Pan-tropical prediction of forest structure from the largest trees

Abstract

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.

Metadata

Item Type:	Article
Authors/Creators:	Bastin, J-F 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 https://orcid.org/0000-0002-5397-5755 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, J-L Dourdain, A Droissart, V Duncan, T Silva Espejo, J Espinosa, S Farwig, N Fayolle, A Feldpausch, TR Ferraz, A Fletcher, C Gajapersad, K Gillet, J-F 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 https://orcid.org/0000-0002-8993-6168 Pitman, NCA Ploton, P Ponette, Q Ramesh, BR Razafimahaimodison, J-C Réjou-Méchain, M Rolim, SG Saltos, HR Rossi, LMB Spironello, WR Rovero, F Saner, P Sasaki, D Schulze, M Silveira, M Singh, J Sist, P Sonke, B Soto, JD De Souza, CR Stropp, J Sullivan, MJP https://orcid.org/0000-0002-5955-0483 Swanepoel, B Steege, HT Terborgh, J Texier, N Toma, T Valencia, R Valenzuela, L Valle Ferreira, L Valverde, FC Van Andel, TR Vasque, R Verbeeck, H Vivek, P Vleminckx, J Vos, VA Wagner, FH Warsudi, PP Wortel, V Zagt, RJ Zebaze, D
Copyright, Publisher and Additional Information:	© 2018 John Wiley & Sons Ltd. This is the peer reviewed version of the following article: Bastin J‐F, Rutishauser E, Kellner JR, et al. Pan‐tropical prediction of forest structure from the largest trees. Global Ecol Biogeogr. 2018; 27:1366–1383, which has been published in final form at https://doi.org/10.1111/geb.12803. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Uploaded in accordance with the publisher's self-archiving policy.
Keywords:	carbon, climate change, forest structure, large trees, pan‐tropical, REDD+, tropical forest ecology
Dates:	Published: 9 November 2018 Published (online): 10 October 2018 Accepted: 13 June 2018
Institution:	The University of Leeds
Academic Units:	The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Ecology & Global Change (Leeds)
Depositing User:	Symplectic Publications
Date Deposited:	30 Jan 2019 12:35
Last Modified:	10 Oct 2019 00:39
Status:	Published
Publisher:	Wiley
Identification Number:	10.1111/geb.12803
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:141737

CORE (COnnecting REpositories)

Pan-tropical prediction of forest structure from the largest trees

Abstract

Metadata

Download

Accepted Version

Export

Statistics