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  • Palaeoecological proxy data (pollen, non-pollen palynomorph (NPP), micro-charcoal, macro-charcoal, loss-on-ignition (LOI) and x-ray fluorescence (XRF)) recovered from lake sediments, cliff exposures, surface soils and moss pollsters within the eastern Andean cloud forest of Ecuador. Palaeoecological proxy data were recovered from lake sediments, surface soil and moss pollsters within the eastern Andean cloud forest of Ecuador. Materials and proxy data were collected with the aim of understanding how ecosystem dynamics were driven by anthropogenic, physical and climatic impact through time (late Quaternary). Here, data are provided for pollen, non-pollen palynomorph (NPP), micro-charcoal, macro-charcoal, loss-on-ignition (LOI) and x-ray fluorescence (XRF). Field samples were collected throughout 2012-2013 from the Napo province of Ecuador and analysed in the laboratory throughout 2014-2015 at The Open University (UK). Full details about this dataset can be found at https://doi.org/10.5285/952e8ddb-b573-44ad-a930-2c8c5164a381

  • [This dataset is embargoed until March 1, 2024]. Data include four charcoal concentration datasets derived from radiocarbon-dated lake sediments in the Brazilian Pantanal, from which regional fire histories can be inferred over the last few millennia. Each dataset provides raw counts of charcoal fragments > 125 um calculated per cubic centimetre of sediment. Samples measuring 0.5 to 1.5 cc were analysed at each 1-cm depth. Contained within each data sheet are radiocarbon dates for several horizons in the lake sediment for the derivation of age-depth models for a chronological framework for each record. Full details about this dataset can be found at https://doi.org/10.5285/56915f15-46b5-4693-8737-28c985f27f80

  • [This dataset is embargoed until February 28, 2024]. Data on peat depth from >250 locations in the Pastaza-Marañón Basin, Amazonian Peru. The data were collected during a series of field campaigns in 2019 and 2020. These data, along with similar data collected under other projects, were used to train a predictive model of peat distribution. Locations of a small number of other sites are given without peat depth measurements (i.e. with NA in the column Peat_depth_cm); these sites relate to data reported elsewhere in the ‘Carbon Storage in Amazonian Peatlands’ data collection. Full details about this dataset can be found at https://doi.org/10.5285/ab13a06f-392f-4bc6-b1bf-06dd8b020307