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DOI10.3389/feart.2019.00083
Individual-Based Modeling of Amazon Forests Suggests That Climate Controls Productivity While Traits Control Demography
Fauset, Sophie1,40; Gloor, Manuel1; Fyllas, Nikolaos M.2; Phillips, Oliver L.1; Asner, Gregory P.3; Baker, Timothy R.1; Bentley, Lisa Patrick4; Brienen, Roel J. W.1; Christoffersen, Bradley O.5,6; del Aguila-Pasquel, Jhon7; Doughty, Christopher E.8; Feldpausch, Ted R.9; Galbraith, David R.1; Goodman, Rosa C.10; Girardin, Cecile A. J.11; Honorio Coronado, Euridice N.7; Monteagudo, Abel12; Salinas, Norma11,13; Shenkin, Alexander11; Silva-Espejo, Javier E.14; van der Heijden, Geertje15; Vasquez, Rodolfo12; Alvarez-Davila, Esteban16; Arroyo, Luzmila17; Barroso, Jorcely G.18; Brown, Foster19; Castro, Wendeson20; Cornejo Valverde, Fernando21; Cardozo, Nallarett Davila22; Di Fiore, Anthony23; Erwin, Terry24; Huamantupa-Chuquimaco, Isau14,25; Nunez Vargas, Percy14; Neill, David26; Pallqui Camacho, Nadir1,14; Parada Gutierrez, Alexander27; Peacock, Julie1; Pitman, Nigel28,29; Prieto, Adriana30; Restrepo, Zorayda31,32; Rudas, Agustin33; Quesada, Carlos A.33; Silveira, Marcos34; Stropp, Juliana35; Terborgh, John36,37,38; Vieira, Simone A.39; Malhi, Yadvinder11
发表日期2019
ISSN2296-6463
卷号7
英文摘要

Climate, species composition, and soils are thought to control carbon cycling and forest structure in Amazonian forests. Here, we add a demographics scheme (tree recruitment, growth, and mortality) to a recently developed non-demographic model-the Trait-based Forest Simulator (TFS)-to explore the roles of climate and plant traits in controlling forest productivity and structure. We compared two sites with differing climates (seasonal vs. aseasonal precipitation) and plant traits. Through an initial validation simulation, we assessed whether the model converges on observed forest properties (productivity, demographic and structural variables) using datasets of functional traits, structure, and climate to model the carbon cycle at the two sites. In a second set of simulations, we tested the relative importance of climate and plant traits for forest properties within the TFS framework using the climate from the two sites with hypothetical trait distributions representing two axes of functional variation ("fast" vs. "slow" leaf traits, and high vs. low wood density). The adapted model with demographics reproduced observed variation in gross (GPP) and net (NPP) primary production, and respiration. However, NPP and respiration at the level of plant organs (leaf, stem, and root) were poorly simulated. Mortality and recruitment rates were underestimated. The equilibrium forest structure differed from observations of stem numbers suggesting either that the forests are not currently at equilibrium or that mechanisms are missing from the model. Findings from the second set of simulations demonstrated that differences in productivity were driven by climate, rather than plant traits. Contrary to expectation, varying leaf traits had no influence on GPP. Drivers of simulated forest structure were complex, with a key role for wood density mediated by its link to tree mortality. Modeled mortality and recruitment rates were linked to plant traits alone, drought-related mortality was not accounted for. In future, model development should focus on improving allocation, mortality, organ respiration, simulation of understory trees and adding hydraulic traits. This type of model that incorporates diverse tree strategies, detailed forest structure and realistic physiology is necessary if we are to be able to simulate tropical forest responses to global change scenarios.


WOS研究方向Geology
来源期刊FRONTIERS IN EARTH SCIENCE
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/96704
作者单位1.Univ Leeds, Sch Geog, Leeds, W Yorkshire, England;
2.Univ Aegean, Dept Environm, Biodivers Conservat Lab, Mitilini, Greece;
3.Arizona State Univ, Ctr Global Discovery & Conservat Sci, Tempe, AZ USA;
4.Sonoma State Univ, Dept Biol, Rohnert Pk, CA 94928 USA;
5.Univ Texas Rio Grande Valley, Dept Biol, Edinburg, TX USA;
6.Univ Texas Rio Grande Valley, Sch Earth Environm & Marine Sci, Edinburg, TX USA;
7.Inst Invest Amazonia Peruana, Iquitos, Peru;
8.No Arizona Univ, Sch Informat Comp & Cyber Syst, Flagstaff, AZ 86011 USA;
9.Univ Exeter, Coll Life & Environm Sci, Geog, Exeter, Devon, England;
10.Swedish Univ Agr Sci SLU, Dept Forest Ecol & Management, Umea, Sweden;
11.Univ Oxford, Sch Geog & Environm, Environm Change Inst, Oxford, England;
12.Jardin Bot Missouri, Oxapampa, Peru;
13.Pontificia Univ Catolica Peru, Inst Ciencias Nat Terr & Energies Renovables San, Lima, Peru;
14.Univ Nacl San Antonio Abed Cusco, Cuzco, Peru;
15.Univ Nottingham, Sch Geog, Nottingham, England;
16.Univ UNAD, Escuele Ciencias Agr Pecuaries & Ambientales, Bogota, Colombia;
17.Univ Autonoma Gabriel Rene Moreno, Museo Hist Nat Noel Kempff Mercado, Santa Cruz, Bolivia;
18.Univ Fed Acre, Campus Floresta, Cruzeiro Do Sul, Brazil;
19.Woods Hole Res Ctr, Falmouth, MA USA;
20.Univ Fed Acre, Programa Posgrad Ecol & Manejo Recursos Nat, Rio Branco, Brazil;
21.Andes Amazon Biodivers Program, Puerto Maldonado, Peru;
22.Univ Estado Amazonas, Programs Mestrado Biotecnol & Recursos Nat, Manaus, Amazonas, Brazil;
23.Univ Texas Austin, Dept Anthropol, Austin, TX 78712 USA;
24.Smithsonian Inst, Washington, DC 20560 USA;
25.Inst Pesquisas Jardim Bot Rio de Janeiro, Escola Nacl Bot Trop, Programa Posgrad Bot, Rio De Janeiro, Brazil;
26.Univ Estatal Amazon, Puyo, Ecuador;
27.Univ Autonoma Gabriel Rene Moreno, Muse Hist Nat Noel Kempff Mercado, Santa Cruz, Bolivia;
28.Field Museum, Sci & Educ, Chicago, IL USA;
29.Duke Univ, Nicholas Sch Environm, Ctr Trop Conservat, Durham, NC 27708 USA;
30.Univ Nacl Colombia, Inst Ciencias Nat, Bogota, Colombia;
31.Corp COL TREE, Grp Serv Ecosistem & Cambio Climat, Medellin, Colombia;
32.Univ Antioquia, Grp GIGA, Medellin, Colombia;
33.Inst Nacl de Pesquisas da Amazonia, Manaus, Amazonas, Brazil;
34.Univ Fed Acre, Museu Univ, Rio Branco, Brazil;
35.Univ Fed Alagoas, Inst Biol & Hlth Sci, Maceio, Brazil;
36.Univ Florida, Dept Biol, Gainesville, FL USA;
37.Univ Florida, Florida Museum Nat Hist, Gainesville, FL 32611 USA;
38.James Cook Univ, Coll Sci & Engn, Cairns, Qld, Australia;
39.Univ Estadual Campinas, Nucleo Estudos & Pesquisas Ambientais, Campinas, SP, Brazil;
40.Univ Plymouth, Sch Geog Earth & Environm Sci, Plymouth, Devon, England
推荐引用方式
GB/T 7714
Fauset, Sophie,Gloor, Manuel,Fyllas, Nikolaos M.,et al. Individual-Based Modeling of Amazon Forests Suggests That Climate Controls Productivity While Traits Control Demography[J],2019,7.
APA Fauset, Sophie.,Gloor, Manuel.,Fyllas, Nikolaos M..,Phillips, Oliver L..,Asner, Gregory P..,...&Malhi, Yadvinder.(2019).Individual-Based Modeling of Amazon Forests Suggests That Climate Controls Productivity While Traits Control Demography.FRONTIERS IN EARTH SCIENCE,7.
MLA Fauset, Sophie,et al."Individual-Based Modeling of Amazon Forests Suggests That Climate Controls Productivity While Traits Control Demography".FRONTIERS IN EARTH SCIENCE 7(2019).
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