# Data for RLE assessment of the Tropical Glacier Ecosystem of the Cordillera de Mérida
This repository contain data used in the IUCN Red List of Ecosystem assessment of the tropical glacier ecosystem of the Cordillera de Merida.
This project component is part of:
> Ferrer-Paris, J. R., Llambí, L. D., & Melfo, A. (2023, October 5). RLE assessment of the Tropical Glacier Ecosystem of the Cordillera de Mérida. https://doi.org/10.17605/OSF.IO/Y3279
## Types of data used in the assessment
![Types of data used in the RLE assessment][1]
Details of the data sources used in several steps of the analysis and visualisation
### Glacier inventories
The [Randolph Glacier Inventory](https://www.glims.org/RGI/), version 6.0 has global coverage (Randolph Glacier Inventory Consortium 2017). We used following links for [data download](http://www.glims.org/RGI/rgi60_dl.html) and [user guidelines](http://www.glims.org/RGI/00_rgi60_TechnicalNote.pdf).
We also considered the Global Land Ice Measurements from Space ([GLIMS](http://www.glims.org/)) glacier database (Raup et al. 2017; GLIMS Consortium)/
For the study region both RGI and GLIMS have the same information about glacier outlines.
### Glacier extent measurements
All measures of extent of glacier ice come from the published work of (Ramirez et al. 2020).
### Climatic data
- For description of current climatic mean conditions in the study region we used:
- Climatic data for one climate station digitised from a published climadiagram in Azocar and Fariñas (2003).
- Two remote sensing time series: Modis Land Surface Temperature data (Wan et al 2015) and CHIRPS monthly precipitation data (Funk et al. 2015).
- For spatial analysis we used interpolated surfaces of bioclimatic variables from Chelsa: https://chelsa-climate.org/exchelsa-extended-bioclim/ (Karger et al. 2017)
### General GIS data for data exploration and visualisation
For data exploration and visualisation we used following layers:
- GMBA mountain inventory [version 1.2](https://ilias.unibe.ch/goto_ilias3_unibe_cat_1000515.html) (Spehn et al. 2011, Körner et al. 2017)
- [RESOLVE terrestrial ecoregions](https://ecoregions2017.appspot.com), see also the [documentation](https://developers.google.com/earth-engine/datasets/catalog/RESOLVE_ECOREGIONS_2017) (Dinerstein et al. 2017)
- Global 1-km Topography from [EarthEnv](http://www.earthenv.org//topography) (Amatulli et al. 2018)
- Copernicus Global Land Service, Land Cover 100m. Links to: [map visualization](https://lcviewer.vito.be/), [webpage](https://land.copernicus.eu/global/documents/lc100/v2/pum) and [more info](https://blog.vito.be/remotesensing/towards-mapping-annual-land-cover-changes) (Buchhorn et al. 2019).
- World Database of Protected Areas at [protectedplanet.net](http://www.protectedplanet.net) (UNEP-WCMC & IUCN, 2023)
- Mosaic of satellite images (Sentinel 2 MultiSpectral Instrument, Level 2A; December 2021 to March 2022; clouds and shadows removed).
## Workflow of data analysis
![Workflow of data analysis][2]
## References
Amatulli, G., Domisch, S., Tuanmu, M.-N., Parmentier, B., Ranipeta, A., Malczyk, J. & Jetz, W. (2018a) A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in GeoTIFF format, 2 datasets.
Amatulli, G., Domisch, S., Tuanmu, M.-N., Parmentier, B., Ranipeta, A., Malczyk, J. & Jetz, W. (2018b) A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific Data, 5, 180040.
Andressen, R. (2007) Circulación atmosférica y tipos de climas. In GeoVenezuela 2. Medio físico y recursos naturales (ed P.C. Grau), pp. 238–329. Fundación Polar, Caracas.
Azócar, A. & Fariñas, M. (2003) Páramos. In Biodiversidad en venezuela (eds M. Aguilera, A. Azócar & E. González Jiménez),. Fundación Empresas Polar - Ministerio de Ciencia y Tecnología. Fondo Nacional de Ciencia, Tecnología e Innovación (Fonacit), Caracas, Venezuela.
Balcazar, W., Rondón, J., Rengifo, M., Ball, M.M., Melfo, A., Gómez, W. & Yarzábal, L.A. (2015) Bioprospecting glacial ice for plant growth promoting bacteria. Microbiological Research, 177, 1–7.
Ball, M.M., Gómez, W., Magallanes, X., Rosales, R., Melfo, A. & Yarzábal, L.A. (2014) Bacteria recovered from a high-altitude, tropical glacier in venezuelan andes. World Journal of Microbiology and Biotechnology, 30, 931–941.
Braun, C. & Bezada, M. (2013) The history and disappearance of glaciers in venezuela. Journal of Latin American Geography, 12, 85–124. University of Texas Press.
Buchhorn, M., Smets, B., Bertels, L., Lesiv, M., Tsendbazar, N.-E., Herold, M. & Fritz, S. (2019) Copernicus Global Land Service: Land Cover 100m: Collection 2: Epoch 2015: Globe. Zenodo. Https://zenodo.org/record/3243509 [accessed 1 June 2023].
GLIMS Consortium GLIMS Glacier Database, Version 1. National Snow; Ice Data Center. Http://nsidc.org/data/nsidc-0272/versions/1 [accessed 1 June 2023].
Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N.D., Wikramanayake, E., et al. (2017) An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm. BioScience, 67, 534–545.
Farinotti, D., Huss, M., Fürst, J.J., Landmann, J., Machguth, H., Maussion, F. & Pandit, A. (2019) A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nature Geoscience, 12, 168–173.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., et al. (2015) The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2. Springer Science; Business Media LLC.
Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., et al. (2017) Climatologies at high resolution for the earth’s land surface areas. Springer Science; Business Media LLC. Scientific Data. Http://dx.doi.org/10.1038/sdata.2017.122.
Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., et al. (2018) Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad. Http://datadryad.org/stash/dataset/doi:10.5061/dryad.kd1d4.
Körner, C., Jetz, W., Paulsen, J., Payne, D., Rudmann-Maurer, K. & M. Spehn, E. (2017) A global inventory of mountains for bio-geographical applications. Alpine Botany, 127, 1–15.
Llambí, L.D., Melfo, A., Gámez, L.E., Pelayo, R.C., Cárdenas, M., Rojas, C., et al. (2021) Vegetation assembly, adaptive strategies and positive interactions during primary succession in the forefield of the last venezuelan glacier. Frontiers in Ecology and Evolution, 9.
Monasterio, M. & Reyes, S. (1980) Diversidad ambiental y variación de la vegetación en los páramos de los andes venezolanos. In Estudios ecológicos en los páramos andinos (ed M. Monasterio),. Universidad de Los Andes, Mérida, Venezuela.
Polissar, P.J., Abbott, M.B., Wolfe, A.P., Bezada, M., Rull, V. & Bradley, R.S. (2006) Solar modulation of little ice age climate in the tropical andes. Proceedings of the National Academy of Sciences, 103, 8937–8942. Proceedings of the National Academy of Sciences.
Pulwarty, R.S., Barry, R.G., Hurst, C.M., Sellinger, K. & Mogollon, L.F. (1998) Precipitation in the venezuelan andes in the context of regional climate. Meteorology and Atmospheric Physics, 67, 217–237. Springer Science; Business Media LLC.
Ramírez, N., Melfo, A., Resler, L.M. & Llambí, L.D. (2020) The end of the eternal snows: Integrative mapping of 100 years of glacier retreat in the venezuelan andes. Arctic, Antarctic, and Alpine Research, 52, 563–581. Taylor & Francis.
Randolph Glacier Inventory Consortium (2017) Randolph glacier inventory 6.0. NSIDC. Http://www.glims.org/RGI/randolph60.html.
Raup, B., Racoviteanu, A., Khalsa, S.J.S., Helm, C., Armstrong, R. & Arnaud, Y. (2007) The GLIMS geospatial glacier database: A new tool for studying glacier change. Global and Planetary Change, 56, 101–110.
Rondón, J., Gómez, W., Ball, M.M., Melfo, A., Rengifo, M., Balcázar, W., et al. (2016) Diversity of culturable bacteria recovered from pico bolívar’s glacial and subglacial environments, at 4950 m, in venezuelan tropical andes. Canadian Journal of Microbiology, 62, 904–917.
Rounce, D.R., Hock, R. & Maussion, F. (2022) Global PyGEM-OGGM glacier projections with RCP and SSP scenarios, version 1. NASA National Snow; Ice Data Center Distributed Active Archive Center. Https://nsidc.org/data/HMA2_GGP/versions/1.
Rounce, D.R., Hock, R., Maussion, F., Hugonnet, R., Kochtitzky, W., Huss, M., et al. (2023) Global glacier change in the 21st century: Every increase in temperature matters. Science, 379, 78–83.
Spehn, E.M., Koerner, C. & Paulsen, J. (2011) GMBA mountain definition_V1.0.
UNEP-WCMC & IUCN (2023) Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM). Cambridge, UK. Www.protectedplanet.net.
Wan, Z., Hook, S. & Hulley, G. (2015) MOD11A2 MODIS/terra land surface temperature/emissivity 8-day L3 global 1km SIN grid V006. NASA EOSDIS Land Processes DAAC. Https://lpdaac.usgs.gov/products/mod11a2v006/.
[1]: https://files.osf.io/v1/resources/n73wk/providers/osfstorage/651f1d11b0a33e0c04591b99?mode=render
[2]: https://files.osf.io/v1/resources/n73wk/providers/osfstorage/651f1e14b0a33e0c04591da4?mode=render
