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Pan-tropical prediction of forest structure from the largest trees

Publication ,  Journal Article
Bastin, JF; Rutishauser, E; Kellner, JR; Saatchi, S; Pélissier, R; Hérault, B; Slik, F; Bogaert, J; De Cannière, C; Marshall, AR; Poulsen, J ...
Published in: Global Ecology and Biogeography
November 1, 2018

Aim: Large tropical trees form the interface between ground and airborne observations, offering a unique opportunity to capture forest properties remotely and to investigate their variations on broad scales. However, despite rapid development of metrics to characterize the forest canopy from remotely sensed data, a gap remains between aerial and field inventories. To close this gap, we propose a new pan-tropical model to predict plot-level forest structure properties and biomass from only the largest trees. Location: Pan-tropical. Time period: Early 21st century. Major taxa studied: Woody plants. Methods: Using a dataset of 867 plots distributed among 118 sites across the tropics, we tested the prediction of the quadratic mean diameter, basal area, Lorey's height, community wood density and aboveground biomass (AGB) from the ith largest trees. Results: Measuring the largest trees in tropical forests enables unbiased predictions of plot- and site-level forest structure. The 20 largest trees per hectare predicted quadratic mean diameter, basal area, Lorey's height, community wood density and AGB with 12, 16, 4, 4 and 17.7% of relative error, respectively. Most of the remaining error in biomass prediction is driven by differences in the proportion of total biomass held in medium-sized trees (50–70 cm diameter at breast height), which shows some continental dependency, with American tropical forests presenting the highest proportion of total biomass in these intermediate-diameter classes relative to other continents. Main conclusions: Our approach provides new information on tropical forest structure and can be used to generate accurate field estimates of tropical forest carbon stocks to support the calibration and validation of current and forthcoming space missions. It will reduce the cost of field inventories and contribute to scientific understanding of tropical forest ecosystems and response to climate change.

Duke Scholars

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Published In

Global Ecology and Biogeography

DOI

EISSN

1466-8238

ISSN

1466-822X

Publication Date

November 1, 2018

Volume

27

Issue

11

Start / End Page

1366 / 1383

Related Subject Headings

  • Ecology
  • 4104 Environmental management
  • 4102 Ecological applications
  • 3103 Ecology
  • 0602 Ecology
  • 0501 Ecological Applications
  • 0406 Physical Geography and Environmental Geoscience
 

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Bastin, J. F., Rutishauser, E., Kellner, J. R., Saatchi, S., Pélissier, R., Hérault, B., … Rolim, S. G. (2018). Pan-tropical prediction of forest structure from the largest trees. Global Ecology and Biogeography, 27(11), 1366–1383. https://doi.org/10.1111/geb.12803
Bastin, J. F., E. Rutishauser, J. R. Kellner, S. Saatchi, R. Pélissier, B. Hérault, F. Slik, et al. “Pan-tropical prediction of forest structure from the largest trees.” Global Ecology and Biogeography 27, no. 11 (November 1, 2018): 1366–83. https://doi.org/10.1111/geb.12803.
Bastin JF, Rutishauser E, Kellner JR, Saatchi S, Pélissier R, Hérault B, et al. Pan-tropical prediction of forest structure from the largest trees. Global Ecology and Biogeography. 2018 Nov 1;27(11):1366–83.
Bastin, J. F., et al. “Pan-tropical prediction of forest structure from the largest trees.” Global Ecology and Biogeography, vol. 27, no. 11, Nov. 2018, pp. 1366–83. Scopus, doi:10.1111/geb.12803.
Bastin JF, Rutishauser E, Kellner JR, Saatchi S, Pélissier R, Hérault B, Slik F, Bogaert J, De Cannière C, Marshall AR, Poulsen J, Alvarez-Loyayza P, Andrade A, Angbonga-Basia A, Araujo-Murakami A, Arroyo L, Ayyappan N, de Azevedo CP, Banki O, Barbier N, Barroso JG, Beeckman H, Bitariho R, Boeckx P, Boehning-Gaese K, Brandão H, Brearley FQ, Breuer Ndoundou Hockemba M, Brienen R, Camargo JLC, Campos-Arceiz A, Cassart B, Chave J, Chazdon R, Chuyong G, Clark DB, Clark CJ, Condit R, Honorio Coronado EN, Davidar P, de Haulleville T, Descroix L, Doucet JL, Dourdain A, Droissart V, Duncan T, Silva Espejo J, Espinosa S, Farwig N, Fayolle A, Feldpausch TR, Ferraz A, Fletcher C, Gajapersad K, Gillet JF, Amaral ILD, Gonmadje C, Grogan J, Harris D, Herzog SK, Homeier J, Hubau W, Hubbell SP, Hufkens K, Hurtado J, Kamdem NG, Kearsley E, Kenfack D, Kessler M, Labrière N, Laumonier Y, Laurance S, Laurance WF, Lewis SL, Libalah MB, Ligot G, Lloyd J, Lovejoy TE, Malhi Y, Marimon BS, Marimon Junior BH, Martin EH, Matius P, Meyer V, Mendoza Bautista C, Monteagudo-Mendoza A, Mtui A, Neill D, Parada Gutierrez GA, Pardo G, Parren M, Parthasarathy N, Phillips OL, Pitman NCA, Ploton P, Ponette Q, Ramesh BR, Razafimahaimodison JC, Réjou-Méchain M, Rolim SG. Pan-tropical prediction of forest structure from the largest trees. Global Ecology and Biogeography. 2018 Nov 1;27(11):1366–1383.
Journal cover image

Published In

Global Ecology and Biogeography

DOI

EISSN

1466-8238

ISSN

1466-822X

Publication Date

November 1, 2018

Volume

27

Issue

11

Start / End Page

1366 / 1383

Related Subject Headings

  • Ecology
  • 4104 Environmental management
  • 4102 Ecological applications
  • 3103 Ecology
  • 0602 Ecology
  • 0501 Ecological Applications
  • 0406 Physical Geography and Environmental Geoscience