CAREER: Plant traits link disturbance history to carbon uptake across spatiotemporal scales

当释放的二氧化碳超过地球陆地和水的吸收能力时，多余的二氧化碳就会在大气中积聚并吸收能量。这些额外的能量使大气变暖，使其容纳更多的水蒸气，导致全球气候变化。气候和天气的变化反过来又影响生态系统和依赖它们的人们。要了解和预测气候变化的影响，关键的第一步是了解地球上的土地和水吸收了多少二氧化碳。在陆地上，大部分二氧化碳被植物吸收，但这种吸收特别难以预测，因为植物吸收碳的能力和对环境变化的反应不同。CAREER奖采用了一种新的方法来应对这一挑战，将卫星的历史数据、机载传感器的新数据和计算机建模结合起来，绘制美国东部森林的森林碳吸收图。该奖项将与教育和推广活动相结合，包括实地课程、教学模块、数字推广材料和公共写作，邀请所有年龄段的学生学习宏观系统生态学。由于缺乏对干扰、植物生理和结构性状（“植物性状”）和总初级产量（GPP）之间联系的机制理解，目前对碳吸收的估计受到限制。该职业奖提出了森林生态系统中“干扰综合征”的概念，该概念认为，即使在不同的生态系统中，对干扰的反应的时间模式也可能相似。该项目针对三个关键问题：(1)干扰综合征在多大程度上解释了植物性状的当前分布？(2)哪些植物性状对预测GPP最重要？(3)干扰综合征将在哪些方面改善按比例估计的GPP？利用近40年的地球资源卫星数据，该项目将绘制美国东部森林的扰动综合征图。这些干扰综合征图，连同环境梯度图，将通过使用国家生态观测站网络的机载观测平台估算植物性状分布介导的GPP预测模型。通过整合研究和教育目标，该项目旨在培养一个更具包容性、多样性和技术技能的社区，以了解陆地生态系统的模式和过程，并加快宏观系统生态学的创新步伐。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

When more carbon dioxide is released than can be absorbed by Earth’s land and water, the extra carbon dioxide builds up in the atmosphere and absorbs energy. This extra energy warms the atmosphere and lets it hold more water vapor, causing global climate change. Changes in climate and weather, in turn, impact ecosystems and the people who rely on them. To understand and predict the impacts of climate change, a key first step is to understand how much carbon dioxide is absorbed by the Earth’s land and water. On land, most carbon dioxide is taken up by plants, but this uptake is especially difficult to predict because plants differ in their abilities to absorb carbon and to respond to changes in the environment. This CAREER award takes a new approach to this challenge by combining historical data from satellites, new data from airborne sensors, and computer modeling to map forest carbon uptake in eastern U.S. forests. This award will be integrated with education and outreach activities, including a field course, teaching modules, digital outreach materials, and public writing, that invite students of all ages to learn about macrosystems ecology.Current estimates of carbon uptake are limited by a lack of mechanistic understanding of the connections between disturbance, plant physiological and structural traits (‘plant traits’), and gross primary production (GPP). This CAREER award develops the concept of ‘disturbance syndromes’ in forest ecosystems, which posits that temporal patterns of response to disturbances may be similar even among diverse ecosystems. The project targets three key questions: (1) How much does disturbance syndrome explain current distributions of plant traits?; (2) Which plant traits are most important to predicting GPP?; and (3) Where will disturbance syndromes improve scaled up estimates of GPP? Using nearly 40 years of Landsat satellite data, the project will map disturbance syndromes across eastern U.S. forests. These maps of disturbance syndromes, along with mapped environmental gradients, will inform a predictive model of GPP mediated by plant trait distributions estimated using the National Ecological Observatory Network’s Airborne Observation Platform. Through the integration of research and education objectives, this project aims to cultivate a more inclusive, diverse, and technically skilled community to understand terrestrial ecosystem patterns and processes and to accelerate the pace of innovation in macrosystems ecology.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.