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

北方针叶林或针叶林储存了全球约30-50%的碳，并面临多种类型的干扰。随着高纬度地区气温持续快速上升，野火、昆虫爆发、冻土融化或永久冻土的增加，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时，最大的未知因素可能是气候变化将如何影响从苔原到森林、从森林到草原的转变，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来减少我们对干扰和气候变化如何影响西伯利亚北方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、研讨会和讲习班加强国际研究合作。研究人员将通过与阿拉斯加夏季研究学院（阿拉斯加的一个实地科学营）合作，支持中学生的教育，并培训博士后科学家。目前，全球模式在我们目前了解植被如何因气候变化而变化方面发挥着关键作用。然而，植被被模拟为功能类型而不是物种，并不是所有重要的干扰都包括在内。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，与全球模式不同的是，景观模式不模拟植被和大气之间的反馈，这在北极和北方地区至关重要，在这些地区，即使植被类型的微小变化也可能对气候变化产生全球影响。本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。这项研究的重点是在俄罗斯西伯利亚一个纬度梯度很大（北纬53-73度）的四个有转变风险的地区（如苔原向北方森林、草地），这是世界上一个研究不足的地区。利用丰富的经验数据集，研究人员将比较景观和全球尺度模型的结果，预测未来物种组成和碳动态。这种比较将：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影响的相对幅度和方向（反之亦然）。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。

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.