Karen Dyson
Coupling remote sensing and eDNA to monitor environmental impact: A pilot to quantify the environmental benefits of sustainable agriculture in the Brazilian Amazon
Dyson, Karen; Nicolau, Andréa P.; Tenneson, Karis; Francesconi, Wendy; Daniels, Amy; Andrich, Giulia; Caldas, Bernardo; Castaño, Silvia; de Campos, Nathanael; Dilger, John; Guidotti, Vinicius; Jaques, Iara; McCullough, Ian M.; McDevitt, Allan D.; Molina, Luis; Nekorchuk, Dawn M.; Newberry, Tom; Pereira, Cristiano Lima; Perez, Jorge; Richards-Dimitrie, Teal; Rivera, Ovidio; Rodriguez, Beatriz; Sales, Naiara; Tello, Jhon; Wespestad, Crystal; Zutta, Brian; Saah, David
Authors
Andréa P. Nicolau
Karis Tenneson
Wendy Francesconi
Amy Daniels
Giulia Andrich
Bernardo Caldas
Silvia Castaño
Nathanael de Campos
John Dilger
Vinicius Guidotti
Iara Jaques
Ian M. McCullough
Allan D. McDevitt
Luis Molina
Dawn M. Nekorchuk
Tom Newberry
Cristiano Lima Pereira
Jorge Perez
Teal Richards-Dimitrie
Ovidio Rivera
Beatriz Rodriguez
Dr Naiara Guimaraes Sales N.GuimaraesSales@salford.ac.uk
Lecturer
Jhon Tello
Crystal Wespestad
Brian Zutta
David Saah
Contributors
Petr Heneberg
Editor
Abstract
Monitoring is essential to ensure that environmental goals are being achieved, including those of sustainable agriculture. Growing interest in environmental monitoring provides an opportunity to improve monitoring practices. Approaches that directly monitor land cover change and biodiversity annually by coupling the wall-to-wall coverage from remote sensing and the site-specific community composition from environmental DNA (eDNA) can provide timely, relevant results for parties interested in the success of sustainable agricultural practices. To ensure that the measured impacts are due to the environmental projects and not exogenous factors, sites where projects have been implemented should be benchmarked against counterfactuals (no project) and control (natural habitat) sites. Results can then be used to calculate diverse sets of indicators customized to monitor different projects. Here, we report on our experience developing and applying one such approach to assess the impact of shaded cocoa projects implemented by the Instituto de Manejo e Certificação Florestal e Agrícola (IMAFLORA) near São Félix do Xingu, in Pará, Brazil. We used the Continuous Degradation Detection (CODED) and LandTrendr algorithms to create a remote sensing-based assessment of forest disturbance and regeneration, estimate carbon sequestration, and changes in essential habitats. We coupled these remote sensing methods with eDNA analyses using arthropod-targeted primers by collecting soil samples from intervention and counterfactual pasture field sites and a control secondary forest. We used a custom set of indicators from the pilot application of a coupled monitoring framework called TerraBio. Our results suggest that, due to IMAFLORA’s shaded cocoa projects, over 400 acres were restored in the intervention area and the community composition of arthropods in shaded cocoa is closer to second-growth forests than that of pastures. In reviewing the coupled approach, we found multiple aspects worked well, and we conclude by presenting multiple lessons learned.
Citation
Dyson, K., Nicolau, A. P., Tenneson, K., Francesconi, W., Daniels, A., Andrich, G., …Saah, D. (in press). Coupling remote sensing and eDNA to monitor environmental impact: A pilot to quantify the environmental benefits of sustainable agriculture in the Brazilian Amazon. PloS one, 19(2), e0289437. https://doi.org/10.1371/journal.pone.0289437
Journal Article Type | Article |
---|---|
Acceptance Date | Dec 1, 2023 |
Online Publication Date | Feb 14, 2024 |
Deposit Date | Feb 21, 2024 |
Publicly Available Date | Feb 21, 2024 |
Journal | PLOS ONE |
Print ISSN | 1932-6203 |
Publisher | Public Library of Science |
Peer Reviewed | Peer Reviewed |
Volume | 19 |
Issue | 2 |
Pages | e0289437 |
DOI | https://doi.org/10.1371/journal.pone.0289437 |
Files
Published Version
(5.6 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
You might also like
Primer biases in the molecular assessment of diet in multiple insectivorous mammals
(2021)
Journal Article
Downloadable Citations
About USIR
Administrator e-mail: library-research@salford.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search