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A new data-driven map predicts substantial undocumented peatland areas in Amazonia

Bibliographic Data

ID15550282
AuthorsAdam Hastie (0000-0003-2098-3510, Charles University, corresponding author), John Ethan Householder (0000-0002-5959-3504, Karlsruhe Institute of Technology), Eurídice N Honorio Coronado (0000-0003-2314-590X, University of St Andrews), Gabriel Hidalgo Pizango (0000-0003-3170-9939, Instituto de Investigaciones de la Amazonía Peruana), Rafael Herrera (0000-0002-1178-5224, Instituto Venezolano de Investigaciones Científicas), Outi Lähteenoja (0000-0002-5730-5804), Johan De Jong (0000-0002-5121-1348, Wageningen University & Research), R Scott Winton (0000-0002-9048-9342, University of California, Santa Cruz), Gerardo A Aymard C (0000-0001-9405-0508, Universidad Nacional Experimental de los Llanos Occidentales Ezequiel Zamora), Gerardo A Aymard Corredor, José Reyna (Instituto de Investigaciones de la Amazonía Peruana), Edwin Montoya (0000-0002-4690-190X, Geociencias Barcelona), Stella Paukku (ETH Zurich), Edward T A Mitchard (0000-0002-5690-4055, University of Edinburgh), C M Åkesson (0000-0002-1442-7019, University of St Andrews), Timothy R Baker (0000-0002-3251-1679, University of Leeds), Lydia E S Cole (0000-0003-3198-6311, University of St Andrews), Jimmy Cesar Cordova Oroche (0000-0002-0692-3186, Instituto de Investigaciones de la Amazonía Peruana), Nállarett Dávila (0000-0003-0826-7167, Instituto de Investigaciones de la Amazonía Peruana), Jhon del Águila Pasquel (0000-0003-2103-7390, Universidad Nacional de la Amazonía Peruana), Jhon Del Águila, Frederick C Draper (0000-0001-7568-0838, University of Liverpool), Etienne Fluet‐chouinard (0000-0003-4380-2153, Pacific Northwest National Laboratory), Julio Grández (Instituto de Investigaciones de la Amazonía Peruana), John P Janovec (Universidad Nacional Agraria La Molina), David Reyna (Instituto de Investigaciones de la Amazonía Peruana), Mathias W Tobler (0000-0002-8587-0560), Dennis Del Castillo Torres (0000-0003-0852-5197, Instituto de Investigaciones de la Amazonía Peruana), Katherine H Roucoux (0000-0001-6757-7267, University of St Andrews), Charlotte Wheeler (0000-0003-4149-5997), Charlotte E Wheeler, María Teresa Fernández Piedade (0000-0002-7320-0498, National Institute of Amazonian Research), Jochen Schöngart (0000-0002-7696-9657, National Institute of Amazonian Research), Florian Wittmann (0000-0002-9890-6091, Karlsruhe Institute of Technology), Marieke Van Der Zon (0000-0002-5767-3535, Wageningen University & Research), Ian T Lawson (0000-0002-3547-2425, University of St Andrews)
Year2024
Volume19
Issue9
Pages094019-094019
Publication date2024-07-25
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ad677b
OpenAlexW4400970010
LanguageEN
Citations received3
References cited65

Tropical peatlands are among the most carbon-dense terrestrial ecosystems yet recorded. Collectively, they comprise a large but highly uncertain reservoir of the global carbon cycle, with wide-ranging estimates of their global area (441 025–1700 000 km 2 ) and below-ground carbon storage (105–288 Pg C). Substantial gaps remain in our understanding of peatland distribution in some key regions, including most of tropical South America. Here we compile 2413 ground reference points in and around Amazonian peatlands and use them alongside a stack of remote sensing products in a random forest model to generate the first field-data-driven model of peatland distribution across the Amazon basin. Our model predicts a total Amazonian peatland extent of 251 015 km 2 (95th percentile confidence interval: 128 671–373 359), greater than that of the Congo basin, but around 30% smaller than a recent model-derived estimate of peatland area across Amazonia. The model performs relatively well against point observations but spatial gaps in the ground reference dataset mean that model uncertainty remains high, particularly in parts of Brazil and Bolivia. For example, we predict significant peatland areas in northern Peru with relatively high confidence, while peatland areas in the Rio Negro basin and adjacent south-western Orinoco basin which have previously been predicted to hold Campinarana or white sand forests, are predicted with greater uncertainty. Similarly, we predict large areas of peatlands in Bolivia, surprisingly given the strong climatic seasonality found over most of the country. Very little field data exists with which to quantitatively assess the accuracy of our map in these regions. Data gaps such as these should be a high priority for new field sampling. This new map can facilitate future research into the vulnerability of peatlands to climate change and anthropogenic impacts, which is likely to vary spatially across the Amazon basin

Amazon basin · Amazon rainforest · Amazonian · Boreal · Geography · Geomorphology · Peat · Physical geography · Structural basin · Atmospheric and Environmental Gas Dynamics · Environmental Science · Fire effects on ecosystems · Peatlands and Wetlands Ecology · Ecology · Geology

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Unique citing works3
Citations per year3
Citation span2025 - 2026 (2)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 3

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