Published September 18, 2020 | Version v1
Dataset Open

L4D - Probability map of giant trees occurrence (> 70 m) in the Brazilian Amazon

  • 1. Universidade Federal dos Vales do Jequitinhonha e Mucuri
  • 2. University of Helsinki
  • 3. University of Cambridge
  • 4. United Sates Forest service
  • 5. Universidade de São Paulo
  • 6. Bangor University
  • 7. Swansea University
  • 8. Instituto Nacional de Pesquisas da Amazônia
  • 9. Universidade de Brasília
  • 10. Instituto Nacional de Pesquisa Espaciais


The probability of giant trees occurrence (> 70m) based on environmental conditions. The observations higher than 70 m were filtered out and used to adjust an envelope model based on maximum entropy. In its optimization routine, the algorithm tracked how much the model gain was improved when small changes were made to each coefficient value associated with a particular variable. The resulting map of predicted occurrence of the tallest trees in the Amazon from the MaxEnt model shows that the probability of maximum tree height occurrence is highest in the northeastern Amazon (Fig. 6), more specifically in the Roraima and Guianan Lowlands. We considered 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m);  (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s-1); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s-1); (5) the number of days not affected by cloud cover (clear days; in days yr-1); (6) the number of days with precipitation above 20 mm (days > 20mm; in days yr-1 ); (7) the number of months with precipitation below 100 mm (months < 100mm; in months yr-1 ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15)  standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %).  


Funding was provided by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES; Finance Code 001); Conselho Nacional de Desenvolvi­mento Científico e Tecnológico (Processes 403297/2016-8 and 301661/2019-7); Amazon Fund (grant 14.2.0929.1); National Academy of Sciences and US Agency for International Development (grant AID-OAA-A-11-00012); Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM); Instituto Nacional de Pesquisas Espaciais (INPE); São Paulo Research Foundation (#2018/21338-3 and #2019/14697-0); INCT-Madeiras da Amazônia and Next Generation Ecosystem Experiments-Tropics (NGEE-Tropics), as part of DOE's Terrestrial Ecosystem Science Program – Contract No. DE-AC02-05CH11231; UK Natural Environment Research Council grant NE/S010750/1; Academy of Finland (decision number 319905); Royal Society University Research Fellowship (URF\R\191014);



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Journal article: 10.1111/gcb.15423 (DOI)


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