Integrating animal tracking datasets at a continental scale for mapping Eurasian lynx habitat
Data files
Oct 05, 2023 version files 3.04 GB
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global_ensemble_scale_integrated.tif
1.09 GB
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local_ensemble_scale_integrated.tif
1.14 GB
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README.md
2.34 KB
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training_data_home_range_level.csv
321.41 MB
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training_data_landscape_level.csv
490.07 MB
Oct 19, 2023 version files 3.04 GB
Abstract
README: Integrating animal tracking datasets at a continental scale for mapping Eurasian lynx habitat
This dataset is a product of the Conservation Biogeography Lab at Humbolt-Universität zu Berlin (hu.berlin/biogeography) and is a result of the following publication:
Oeser, J., Heurich, M., Kramer-Schadt, S., Mattisson, J., Krofel, M., Krojerová-Prokešová, J., Zimmermann, F., Anders, O., Andrén, H., Bagrade, G., Belotti, E., Breitenmoser-Würsten, C., Bufka, L., Černe, R., Drouet-Hoguet, N., Duľa, M., Fuxjäger, C., Gomerčić, T., Jędrzejewski, W. … Kuemmerle, T. (2023). Integrating animal tracking datasets at a continental scale for mapping Eurasian lynx habitat. Diversity and Distributions, 00, 1–15. https://doi.org/10.1111/ddi.13784
Description of the data and file structure
GEOTIFF files contain habitat suitability raster maps of the best-performing global and local habitat models, respectively, using an algorithm ensemble of the best-performing modeling algorithms (Maxent and random forest). Maps represent scale-integrated predictions obtained by combining (i.e., multiplying) habitat models built at the landscape and home range levels of selection (selection of home ranges in the wider landscape and selection of locations within home ranges). This means that areas with suitable habitat too small for the establishment of lynx home ranges will be 'masked out' in the scale-integrated prediction. Predictions represent a relative habitat suitability index rescaled to a 0-1000 scale, best understood as a relative ranking of raster cells in terms of their habitat suitability. Raster maps are in EPSG 3035 projection and have 100m spatial resolution.
CSV tables contain data frames used for building habitat models. Column description:
"animals_id" - ID of the lynx individual
"site" - name of the study site
"occ" - occurrence status: 1 for presence locations and 0 for available locations (background points)"sex" - sex of the individual"acquisition_time" - date and time of observation
"ruggedness" - terrain ruggedness
"forest" - forest cover"forest_integrity" - forest integrity
"human_modification" - human modification index
"accessibility" - accessibility (travel time to cities)
"snow_cover" - snow cover frequency
"road_density" - road density
"greenness_median" - median Landsat tasseled cap greenness
"greenness_variability" - amplitude of Landsat tasseled cap greenness
"brightness_median" - median Landsat tasseled cap brightness
"brightness_variability" - amplitude of Landsat tasseled cap brightness
"wetness_median" - median Landsat tasseled cap wetness
"wetness_variability" - amplitude of Landsat tasseled cap wetness
The numbers following variable names indicate spatial scale of the model variables (e.g., _22000 for 22km diameter moving window).