MCA: New approaches to studying mast-seeding dynamics

了解植物繁殖的模式和驱动因素是生态学中的一个长期挑战。随着时间的推移，多年生植物产生的种子数量在物种和地点之间表现出令人难以置信的变化，从每年繁殖的一致水平到种子生产的繁荣和萧条年份，表现出极高的时间变异性，称为“肥大播种”。植物繁殖，环境驱动因素和物种相互作用之间的联系主要集中在地上因素，如传粉者和天气条件，而地下植物性状，如根属性和共生关系与菌根真菌网络，在很大程度上被忽视。该项目的目的是利用多个全球数据库和长期实地数据，将桅杆播种的地上现象与地下特征和地下物种相互作用结合起来。这项研究有可能揭示自然和管理的林业系统繁殖年度波动的新驱动因素。除了探索真菌对繁殖动力学的影响外，该项目还可以为林业实践提供信息。许多林业树木都接种了菌根真菌，这项工作可以让林业工作者开发更有效的树木再生技术。该项目将提供一个职业生涯中期的科学家保护的时间和跨学科的培训。来自不同背景的本科生和博士后研究人员的培训机会也将通过这种跨学科的研究。这项工作将使用五个全球数据库，关于肥大播种、菌根真菌协会和植物性状（例如，TRY，FRED，MycoBD，FungalRoot），数据来自10年的大田试验。研究人员具有桅杆播种的专业知识，并将与具有植物-土壤-微生物相互作用和结构方程建模专业知识的合作伙伴合作。该项目将测试关于地下植物性状和菌根协会如何影响桅杆播种条件范围的假设（例如，时间变异性，时滞），在一系列环境条件下，在全球范围内观察到的分类群。多元统计方法将通过结构方程模型来应用，以产生对桅杆播种模式驱动因素的新见解。一名本科生将协助探索数据库，一名博士后研究员将与研究人员合作，使用GitHub实现实验室工作流程的现代化，并创建闪亮的应用程序。这项工作将促进新的概念和分析的可能性，以领域的桅杆播种和植物繁殖的研究更广泛。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。

Understanding the patterns and drivers of plant reproduction is a long-standing challenge in ecology. The number of seeds produced by perennial plants over time shows incredible variation across species and locations, from consistent levels of annual reproduction to boom and bust years of seed production exhibiting extremely high temporal variability, known as ‘mast seeding’. Linking relationships among plant reproduction, environmental drivers, and species interactions has primarily focused on aboveground factors such as pollinators and weather conditions; while belowground plant traits, such as root attributes and symbiotic relationships with mycorrhizal fungal networks, have been largely ignored. The purpose of this project is to bring together the aboveground phenomenon of mast seeding, with belowground traits and belowground species interactions using multiple global databases and long-term field data. This study has the potential to reveal new drivers of annual fluctuations in reproduction in natural and managed forestry systems. In addition to exploring the impact of mycorrhizae on reproductive dynamics, this project may inform forestry practices. Many forestry trees are inoculated with mycorrhizal fungi, and this work could allow foresters to develop more efficient tree regeneration techniques. The project will provide a mid-career scientist protected time and interdisciplinary training. Training opportunities for an undergraduate student and a postdoctoral researcher from diverse backgrounds will also be enable by this interdisciplinary study. This work will use five global databases on mast seeding, and mycorrhizal fungal associations and plant-traits (e.g., TRY, FRED, MycoBD, FungalRoot), with data from a ten-year field study of mast seeding. The investigator has expertise in mast seeding and will work with a collaborative partner with expertise in plant-soil-microbe interactions and structural equation modeling. The project will test hypotheses about how belowground plant traits and mycorrhizal associations influence the range of mast-seeding conditions (e.g., temporal variability, time-lags) observed globally across taxa, over a range of environmental conditions. Multivariate statistical approaches will be applied through structural equation modelling to generate new insight into drivers of the patterns of mast seeding. An undergraduate student will assist with exploring databases, and a postdoctoral researcher will work with the investigator to modernize lab workflows using GitHub and creating Shiny Apps. This work will facilitate new conceptual and analytical possibilities to the field of mast seeding and to plant reproduction studies more broadly.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.