Climate Change Data Portal
| Collaborative Research: Predicting ecosystem resilience to climate and disturbance events with a multi-scale hydraulic trait framework | |
| 项目编号 | 2003205 |
| Anna Trugman (Principal Investigator) | |
| 项目主持机构 | University of California-Santa Barbara |
| 开始日期 | 2020-10-01 |
| 结束日期 | 2025-09-30 |
| 英文摘要 | Forests provide numerous services to society and play a critical role in governing the flux of carbon between the atmosphere and the biosphere. Currently, there is considerable uncertainty about the capacity of forests to continue providing services under altered precipitation regimes. By making detailed measurements related to drought-induced mortality for multiple, widespread tree species, using a continental-scale forest composition data set to extrapolate these measurements across the United States, and by incorporating predictions of future rainfall patterns, this project aims to improve our ability to predict the susceptibility of forests throughout the United States to drought. The investigators plan to make new measurements related to how low water availability in soil reduces water flow through trees, reduces rates of photosynthesis, and causes trees to die. The planned measurements, which are associated with key traits that influence coupled carbon and water flow in trees, provide a new basis for understanding how environmental variability has given rise to the forests of today, and for predicting how the species composition of forests is likely to change in the future. Furthermore, comparing the trait combinations of individual trees, and average trait values across species, to optimal values derived from a model enables a deeper understanding of why some species are more susceptible to drought than others. The team, composed of plant physiologists, forest ecologists, and Earth system modelers, plans to develop new measurement and modeling techniques, and plans to publicly disseminate datasets that will be used to identify regions at particular risk to changing rainfall patterns. In the process, the team will conduct interdisciplinary undergraduate and graduate training to prepare diverse, next-generation scientists to tackle ecological and data science challenges. The project team will combine a simple mechanistic vegetation model that links plant hydraulic traits to plant fitness, given local environmental conditions, and a wide range of datasets including forest community surveys from the USDA Forest Service Forest Inventory and Analysis (FIA) program, hydraulic trait databases, and new measurements of within-species variation of plant hydraulic traits. The team will aim to predict observed patterns in forest mortality at FIA plots, and across multiple long-term forest demography networks, using observed community hydraulic trait distributions and mechanistic model simulations driven by local environmental conditions. The team will identify the extent to which mortality during extreme events is dictated by community-weighted mean hydraulic traits and ecosystem hydraulic diversity. Finally, the team will apply these concepts to understand the limits of plant physiological plasticity/acclimation within a species. The project team will generate new continental-scale datasets documenting community-weighted hydraulic traits, community physiological function and community resilience. The knowledge gained will inform ecosystem management and conservation efforts, as well as future Earth system model development. The research team plans to engage local land managers about the impacts of climate on forest resilience, to incorporate undergraduate and high school researchers from local communities and underrepresented groups in STEM in project activities, and to reach out to local prison populations through an annual lecture series focused on the effects of variation in environmental conditions and terrestrial ecosystem health. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. |
| 学科分类 | 09 - 环境科学;0903 - 环境生物学 |
| 资助机构 | US-NSF |
| 项目经费 | 955064 |
| 项目类型 | Continuing Grant |
| 国家 | US |
| 语种 | 英语 |
| 文献类型 | 项目 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/190955 |
| 推荐引用方式 GB/T 7714 | Anna Trugman .Collaborative Research: Predicting ecosystem resilience to climate and disturbance events with a multi-scale hydraulic trait framework.2020. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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