MCA: Data Science for Global Change-Does Plant Diversity Imply Forest Resilience?

MCA：全球变化的数据科学——植物多样性是否意味着森林恢复力？

• 批准号: 2220980
• 负责人: Martin Dovciak
• 金额: $ 29.3万
• 依托单位: SUNY College of Environmental Science and Forestry
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2220980&HistoricalAwards=false
• 关键词: MCA; Data; Science; Global; Change

Two ongoing global crises – climate warming and species loss – make it difficult to predict how our planet’s forests will look in the future. Are species-rich forests more resilient to changing climate, or more vulnerable to change? Should we expect greater changes in species-rich tropical forests, or in species-poor northern conifer forests? All forests deliver important ecosystem services to humanity as they moderate climate and provide wood, clean water, wildlife habitat, and livelihoods and recreation opportunities. This project will help us understand how the world’s forests are changing and what role biodiversity plays in forests’ resilience to changing climate. This work will focus on the most vulnerable stage in forest development – tree seedlings – to create new conceptual and statistical models to aid forest management and conservation in varied settings across the globe. The project will also compare how different countries monitor early stages of forest change and will contribute to the sharing of best practices and the best science across borders. The findings will be widely disseminated to major agencies in forest management and monitoring. Ongoing societal conversations about climate change and biodiversity loss will be aided by sharing the project’s findings with the public via interviews, press releases, and lectures at regional high schools and nature centers. Advanced training in ecology, forest monitoring, and data science will be given to 15-30 graduate students, including those from traditionally underrepresented backgrounds. Long-term impacts will include a newly developed course, new teaching materials, and new study plots established to monitor future forest changes.As global forest ecosystems experience interconnected contemporary crises – climate change and biodiversity loss – it is increasingly important to understand if forests composed of more species are also more resilient to changing climate. This proposal will help answer this question by studying how forests change in time and space along the global latitudinal diversity gradient (LDG), focusing particularly on the early stages of forest change (tree seedlings) that influence future forest character. The proposal seeks to use diverse data science approaches to help us understand how tree seedling banks change along the LDG with climate and how they relate to characteristics (such as diversity and resilience to change) of forest overstory and understory plant communities. We will specifically address key aspects of tree seedling bank dynamics such as tree seedling diversity, resilience to change, abundance, and composition. The project integrates four activities: (1) fine-scale analyses of field data from sites along the tropical-temperate-boreal gradient, (2) broad-scale analyses of forest inventories spanning the LDG (Big Data), (3) systematic review and meta-analysis of published papers, and (4) review and synthesis of data science approaches used for monitoring tree seedlings in major forest monitoring systems of the world. Data will be analyzed using generalized linear mixed models (GLMMs) and meta-analyses. The proposed project will address decades old, yet still unresolved discussions of the effects of diversity on forest ecosystem resilience and stability, while focusing on the most sensitive stage of forest dynamics using modern data science approaches along the global diversity gradient.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.

气候变暖和物种丧失这两项持续的全球危机使我们很难预测地球森林未来的面貌。物种丰富的森林对气候变化的适应能力更强，还是更容易受到变化的影响？我们应该期待物种丰富的热带森林或物种贫乏的北方针叶林发生更大的变化吗？所有森林都为人类提供重要的生态系统服务，因为它们调节气候并提供木材、清洁水、野生动物栖息地以及生计和娱乐机会。该项目将帮助我们了解世界森林正在如何变化以及生物多样性在森林抵御气候变化的能力中发挥什么作用。这项工作将重点关注森林发展中最脆弱的阶段——树苗——创建新的概念和统计模型，以帮助全球不同环境下的森林管理和保护。该项目还将比较不同国家如何监测森林变化的早期阶段，并将有助于跨境分享最佳实践和最佳科学。研究结果将广泛传播给森林管理和监测的主要机构。通过采访、新闻稿以及在地区高中和自然中心举办的讲座与公众分享该项目的研究结果，将有助于正在进行的有关气候变化和生物多样性丧失的社会对话。将为 15-30 名研究生提供生态学、森林监测和数据科学方面的高级培训，其中包括来自传统上代表性不足的背景的研究生。长期影响将包括新开发的课程、新的教材以及为监测未来森林变化而建立的新研究地块。随着全球森林生态系统经历相互关联的当代危机——气候变化和生物多样性丧失——了解由更多物种组成的森林是否也更能适应气候变化变得越来越重要。该提案将通过研究森林如何沿着全球纬度多样性梯度（LDG）在时间和空间上发生变化，特别关注影响未来森林特征的森林变化（树苗）的早期阶段，来帮助回答这个问题。该提案旨在利用不同的数据科学方法来帮助我们了解树苗库如何随着 LDG 随气候而变化，以及它们与森林上层和林下植物群落的特征（例如多样性和变化恢复能力）之间的关系。我们将具体解决树苗库动态的关键方面，例如树苗多样性、变化恢复能力、丰度和组成。该项目整合了四项活动：（1）对热带-温带-寒带梯度沿线站点的实地数据进行精细分析，（2）对跨越LDG（大数据）的森林清单进行大规模分析，（3）对已发表论文进行系统回顾和荟萃分析，以及（4）对世界主要森林监测系统中用于监测树苗的数据科学方法进行回顾和综合。将使用广义线性混合模型（GLMM）和荟萃分析来分析数据。拟议的项目将解决几十年前但仍未解决的关于多样性对森林生态系统恢复力和稳定性影响的讨论，同时利用现代数据科学方法沿着全球多样性梯度关注森林动态最敏感的阶段。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。