Fish welfare based classification method of ocean current speeds at aquaculture sites

被引:25
|
作者
Jonsdottir, Kristbjorg Edda [1 ]
Hvas, Malthe [2 ]
Alfredsen, Jo Arve [1 ]
Fore, Martin [1 ,3 ]
Alver, Morten Omholt [1 ,3 ]
Bjelland, Hans Vanhauwaert [3 ]
Oppedal, Frode [2 ]
机构
[1] Norwegian Univ Sci & Technol, Dept Engn Cybernet, N-7491 Trondheim, Norway
[2] Inst Marine Res, Res Grp Anim Welf, N-5984 Matredal, Norway
[3] SINTEF Ocean, N-7465 Trondheim, Norway
关键词
Atlantic salmon; Lumpfish; U-crit; Exposed farming; Ocean current speed; Swimming behaviour; Sea cage environment; SALMON SALMO-SALAR; FARMED ATLANTIC SALMON; CRITICAL SWIMMING SPEED; SEA-CAGE; BEHAVIOR; DEFORMATION; CAPACITY; HYPOXIA; GROWTH; L;
D O I
10.3354/aei00310
中图分类号
S9 [水产、渔业];
学科分类号
0908 ;
摘要
A major trend in marine aquaculture is to move production to more exposed sites with occasionally rough ocean current events. However, it is unclear whether fish will thrive in these extreme environments, since thorough descriptions of ambient current conditions with regards to fish welfare is lacking. In the present study, ocean current data were collected using acoustic Doppler current profilers at 5 exposed sites along the Norwegian coast over minimum periods of 5 mo. To evaluate welfare risks, current data was compared to known limits of swimming capabilities, such as onset of behavioural changes and critical swimming speeds (U-crit), of Atlantic salmon Salmo salar and lumpfish Cyclopterus lumpus. Specifically, at each site, current speeds were classified into 6 categories based on expected impact on swimming behaviours of Atlantic salmon, and duration of currents within each category were inspected using a homogeneous and non-homogeneous criterion for the water column. Current speeds were then compared with projected U-crit at relevant temperatures and fish sizes of Atlantic salmon and lumpfish. Furthermore, a detailed characterization of extreme events at the most exposed site was performed. Of the 5 locations, only 1 exceeded the U-crit of Atlantic salmon, while all sites featured currents above U-crit of lumpfish for up to 33 h at a time. These results suggest that responsible Atlantic salmon farming is possible at sites considered exposed, while lumpfish should be restricted to more sheltered environments. The presented method can be applied for other aquaculture fish species if adequate data are available.
引用
收藏
页码:249 / 261
页数:13
相关论文
共 35 条
  • [1] Fish welfare: Current issues in aquaculture
    Ashley, Paul J.
    [J]. APPLIED ANIMAL BEHAVIOUR SCIENCE, 2007, 104 (3-4) : 199 - 235
  • [2] Prioritization of fish welfare issues in European salmonid aquaculture using the Delphi method
    Boogaart, Lucia van den
    Slabbekoorn, Hans
    Scherer, Laura
    [J]. AQUACULTURE, 2023, 572
  • [3] A Copula Based Method For Fish Species Classification
    Dhawal, Raj Singh
    Chen, Liang
    [J]. 2016 IEEE 15TH INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS & COGNITIVE COMPUTING (ICCI*CC), 2016, : 471 - 478
  • [4] A copula based method for the classification of fish species
    Dhawal R.S.
    Chen L.
    [J]. 1600, IGI Global (11): : 29 - 45
  • [5] Logistic regression and fuzzy logic as a classification method for feral fish sampling sites
    Rachel Ann Hauser-Davis
    Terezinha Ferreira de Oliveira
    Antônio Morais da Silveira
    João Marcelo Brazão Protázio
    Roberta Lourenço Ziolli
    [J]. Environmental and Ecological Statistics, 2012, 19 : 473 - 483
  • [6] Logistic regression and fuzzy logic as a classification method for feral fish sampling sites
    Hauser-Davis, Rachel Ann
    de Oliveira, Terezinha Ferreira
    da Silveira, Antonio Morais
    Brazao Protazio, Joao Marcelo
    Ziolli, Roberta Lourenco
    [J]. ENVIRONMENTAL AND ECOLOGICAL STATISTICS, 2012, 19 (04) : 473 - 483
  • [7] A method of fish classification based on wavelet packet and bispectrum
    Zhang, Qiao
    Xu, Feng
    Wen, Tao
    Yu, Tianze
    [J]. Sensors and Transducers, 2014, 164 (02): : 272 - 277
  • [8] A Method Based on Knowledge Distillation for Fish School Stress State Recognition in Intensive Aquaculture
    Mei, Siyuan
    Chen, Yingyi
    Qin, Hanxiang
    Yu, Huihui
    Li, Daoliang
    Sun, Boyang
    Yang, Ling
    Liu, Yeqi
    [J]. CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES, 2022, 131 (03): : 1315 - 1335
  • [9] Underwater abnormal classification system based on deep learning: A case study on aquaculture fish farm in Taiwan
    Chen, James C.
    Chen, Tzu-Li
    Wang, Hsiang-Leng
    Chang, Ping-Chen
    [J]. AQUACULTURAL ENGINEERING, 2022, 99
  • [10] An ocean current prediction method for profiling float based on RBFNN
    Zhou, Puzhe
    Jiao, Kuikui
    Wang, Zhaohong
    Cheng, Xi
    Zhang, Xiangdong
    [J]. 2022 OCEANS HAMPTON ROADS, 2022,