Interpretation of interannual variability in long-term aquatic ecological surveys

被引:10
|
作者
Cauvy-Fraunie, Sophie [1 ]
Trenkel, Verena M. [2 ]
Daufresne, Martin [3 ]
Maire, Anthony [4 ]
Capra, Herve [1 ]
Olivier, Jean-Michel [5 ]
Lobry, Jeremy [6 ]
Gazelles, Bernard [7 ,8 ]
Lamouroux, Nicolas [1 ]
机构
[1] Irstea, UR RIVERLY, Ctr Lyon Villeurbanne, Villeurbanne, France
[2] IFREMER, BP 21105, Nantes 44311 3, France
[3] Irstea, UR Recover, Aix En Provence 13182, France
[4] EDF R&D, LNHE Lab Natl Hydraul & Environm, Chatou, France
[5] Univ Claude Bernard Lyon 1, Univ Lyon, CNRS, ENTPE,UMR 5023,LEHNA, F-69622 Villeurbanne, France
[6] Irstea, UR EABX, Ctr Bordeaux, F-33612 Cestas, France
[7] UMMISCO, IRD UPMC UMI 209, Bondy 93143, France
[8] Ecole Normale Super, IBENS, CNRS, UMR 8197, Paris 75005, France
关键词
AGE-0; RAINBOW-TROUT; FISH COMMUNITY; TIME-SERIES; BROWN TROUT; STATISTICAL POWER; HABITAT USE; RIVER; DENSITY; POPULATIONS; SYNCHRONY;
D O I
10.1139/cjfas-2019-0146
中图分类号
S9 [水产、渔业];
学科分类号
0908 ;
摘要
Long-term ecological surveys (LTES) often exhibit strong variability among sampling dates. The use and interpretation of such interannual variability is challenging due to the combination of multiple processes involved and sampling uncertainty. Here, we analysed the interannual variability in similar to 30 years of 150 species density (fish and invertebrate) and environmental observation time series in four aquatic systems (stream, river, estuary, and marine continental shelf) with different sampling efforts to identify the information provided by this variability. We tested, using two empirical methods, whether we could observe simultaneous fluctuation between detrended time series corresponding to widely acknowledged assumptions about aquatic population dynamics: spatial effects, cohort effects, and environmental effects. We found a low number of significant results (36%, 9%, and 0% for spatial, cohort, and environmental effects, respectively), suggesting that sampling uncertainty overrode the effects of biological processes. Our study does not question the relevance of LTES for detecting important trends, but clearly indicates that the statistical power to interpret interannual variations in aquatic species densities is low, especially in large systems where the degree of sampling effort is always limited.
引用
收藏
页码:894 / 903
页数:10
相关论文
共 50 条
  • [21] Inconsistencies of interannual variability and trends in long-term satellite leaf area index products
    Jiang, Chongya
    Ryu, Youngryel
    Fang, Hongliang
    Myneni, Ranga
    Claverie, Martin
    Zhu, Zaichun
    GLOBAL CHANGE BIOLOGY, 2017, 23 (10) : 4133 - 4146
  • [22] Long-term trend and interannual variability of precipitation-use efficiency in Eurasian grasslands
    Zhang, Tianyou
    Chen, Zhi
    Zhang, Weikang
    Jiao, Cuicui
    Yang, Meng
    Wang, Qiufeng
    Han, Lang
    Fu, Zheng
    Sun, Zhongyi
    Li, Wenhua
    Yu, Guirui
    ECOLOGICAL INDICATORS, 2021, 130
  • [23] Long-term trends and interannual variability of forest, savanna and agricultural fires in South America
    Chen, Yang
    Morton, Douglas C.
    Jin, Yufang
    Gollatz, G. James
    Kasibhatla, Prasad S.
    van der Werf, Guido R.
    DeFries, Ruth S.
    Randerson, James T.
    CARBON MANAGEMENT, 2013, 4 (06) : 617 - 638
  • [24] Interannual and long-term variability in the North Atlantic Oscillation and Indian Summer monsoon rainfall
    S. S. Dugam
    S. B. Kakade
    R. K. Verma
    Theoretical and Applied Climatology, 1997, 58 : 21 - 29
  • [25] Climate variability and ecosystem response at long-term ecological research sites
    Greenland, D
    15TH CONFERENCE ON BIOMETEOROLOGY AND AEROBIOLOGY JOINT WITH THE 16TH INTERNATIONAL CONGRESS ON BIOMETEOROLOGY, 2002, : 317 - 318
  • [26] Interannual and long-term sea level variability in the eastern Indian Ocean and South China Sea
    Soumya Mohan
    P. Vethamony
    Climate Dynamics, 2018, 50 : 3195 - 3217
  • [27] The Long-Term Trends and Interannual Variability in Surface Ozone Levels in Beijing from 1995 to 2020
    Hong, Jin
    Wang, Wuke
    Bai, Zhixuan
    Bian, Jianchun
    Tao, Mengchu
    Konopka, Paul
    Ploeger, Felix
    Muller, Rolf
    Wang, Hongyue
    Zhang, Jinqiang
    Zhao, Shuyun
    Zhu, Jintao
    REMOTE SENSING, 2022, 14 (22)
  • [28] Seasonal, interannual and long-term variability of precipitation and snow depth in the region of the Barents and Kara seas
    Aleksandrov, YI
    Bryazgin, NN
    Forland, EJ
    Radionov, VF
    Svyashchennikov, PN
    POLAR RESEARCH, 2005, 24 (1-2) : 69 - 85
  • [29] LONG-TERM INDIVIDUAL DIETARY SURVEYS
    CHAPPELL, GM
    BRITISH JOURNAL OF NUTRITION, 1955, 9 (04) : 323 - 339
  • [30] Snow conditions in northern Europe: the dynamics of interannual variability versus projected long-term change
    Raisanen, Jouni
    CRYOSPHERE, 2021, 15 (04): : 1677 - 1696