Collaborative Research EAGER-NEON: Probabilistic Forecasting of Biodiversity Response to Intensifying Drought by Combining NEON, National Climate, Species, and Trait Data Bases

合作研究 EAGER-NEON：结合 NEON、国家气候、物种和性状数据库，对生物多样性对加剧干旱的反应进行概率预测

• 批准号: 1550911
• 负责人: James Clark
• 金额: $ 26.63万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1550911&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; NEON; Probabilistic

Drought is a nationwide threat to biodiversity, one of the challenges that motivated development of the National Ecological Observatory Network (NEON). Droughts are already affecting organisms from microbes to vertebrates and higher plants. The long-term consequences are unpredictable because each species responds to other species, as they too respond to drought. For example, a species that is insensitive to drought will still respond if the plants on which it feeds or the predators that consume it respond to drought. Ecologists could better anticipate drought effects by developing tools to integrate the information from many species as they respond both to climate and each other. With data from key taxonomic groups monitored by NEON the researchers will use a new approach, joint modeling of species and drought, to develop a predictive framework for biodiversity analysis. Using relevant NEON, predictive models, which combine multiple species, functional types, and functional traits, will be developed to identify commonalities in species responses to changing drought, reducing the dimensionality of thousands of species to groups that can be predicted. Indirect effects of species interactions will be emphasized in planned modeling activities. For example, a tree species may increase in crowded stands only when others decrease. The effects of temperature usually depend on precipitation. The results of this collaboration involving ecology and statistics will be of interest not only ecologists, but for all global change scientists and policy makers.

干旱是对生物多样性的一个全国性威胁，这是促使国家生态观测网（氖）发展的挑战之一。 干旱已经影响到从微生物到脊椎动物和高等植物的生物体。 长期后果是不可预测的，因为每个物种都会对其他物种做出反应，因为它们也会对干旱做出反应。 例如，一个对干旱不敏感的物种，如果它赖以为生的植物或捕食它的捕食者对干旱做出反应，它仍然会做出反应。 生态学家可以通过开发工具来整合来自许多物种的信息，更好地预测干旱的影响，因为它们对气候和彼此都有反应。 利用氖监测的关键分类组的数据，研究人员将使用一种新的方法，即物种和干旱的联合建模，为生物多样性分析开发一个预测框架。使用相关的氖，预测模型，结合联合收割机多个物种，功能类型和功能特性，将开发识别物种响应变化的干旱的共性，减少数千个物种的维度，可以预测的群体。 在计划的模拟活动中，将强调物种相互作用的间接影响。 例如，只有当其他树种减少时，一个树种才能在拥挤的林分中增加。 温度的影响通常取决于降水量。这项涉及生态学和统计学的合作成果不仅会引起生态学家的兴趣，而且会引起所有全球变化科学家和政策制定者的兴趣。