Published October 5, 2020 | Version 14042020
Software Open

Maximum entropy model trained to estimate probability to host trees taller then 70 m based on environmental factors

  • 1. Universidade Federal dos Vales do Jequitinhonha e Mucuri
  • 2. University of Helsinki
  • 3. University of Cambridge
  • 4. United States 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 Pesquisas Espaciais


Focusing only on the tallest trees - those over 70 m in height – we built an environmental envelope model to assess the conditions which allow them to occur. We employed the maximum entropy approach (MaxEnt) commonly applied to modelling species geographic distributions with presence-only data to discriminate suitable versus unsuitable areas for the species. We initially considered a total of 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) lightning frequency (flashes rate); (8) annual precipitation (in mm); (9) potential evapotranspiration (in mm); (10) coefficient of variation of precipitation (precipitation seasonality; in %); (11) amount of precipitation on the wettest month (precip. wettest; in mm); (12) mean annual temperature (in °C); (13)  standard deviation of temperature (temp. seasonality; in °C); (14) annual maximum temperature (in °C); (15) soil clay content (in %); and (16) 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); 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; National Academy of Sciences and US Agency for International Development (grant AID-OAA-A-11-00012); Royal Society University Research Fellowship (URF\R\191014).


Files (331.9 kB)

Name Size Download all
331.9 kB Download

Additional details

Related works

Is referenced by
Journal article: 10.1111/gcb.15423 (DOI)


Understanding mechanisms of habitat change in fragmented tropical forests for improving conservation 319905
Academy of Finland
A 3D perspective on the effects of topography and wind on forest height and dynamics NE/S010750/1
UK Research and Innovation