TY - JOUR
T1 - Effect of microsite quality and species composition on tree growth
T2 - A semi-empirical modeling approach
AU - Mayoral, Carolina
AU - van Breugel, Michiel
AU - Turner, Benjamin L.
AU - Asner, Gregory P.
AU - Vaughn, Nicholas R.
AU - Hall, Jefferson S.
N1 - Funding Information:
This work is a contribution of the Agua Salud Project and the Smart Reforestation® program of the Smithsonian Tropical Research Institute (STRI). Agua Salud is part of ForestGEO and is a collaboration with the Panama Canal Authority (ACP), the Ministry of the Environment (MiAmbiente) of Panama, and other partners. The plantation is also part of the TreeDiVNet network. Plantation management is supported by the ACP. Funding for this work came from Stanley Motta, the Silicon Valley Foundation, the Hoch Family, the Smithsonian Institution's Competitive Grants for Science, the Smithsonian Institution's Grand Challenge grant to BiodiversiTREE, the Heising-Simons Foundation and the National Science Foundation grant (NSF grant EAR-1360391). We thank Daniela Weber, Estrella Yanguas, and Federico Davies for their work in helping to manage the plantation. We are grateful to Mario Bailon, Anabel Rivas, Johana Balbuena, Miguel Nunez, Guillermo Fernandez, Julia Gonzalez, and numerous other interns and technicians for their help in measuring the plantations and data entry and management.
Funding Information:
This work is a contribution of the Agua Salud Project and the Smart Reforestation® program of the Smithsonian Tropical Research Institute (STRI). Agua Salud is part of ForestGEO and is a collaboration with the Panama Canal Authority (ACP), the Ministry of the Environment (MiAmbiente) of Panama, and other partners. The plantation is also part of the TreeDiVNet network. Plantation management is supported by the ACP. Funding for this work came from Stanley Motta, the Silicon Valley Foundation, the Hoch Family, the Smithsonian Institution’s Competitive Grants for Science, the Smithsonian Institution’s Grand Challenge grant to BiodiversiTREE, the Heising-Simons Foundation and the National Science Foundation grant ( NSF grant EAR-1360391 ). We thank Daniela Weber, Estrella Yanguas, and Federico Davies for their work in helping to manage the plantation. We are grateful to Mario Bailon, Anabel Rivas, Johana Balbuena, Miguel Nunez, Guillermo Fernandez, Julia Gonzalez, and numerous other interns and technicians for their help in measuring the plantations and data entry and management.
Funding Information:
Carnegie Airborne Observatory (CAO) participation in this study was supported by grants and donations to G.P. Asner from the Avatar Alliance Foundation, Margaret A. Cargill Foundation, David and Lucile Packard Foundation, Gordon and Betty Moore Foundation, Grantham Foundation for the Protection of the Environment, W. M. Keck Foundation, John D. and Catherine T. MacArthur Foundation, Andrew Mellon Foundation, Mary Anne Nyburg Baker and G. Leonard Baker Jr, and William R. Hearst III.
Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/1/15
Y1 - 2019/1/15
N2 - Reforestation in the tropics mitigates the negative effects of climate change by sequestering carbon in biomass. However, tree growth is limited by nutrient availability in many tropical regions. A clear understanding of nutrient constraints and topography on growth of native timber species is thus essential to improve both the economic return on reforestation and the ecosystem services in tropical degraded lands. To address this, we use 7-year growth data from a 75-ha reforestation experiment in central Panama to test a modeling approach to predict growth of these species. The experiment includes five valuable timber species in 21 treatments, including monocultures and mixtures. We first fit a non-linear growth model as a function of tree age, then expand the former model parameters as a function of variables related to species mixture and micro-site soil conditions. Finally, we built a final model for each species to predict growth along three axes: nutrient availability, slope and species mixture. The models successfully identified how variation in growth was related to micro-site conditions and the species mixture. Although all species were long-lived pioneers, most were overall more sensitive to nutrient availability and between-trees interactions than to slope. However, the fastest growing species on average was more sensitive to slope than the other species and less sensitive to nutrient availability, showing better performance than the other species even under adverse conditions. Our models aid identification of species with the best growth potential to use in reforestation on infertile soils, leading to a better species selection according to site conditions.
AB - Reforestation in the tropics mitigates the negative effects of climate change by sequestering carbon in biomass. However, tree growth is limited by nutrient availability in many tropical regions. A clear understanding of nutrient constraints and topography on growth of native timber species is thus essential to improve both the economic return on reforestation and the ecosystem services in tropical degraded lands. To address this, we use 7-year growth data from a 75-ha reforestation experiment in central Panama to test a modeling approach to predict growth of these species. The experiment includes five valuable timber species in 21 treatments, including monocultures and mixtures. We first fit a non-linear growth model as a function of tree age, then expand the former model parameters as a function of variables related to species mixture and micro-site soil conditions. Finally, we built a final model for each species to predict growth along three axes: nutrient availability, slope and species mixture. The models successfully identified how variation in growth was related to micro-site conditions and the species mixture. Although all species were long-lived pioneers, most were overall more sensitive to nutrient availability and between-trees interactions than to slope. However, the fastest growing species on average was more sensitive to slope than the other species and less sensitive to nutrient availability, showing better performance than the other species even under adverse conditions. Our models aid identification of species with the best growth potential to use in reforestation on infertile soils, leading to a better species selection according to site conditions.
KW - Modeling tree diameter
KW - Parameter expansion
KW - Poor soils
KW - Steep slope
KW - Tropical timber species
UR - http://www.scopus.com/inward/record.url?scp=85054247837&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054247837&partnerID=8YFLogxK
U2 - 10.1016/j.foreco.2018.09.047
DO - 10.1016/j.foreco.2018.09.047
M3 - Article
AN - SCOPUS:85054247837
SN - 0378-1127
VL - 432
SP - 534
EP - 545
JO - Forest Ecology and Management
JF - Forest Ecology and Management
ER -