Will tree seedling fitness and survival decline in the face of climate change? A novel quantitative approach with implications on forest regeneration

去年，也就是三年计划的第一年，我们建立了田间和实验室实验，并开始收集物候和植物表现数据。2022年，即项目的第二年，我们专注于在田间和生长室收集数据。本研究的重要内容，即1）整个生长季节的详细环境数据（光照水平和温度），2）目标物种从叶片出现到叶片衰老的叶片物候，以及3）叶片性能（光合作用对光和温度变化的反应）和目标植物的叶片成本（LMA测定）在一年中进行了监测。这些数据将构成叶功能奇偶性（LFP）计算的基础，该计算将在本研究的第3年开始。我们亦已于2022年开始调查文献中的森林动态建模方法，并已识别多个潜在有用的模型，并将跟进试验测试这些模型是否适合纳入LFP输入。森林动态模型旨在处理我们对植物性能（季节性）的LFP估计，并在当前林下环境中生成生存概率，最重要的是，在未来受气候变化影响的环境中。

Last year, the first year of the three-year project, we established field and laboratory experiments and began collecting phenological and plant performance data. In 2022, the second year of the project, we focused on data gathering both in the field and in the growth chambers. Important elements of this study, i.e. 1) detailed environmental data (light level and temperature) across the growing season, 2) leaf phenology of target species from leaf emergence to leaf senescence and, 3) leaf performance (photosynthetic response to light and temperature variations) and leaf cost (LMA determination) of target plants were monitored during the year. These data will form the basis of the leaf functional parity (LFP) calculations that will begin in the 3rd year of this study. We have also begun a survey of forest dynamic modeling approaches in the literature during 2022 and we have identified a number of potentially useful models and will follow up with trial testing these models for their suitability in incorporating LFP input. The forest dynamic models are intended to process our LFP estimates on plant performance (seasonal) and generate probabilities of survivorship in current understory environment and, most importantly, in a future environment affected by climate change.