U.S. Department of EnergyOffice of Scientific and Technical Information
Simulated tree recruitment at PA-BCI under four precipitation scenarios
Abstract
Simulated recruitment rates are provided for four tropical tree plant functional types (PFTs) which vary in drought and shade tolerance, at Barro Colorado Island, Panama (PA-BCI). Recruitment rates are predicted using the Tree Recruitment Scheme (TRS; Hanbury-Brown et al., 2022) and model data output from the Ecosystem Demography model version 2 with hydrodynamics (ED2; Medvigy et al., 2009, Powell et al., 2018). Predictions of recruitment are provided under four precipitation scenarios: “BASE” = recycled 2008-2014 observed meteorology (Faybishenko & Paton, 2021), “SYN-ENSO” = two exceptionally strong El Niño events within 30 years (Powell et al., 2018), “WET” = 30% increase in precipitation compared to BASE, and DRY-DS = dry season (January–April) precipitation reduced by 75% compared to BASE. In addition to recruitment rates, this dataset provides TRS predictions of PFT-specific seed bank and seedling pool dynamics (tracked in units of carbon) and ED predictions of understory photosynthetically active radiation and soil matric potential at 6 cm soil depth.
- Authors:
-
- University of California Berkeley
- Lawrence Berkeley National Laboratory
- Smithsonian Tropical Research Institute
- Publication Date:
- Other Number(s):
- NGT0185
- DOE Contract Number:
- DE-AC02-05CH11231
- Research Org.:
- Next-Generation Ecosystem Experiments Tropics; University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Smithsonian Research Institute
- Sponsoring Org.:
- Department of Energy, Office of Science, Office of Biological and Environmental Research
- Subject:
- 54 ENVIRONMENTAL SCIENCES
- OSTI Identifier:
- 1855609
- DOI:
- https://doi.org/10.15486/ngt/1855609
Citation Formats
Hanbury-Brown, Adam, Powell, Thomas, Muller-Landau, Helene, Wright, Joseph, and Kueppers, Lara. Simulated tree recruitment at PA-BCI under four precipitation scenarios. United States: N. p., 2022.
Web. doi:10.15486/ngt/1855609.
Hanbury-Brown, Adam, Powell, Thomas, Muller-Landau, Helene, Wright, Joseph, & Kueppers, Lara. Simulated tree recruitment at PA-BCI under four precipitation scenarios. United States. doi:https://doi.org/10.15486/ngt/1855609
Hanbury-Brown, Adam, Powell, Thomas, Muller-Landau, Helene, Wright, Joseph, and Kueppers, Lara. 2022.
"Simulated tree recruitment at PA-BCI under four precipitation scenarios". United States. doi:https://doi.org/10.15486/ngt/1855609. https://www.osti.gov/servlets/purl/1855609. Pub date:Sat Jan 01 00:00:00 EST 2022
@article{osti_1855609,
title = {Simulated tree recruitment at PA-BCI under four precipitation scenarios},
author = {Hanbury-Brown, Adam and Powell, Thomas and Muller-Landau, Helene and Wright, Joseph and Kueppers, Lara},
abstractNote = {Simulated recruitment rates are provided for four tropical tree plant functional types (PFTs) which vary in drought and shade tolerance, at Barro Colorado Island, Panama (PA-BCI). Recruitment rates are predicted using the Tree Recruitment Scheme (TRS; Hanbury-Brown et al., 2022) and model data output from the Ecosystem Demography model version 2 with hydrodynamics (ED2; Medvigy et al., 2009, Powell et al., 2018). Predictions of recruitment are provided under four precipitation scenarios: “BASE” = recycled 2008-2014 observed meteorology (Faybishenko & Paton, 2021), “SYN-ENSO” = two exceptionally strong El Niño events within 30 years (Powell et al., 2018), “WET” = 30% increase in precipitation compared to BASE, and DRY-DS = dry season (January–April) precipitation reduced by 75% compared to BASE. In addition to recruitment rates, this dataset provides TRS predictions of PFT-specific seed bank and seedling pool dynamics (tracked in units of carbon) and ED predictions of understory photosynthetically active radiation and soil matric potential at 6 cm soil depth.},
doi = {10.15486/ngt/1855609},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2022},
month = {1}
}