Collaborative Research: Individual Based Approaches to Understanding Krill Distributions and Aggregations

南极磷虾(Euphausia Superba)是南大洋食物网的重要生态组成部分，但人们对它们在水环境中的许多特征的行为知之甚少。这个项目将通过研究个体对光照水平、水流速度、吸引和排斥的化学线索的存在的反应，来提高对磷虾游泳和觅食行为的理解。流动、光线和化学条件将在配备了高速数码相机系统的专用水箱中进行控制和改变，以便在相关的学校环境中测量个体磷虾对这些因素的反应。这一分析将被用来预测优先环境，定义磷虾检测和移动到它们(以及远离不利环境)的能力。然后，这些信息将被用来改进模型，以估计与不同类型环境相关的行为的能量成本。将个体行为与较大磷虾群体的行为联系起来，也将改善对磷虾密度的声学评估。了解磷虾对环境扰动的反应能力将提高我们对高纬度生态系统生态的了解，并为磷虾渔业的管理提供相关信息。该项目将支持研究生和本科生，并提供作为博士后助理的培训。课程材料和公众参与活动将以项目的目标和活动为基础。项目调查人员将与对磷虾保护和管理感兴趣的专家分享磷虾运动和学校结构的模型结果和预测。该项目将使用水平和垂直层流隧道来检查自然相关条件下的磷虾行为。水平(每秒1-10厘米)和垂直(每秒1-3毫米)的流动速度模拟了自然相关的电流模式，而光的级别和光谱质量将从完全黑暗到磷虾栖息的深度范围内经历的强度变化。通过添加水槽以获得接近平均和水华条件的背景叶绿素a水平，将产生诱人的浮游植物气味，而企鹅鸟粪将产生令人厌恶的线索。在所有条件下，单个磷虾的行为将被摄像机以X-Y和Y-Z平面可视化的视频记录下来，并将通过视频运动分析以5赫兹的采样率重建3D运动。水平和垂直游动角度和速度的分布将被用来创建基于个体的磷虾运动模型(IBM)，以响应每种情况，其中磷虾在每个模型时间步长的行为基于从速度和角度分布中随机提取的结果。由于磷虾通常成群旅行，进一步的实验将检查小型磷虾在相同条件下的行为，以进一步参数化变量，如个体间距。研究人员将从磷虾在专门设计的克里塞尔水箱中游泳的3D视频记录中检查磷虾聚集结构，并计算聚集中个体之间的最近邻距离(NND)和游泳角度的相关性。IBM中的磷虾运动将受到约束，以遵守观察到的NND和角度相关性。大型海洋模型将被用来定义空间环境，在空间环境中，将允许模拟的磷虾使用1000-100,000个磷虾群进行移动。模型输出将包括学校游泳的速度、方向和结构(堆积密度，NND)。研究人员将比较磷虾种群在测量流量和浮游植物丰度方面的现有声学数据集，以评估模型预测。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Antarctic krill (Euphausia superba) are an ecologically important component of the Southern Ocean's food web, yet little is known about their behavior in response to many features of their aquatic environment. This project will improve understanding of krill swimming and schooling behavior by examining individual responses to light levels, water flow rates, the presence of attractive and repulsive chemical cues. Flow, light and chemical conditions will be controlled and altered in specialized tanks outfitted with high speed digital camera systems so that individual krill responses to these factors can be measured in relevant schooling settings. This analysis will be used to predict preferred environments, define the capacity of krill to detect and move to them (and away from unfavorable ones). Such information will then be used to improve models that estimate the energetic costs of behaviors associated with different types of environments. Linking individual behavior to those of larger krill aggregations will also improve acoustic assessments of krill densities. Understanding the capacity of krill to respond to environmental perturbations will improve our understanding of the ecology of high latitude ecosystems and provide relevant information for the management of krill fisheries. The project will support graduate and undergraduate students and provide training for as post-doctoral associate. Curricular materials and public engagement activities will be based on the project's aims and activities. Project investigators will share model results and predictions of krill movements and school structure with experts interested in krill conservation and management. The project will use horizontal and vertical laminar flow tunnels to examine krill behavior under naturally relevant conditions. Horizontal (1-10 cm per second) and vertical (1-3 mm per second) flow velocities mimic naturally relevant current patterns, while light levels and spectral quality will be varied from complete darkness to intensities experienced across the depth range inhabited by krill. Attractive phytoplankton odor will be created by dosing the flumes to obtain background chlorophyll a levels approximating average and bloom conditions, while repulsive cues will be generated from penguin guano. Behavior of individual krill in all conditions will be video recorded with cameras visualizing X-Y and Y-Z planes, and 3D movements will be reconstructed by video motion analysis at a 5 Hz sampling rate. The distribution of horizontal and vertical swimming angles and velocities will be used to create an individual based model (IBM) of krill movement in response to each condition, where krill behavior at each model time step is based on random draws from the velocity and angular distributions. Since krill commonly travel in groups, further experiments will examine the behavior of small krill schools in these same conditions to further parameterize variables such as individual spacing. Researchers will examine krill aggregation structure from 3D video records of krill swimming in a specially designed kriesel tank, and compute nearest neighbor distances (NND) and correlations of swimming angles among individuals within the aggregation. Krill movements in the IBM will be constrained to adhere to observed NND and angular correlations. Large scale oceanographic models will be used to define spatial environments in which the modelled krill will be allowed to move using simulated schools of 1000-100,000 krill. Model output will include the school swimming speed, direction and structure (packing density, NND). Researchers will compare available acoustic data sets of krill schools in measured flow and phytoplankton abundance to evaluate the model predictions.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.