Collaborative Research: Climate Change Impacts on Forest Biodiversity: Individual Risk to Subcontinental Impacts
合作研究:气候变化对森林生物多样性的影响:次大陆影响的个体风险
基本信息
- 批准号:1137309
- 负责人:
- 金额:$ 44.92万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2012
- 资助国家:美国
- 起止时间:2012-02-15 至 2018-01-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Climate change is rapidly transforming forests over much of the globe in ways that are not anticipated by current science. Large scale forest diebacks, apparently linked to interactions involving drought, warm winters, and other species, are becoming alarmingly frequent. Models of biodiversity and climate have not provided guidance on if, where or when such responses will occur. Instead models tend to provide potential numbers of extinctions, but such forecasts are not linked in any mechanistic way to the processes that could cause them. Both modeling and field studies rely on aggregate measures of species presence or absence, or their relative abundance at regional scales. However, climate acts on individuals. Aggregating data on individual trees to the level of a whole species hides or may even change predictions of climate effects. This study aims to link individual scale tree processes to regional species level responses by sampling and analyzing data about individuals across their entire range and corresponding range in climate conditions. It will use data from existing research sites, plus the platform of sites that form the core of the new National Ecological Observatory Network. These data will be collectively synthesized and used to develop computer models that can help determine when and where predicting climate impacts on biodiversity is a plausible goal. The models will also reveal where surprises are likely to occur and can provide feedback to expectations of individual tree health and vulnerability to environmental changes. This study will provide the first forecasts of the vulnerability of forest biodiversity to changes in climate that are directly linked to the biological processes that are most sensitive. The goal is to provide forecasts of the distribution, growth, reproduction and risks of mortality for tree species making up the nation's forests. These predictions will help scientists, forest managers and policy makers anticipate the combined risks of increasing drought and longer growing seasons. Methods and results developed during this project will be disseminated through workshops for training resource managers, as well as graduate students and postdoctoral associates at a number of universities.
气候变化正在以当前科学无法预料的方式迅速改变全球大部分地区的森林。大规模的森林枯死,显然与干旱、暖冬和其他物种的相互作用有关,正变得令人担忧地频繁。生物多样性和气候模型并没有就是否、在何处或何时会发生这种反应提供指导。相反,模型倾向于提供潜在的灭绝数量,但这种预测与可能导致灭绝的过程没有任何机械的联系。模型和实地研究都依赖于物种存在或缺失的总体测量,或者它们在区域尺度上的相对丰度。然而,气候对个人也有影响。将单个树木的数据汇总到整个物种的水平,隐藏甚至可能改变对气候影响的预测。本研究旨在通过采样和分析气候条件下整个范围内的个体数据,将个体尺度的树木过程与区域物种水平的响应联系起来。它将使用现有研究站点的数据,以及构成新国家生态观测站网络核心的站点平台。这些数据将被综合起来用于开发计算机模型,以帮助确定何时何地预测气候对生物多样性的影响是一个合理的目标。这些模型还将揭示意外可能发生的地方,并可以为个体树木的健康状况和对环境变化的脆弱性的预期提供反馈。这项研究将首次预测森林生物多样性对气候变化的脆弱性,而气候变化与最敏感的生物过程直接相关。其目标是为构成国家森林的树种的分布、生长、繁殖和死亡风险提供预测。这些预测将帮助科学家、森林管理者和决策者预测干旱加剧和生长季节延长的综合风险。在这个项目中发展的方法和结果将通过培训资源管理人员以及一些大学的研究生和博士后的讲习班来传播。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Andrew Finley其他文献
Small Area Estimates for National Applications: A Database to Dashboard Strategy Using FIESTA
国家应用的小面积估算:使用 FIESTA 的数据库到仪表板策略
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:3.2
- 作者:
Andrew Finley;T. Frescino;K. McConville;Grayson W. White;J. C. Toney;G. Moisen - 通讯作者:
G. Moisen
Andrew Finley的其他文献
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{{ truncateString('Andrew Finley', 18)}}的其他基金
Collaborative Proposal: Redefining the ecological memory of disturbance over multiple temporal and spatial scales in forest ecosystems
合作提案:重新定义森林生态系统多个时空尺度扰动的生态记忆
- 批准号:
1946007 - 财政年份:2021
- 资助金额:
$ 44.92万 - 项目类别:
Standard Grant
Collaborative Research: High-Dimensional Spatial-Temporal Modeling and Inference for Large Multi-Source Environmental Monitoring Systems
合作研究:大型多源环境监测系统的高维时空建模与推理
- 批准号:
1916395 - 财政年份:2019
- 资助金额:
$ 44.92万 - 项目类别:
Standard Grant
Collaborative Research: Hierarchical Sparsity-Inducing Gaussian Process Models for Bayesian Inference on Large Spatiotemporal Datasets
合作研究:大型时空数据集贝叶斯推理的层次稀疏诱导高斯过程模型
- 批准号:
1513481 - 财政年份:2015
- 资助金额:
$ 44.92万 - 项目类别:
Standard Grant
CAREER: Advancements in Spatio-temporal Modeling and Education in Support of NEON and Large-scale and Long-term Ecological Research
职业:支持 NEON 和大规模长期生态研究的时空建模和教育进展
- 批准号:
1253225 - 财政年份:2013
- 资助金额:
$ 44.92万 - 项目类别:
Continuing Grant
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