Pan‐tropical prediction of forest structure from the largest trees
Authors: Bastin Jean‐François , Rutishauser Ervan , Kellner James R. , Saatchi Sassan , Pélissier Raphael , Hérault Bruno , Slik Ferry , Bogaert Jan , De Cannière Charles , Marshall Andrew R. , Poulsen John , Alvarez‐Loyayza Patricia , Andrade Ana , Angbonga‐Basia Albert , Araujo‐Murakami Alejandro , Arroyo Luzmila , Ayyappan Narayanan , de Azevedo Celso Paulo , Banki Olaf , Barbier Nicolas , Barroso Jorcely G. , Beeckman Hans , Bitariho Robert , Boeckx Pascal , Boehning‐Gaese Katrin , Brandão Hilandia , Brearley Francis Q. , Breuer Ndoundou Hockemba Mireille , Brienen Roel , Camargo Jose Luis C. , Campos‐Arceiz Ahimsa , Cassart Benoit , Chave Jérôme , Chazdon Robin , Chuyong Georges , Clark David B. , Clark Connie J. , Condit Richard , Honorio Coronado Euridice N. , Davidar Priya , de Haulleville Thalès , Descroix Laurent , Doucet Jean‐Louis , Dourdain Aurelie , Droissart Vincent , Duncan Thomas , Silva Espejo Javier , Espinosa Santiago , Farwig Nina , Fayolle Adeline , Feldpausch Ted R. , Ferraz Antonio , Fletcher Christine , Gajapersad Krisna , Gillet Jean‐François , Amaral Iêda Leão do , Gonmadje Christelle , Grogan James , Harris David , Herzog Sebastian K. , Homeier Jürgen , Hubau Wannes , Hubbell Stephen P. , Hufkens Koen , Hurtado Johanna , Kamdem Narcisse G. , Kearsley Elizabeth , Kenfack David , Kessler Michael , Labrière Nicolas , Laumonier Yves , Laurance Susan , Laurance William F. , Lewis Simon L. , Libalah Moses B. , Ligot Gauthier , Lloyd Jon , Lovejoy Thomas E. , Malhi Yadvinder , Marimon Beatriz S. , Marimon Junior Ben Hur , Martin Emmanuel H. , Matius Paulus , Meyer Victoria , Mendoza Bautista Casimero , Monteagudo‐Mendoza Abel , Mtui Arafat , Neill David , Parada Gutierrez Germaine Alexander , Pardo Guido , Parren Marc , Parthasarathy N. , Phillips Oliver L. , Pitman Nigel C. A. , Ploton Pierre , Ponette Quentin , Ramesh B. R. , Razafimahaimodison Jean‐Claude , Réjou‐Méchain Maxime , Rolim Samir Gonçalves , Saltos Hugo Romero , Rossi Luiz Marcelo Brum , Spironello Wilson Roberto , Rovero Francesco , Saner Philippe , Sasaki Denise , Schulze Mark , Silveira Marcos , Singh James , Sist Plinio , Sonke Bonaventure , Soto J. Daniel , de Souza Cintia Rodrigues , Stropp Juliana , Sullivan Martin J. P. , Swanepoel Ben , Steege Hans ter , Terborgh John , Texier Nicolas , Toma Takeshi , Valencia Renato , Valenzuela Luis , Ferreira Leandro Valle , Valverde Fernando Cornejo , Van Andel Tinde R. , Vasque Rodolfo , Verbeeck Hans , Vivek Pandi , Vleminckx Jason , Vos Vincent A. , Wagner Fabien H. , Warsudi Papi Puspa , Wortel Verginia , Zagt Roderick J. , Zebaze Donatien 
Published: October 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.