author = "Withey, Kieran and Berenguer, Erika and Palmeira, Alessandro 
                         Ferraz and Espirito Santo, Fernando D. B. and Lennox, Gareth D. 
                         and Silva, Camila V. J. and Arag{\~a}o, Luiz Eduardo Oliveira e 
                         Cruz de and Ferreira, Joice and Fran{\c{c}}a, Filipe and Malhi, 
                         Yadvinder and Rossi, Liana Chesini and Barlow, Jos",
          affiliation = "{Lancaster University} and {Lancaster University} and 
                         {Universidade Federal do Par{\'a} (UFPA)} and {University of 
                         Leiceste} and {Lancaster University} and {Lancaster University} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Embrapa Amaz{\^o}nia Oriental} and {Lancaster University} and 
                         {University of Oxford} and {Universidade Estadual Paulista 
                         (UNESP)} and {Lancaster University}",
                title = "Quantifying immediate carbon emissions from El Niño-mediated 
                         wildfires in humid tropical forests",
              journal = "Philosophical Transactions of the Royal Society B: Biological 
                 year = "2018",
               volume = "373",
               number = "1760",
                month = "Oct.",
             keywords = "ENSO, forest degradation, climate change, necromass, drought, 
             abstract = "Wildfires produce substantial CO2 emissions in the humid tropics 
                         during El Niño-mediated extreme droughts, and these emissions are 
                         expected to increase in coming decades. Immediate carbon emissions 
                         from uncontrolled wildfires in human-modified tropical forests can 
                         be considerable owing to high necromass fuel loads. Yet, data on 
                         necromass combustion during wildfires are severely lacking. Here, 
                         we evaluated necromass carbon stocks before and after the 
                         2015-2016 El Niño in Amazonian forests distributed along a 
                         gradient of prior human disturbance. We then used Landsat-derived 
                         burn scars to extrapolate regional immediate wildfire CO2 
                         emissions during the 2015-2016 El Niño. Before the El Niño, 
                         necromass stocks varied significantly with respect to prior 
                         disturbance and were largest in undisturbed primary forests (30.2 
                         ± 2.1 Mg ha-1, mean ± s.e.) and smallest in secondary forests 
                         (15.6 ± 3.0 Mg ha-1). However, neither prior disturbance nor our 
                         proxy of fire intensity (median char height) explained necromass 
                         losses due to wildfires. In our 6.5 million hectare (6.5 Mha) 
                         study region, almost 1 Mha of primary (disturbed and undisturbed) 
                         and 20 000 ha of secondary forest burned during the 2015-2016 El 
                         Niño. Covering less than 0.2% of Brazilian Amazonia, these 
                         wildfires resulted in expected immediate CO2 emissions of 
                         approximately 30 Tg, three to four times greater than comparable 
                         estimates from global fire emissions databases. Uncontrolled 
                         understorey wildfires in humid tropical forests during extreme 
                         droughts are a large and poorly quantified source of CO2 
                         emissions.This article is part of a discussion meeting issue 'The 
                         impact of the 2015/2016 El Niño on the terrestrial tropical carbon 
                         cycle: patterns, mechanisms and implications'.",
                  doi = "10.1098/rstb.2017.0312",
                  url = "http://dx.doi.org/10.1098/rstb.2017.0312",
                 issn = "1552-2814",
             language = "en",
           targetfile = "withey_quantifying.pdf",
        urlaccessdate = "26 nov. 2020"