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Inferring plant–plant interactions using remote sensing.

Authors :
Chen, Bin J. W.
Teng, Shuqing N.
Guang Zheng
Lijuan Cui
Shao-peng Li
Staal, Arie
Eitel, Jan U. H.
Crowther, Thomas W.
Berdugo, Miguel
Lidong Mo
Haozhi Ma
Bialic-Murphy, Lalasia
Zohner, Constantin M.
Maynard, Daniel S.
Averill, Colin
Jian Zhang
Qiang He
Evers, Jochem B.
Anten, Niels P. R.
Yizhaq
Source :
Journal of Ecology; Oct2022, Vol. 110 Issue 10, p2268-2287, 20p
Publication Year :
2022

Abstract

1. Rapid technological advancements and increasing data availability have improved the capacity to monitor and evaluate Earth's ecology via remote sensing. However, remote sensing is notoriously ‘blind’ to fine-scale ecological processes such as interactions among plants, which encompass a central topic in ecology. 2. Here, we discuss how remote sensing technologies can help infer plant–plant interactions and their roles in shaping plant-based systems at individual, community and landscape levels. At each of these levels, we outline the key attributes of ecosystems that emerge as a product of plant–plant interactions and could possibly be detected by remote sensing data. We review the theoretical bases, approaches and prospects of how inference of plant–plant interactions can be assessed remotely. 3. At the individual level, we illustrate how close-range remote sensing tools can help to infer plant–plant interactions, especially in experimental settings. At the community level, we use forests to illustrate how remotely sensed community structure can be used to infer dominant interactions as a fundamental force in shaping plant communities. At the landscape level, we highlight how remotely sensed attributes of vegetation states and spatial vegetation patterns can be used to assess the role of local plant–plant interactions in shaping landscape ecological systems. 4. Synthesis. Remote sensing extends the domain of plant ecology to broader and finer spatial scales, assisting to scale ecological patterns and search for generic rules. Robust remote sensing approaches are likely to extend our understanding of how plant–plant interactions shape ecological processes across scales—from individuals to landscapes. Combining these approaches with theories, models, experiments, data-driven approaches and data analysis algorithms will firmly embed remote sensing techniques into ecological context and open new pathways to better understand biotic interactions. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00220477
Volume :
110
Issue :
10
Database :
Complementary Index
Journal :
Journal of Ecology
Publication Type :
Academic Journal
Accession number :
160296496
Full Text :
https://doi.org/10.1111/1365-2745.13980