L4D - Probability map of giant trees occurrence (> 70 m) in the Brazilian Amazon
Creators
- Gorgens, Eric Bastos1
- Nunes, Matheus Henrique2
- Jackson, Tobias3
- Coomes, David3
- Keller, Michael4
- Reis, Cristiano Rodrigues5
- Valbuena, Rubén6
- Rosette, Jacqueline7
- Almeida, Danilo Roberti Alves5
- Gimenez, Bruno8
- Cantinho, Roberta9
- Motta, Alline Zagnoli1
- Assis, Mauro10
- Pereira, Francisca Rocha de Souza10
- Spanner, Gustavo8
- Higuchi, Niro8
- Ometto, Jean Pierre10
- 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
Description
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 %).
Notes
Files
meprobHeightMap70mCor80v15042020.tif
Files
(79.7 MB)
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Additional details
Related works
- Is documented by
- Preprint: https://www.biorxiv.org/content/10.1101/2020.05.15.097683v2 (URL)
- Is referenced by
- Journal article: 10.1111/gcb.15423 (DOI)
Funding
- A 3D perspective on the effects of topography and wind on forest height and dynamics NE/S010750/1
- UK Research and Innovation
- Understanding mechanisms of habitat change in fragmented tropical forests for improving conservation 319905
- Academy of Finland