Collaborative Research: Quantifying rates of biome shifts under climate change in arctic and boreal ecosystems

北方或泰加森林储存了全球约30%-50%的碳，并面临着多种类型的干扰。随着高纬度地区气温继续快速上升，野火、昆虫暴发和冻土融化的增加向大气中释放了更多的碳。此外，从该地区采伐木材的压力越来越大。在预测这些系统的未来方面，最大的未知数可能是气候变化将如何影响从冻土带到森林和森林到草原的转变，以及这些变化将如何反馈到气候系统。该项目将使用多尺度建模方法，以减少我们对干扰和气候变化将如何影响西伯利亚北部陆地生态系统的理解的不确定性。它将通过在美国、奥地利和俄罗斯的数据共享、会议、研讨会和研讨会加强国际研究合作。研究人员将通过与阿拉斯加夏季研究院(Alaska Summer Research Academy)的合作，支持中学生的教育，并培训博士后科学家。阿拉斯加夏季研究院是阿拉斯加的一个野外科学夏令营。目前，全球模型在我们目前对气候变化导致的植被如何变化的理解中发挥着关键作用。然而，植被被模拟为功能型而不是物种，并且并不包括所有重要的干扰。相比之下，森林景观模型能够在物种水平上模拟演替，并包括昆虫和风等干扰。然而，与全球模型不同，景观模型不模拟植被和大气之间的反馈，这在北极和北部地区至关重要，因为在这些地区，植被类型的微小变化可能会对气候变化产生全球影响。这项研究的目的是确定气候变化和干扰将如何影响北方生态系统，并在全球模型和景观模型之间比较这些估计。这项研究集中在俄罗斯西伯利亚的四个有转换风险的地区(例如，冻土带到北方森林、草原)，横跨大纬度梯度(北纬53-73度)，这是一个世界上研究不足的地区。利用丰富的经验数据集，研究人员将比较预测未来物种组成和碳动态的景观和全球尺度模型的结果。这一比较将：1)量化气候变化下植被的潜在变化，2)估计北部地区植被对气候(反之亦然)影响的相对大小和方向。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Boreal, or taiga, forests store approximately 30-50% of carbon worldwide and are facing multiple types of disturbance. As temperatures continue to rise rapidly at high latitudes, increases in wildfire, insect outbreaks, and thawing frozen soil, or permafrost, release more carbon into the atmosphere. In addition, the pressure to harvest timber from this region is growing. Perhaps the biggest unknown in predicting the future of these systems is how climate change will affect conversions from tundra to forest and forest to grasslands, and in turn, how these changes will feed back to the climate system. This project will use a multi-scale modeling approach to reduce uncertainty in our understanding of how disturbance and climate change will affect boreal terrestrial ecosystems in Siberia. It will enhance international research collaboration through data sharing, conferences, seminars, and workshops in the United States, Austria, and Russia. The investigators will support education of middle school students through a collaboration with the Alaska Summer Research Academy, a field-based science camp in Alaska, and train postdoctoral scientists. Currently, global models play a key role in our current understanding of how vegetation may shift due to climate change. However, vegetation is simulated as functional types rather than species, and not all important disturbances are included. In contrast, forest landscape models are able to simulate succession at the species-level and include disturbances like insects and wind. Unlike global models, however, landscape models do not simulate feedbacks between vegetation and the atmosphere, which is critical in arctic and boreal regions where even small shifts in vegetation type may have global repercussions for climate change. The objective of this study is to determine how climate change and disturbance will affect boreal ecosystems and to compare these estimates between global and landscape models. The research focuses on four areas at risk of conversion (e.g, tundra to boreal forest, grassland) across a large latitudinal gradient (53-73 degrees N) in Siberian Russia, an understudied region of the world. Capitalizing on a rich empirical dataset, the investigators will compare results from landscape and global scale models predicting future species composition and carbon dynamics. This comparison will: 1) quantify potential shifts in vegetation under climate change and 2) estimate the relative magnitude and direction of the influence of vegetation on climate (and vice-versa) in northern regions.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.