Scaling-up ecological understanding with remote sensing and causal inference
Current biodiversity policy goals demand large-scale conservation and management actions.Ecological relationships operating in fine-scale experiments and observational datasets do not necessarily apply at management-relevant spatial scales.Remote sensing offers a solution for understanding ecologica...
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| Published in: | Trends in ecology & evolution (Amsterdam) Vol. 40; no. 2; pp. 122 - 135 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
Elsevier Ltd
01.02.2025
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| Subjects: | |
| ISSN: | 0169-5347, 1872-8383, 1872-8383 |
| Online Access: | Get full text |
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| Summary: | Current biodiversity policy goals demand large-scale conservation and management actions.Ecological relationships operating in fine-scale experiments and observational datasets do not necessarily apply at management-relevant spatial scales.Remote sensing offers a solution for understanding ecological relationships at scale, but inferring causality from remotely sensed data presents various challenges.Confounding variables and measurement errors, which are inevitable in large-scale observational datasets, can create bias and lead to spurious conclusions about causal relationships.Remote sensing products can help to scale-up causal ecological relationships, but only if they are accompanied by careful analyses and inferences that address challenges posed by confounding variables and measurement errors.
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.
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. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 |
| ISSN: | 0169-5347 1872-8383 1872-8383 |
| DOI: | 10.1016/j.tree.2024.09.006 |