Collaborative Research and NEON: MSB Category 2: PalEON - a PaleoEcological Observatory Network to Assess Terrestrial Ecosystem Models

合作研究和 NEON：MSB 类别 2：PalEON - 评估陆地生态系统模型的古生态观测站网络

• 批准号: 1241874
• 负责人: Jason McLachlan
• 金额: $ 150.47万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1241874&HistoricalAwards=false
• 关键词: Collaborative; Research; NEON; MSB; Category

Because of the slow pace of terrestrial ecosystem processes, including the slow generation time, growth rate, and decomposition rate of trees, the impact of changing climate and disturbance on forests plays out over hundreds of years. For this reason, terrestrial ecosystem models are used to anticipate the centennial scale projections of forest response to environmental change. Current terrestrial ecosystem model predictions vary widely and results have large statistical uncertainties. Furthermore, testing and calibration of these models relies on short term (sub-daily to decadal) data that fail to capture longer term trends and infrequent extreme events. The capacity of ecosystem models for scientific inference and long-term prediction would be greatly improved if uncertainties can be reduced through rigorous testing against observational data. PalEON is an interdisciplinary team of paleoecologists, statisticians, and modelers that have partnered to rigorously synthesize longer term paleoecological data and incorporate into ecosystem models to provide a deeper understanding of past dynamics and to use this knowledge to improve long-term forecasting capabilities.Funds are provided to address four objectives and associated research questions: 1) Validation: How well do ecosystem models simulate decadal-to-centennial dynamics when confronted with past climate change, and what limits model accuracy? 2) Initialization: How sensitive are ecosystem models to initialization state and equilibrium assumptions? Do data-constrained simulations of centennial-scale dynamics improve 20thcentury simulations? 3) Inference: Was the terrestrial biosphere a carbon sink or source during the Little Ice Age and Medieval Climate Anomaly? and 4) Improvement: How can parameters and processes responsible for data-model divergences be improved? The data synthesis will include wide range of ecosystems, encompasses past climate variations that were large enough to affect tree growth rates, disturbance regimes, and forest demography, and leverages available paleodata. The synthesis will include 1) fossil pollen and Public Land Survey data to reconstruct forest composition, 2) sedimentary charcoal, stand-age and firescar indicators of past disturbance regimes, 3) tree-ring records of tree growth rates, and 4) multiple paleoclimatic proxies and paleoclimatic simulations. Bayesian hierarchical statistical models will be used to reconstruct key ecological variables and their associated uncertainty estimates. A standardized model intercomparison involving 13 ecosystem modeling groups will be used to evaluate the robustness of the modeling approach.Three areas will be emphasized for PalEON's broader impacts. Community Building: The PalEON research community has doubled over the past 10 months, with more than 60 participants now. It is anticipated to nearly another doubling over the next five years, and the funds will allow the ongoing community-building via annual large meetings and task-oriented workshops. Interdisciplinary Training and Mentoring: A new generation of researchers will be trained to naturally conceptualize large spatial and temporal scales and to approach ecological forecasting as an integrative activity spanning data collection to model prediction. Eight postdocs and seven graduate students will be involved in proposed PalEON research, with multiple opportunities for cross-training. Additionally, the PalEON Summer Short Course provides an intensive cross-training experience for young scientists in all areas encompassed by PalEON. The 2012 course will be followed by courses in 2014 and 2016. Building Scientific Infrastructure: All PalEON datasets will be made publicly available upon publication, as will our new data-assimilation methods and model intercomparison protocols. Tools will be developed for optimal site selection (given the goal of reducing the integrated prediction uncertainty about past vegetation and climate over space and time) and will distribute a publicly available webtool version that will be linked directly to the Neotoma Paleoecology Database.

由于陆地生态系统进程的缓慢，包括树木的生成时间、生长速度和分解速度缓慢，气候变化和干扰对森林的影响要持续数百年。因此，陆地生态系统模型被用来预测森林对环境变化的百年尺度预测。目前的陆地生态系统模型预测差异很大，结果有很大的统计不确定性。此外，这些模型的测试和校准依赖于短期（每日以下至十年）数据，无法捕捉长期趋势和罕见的极端事件。如果能够通过对观测数据进行严格测试来减少不确定性，生态系统模型进行科学推断和长期预测的能力将大大提高。PalEON是一个由古生态学家、统计学家和建模师组成的跨学科团队，他们合作严格综合长期的古生态数据，并将其纳入生态系统模型，以提供对过去动态的更深入了解，并利用这些知识提高长期预测能力。基金提供用于解决四个目标和相关研究问题：1）验证：面对过去的气候变化，生态系统模型模拟十年到百年动态的效果如何？是什么限制了模型的准确性？2)生态系统模型对初始状态和平衡假设的敏感程度如何？百年尺度动力学的数据约束模拟是否改善了20世纪的模拟？3)推论：在小冰期和中世纪气候异常期间，陆地生物圈是碳汇还是碳源？4）改进：如何改进导致数据模型分歧的参数和过程？数据合成将包括广泛的生态系统，包括过去的气候变化，大到足以影响树木生长率，干扰制度和森林人口，并利用现有的古数据。该合成将包括1）化石花粉和公共土地调查数据，以重建森林组成，2）过去干扰制度的沉积木炭、林分年龄和火灾指标，3）树木生长率的树木年轮记录，以及4）多个古气候代理和古气候模拟。贝叶斯分层统计模型将用于重建关键生态变量及其相关的不确定性估计。一个涉及13个生态系统建模小组的标准化模型相互比较将被用来评估建模方法的鲁棒性。社区建设：PalEON研究社区在过去10个月里翻了一番，现在有60多个参与者。预计在未来五年内将再增加近一倍，这些资金将允许通过年度大型会议和以任务为导向的研讨会进行持续的社区建设。跨学科培训和指导：新一代的研究人员将接受培训，自然地概念化大的空间和时间尺度，并将生态预测作为一项综合活动，涵盖数据收集和模型预测。八名博士后和七名研究生将参与拟议的PalEON研究，并有多种交叉培训机会。此外，PalEON夏季短期课程为PalEON所涵盖的所有领域的年轻科学家提供了密集的交叉培训体验。继2012年课程之后，将在2014年和2016年举办课程。 建设科学基础设施：所有PalEON数据集将在出版后公开提供，我们新的数据同化方法和模型相互比较协议也将公开提供。将为最佳选址开发工具（考虑到减少过去植被和气候在空间和时间上的综合预测不确定性的目标），并将分发一个公开的网络工具版本，该版本将直接与Neotoma古生态学数据库链接。

项目成果

Sharp Savanna-forest Transitions in the Midwest Followed Environmental Gradients but are Absent from the Modern Landscape

中西部稀树草原-森林的急剧转变遵循环境梯度，但在现代景观中并不存在

• DOI: 10.1674/0003-0031-180.1.1
• 发表时间: 2018
• 期刊: The American Midland Naturalist
• 作者: Broderick, Caitlin M.;Heilman, Kelly A.;Patterson, Tamatha A.;Peters, Jody A.;McLachlan, Jason S.
• 通讯作者: McLachlan, Jason S.