The MARAS dataset, vegetation and soil characteristics of dryland rangelands across Patagonia

Gabriel Oliva*, Eder dos Santos, Osiris Sofía, Fernando Umaña, Virginia Massara, Guillermo García Martínez, Cecilia Caruso, German Cariac, Daniela Echevarría, Anabella Fantozzi, Lucas Butti, Donaldo Bran, Juan Gaitán, Daniela Ferrante, Paula Paredes, Erwin Domínguez, Fernando T. Maestre

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

We present the MARAS (Environmental Monitoring of Arid and Semiarid Regions) dataset, which stores vegetation and soil data of 426 rangeland monitoring plots installed throughout Patagonia, a 624.500 km2 area of southern Argentina and Chile. Data for each monitoring plot includes basic climatic and landscape features, photographs, 500 point intercepts for vegetation cover, plant species list and biodiversity indexes, 50-m line-intercept transect for vegetation spatial pattern analysis, land function indexes drawn from 11 measures of soil surface characteristics and laboratory soil analysis (pH, conductivity, organic matter, N and texture). Monitoring plots were installed between 2007 and 2019, and are being reassessed at 5-year intervals (247 have been surveyed twice). The MARAS dataset provides a baseline from which to evaluate the impacts of climate change and changes in land use intensity in Patagonian ecosystems, which collectively constitute one of the world´s largest rangeland areas. This dataset will be of interest to scientists exploring key ecological questions such as biodiversity-ecosystem functioning relationships, plant-soil interactions and climatic controls on ecosystem structure and functioning.

Original languageEnglish (US)
Article number327
JournalScientific data
Volume7
Issue number1
DOIs
StatePublished - Dec 1 2020

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

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