Doctoral Dissertation Research: Resolving Novel Arctic Late Season Phytoplankton Blooms

太平洋和北极中部的气温变暖和海冰覆盖减少导致海藻（浮游植物）的年度生长模式发生变化，最明显的是在秋季出现新的生长。浮游植物是北极海洋食物网的基础，生长时间的中断可能对当前的生态系统有害。卫星遥感的局限性，如无法检测浮游植物水华活动在整个水柱，冰下，并在多云的条件下，需要船上的测量，以提供更多的信息，水华动态。该项目改进了一种新的方法，利用卫星图像作为参考，估计整个水体中浮游植物水华的生长阶段。估计的夏季水华阶段将沿着大气和海洋条件，以确定秋季浮游植物水华的驱动因素，以更好地估计它们可能发生的地方。这些信息将有利于预测后期的生物热点保护，科学和海洋生存的影响。重复巡航测量将被用来映射模拟的水华阶段，整个水柱在夏季和秋季整个太平洋北极和向北的冰缘。这项研究还将开发两个模型，以（1）确定观察到的晚花生长的直接驱动因素，（2）确定秋季开花的仲夏预处理方案，并通过现场观察验证模型预测。这项研究利用了两个NSF资助的巡航：2013年7月至2022年7月的分布式生物观测站和2022年秋季的天气北极调查。将在2022年的巡航中收集浮游植物叶绿素a和脱镁叶绿素（降解叶绿素）的浓度。美国航天局MODIS-Aqua卫星叶绿素a和原位脱镁叶绿素比例将用于开发区分生长期和衰老期水华的水华阶段模型。水华阶段模型将应用于整个水柱收集的测量结果，以从巡航样本中提取更多信息，扩大观测范围，并了解水华进展。秋霜驱动和预处理模型将使用机器学习回归方法开发。将被检查的变量包括高风速事件，风向，海冰破裂和冻结日期的遥感和模拟数据集，以及营养物，温度，盐度和分层的现场测量。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Warming temperatures and decreased sea ice cover in the Pacific and Central Arctic have led to changes in annual growth patterns of marine algae (phytoplankton), most notably permitting novel growth to occur during the fall season. Phytoplankton serve as the foundation of the Arctic marine food web, and disruptions in the timing of growth can be detrimental to current ecosystems. Limitations of satellite remote sensing such as the inability to detect phytoplankton bloom activity throughout the water column, under ice, and in cloudy conditions dictate the need for shipboard based measurements to provide more information on bloom dynamics. This project refines a novel methodology to estimate the growing stage of phytoplankton blooms throughout the water column using satellite imagery as a reference. Estimated summer bloom stages will be used along with atmospheric and oceanic conditions to identify the drivers of fall phytoplankton blooms to better estimate where they might occur. This information will be beneficial for predicting late season biological hotspots for conservation, scientific, and marine subsistence impacts.Repeat cruise measurements will be used to map modeled bloom stages throughout the water column in the summer and fall throughout the Pacific Arctic and northward to the ice edge. This research will also develop two models to (1) identify immediate drivers of observed late bloom growth and (2) identify midsummer preconditioning scenarios for fall blooms and validate model predictions with in situ observations. This research utilizes two NSF-funded cruises: the Distributed Biological Observatory in July 2013–2022 and the Synoptic Arctic Survey in fall 2022. Phytoplankton chlorophyll-a and pheophytin (degraded chlorophyll) concentrations will be collected on the 2022 cruises. NASA MODIS-Aqua satellite chlorophyll-a and in situ pheophytin proportions will be used to develop bloom stage models that distinguish growing versus senescent blooms. The bloom stage model will be applied to measurements collected throughout the water column to extract more information from cruise samples, broaden the context of observations, and understand bloom progression. The fall bloom driver and preconditioning models will be developed using a machine learning regression approach. Variables that will be examined include remotely sensed and modelled datasets of high wind speed events, wind direction, sea ice breakup and freeze-up dates, and in situ measurements of nutrients, temperature, salinity, and stratification.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.