Collaborative Proposal: MRA: Using NEON data to elucidate the ecological effects of global environmental change on phenology across time and space

全球环境变化正在引起物种物候变化（生态过程的时间）和适应速度（对环境变化的生理调整）的变化，物候变化的生理前兆。这些变化很重要，因为它们会在相互作用的物种（如捕食者和猎物、宿主和寄生虫、竞争对手、植物和传粉者）的表现和时间上造成“不匹配”，从而对生物多样性和生态系统为人类提供的服务产生不利影响。尽管有大量关于单个物种物候和适应的数据，但没有一个普遍的框架可以预测物种的物候反应——以及物种相互作用——将如何对环境变化做出反应。为了解决这一知识差距并对社会产生更广泛的影响，一个研究小组已经聚集了全球变化和热生物学、生态信息学、数学和统计建模以及地理信息系统方面的专业知识。该项目的更广泛影响包括：培养不同种族背景和性别的下一代STEM本科生和研究生，一门关于生物学大规模和“大数据”的研究生课程，以及为子孙后代提供指导保护和监测物种入侵和传染病的新数据库。从科学的角度来看，该团队建议i)扩展物候数据集，ii)从环境变化对物种相互作用的影响文献中收集数据，iii)开发一个定量的全球框架，用于预测基于纬度、气候和生物性状对单个物种物候和物种相互作用的影响方向和程度。iv)通过使用至少13个现有的国家生态观测站网络（NEON）数据集来验证该模型的当前预测。具体目标是：1)建立一个描述物候变化、当地气候驱动因素、纬度和海拔以及生物体型的时间序列数据库；2)通过量化系统发育、体型、栖息地、纬度、物候特征和环境（温度和降水）对物候的影响，建立预测物种物候响应的数学框架；3)利用NEON数据验证这一框架，测试它是否能准确预测单个物种对当前气候变化的物候响应，从局地到大陆尺度；4)利用NEON数据评估该框架预测当前气候变率如何影响物种相互作用和生态系统功能的强度和结果的能力；5)一旦得到验证，将该框架与当地环境变化预测相结合，以预测全球对环境变化特别敏感的物种和地点。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。

Global environmental change is causing shifts in species’ phenologies (the timing of ecological processes) and rates of acclimation (physiological adjustments to environmental change), the physiological precursors to phenological shifts. These shifts are important because they can create 'mismatches' in the performance and timing of interacting species, such as predators and prey, hosts and parasites, competitors, and plants and pollinators, which can adversely affect biodiversity and the services that ecosystems provide to humans. Despite extensive data on the phenology and acclimation of individual species, no general framework exists that can predict how phenological responses of species – and by extension species interactions – will respond to environmental change. To address this knowledge gap and provide broader impacts to society, a research team has been assembled with expertise in global change and thermal biology, ecoinformatics, mathematical and statistical modeling, and geographic information systems. The broader impacts of the project include: training the next generation of STEM undergraduates and graduate students across diverse ethnic backgrounds and genders, a graduate course on large scales and ‘big data’ in biology, and new databases for posterity that will guide conservation and monitoring for species invasions and infectious diseases.From a scientific perspective, this team proposes to i) expand phenological datasets, ii) gather data from the literature on environmental change effects on species interactions, iii) develop a quantitative global framework for predicting the direction and magnitude of effects on individual species’ phenologies and species interactions based on latitude, climate, and organismal traits, and iv) validate present-day predictions of the model by working with at least 13 existing National Ecological Observatory Network (NEON) datasets. The specific objectives are to: 1) assemble a database of time series describing phenological shifts, local climatic drivers, latitude and elevation, and organismal body sizes; 2) develop a mathematical framework for predicting the phenological responses of species to environmental change, by quantifying how phylogeny, body size, habitat, latitude, phenological trait, and environment (temperature and precipitation) affect phenology; 3) use NEON data to validate this framework by testing whether it can accurately predict the phenological responses of individual species to variability in present-day climate, from local to continental scales; 4) use NEON data to evaluate the ability of this framework to predict how variability in present-day climate will affect the strength and outcome of species interactions and ecosystem functions; and 5) once validated, couple the framework to local environmental change projections to predict species and locations around the globe that will be particularly sensitive to changing environments.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.