Scaling-up ecological understanding with remote sensing and causal inference

Elisa Van Cleemput; Peter B Adler; Katharine Nash Suding; Alanna Jane Rebelo; Benjamin Poulter; Laura E Dee

doi:10.1016/j.tree.2024.09.006

Scaling-up ecological understanding with remote sensing and causal inference

Trends Ecol Evol. 2024 Nov 6:S0169-5347(24)00241-6. doi: 10.1016/j.tree.2024.09.006. Online ahead of print.

Authors

Elisa Van Cleemput¹, Peter B Adler², Katharine Nash Suding³, Alanna Jane Rebelo⁴, Benjamin Poulter⁵, Laura E Dee³

Affiliations

¹ Leiden University College The Hague, Leiden University, 2595 DG Den Haag, The Netherlands; Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO 80309, USA. Electronic address: [email protected].
² Department of Wildland Resources and the Ecology Center, Utah State University, Logan, UT 84322, USA.
³ Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO 80309, USA.
⁴ Water Science Unit, Natural Resources and Engineering, Agricultural Research Council, Pretoria 0001, South Africa; Department of Conservation Ecology and Entomology, Stellenbosch University, Stellenbosch 7602, South Africa; Africa's Search for Sound Economic Trajectories (ASSET) Research, Knysna District, South Africa.
⁵ NASA Goddard Space Flight Center, Earth Sciences Division, Biospheric Sciences Laboratory, Greenbelt, MD 20771, USA.

PMID: 39510921
DOI: 10.1016/j.tree.2024.09.006

Abstract

Decades of empirical ecological research have focused on understanding ecological dynamics at local scales. Remote sensing products can help to scale-up ecological understanding to support management actions that need to be implemented across large spatial extents. This new avenue for remote sensing applications requires careful consideration of sources of potential bias that can lead to spurious causal relationships. We propose that causal inference techniques can help to mitigate biases arising from confounding variables and measurement errors that are inherent in remote sensing products. Adopting these statistical techniques will require interdisciplinary collaborations between local ecologists, remote sensing specialists, and experts in causal inference. The insights from integrating 'big' observational data from remote sensing with causal inference could be essential for bridging biodiversity science and conservation.

Keywords: causal inference; confounding variables; landscape ecology; measurement error; remote sensing; scale.

Publication types

Review