Collaborative Research: Will changes in vegetation composition slow climate-driven wildfire growth in the boreal forests of northwestern North America?

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Arctic and boreal forests, tundra, and cold underlying soils together store 30% of the world’s terrestrial carbon, an amount twice that currently stored in the atmosphere. As the climate warms, Arctic and boreal wildfires are becoming more frequent and intense, burning larger areas. These fires release older carbon to the atmosphere, and by doing so could affect global climate, change the forest ecosystems, and possibly slow climate-driven increases in fire activity. This project is exploring these system-level feedbacks between fire and vegetation, also called fire self-regulation, using techniques from ecosystem ecology, remote sensing, geostatistics, and simulation modeling. The team is producing new models that will enable more accurate forecasts of ecosystem, landscape, and regional change. The team is partnering with the Alaska Fire Science Consortium to develop new maps that enhance tools available for regional land-use and fire management and provide new training opportunities to land managers. The project also promotes interdisciplinary training and professional development for ecological scientists and supports career development of early career scientists and students, including members of underrepresented groups.This project is addressing two primary questions: (1) What is the current evidence for the ecological process of fire self-regulation, and can it slow the effects of climate warming on fire activity? (2) How does consideration of fire self-regulation affect projections of future ecosystem and landscape structure, function, and climate forcing? Multiple lines of evidence are being developed to test hypotheses about fire self-regulation in boreal forests of Interior Alaska, USA, and Northwest Territories, Canada, cold regions on discontinuous permafrost ground that have recently experienced unprecedented fire activity. The team is combining field and remote sensing data to quantify decadal patterns of burning and estimate the direction and magnitude of fire self-regulation processes. Researchers are integrating statistical inference with simulation modeling that is process-based and landscape-scale to predict current and future ecological dynamics and feedbacks. The results are informing improved tools for wildland and fire managers across Alaska, assisting with efforts to mitigate wildfire effects on residents and critical infrastructure.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.