MRA: Disentangling cross-scale influences on tree species, traits, and diversity from individual trees to continental scales

树木对生态系统至关重要。它们储存碳，减少侵蚀，并为其他物种提供栖息地。影响树木的因素以及管理树木的空间尺度从单个树木到整个大陆不等。由于世界上大约有3万亿棵树，使用传统方法在大范围内收集每棵树的数据是不可能的。因此，有必要利用新技术来测量和描述大地理区域内的单个树木。这项研究将通过将高分辨率遥感数据与树木实地数据相结合来解决这一根本性挑战。遥感和实地数据将一起用于了解是什么影响了树木的数量、大小、不同物种出现的地方，以及这些因素如何从当地公园的空间尺度变化到整个美国。该项目还将通过开发软件、生产数据产品以及为使用这些新数据集提供培训和合作机会，使其他科学家更容易研究大面积的树木。这将有助于推动对树木生态学的跨尺度理解的快速发展，并在林业、管理和基础科学理解方面有广泛的应用。该项目结合了国家生态观测站网络（NEON）的航空遥感数据和实地数据收集。这些数据将用于开发基于机器学习的方法，以识别、测量和表征位于每个森林NEON站点内的所有冠层树木的物种。这将产生美国大约40个地点大约5000万棵树的数据。NEON的这些数据将与美国林务局森林库存和分析项目的数据相结合，该项目在美国超过10万个地点对数百万棵树进行了采样。这些综合数据将用于开发树木分布、丰度和结构特征的联合模型，这些模型明确地包含了规模的概念。通过比较不同因素的重要性，这些模型将被用来了解影响树木分布和特征的过程如何在不同尺度上发生变化，这些尺度从几米的尺度（单个树木直接相互作用）到整个美国的尺度（气候和土地利用的大梯度很重要）。本研究将解决生态学中的三大问题：1)在不同尺度上，什么过程控制着物种的分布和丰度？它们是如何相互作用的？2)物种共存的景观和区域过程与当地生物多样性的关系如何？3)影响树木性状的过程变化如何影响生物量和碳储量的估算？该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Trees are essential to ecosystems. They store carbon, reduce erosion, and serve as habitat for other species. The factors influencing trees, and the spatial scales at which they are managed, range from an individual tree to entire continents. Since there are approximately three trillion trees in the world collecting data on every tree over large areas is impossible using traditional methods. Therefore, it is necessary to use new technology to measure and describe individual trees over large geographic areas. This research will address this fundamental challenge by combining high resolution remote sensing data with field data on trees. Together, the remote sensing and field data will be used to understand what influences the number of trees, their size, where different species occur, and how this changes from spatial scales of local parks to the entire United States. This project will also make it easier for other scientists to study trees over large areas by developing software, producing data products, and providing training and collaboration opportunities for working with these novel datasets. This will help drive rapid advances in the cross-scale understanding of tree ecology with broad applications in forestry, management, and fundamental scientific understanding.This project combines National Ecological Observatory Network (NEON) data from airborne remote sensing and field data collection. These data will be used to develop machine learning based approaches to identify, measure, and characterize to species all of the canopy trees located within each forested NEON site. This will yield data on approximately 50 million individual trees at about 40 sites across the United States. These data from NEON will be combined with data from the US Forest Service Forest Inventory and Analysis Project, which samples millions of trees at over 100,000 locations across the United States. These combined data will be used to develop joint models of the distribution, abundance, and structural traits of trees, that explicitly incorporate the concept of scale. These models will be used to understand how the processes influencing tree distribution and traits change across scales by comparing the importance of different factors at scales ranging from a few meters, where individual trees directly interact, to the entire United States, where large gradients in climate and land use are important. This research will address three broad questions in ecology: 1) what processes govern species distribution and abundance at different scales and how do they interact? 2) how are landscape and regional process of species coexistence connected to local biodiversity? 3) how do changes in the processes influencing tree traits across scales impact estimates of biomass and carbon storage?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.