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

虽然单细胞浮游植物与海洋食物网中掠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（例如它们突出的大小结构）如何影响能量通量和向更高营养水平转移效率的理解仍然有限。这对渔业生产力、营养物质循环和碳进入海底沉积物都有影响。该项目旨在通过以下方式提高我们对海洋食物网低热带水平过程的了解：(a)应用最先进的海洋颜色算法，并进一步发展基于遥感的模型，从遥感数据中得出浮游植物的大小结构；(b)将浮游植物大小结构的卫星资料与浮游植物和浮游动物分类群以及浮游鱼类的现场观测资料相结合；(c)建立三营养食物网模型，以评估这些大小结构和环境参数对能量向更高营养水平转移的影响。该学生将与ICES （S. Jennings）密切合作，研究基于计算机的建模、卫星遥感（在雷丁大学与S. Roy合作）、实验室和实地观测（在CEFAS与E. Capuzzo和J. Van Der Kooij合作）之间的接口。特别是，学生将(a)参加年度多学科鱼类调查（由Cefas进行）的活动，以估计鱼类群落（如鲱鱼、沙丁鱼、鲭鱼、鲱鱼）的生物量及其与环境的分布；(b)利用最先进的卫星算法（例如[1]）产生关于浮游植物大小和群落结构的卫星衍生变量，并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)利用声学数据（在远洋拖网作业期间收集）、环境数据（地下温度、盐度和叶绿素）以及主要浮游生物功能群、浮游植物色素（HPLC）、微型浮游动物和中浮游动物）的丰度、生物量和大小获得的远洋鱼类丰度和分布数据，以改善鱼类观测的生态背景（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，以估计营养能量通量和转移效率，以及不确定的消费者生物量（例如b[3]）。(1) Roy et al.（2013）利用卫星数据提取浮游植物的光吸收光谱，研究浮游植物的尺寸光谱和尺寸类别的全球分布。环境遥感，139,185-197；(2) Capuzzo et al.(2015) 20世纪北海南部和中部海水清澈度下降。全球气候变化研究进展[j] .地球科学进展，2016(6),444 - 444。(3) Jennings and Collingridge（2015）预测世界海洋生态系统中消费者生物量、大小结构、产量、捕捞潜力、对捕捞的响应和相关不确定性。科学通报10 (7),e0133794。

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.