CAREER: Dissecting Snow Algal Community Interactions to Understand Environmental Change Impacts

在雪、冰和冰川（通常被称为粉红雪）上盛开的藻类加速融化，因为它们的深色色素吸收光。随着气温上升，冰川和积雪融化的额外因素正变得越来越令人担忧。雪藻与其他微生物如细菌和真菌一起生长。目前，人们对藻类和非藻类物种之间的相互作用以及它们如何对环境条件做出反应知之甚少。本项目采用自下而上的方法对蒙大拿州的雪藻群落进行调查，首先描述了雪藻群落的组成特征，并将其成员分离出来进行受控的实验室生长实验，从而建立了一个详细描述单个物种如何促进群落生长的模型。通过对同一地点的雪藻群落进行为期五年的采样，该项目将收集有关雪藻季节性和年度动态的重要信息。这个CAREER项目将涉及国家森林和公园合作伙伴关系，将本科生研究人员整合到课堂内外的研究过程中，并让蒙大拿州农村中学参与数据收集过程和潜在的科学概念。该项目的研究结果将有助于进一步了解雪藻群落的组成、功能和对环境变化的响应。了解雪藻群落的运作方式也有助于制定策略，更好地管理它们对融雪的影响。总体目标是建立一个基于实验的雪藻群落模型系统，预测它们对关键环境变量（如光照和养分可用性）的反应。为了发展对物种间相互作用的代谢尺度理解，将采用三种主要方法来调查冰川国家公园和蒙大拿州熊齿山的雪藻群落：(1)通过纳米孔DNA序列分析表征雪藻群落结构，识别遗传潜力；(2)利用基因组信息构建具有代表性的雪藻群落代谢模型，通过代谢途径的计算分析预测对关键环境变量的响应；(3)在实验室条件下培养选择的藻类，测试对环境因素的响应，并提供反馈，以完善模型。每一个目标都与本科教育交织在一起，通过将研究带入基于课程的实验室，并提供个性化的暑期研究体验。这项工作将促进目前对雪藻群落中微生物相互作用的理解，并深入了解生态系统的影响；了解随着时间的推移对不断变化的环境因素的响应的生产力和稳定性对于预测雪藻在未来几年如何影响全球气候危机至关重要。研究过程将建立一个结构，将大型环境基因组数据集转化为预测模型，帮助描述和量化微生物相互作用和营养循环对环境压力的反应。该项目由环境生物学部人口与社区生态学部、刺激竞争研究的既定计划（EPSCoR）以及分子与细胞生物科学部的系统与合成生物学学部共同资助。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。

Algae blooming on snow, ice, and glaciers (commonly called pink snow) accelerate melting due to the absorbance of light by their dark pigments. With rising temperatures, additional contributions to glacier and snow melt are becoming of greater concern. Snow algae grow in communities with other microorganisms such as bacteria and fungi. Currently, little is known about the interactions between algal and non-algal species or how they respond to environmental conditions. This project investigates snow algal community in Montana using a bottom-up approach, first characterizing the makeup of snow algal communities and isolating members for controlled laboratory growth experiments, leading to development of a model detailing how individual species contribute to community growth. By sampling snow algal communities in the same locations over five years, this project will gather important information about the seasonal and annual dynamics of snow algae. This CAREER project will involve National Forest and Park partnerships, integrate undergraduate researchers in the research process both inside and outside of the classroom, and engage rural Montana middle schools with the data collection process and underlying scientific concepts. The results of this project are expected to advance current understanding of snow algal community composition, function, and response to changing environments. Gaining an understanding of how snow algal communities operate is anticipated to also aid in devising strategies to better manage their effects on snow melt.The overarching goal is to develop an experimentally grounded model system of snow algal communities predicting their responses to key environmental variables, such as light and nutrient availability. To develop a metabolic scale understanding of interspecies interactions, three major approaches will be used to investigate snow algal communities in Glacier National Park and the Beartooth Mountains in Montana: (1) characterize snow algal community structure and identify genetic potential through Nanopore DNA sequence profiling, (2) develop a representative snow algal community metabolic model from genomic information and predict responses to key environmental variables through computational analysis of metabolic pathways, and (3) cultivate select algal species under laboratory conditions to test responses to environmental factors and provide feedback to refine the model. Each of these objectives is intertwined with undergraduate education through bringing research into course-based labs and offering individualized summer research experiences. This work will advance current understanding of microbial interactions in snow algal communities and gain insight into ecosystem impacts; understanding productivity and stability over time in response to changing environmental factors is essential to predict how snow algae may affect the global climate crisis in years ahead. The research process will establish a structure for transforming large environmental genomic data sets into predictive models, helping to both describe and quantify microbial interactions and nutrient cycling in response to environmental stressors.This project is jointly funded by the Population and Community Ecology Cluster, Division of Environmental Biology and the Established Program to Stimulate Competitive Research (EPSCoR) and the Systems and Synthetic Biology Cluster in the Division of Molecular and Cellular Biosciences.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.