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全国性威胁，这是激发国家生态观测网络（NEON）发展的挑战之一。 干旱已经在影响从微生物到脊椎动物和较高植物的生物。 长期后果是不可预测的，因为每个物种也对干旱做出反应，因此对其他物种做出了反应。 例如，如果植物所饲养的植物或食用捕食者对干旱的反应，对干旱不敏感的物种仍然会做出反应。 生态学家可以通过开发工具来整合许多物种的信息，因为它们对气候和彼此的反应，可以更好地预期干旱影响。 通过霓虹灯监测的关键分类群体的数据，研究人员将使用一种新的方法，对物种和干旱的联合建模，以开发生物多样性分析的预测框架。将使用相关的霓虹灯，结合多种物种，功能类型和功能性状的预测模型，以确定物种对变化干旱的反应中的共同点，从而降低了数千种物种对可以预测的群体的维度。 在计划的建模活动中将强调物种相互作用的间接影响。 例如，只有当其他人减少时，树种可能会增加拥挤的林分。 温度的影响通常取决于降水。这种涉及生态学和统计的合作的结果不仅是生态学家，而且对所有全球变革科学家和政策制定者都感兴趣。