PT Journal
AU Bastin, J
   Rutishauser, E
   Kellner, J
   Saatchi, S
   Pélissier, R
   Hérault, B
   Slik, F
   Bogaert, J
   De Cannière, C
   Marshall, A
   Poulsen, J
   Alvarez-Loyayza, P
   Andrade, A
   Angbonga-Basia, A
   Araujo-Murakami, A
   Arroyo, L
   Ayyappan, N
   de Azevedo, C
   Banki, O
   Barbier, N
   Barroso, J
   Beeckman, H
   Bitariho, R
   Boeckx, P
   Boehning-Gaese, K
   Brandão, H
   Brearley, F
   Breuer Ndoundou Hockemba, M
   Brienen, R
   Camargo, J
   Campos-Arceiz, A
   Cassart, B
   Chave, J
   Chazdon, R
   Chuyong, G
   Clark, D
   Clark, C
   Condit, R
   Honorio Coronado, E
   Davidar, P
   de Haulleville, T
   Descroix, L
   Doucet, J
   Dourdain, A
   Droissart, V
   Duncan, T
   Silva Espejo, J
   Espinosa, S
   Farwig, N
   Fayolle, A
   Feldpausch, T
   Ferraz, A
   Fletcher, C
   Gajapersad, K
   Gillet, J
   Amaral, Id
   Gonmadje, C
   Grogan, J
   Harris, D
   Herzog, S
   Homeier, J
   Hubau, W
   Hubbell, S
   Hufkens, K
   Hurtado, J
   Kamdem, N
   Kearsley, E
   Kenfack, D
   Kessler, M
   Labrière, N
   Laumonier, Y
   Laurance, S
   Laurance, W
   Lewis, S
   Libalah, M
   Ligot, G
   Lloyd, J
   Lovejoy, T
   Malhi, Y
   Marimon, B
   Marimon Junior, B
   Martin, E
   Matius, P
   Meyer, V
   Mendoza Bautista, C
   Monteagudo-Mendoza, A
   Mtui, A
   Neill, D
   Parada Gutierrez, G
   Pardo, G
   Parren, M
   Parthasarathy, N
   Phillips, O
   Pitman, N
   Ploton, P
   Ponette, Q
   Ramesh, B
   Razafimahaimodison, J
   Réjou-Méchain, M
   Rolim, S
   Saltos, H
   Rossi, L
   Spironello, W
   Rovero, F
   Saner, P
   Sasaki, D
   Schulze, M
   Silveira, M
   Singh, J
   Sist, P
   Sonke, B
   Soto, J
   de Souza, C
   Stropp, J
   Sullivan, M
   Swanepoel, B
   Steege, Ht
   Terborgh, J
   Texier, N
   Toma, T
   Valencia, R
   Valenzuela, L
   Ferreira, L
   Valverde, F
   Van Andel, T
   Vasque, R
   Verbeeck, H
   Vivek, P
   Vleminckx, J
   Vos, V
   Wagner, F
   Warsudi, P
   Wortel, V
   Zagt, R
   Zebaze, D
TI Pan-tropical prediction of forest structure from the largest trees
SO Global Ecology and Biogeography
JI Global Ecol Biogeogr
PY 2018
BP 1366
EP 1383
VL 27
IS 11
DI 10.1111/geb.12803
DE carbon; climate change; forest structure; large trees; pan-tropical; Redd+; tropical forest ecology
AB 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.
ER