Global Mountain Biodiversity Assessment (GMBA)

Working groups

Species distribution modeling and remote sensing

From field observations to distribution maps using species distribution models

Short description

Understanding the past and present distribution of species is important for many applications, including predictions of future biodiversity patterns and the development of sustainable management and conservation strategies. Species occurrence is commonly obtained from field data (e.g., collections, surveys, checklists, expert range maps) and increasingly provided by remote sensing. Species distribution models (SDMs) in turn relate occurrence data to spatial variables (e.g., climate, topography, land use).

Incorporating spatial information in SDMs raises a number of methodological issues. These issues are associated with the type of spatial variable (e.g., climate, topography, land use), their quality and associated error and uncertainties, their typical process length, and their context of use; with the quality and spatio-temporal resolution of the species data; and with the context in which SDMs are applied. This working group aims at exploring these challenges.

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Deliverable Status Lead
Article: Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models Completed Christophe Randin
Article: Climate data for ecological predictions in mountains*
* preliminary title
Ongoing Christophe Randin
Name Affiliation  
Christophe Randin
University of Lausanne & Jardin Flore-Alp, Switzerland GMBA Network
     
Nigel Yoccoz The Arctic University of Tromsoe, Norway GMBA SSC
Walter Jetz Yale University, United States GMBA SSC
Xuefei Yang Chinese Academy of Sciences, Kunming Institute of Botany, China GMBA SSC
Davnah Payne
GMBA
GMBA office

Past Event

WORKSHOP
Informing Species Distribution Models and Essential Biodiversity Variables using Remote Sensing

University of Zurich (Irchel Campus), Switzerland, 05-09.02.2018
 

Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models