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

合作研究：植被组成的变化是否会减缓北美西北部北方森林中气候驱动的野火增长？

基本信息

批准号：
2116862
负责人：
Michelle Mack
金额：
$ 109.94万
依托单位：
Northern Arizona University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-11-01 至 2024-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2116862&HistoricalAwards=false
关键词：
Collaborative Research Will changes vegetation

项目摘要

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.

北极和北方森林，苔原和寒冷的底层土壤一起储存了30％的陆地碳，这是目前在大气中储存的两倍。随着气候的温暖，北极和北方野火变得越来越频繁，强烈，燃烧了更大的区域。这些火灾将较旧的碳释放到大气中，通过这样做可能会影响全球气候，改变森林生态系统，并可能缓慢的气候驱动驱动活动增加。项目正在使用生态系统生态学，遥感，地统计学和模拟建模的技术探索火灾和植被之间的这些系统级反馈，也称为火灾自我调节。该团队正在生产新的模型，以使生态系统，景观和区域变化的森林更加准确。该团队正在与阿拉斯加消防科学联盟合作开发新的地图，以增强可用于地区土地使用和消防管理的工具，并为土地管理人员提供新的培训机会。该项目还促进了生态科学家的跨学科培训和专业发展，并支持早期职业科学家和学生的职业发展，包括代表人数不足的成员。该项目正在解决两个主要问题：（1）当前的火灾自我调节生态学过程的证据是什么，这可以放慢气候变暖对火灾活动的影响吗？（2）对火自我调节的考虑如何影响未来生态系统和景观结构，功能和气候强迫的项目？正在开发多种证据，以检验有关阿拉斯加北部北部森林和加拿大西北地区的北方森林中的火灾自我调节的假设，在最近经历过前所未有的火灾活动的不连续的永久冻土地面上的寒冷地区。该团队正在将场和遥感数据组合在一起，以量化燃烧的十年模式，并估计火灾自我调节过程的方向和大小。研究人员正在将统计推断与基于过程和景观规模的模拟建模相结合，以预测当前和未来的生态动态和反馈。结果为阿拉斯加各地的野生世界和消防员提供了改进的工具，以帮助减轻对居民和关键基础设施的野火影响。该奖项反映了NSF的法定使命，并通过使用基金会的知识分子优点和更广泛的审查标准评估来诚实地获得支持。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Shifts in Ecological Legacies Support Hysteresis of Stand Type Conversions in Boreal Forests

生态遗产的变化支持北方森林林分类型转换的滞后性

DOI：
10.1007/s10021-023-00866-w
发表时间：
2023
期刊：
Ecosystems
影响因子：
3.7
作者：
Walker, X. J.;Okano, K.;Berner, L. T.;Massey, R.;Goetz, S. J.;Johnstone, J. F.;Mack, M. C.
通讯作者：
Mack, M. C.

‘LandsatTS': an R package to facilitate retrieval, cleaning, cross‐calibration, and phenological modeling of Landsat time series data