Responses of the mesozooplankton community to marine heatwaves: Challenges and solutions based on a long-term time series

被引:0
|
作者
Deschamps, Margot M. [1 ]
Boersma, Maarten [1 ,2 ]
Gimenez, Luis [1 ,3 ]
机构
[1] Alfred Wegener Inst Helmholtz, Biolog Anstalt Helgoland, Zentrum Polar & Meeresforsch, Helgoland, Germany
[2] Univ Bremen, Bremen, Germany
[3] Bangor Univ, Sch Ocean Sci, Bangor, Wales
关键词
BACI design; community structure; Helgoland Roads; marine ecosystems; marine heatwaves; North Sea; zooplankton; PLANKTON ECOSYSTEMS; ZOOPLANKTON; OCEAN; SHIFTS; VARIABILITY; RECRUITMENT; IMPACTS;
D O I
10.1111/1365-2656.14165
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Marine heatwaves (MHWs) are extreme weather events that have major impacts on the structure and functioning of marine ecosystems worldwide. Due to anthropogenic climate change, the occurrence of MHWs is predicted to increase in future. There is already evidence linking MHWs with reductions in biodiversity and incidence of mass mortality events in coastal ecosystems. However, because MHWs are unpredictable, the quantification of their effects on communities is challenging. Here, we use the Helgoland Roads long-term time series (German Bight, North Sea), one of the richest marine time series in the world, and implement a modified before-after control-impact (BACI) design to evaluate MHW effect on mesozooplankton communities. Mesozooplankton play an essential role in connecting primary producers to higher trophic levels, and any changes in their community structure could have far-reaching impacts on the entire ecosystem. The responses of mesozooplankton community to MHWs in terms of community structure and densities occurred mainly in spring and autumn. Abundances of seven taxa, including some of the most abundant groups (e.g. copepods), were affected either positively or negatively in response to MHWs. In contrast, we observed no clear evidence of an impact of summer and winter MHWs; instead, the density of the most common taxa remained unchanged. Our results highlight the seasonally dependent impacts of MHWs on mesozooplankton communities and the challenges in evaluating those impacts. Long-term monitoring is an important contributor to the quantification of effects of MHWs on natural populations. This study uses 43 years of the Helgoland Roads time series, one of the world's richest marine datasets, and implements a novel BACI design, revealing season-specific impacts of marine heatwaves on mesozooplankton in the North Sea. This highlights the importance of long-term monitoring for understanding marine heatwave effects on communities.image
引用
收藏
页码:1524 / 1540
页数:17
相关论文
共 50 条
  • [31] Long-term trends and extreme events of marine heatwaves in the Eastern China Marginal Seas during summer
    Xu, Jing
    Yan, Yunwei
    Zhang, Lei
    Xing, Wen
    Meng, Linxi
    Yu, Yi
    Chen, Changlin
    FRONTIERS IN MARINE SCIENCE, 2024, 11
  • [32] Role of jellyfish in mesozooplankton community stability in a subtropical bay under the long-term impacts of temperature changes
    Zhao J.
    Zhang H.
    Liu J.
    Ke Z.
    Xiang C.
    Zhang L.
    Li K.
    Lai Y.
    Ding X.
    Tan Y.
    Science of the Total Environment, 2022, 849
  • [33] EFFECTS OF COMMUNITY BASED LONG-TERM CARE
    BORUP, J
    LINTZ, L
    VANORMAN, R
    GERONTOLOGIST, 1986, 26 : A268 - A268
  • [34] Long-term shifts in seasonal patterns of biodiversity in a marine fouling community
    Dijkstra, JA
    Harris, LG
    INTEGRATIVE AND COMPARATIVE BIOLOGY, 2005, 45 (06) : 1125 - 1125
  • [35] Network traffic forecasting model based on long-term intuitionistic fuzzy time series
    Fan, Xiaoshi
    Wang, Yanan
    Zhang, Mengyu
    INFORMATION SCIENCES, 2020, 506 : 131 - 147
  • [36] Periodformer: An efficient long-term time series forecasting method based on periodic attention
    Liang, Daojun
    Zhang, Haixia
    Yuan, Dongfeng
    Zhang, Minggao
    KNOWLEDGE-BASED SYSTEMS, 2024, 304
  • [37] Long-term Prediction of Time Series Based on Fuzzy Cognitive Map And Ensemble Learning
    Zhu, Meishu
    Lu, Wei
    Liu, Xiaodong
    2022 41ST CHINESE CONTROL CONFERENCE (CCC), 2022, : 2459 - 2464
  • [38] Long-term Trend Prediction Algorithm Based on Neural Network for Short Time Series
    Xin Zexi
    Zhang Haiyang
    Yue, Ma
    2019 IEEE INTL CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, BIG DATA & CLOUD COMPUTING, SUSTAINABLE COMPUTING & COMMUNICATIONS, SOCIAL COMPUTING & NETWORKING (ISPA/BDCLOUD/SOCIALCOM/SUSTAINCOM 2019), 2019, : 1233 - 1238
  • [39] Hidden Markov Models Based Approaches to Long-Term Prediction for Granular Time Series
    Guo, Hongyue
    Pedrycz, Witold
    Liu, Xiaodong
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018, 26 (05) : 2807 - 2817
  • [40] The Long-Term Prediction of Time Series: A Granular Computing-Based Design Approach
    Ma, Cong
    Zhang, Liyong
    Pedrycz, Witold
    Lu, Wei
    IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2022, 52 (10): : 6326 - 6338