ABSTRACT

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.

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