Acknowledgements: This work was funded by NERC (Grant ref. NE/R000751/1) to I T L, A H, K H R, E T A M, C M A, and T R B; Charles University (PRIMUS/23/SCI/013) to AH; Charles University Research Centre program UNCE/24/SCI/006 to AH; Leverhulme Trust (grant ref. RPG-2018-306) to K H R, L E S C and C E W E N H C acknowledges support from her NERC Knowledge Exchange Fellowship (NE/V018760/1) and from Green Climate Fund and KOICA to PROFONANPE and IIAP to sample peatlands in the Datem del Marañón in Peru. A H and I T L acknowledge support from the Charles University and University of St Andrews Joint Seed Funding Programme. J E H, F W, and M T F P acknowledge support from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq—process number 141727/2011-0 as well as Universal 14/2011) for Brazilian field sampling. J E H, J P J and M T acknowledge support from the Discovery Fund of Fort Worth, Texas, the Gordon and Betty Moore Foundation (Grant Nos. 484), the U.S. National Science Foundation (Grant No. 0717453), and the Programa de Ciencia y Tecnologia—FINCYT (co-financed by the Banco Internacional de Desarollo, BID) Grant Number PIBAP-2007-005. We thank SERNANP, SERFOR and GERFOR for providing research permits, and the different Indigenous and local communities, research stations and tourist companies for giving consent and allowing access to the forests, including the Santa Rosa de Lagarto community (Parinari, Loreto). We acknowledge the invaluable support of technicians J Irarica, J Sanchez, H Vásquez and R Flores, without whom much of the fieldwork would not have been possible. Finally, we would like to thank the reviewers for their insightful comments and suggestions which substantially improved the manuscript.Funder: Gordon and Betty Moore Foundation; doi: http://dx.doi.org/10.13039/100000936Funder: Banco InternacionalFunder: Programa de Ciencia y TecnologiaFunder: Charles University/ University of St Andrews Joint Seed FundingTropical 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 km2) 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 km2 (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
