Energy transfer in marine ecosystems based on phytoplankton size structure from satellite remote sensing

基于卫星遥感浮游植物大小结构的海洋生态系统能量转移

• 批准号: 2108367
• 金额: --
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2108367
• 关键词: Energy; transfer; marine; ecosystems; based

Although the connection from unicellular phytoplankton to predatory fish in the marine food web is well known, our current understanding on how the community composition of phytoplankton (e.g. their prominent size structure) may affect energy fluxes and transfer efficiency to higher trophic levels is still limited. This has implications for fisheries productivity, nutrient recycling and incorporation of carbon into seabed sediments. This project will aim at enhancing our understanding of low tropic level processes in marine food webs by (a) applying the state-of-the-art ocean colour algorithms, and further developing remote-sensing-based models to derive phytoplankton size structure from remote-sensing data;(b) combining satellite-derived information on phytoplankton size structure to the in situ observations on phytoplankton and zooplankton taxa, and planktivorous fish; and (c) developing a tri-trophic food web model to evaluate the implications of these size-structures and environmental parameters for energy transfer to higher trophic levels.The student will work on the interface between computer-based modelling, satellite remote sensing (at the University of Reading with S. Roy), and laboratory and field observations (at CEFAS with E. Capuzzo and J. Van Der Kooij), in close collaboration with ICES (S. Jennings). In particular, the student will (a) take part in the activities within annual multidisciplinary fish surveys (carried out by Cefas), to estimate biomass of fish communities (e.g. sprat, sardine, mackerel, herring), as well as their distribution in relation to their environment; (b) generate satellite-derived variables on phytoplankton size and community structure, using the state-of-the-art satellite algorithms (e.g. [1]), and validate the satellite-based estimates with the survey data that covered the western English Channel and eastern Celtic Sea; (c) use the abundance and distribution data on pelagic fish obtained from acoustic data (collected during pelagic trawl operations), the environmental data (subsurface temperature, salinity, and chlorophyll), and the abundance, biomass and size of the main planktonic functional groups, phytoplankton pigments by HPLC, microzooplankton and mesozooplankton, to improve the ecological context to the fish observations (e.g. [2]);(d) build a tri-trophic food-web model connecting satellite-derived phytoplankton size structure and survey-based biological data to estimate trophic energy fluxes and transfer efficiencies, and uncertainties consumer biomass (e.g. [3]).Key references:(1) Roy et al. (2013) The global distribution of phytoplankton size spectrum and size classes from their light-absorption spectra derived from satellite data. Remote Sensing of Environment, 139, 185-197;(2) Capuzzo et al. (2015) Decrease in water clarity of the southern and central North Sea during the 20th century. Global change biology 21(6), 2206-2214.(3) Jennings and Collingridge (2015) Predicting consumer biomass, size-structure, production, catch potential, responses to fishing and associated uncertainties in the world's marine ecosystems. PloS one 10 (7), e0133794.

虽然单细胞浮游植物与海洋食物网中的捕食性鱼类之间的联系是众所周知的，但我们目前对浮游植物群落组成（例如，其突出的大小结构）如何影响能量通量和向更高营养级转移效率的理解仍然有限。这对渔业生产力、养分循环和碳进入海底沉积物都有影响。这一项目的目的是通过以下方式增进我们对海洋食物网低热带水平过程的了解：（a）应用最新的海洋颜色算法，进一步开发基于遥感的模型，以便从遥感数据中得出浮游植物的大小结构;（B）将卫星获得的关于浮游植物大小结构的信息与关于浮游植物和浮游动物分类群以及食鱼鱼类的现场观测相结合;以及（c）发展一个三营养食物网模型，以评估这些尺寸结构和环境参数对能量向更高营养级转移的影响。Roy），以及实验室和实地观察（在CEFAS与E. Capuzzo和J.货车Der Kooij）与ICES（S. Jennings）。特别是，学生将（a）参加年度多学科鱼类调查范围内的活动（由Cefas进行），以估计鱼类群落的生物量（如鲱鱼、沙丁鱼、鲭鱼、鲱鱼）及其在环境中的分布情况;（B）利用最先进的卫星算法，生成关于浮游植物大小和群落结构的卫星衍生变量（例如[1]），并利用覆盖英吉利海峡西部和凯尔特海东部的调查数据验证基于卫星的估计;（c）使用从声学数据中获得的关于中上层鱼类的丰度和分布数据（在中上层拖网作业期间收集的），（次表层温度、盐度和叶绿素），以及主要浮游生物功能群的丰度、生物量和大小，通过HPLC测定的浮游植物色素，微型浮游动物和中型浮游动物，改善鱼类观测的生态环境（例如[2]）;（d）建立一个三营养食物网模型，将卫星获得的浮游植物大小结构和基于调查的生物数据联系起来，以估计营养能通量和转移效率以及消费者生物量的不确定性（例如[3]）.主要参考资料：（1）Roy et al.（2013）根据卫星数据得出的光吸收光谱，浮游植物大小谱和大小类别的全球分布。环境遥感，139，185 - 197;（2）Capuzzo et al.（2015）世纪北海南部和中部海水透明度的下降。全球变化生物学21（6），2206 - 2214。(3)Jennings和Collingridge（2015）预测消费者生物量，大小结构，产量，渔获量潜力，对捕捞的反应以及世界海洋生态系统中的相关不确定性。PloS one 10（7），e0133794.