EAGER: Quantifying the relative importance of reproduction in forest dynamics under historical and future climate change

EAGER：量化历史和未来气候变化下森林动态繁殖的相对重要性

• 批准号: 2135448
• 负责人: Rebecca Snell
• 金额: $ 19.41万
• 依托单位: Ohio University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2135448&HistoricalAwards=false
• 关键词: EAGER; Quantifying; relative; importance; reproduction

Environmental change will alter forest dynamics and species composition. Tree reproduction and recruitment are critical processes for tree populations and ultimately, determine the future species composition of forests. Even though tree reproduction is sensitive to climate, the relative importance of this process for forest dynamics is unclear, especially compared to anticipated climate-change effects on tree growth and mortality. Seed production and seedling survival are also determined by individual and stand level properties. For example, seedling survival is affected by light availability. Increasing mortality due to climate change will alter the amount of light hitting the forest floor, and affect seedling survival. Seed production is also affected by size (i.e., larger individuals tend to produce more seed). If tree growth responds positively to climate change, this could indirectly cause an increase in seed production. Thus, to capture all these direct and indirect effects, the project will update a dynamic vegetation model (DVM) to explicitly simulate reproduction as a function of weather and stand structure. Including tree regeneration dynamics in process-based vegetation models is a major research gap and a critical step for accurately projecting forest responses to climate change. The project broader impacts include (1) training for students in computer programming, database management, data analysis, and communicating science, and (2) outreach activities to broaden participation of women in computer science.Using forests located in the Pacific Northwest (PNW) as a case study, the project will quantify the relationships for seed production (as a function of weather and size) and seedling survival (as a function of weather and stand structure), for common tree species in the PNW. These relationships will be incorporated into a forest model, that already includes species-specific simulations of growth, competition and mortality as a function of the environment. The updated model will be used to simulate forest stands located in the PNW under historical climate and various climate change scenarios. By comparing model versions, the research will be able to quantify the relative role of reproduction on forest dynamics and species turnover and identify species vulnerable to climate change.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.

环境变化将改变森林动态和物种组成。树木的繁殖和补充是树木种群的关键过程，并最终决定森林未来的物种组成。尽管树木繁殖对气候敏感，但这一过程对森林动态的相对重要性尚不清楚，特别是与预期的气候变化对树木生长和死亡的影响相比。种子生产和幼苗存活也取决于个体和林分水平特性。例如，幼苗存活受光照可用性的影响。由于气候变化导致的死亡率增加将改变照射到森林地面的光量，并影响幼苗的存活。种子产量也受大小的影响（即，较大的个体倾向于产生更多的种子）。如果树木生长对气候变化做出积极反应，这可能间接导致种子产量的增加。因此，为了捕捉所有这些直接和间接的影响，该项目将更新动态植被模型（DVM），以明确模拟作为天气和林分结构函数的繁殖。在基于过程的植被模型中纳入树木更新动态是一个主要的研究空白，也是准确预测森林对气候变化反应的关键一步。该项目的广泛影响包括：（1）对学生进行计算机编程、数据库管理、数据分析和通信科学方面的培训;（2）开展外联活动，扩大妇女对计算机科学的参与。该项目将量化种子生产的关系，（作为天气和大小的函数）和幼苗存活率（作为天气和林分结构的函数）。这些关系将被纳入一个森林模型，该模型已经包括作为环境功能的特定物种的生长、竞争和死亡率的模拟。更新后的模型将用于模拟历史气候和各种气候变化情景下位于PNW的森林。通过比较模型版本，该研究将能够量化繁殖对森林动态和物种周转的相对作用，并确定易受气候变化影响的物种。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。