and Paraguay (Tierras Indígenas)01_08_download_anthropogenic_urban_areas.R: download urban areas data from official databases from Brazil (IBGE), Argentina (IGN) and Paraguay (INE)02_01_grassgis_import_prepare_data_limit.R: import and prepare Atlantic Forest limit data on the GRASS GIS\02_02_grassgis_import_prepare_data_landscape.R: import and prepare Mapbiomas LULC rasters data on the GRASS GIS02_03_grass_gis_import_prepare_data_topographic.R: import and prepare FABDEM rasters data on the GRASS GIS02_04_grassgis_import_prepare_data_hydrologic.R: import and prepare hydrologic data on the GRASS GIS02_05_grassgis_import_anthropogenic_roads_railways.R: import and prepare road and railway data on the GRASS GIS02_06_grassgis_import_anthropogenic_protected_areas.R: import and prepare protected areas data on the GRASS GIS02_07_grassgis_import_anthropogenic_indigenous_territories.R: import and prepare indigenous territories data on the GRASS GIS02_08_grassgis_import_anthropogenic_urban_areas.R: import and prepare urban area data on the GRASS GIS03_02_grassgis_metrics_landscape.R: calculate landscape metrics on the GRASS GIS03_03_grassgis_metrics_topographic.R: calculate topographic metrics on the GRASS GIS03_04_grassgis_metrics_hydrologic.R: calculate hydrologic metrics on the GRASS GIS03_05_grassgis_metrics_anthropogenic.R: calculate anthropogenic metrics on the GRASS GIS04_grass_gis_color.R: define raster colors on the GRASS GIS05_googledrive_organize_data.R: organize data on the Google Drive05_zenodo_organize_data.R: organize data on Zenodo repositorieslsm_hydrologic.R: function to calculate spring and streams from DEM rasterlsm_hydrologic_basin.R: function to calculate spring and streams by basins from DEM raster
Table with layer information and download links.
All figures in the paper in high definition.
Universidade Estadual Paulista (UNESP), Instituto de Biociências, Departamento de Ecologia, Laboratório de Ecologia Espacial e Conservação, Rio Claro, Brazil
Norwegian Institute for Nature Research (NINA), Oslo, Norway
Molecular Epidemiology and Public Health Laboratory, Hopkirk Research Institute, Massey University, Palmerston North, New Zealand
Universidade Estadual Paulista (UNESP), Instituto de Biociências, Departamento de Ecologia, Laboratório de Ecologia Espacial e Conservação, Rio Claro, Brazil
Universidade Estadual Paulista (UNESP), Instituto de Biociências, Departamento de Zoologia e Centro de Aquicultura (CAUNESP), Rio Claro, SP, Brazil