Simulating environmentally sensitive tree recruitment shows promise for helping ecologists predict where trees will grow in the future.
Forests will only persist where future trees are able to reproduce, disperse, germinate, and grow into mature trees (i.e. “recruit”). These critical regeneration processes are generally not represented in the models that ecologists use to predict future forests. The recently developed Tree Recruitment Scheme (TRS) was developed specifically to capture how changing environmental conditions will affect future trees’ ability to recruit. The TRS is shown to improve predictions of tree recruitment rates in a tropical forest in Panama and captures how reduced soil moisture and light constrains tree recruitment.
By improving predictions of tree recruitment using environmentally sensitive processes the TRS is well-positioned to improve predictions of future forest range boundaries, composition, and function. This is important for predicting the role that forests will play in sequestering and storing carbon, providing habitat for biodiversity, and provisioning critical natural resources for people. By representing the early stages of tree development the TRS will allow ecosystem modelers to simulate more complicated interactions between vegetation and changing disturbance regimes such as the affect of more severe fire on vegetation composition.
The TRS was developed and evaluated at Barro Colorado Island (BCI), Panama where ecologists have collected a significant amount of forest demography and meteorological data since the early 1980s. These data allowed researchers at Lawrence Berkeley National Lab to parameterize TRS algorithms that represent how soil moisture and light affect critical regeneration processes such as seedling emergence, seedling mortality, and seedling to sapling transition rates. By simulating recruitment under observed meteorological conditions researchers were able to compare TRS predictions of recruitment to census observations at BCI. Compared to prior models the TRS makes significant improvements in predicting which types of trees recruit and at what rate under the current climate. By also running the TRS under El Niño, wetter-than-observed, and drier-than-observed precipitation scenarios researchers found that the TRS predicts recruitment responses to varying soil moisture and light levels that are consistent with ecological expectations.
Contact: Adam Hanbury-Brown, University of California, Berkeley, firstname.lastname@example.org
Funding was provided via Next Generation Ecosystem Experiments-Tropics (NGEE-Tropics), funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research. The lead author also received support from the National Science Foundation and the National Aeronautics and Space Administration during this research.
A.R. Hanbury-Brown et al., “Simulating environmentally-sensitive tree recruitment in vegetation demographic models”. New Phytologist (2022). [DOI: 10.1111/nph.18059]