Skip to main navigation Skip to search Skip to main content

Predicting species distributions across the Amazonian and Andean regions using remote sensing data

  • Wolfgang Buermann*
  • , Sassan Saatchi
  • , Thomas B. Smith
  • , Brian R. Zutta
  • , Jaime A. Chaves
  • , Borja Milá
  • , Catherine H. Graham
  • *Corresponding author for this work
  • University of California at Los Angeles
  • Jet Propulsion Laboratory, California Institute of Technology
  • Stony Brook University

Research output: Contribution to journalArticlepeer-review

199 Scopus citations

Abstract

Aim: We explore the utility of newly available optical and microwave remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and QuikSCAT (QSCAT) instruments for species distribution modelling at regional to continental scales. Using eight Neotropical species from three taxonomic groups, we assess the extent to which remote sensing data can improve predictions of their geographic distributions. For two bird species, we investigate the specific contributions of different types of remote sensing variables to the predictions and model accuracy at the regional scale, where the benefits of the MODIS and QSCAT satellite data are expected to be most significant. Location: South America, with a focus on the tropical and subtropical Andes and the Amazon Basin. Methods: Potential geographic distributions of eight species, namely two birds, two mammals and four trees, were modelled with the maxent algorithm at 1-km resolution over the South American continent using climatic and remote sensing data separately and combined. For each species and model scenario, we assess model performance by testing the agreement between observed and simulated distributions across all thresholds and, in the case of the two focal bird species, at selected thresholds. Results: Quantitative performance tests showed that models built with remote sensing and climatic layers in isolation performed well in predicting species distributions, suggesting that each of these data sets contains useful information. However, predictions created with a combination of remote sensing and climatic layers generally resulted in the best model performance across the three taxonomic groups. In Ecuador, the inclusion of remote sensing data was critical in resolving the known geographically isolated populations of the two focal bird species along the steep Amazonian-Andean elevational gradients. Within remote sensing subsets, microwave-based data were more important than optical data in the predictions of the two bird species. Main conclusions: Our results suggest that the newly available remote sensing data (MODIS and QSCAT) have considerable utility in modelling the contemporary geographical distributions of species at both regional and continental scales and in predicting range shifts as a result of large-scale land-use change.

Original languageEnglish
Pages (from-to)1160-1176
Number of pages17
JournalJournal of Biogeography
Volume35
Issue number7
DOIs
StatePublished - Jul 2008
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 15 - Life on Land
    SDG 15 Life on Land

Keywords

  • Conservation biogeography
  • Ecological niche characterization
  • MODIS
  • Microwave remote sensing
  • Optical remote sensing
  • QSCAT
  • South America
  • Spatial scale
  • Species distribution modelling

Fingerprint

Dive into the research topics of 'Predicting species distributions across the Amazonian and Andean regions using remote sensing data'. Together they form a unique fingerprint.

Cite this