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

Christophe F. Randin, Michael B. Ashcroft, Janine Bolliger, Jeannine Cavender-Bares, Nicholas C. Coops, Stefan Dullinger, Thomas Dirnböck, Sandra Eckert, Erle Ellis, Néstor Fernández, Gregory Giuliani, Antoine Guisan, Walter Jetz, Stéphane Joost, Dirk Karger, Jonas Lembrechts, Jonathan Lenoir, Miska Luoto, Xavier Morin, Bronwyn PriceDuccio Rocchini, Michael Schaepman, Bernhard Schmid, Peter Verburg, Adam Wilson, Paul Woodcock, Nigel Yoccoz, Davnah Payne*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

1233 Downloads (Pure)

Abstract

In the face of the growing challenges brought about by human activities, effective planning and decision-making in biodiversity and ecosystem conservation, restoration, and sustainable development are urgently needed. Ecological models can play a key role in supporting this need and helping to safeguard the natural assets that underpin human wellbeing and support life on land and below water (United Nations Sustainable Development Goals; SDG 15 & 14). The urgency and complexity of safeguarding forest (SDG 15.2) and mountain ecosystems (SDG 15.4), for example, and halting decline in biodiversity (SDG 15.5) in the Anthropocene requires a re-envisioning of how ecological models can best support the comprehensive assessments of biodiversity and its change that are required for successful action. A key opportunity to advance ecological modeling for both predictive and explanatory purposes arises through a collaboration between ecologists and the Earth observation community, and a close integration of remote sensing and species distribution models. Remote sensing products have the capacity to provide continuous spatiotemporal information about key factors driving the distribution of organisms, therefore improving both the use and accuracy of these models for management and planning. Here we first survey the literature on remote sensing data products available to ecological modelers interested in improving predictions of species range dynamics under global change. We specifically explore the key biophysical processes underlying the distribution of species in the Anthropocene including climate variability, changes in land cover, and disturbances. We then discuss potential synergies between the ecological modeling and remote sensing communities, and highlight opportunities to close the data and conceptual gaps that currently impede a more effective application of remote sensing for the monitoring and modeling of ecological systems. Specific attention is given to how potential collaborations between the two communities could lead to new opportunities to report on progress towards global agendas - such as the Agenda 2030 for sustainable development of the United Nations or the Post-2020 Global Biodiversity Framework of the Convention for Biological Diversity, and help guide conservation and management strategies towards sustainability.

Original languageEnglish
Article number111626
Pages (from-to)1-18
Number of pages18
JournalRemote Sensing of Environment
Volume239
Early online date13 Jan 2020
DOIs
Publication statusPublished - 15 Mar 2020

Funding

We thank the European Space Agency , Future Earth , the Swiss National Science Foundation (grant IZSEZ0_178727 ), and the University of Zurich for financially supporting the workshop that led to this paper. This workshop was an initiative of the Global Mountain Biodiversity Assessment.

FundersFunder number
European Space Agency
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungIZSEZ0_178727
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Universität Zürich

    Keywords

    • Anthropocene
    • Monitoring
    • Remote sensing
    • Species distribution models
    • Sustainable development
    • Terrestrial ecosystems

    Fingerprint

    Dive into the research topics of 'Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models'. Together they form a unique fingerprint.

    Cite this