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Remotely Sensing Ecological Genomics

NOMIS Project 2018

— 2024

The world’s ecosystems are losing biodiversity at unprecedented rates. Humans, who have become a dominant evolutionary force in the Anthropocene, are strongly impacting biodiversity, and there is increasing evidence that the sixth mass extinction event may be underway. However, we currently lack consistent data on global biodiversity since it is difficult to precisely quantify, leading to nonattributable loss of the goods and services biodiversity provides. Capturing and understanding the diversity of plants, their physiological and morphological properties, and their genetic variation is vitally important to monitoring plant diversity, the processes contributing to coexistence and ecosystem functioning, and how diversity responds to environmental change. Novel remote sensing methods have great potential for capturing this information with sufficiently high resolution at global scales.

The Remotely Sensing Ecological Genomics project seeks to measure and understand the genetic mechanisms underlying the behavior of plants in their natural environment by linking genomics (function and structure of genes), phenomics (physical and biochemical traits) and spectranomics (mapping phylogenies as well as composition and chemistry of plants) at different spatial and temporal scales using remote sensing. The approach is a unique combination of new theory, modeling, experiments, observations and big data approaches to create a new integrative research field of remotely sensing ecological genomics.

By establishing a biodiversity observatory to systematically measure plant functional traits, phylogenies and intraspecific genetic variation in a variety of ecosystems, the project’s scientists will be able to predict how ecosystems respond to accelerating global change drivers with comprehensive, consistent and replicated data on the patterns and dynamics of plant functional, phylogenetic and genetic diversity. Linking these diversity observations with observations of ecosystem processes and environmental conditions at similar resolution and scale will greatly advance knowledge of biodiversity environment-ecosystem feedback, such that global Earth system models (ESMs) with biodiversity information can assess the ecological, environmental and social impacts of global change. This will enable the team to answer the unanswered scientific question of how biodiversity will fare — or collapse — given global change scenarios and how the world will change in response when one of its drivers, namely biodiversity, is changing.

NOMIS is supporting the Remotely Sensing Ecological Genomics project together with the Global Change and Biodiversity University Research Priority Program (URPP Global Change and Biodiversity) and the UZH Foundation. The project is being led by Michael Schaepman at the University of Zurich, Zurich, Switzerland.


NOMIS Researcher(s)


Project News

In a feature story in Science, Elizabeth Pennisi discusses the importance of remote sensing research by plant ecologist Jeannine Cavender-Bares and colleagues, including NOMIS researcher and University of Zurich President […]

Perrine Huber of swissnex San Francisco explains how a collaboration between NASA in California and the University of Zurich will help scientists better understand how the Earth and its climate […]

NOMIS scientist Michael Schaepman’s remote sensing and biodiversity research has been featured in an article by Swiss newspaper Neue Zürcher Zeitung (NZZ). The article, “Ein fliegender Wächter für die Ökosysteme […]

The University of Zurich (UZH) has published an article about remote sensing expert Michael Schaepman’s plans to use a new aerial sensing method to investigate the complex interplay between ecosystems, […]


Project Insights

Abstract: Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution (“hyperspectral”) spectroscopy can characterize trait variation at
Abstract: Aim: Globally distributed plant trait data are increasingly used to understand relationships between biodiversity and ecosystem processes. However, global trait databases are sparse because they are compiled from many, mostly small databases. This sparsity in both trait space completeness and geographical distribution limits the potential for both multivariate and global
Abstract: Genetic diversity influences the evolutionary potential of forest trees under changing environmental conditions, thus indirectly the ecosystem services that forests provide. European beech (Fagus sylvatica L.) is a dominant European forest tree species that increasingly suffers from climate change-related die-back. Here, we conducted a systematic literature review of neutral genetic