热带海洋学报 ›› 2025, Vol. 44 ›› Issue (6): 91-107.doi: 10.11978/2025051CSTR: 32234.14.2025051

• 海洋生物学 • 上一篇    下一篇

网采镜检与全息粒子成像综合分析贻贝养殖区浮游植物群落特征

杨天炜1(), 林军1,2,3,4(), 焦俊鹏1, 吴越1   

  1. 1 上海海洋大学海洋科学与生态环境学院, 上海 201306
    2 上海海洋大学海洋牧场工程技术研究中心, 上海 201306
    3 船舶与海洋工程特种装备和动力系统国家工程研究中心, 上海 200030
    4 上海海洋大学自然资源部海洋生态监测与修复技术重点实验室, 上海 201306
  • 收稿日期:2025-04-04 修回日期:2025-05-23 出版日期:2025-11-10 发布日期:2025-12-03
  • 通讯作者: 林军。email:
  • 作者简介:

    杨天炜(2000—), 男, 浙江省衢州市人, 硕士研究生, 从事海洋生态及渔业碳汇研究。email:

  • 基金资助:
    国家自然科学基金项目(42376207); 国家重点研发计划项目(2023YFD2401902)

Integrated analysis of phytoplankton community structure in mussel aquaculture areas using net sampling with microscopy and LISST Holo2

YANG Tianwei1(), LIN Jun1,2,3,4(), JIAO Junpeng1, WU Yue1   

  1. 1 College of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai 201306, China
    2 Marine Ranching Engineering Technology Research Center, Shanghai Ocean University, Shanghai 201306, China
    3 National Engineering Research Center for Special Equipment and Power Systems of Naval Architecture and Ocean Engineering, Shanghai 200030, China
    4 Key Laboratory of Marine Ecological Monitoring and Restoration Technology, Ministry of Natural Resources, Shanghai Ocean University, Shanghai 201306, China
  • Received:2025-04-04 Revised:2025-05-23 Online:2025-11-10 Published:2025-12-03
  • Contact: LIN Jun. email:
  • Supported by:
    National Natural Science Foundation of China(42376207); National Key Research and Development Program of China(2023YFD2401902)

摘要:

本文基于2023年8月期间在浙江省枸杞岛贻贝浮筏式养殖场利用网采镜检以及水下数字全息粒子成像系统(LISST Holo2)进行调查获得的浮游植物实测数据, 对网采和粒子影像数据进行综合比较研究, 并分析其群落结构晨昏动态变化及影响因子。调查期间共鉴定出5门38属75种浮游植物, 其中夜光藻(Noctiluca scintillans)和笔尖形根管藻(Rhizosolenia styliformis)等为主要优势种, 且养殖区正处于夜光藻赤潮暴发阶段。养殖水层浮游植物种类与丰度低于全水层, 但其共现网络复杂度更高, 群落稳定性及种间互作更强。潮汐对全水层浮游植物分布影响显著[广义相加模型(generalized additive models, GAM)解释量57.4%], 而养殖水层丰度变化主要受光照主导(P=0.016)。LISST Holo2共鉴定出3门34属浮游植物, 占网采镜检鉴定结果的89.4%, 在大粒径藻类(如夜光藻)监测中与传统网采镜检结果高度一致(优势度曲线R²>0.8)。LISST Holo2观测与传统采样相比, 解决了其样品保存和处理繁琐, 数据获取滞后以及难以测量浮游植物粒径大小等问题, 本研究验证其在大粒径藻类监测中的可靠性, 有助于未来海洋生态系统监测与保护的研究。

关键词: 全息粒子成像, 贻贝筏式养殖, 浮游植物群落, 环境因子, 浮游植物共现网络

Abstract:

This study integrated net sampling with microscopic examination and an underwater digital holographic particle imaging system (LISST Holo2) to analyze the diurnal dynamics and environmental drivers of phytoplankton communities in suspended mussel raft aquaculture areas near Gouqi Island, Zhejiang Province. Field data collected in August 2023 identified 75 phytoplankton species across 5 phyla and 38 genera, with Noctiluca scintillans and Rhizosolenia styliformis as dominant species, coinciding with a Noctiluca scintillans red tide outbreak. The aquaculture layer exhibited fewer phytoplankton species and lower abundance compared to the entire water column, but demonstrated higher co-occurrence network complexity, greater community stability, and stronger inter-species interactions. Tidal cycles significantly influenced phytoplankton distribution in the entire water column [GAM (generalized additive models) explained 57.4% of variation], while temporal factors dominated abundance fluctuations in the aquaculture layer (P=0.016). The LISST Holo2 detected 34 genera from 3 phyla of phytoplankton, accounting for 89.4% of the taxa identified by net sampling and microscopy. For large-sized phytoplankton (e.g., Noctiluca scintillans), LISST Holo2 showed high consistency with traditional net sampling and microscopy results, with a dominance curve correlation coefficient of R²>0.8. Compared to conventional methods, LISST Holo2 addressed challenges such as laborious sample preservation, delayed data acquisition, and particle size measurement difficulties. This study validates its reliability for large-sized algal monitoring and underscores the roles of mussel filtration pressure and raft-induced hydrodynamic effects in reshaping phytoplankton communities, providing critical insights for optimizing aquaculture practices and advancing marine ecosystem monitoring technologies.

Key words: LISST Holo2, suspended mussel farms, phytoplankton community, environment factors, phytoplankton co-occurrence network

中图分类号: 

  • Q948.8