RESEARCH PAPER https://doi.org/10.1071/MF22111 Mapping the spatial distribution of wetlands in Argentina (South America) from a fusion of national databases Irene Fabricante A , Priscilla Minotti A and Patricia Kandus A, * For full list of author affiliations and declarations see end of paper *Correspondence to: Patricia Kandus Laboratorio de Ecología, Teledetecci ´ on y Ecoinformática (LETyE), Instituto de Investigaci ´ on e Ingeniería Ambiental (3iA), Universidad Nacional de San Martín (UNSAM), Campus Miguelete, 25 de Mayo y Francia, CP 1650 San Martín, Argentina Email: pkandus@unsam.edu.ar Handling Editor: Nicholas Davidson Received: 24 September 2022 Accepted: 16 October 2022 Published: 14 November 2022 Cite this: Fabricante I et al. (2022) Marine and Freshwater Research doi:10.1071/MF22111 © 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing. ABSTRACT Context. There a large information gap on the spatial distribution and diversity of wetland types in South America. Aims. We focus on mapping the spatial distribution of broad wetland types in Argentina, based on the integration of open spatial data sources developed by national government agencies. Methods. We designed a two-tier process, as follows: we filtered broad wetland types described in the attributes of the spatial datasets and created a separate vector layer for each wetland class; we then ensembled the layers by populating a 25-m cell raster template. Key results. Our WetCarto_AR layer indicates that wetlands cover 13.5% of mainland Argentina, being distributed throughout the country with a greater concentration towards the north-east, but patchy in the rest of the country. Palustrine is the dominant wetlands class followed by Riparian and Lacustrine. Global datasets underestimated wetland coverage, although the same large wetlands are recognised in all. Conclusions. Our results make visible the known spatial extent of wetlands in Argentina and provide information to feed or validate global models. Implications. Results stress the importance of existing local databases, which, even when generated for other purposes, can be a starting point for country or region wetland mapping. Keywords: Argentina, global wetland datasets, local spatial datasets, national databases, spatial dataset integration, spatial distribution of wetlands, wetland mapping, wetland types diversity. Introduction There is worldwide recognition of the social, economic, and ecological values of wetlands, although their global extent has significantly declined in the 20th century (Convention on Wetlands 2021). Economic growth and population density are identified as the main underlying forces leading to systematic degradation and loss of wetlands (Finlayson and D’Cruz 2005; van Asselen et al. 2013; Davidson 2014). In addition, the close relationship of the ecological functions of wetlands with the hydrological regime highlights their sensitivity to climate-change processes. Changes in the water balance owing to increases or decreases in evapotranspiration, the increase in extreme events, and increase in floods as a result of sea-level rise, pose serious risks to biodiversity and ecosystem functions (Day et al. 2008; Erwin 2009; Sandi et al. 2018, 2020; Were et al. 2019; Taillardat et al. 2020; Xi et al. 2021), and can trigger synergistic processes with local and regional land uses (Faleiro et al. 2013; Finlayson et al. 2019; Ponzio et al. 2019). In this rapidly changing world, there is a race against time to prevent wetland loss and degradation with the development of effective conservation policies. Wetland policy and management require reliable information about wetland occurrence, extent, and diversity, which can be fulfilled by developing wetland maps and inventories as stated by Ramsar Convention on Wetlands (Ramsar Convention 1990; Davidson et al. 2018). Wetland mapping is not a trivial matter. Reports on the extent of wetlands at the global scale show large differences from 8% (9.167 × 10 6 km 2 ) reported by Lehner and Döll (2004) to 21.1–21.6% (27.5 × 10 6 –29 × 10 6 km 2 ) given by Tootchi et al. (2019). Factors responsible for the disparity in the estimates in global databases can be attributed to