Volume 20 Issue 6
Dec.  2024
Turn off MathJax
Article Contents
Abstract
References
Related Articles
ZHANG Xianbing, QIN Yiwen, YANG Wei, LI Geng, HU Yupeng, YANG Shengfa, HU Jiang, LI Wenjie. Progress in study and application of fish bioenergetics models[J]. South China Fisheries Science, 2024, 20(6): 53-61. DOI: 10.12131/20240106
Citation: ZHANG Xianbing, QIN Yiwen, YANG Wei, LI Geng, HU Yupeng, YANG Shengfa, HU Jiang, LI Wenjie. Progress in study and application of fish bioenergetics models[J]. South China Fisheries Science, 2024, 20(6): 53-61. DOI: 10.12131/20240106
shu

Progress in study and application of fish bioenergetics models

  • Chongqing Jiaotong University/National Inland Waterway Regulation Engineering Technology Research Center/Chongqing Key Laboratory of Ecological Waterway, Chongqing 400074, China

More Information
  • Received Date: May 13, 2024
  • Revised Date: July 02, 2024
  • Accepted Date: September 01, 2024
  • Available Online: September 02, 2024
    Graphical Abstract
    • Abstract

  • Fish bioenergetics plays an important role in the management and sustainable utilization of fisheries resources. This paper provides an overview of recent advance in this field, focusing on the development of bioenergetics models. It also delves into the intricate interplay of environmental factors such as water temperature and flow on fish energy budgets. In practical scenarios, fish bioenergetics models are used to predict the dynamics of natural fish resources and the emission of pollutants from aquaculture, providing scientific support for fisheries management. However, the adaptability and accuracy of these models in dealing with complex environmental changes still need to be improved. Additionally, existed models often overlook the complexity of interactions among influencing factors. Future research should focus on multidisciplinary data fusion, application of advanced technologies, and innovation of individualised models, so as to facilitate the continuous improvement of fish bioenergetics models and promote their development towards precision, practicality and sustainable management.

    • FullText(HTML)
    • References (83)
  • [1]
    FAO. The state of world fisheries and aquaculture 2022[R]. Rome: FAO, 2022: 4-6.
    [2]
    农业农村部渔业渔政管理局, 全国水产技术推广总站, 中国水产学会. 2023中国渔业统计年鉴[M]. 北京: 中国农业出版社, 2023: 17-20.
    [3]
    李纯厚, 齐占会. 中国渔业生态环境学科研究进展与展望[J]. 水产学报, 2023, 47(11): 132-147.
    [4]
    崔奕波. 鱼类生物能量学的理论与方法[J]. 水生生物学报, 1989(4): 369-383.
    [5]
    HATZONIKOLAKIS Y, TSIARAS K, TSERPES G, et al. Investigating growth and reproduction of the Mediterranean swordfish Xiphias gladius through a full life cycle bioenergetics model[J]. Mar Ecol Prog Ser, 2021, 680: 51-77. doi: 10.3354/meps13861
    [6]
    SCHNEIDER J E. Energy balance and reproduction[J]. Physiol Behav, 2004, 81(2): 289-317. doi: 10.1016/j.physbeh.2004.02.007
    [7]
    WARREN C E, DAVIS G. Laboratory studies on the feeding, bioenergetics and growth of fish[M]//GERKIN S D. The biological basis of freshwater fish production. Oxford: Blackwell Scientific Publication, 1967: 175-214.
    [8]
    IGAMBERDIEV A U. Toward the relational formulation of biological thermodynamics[J]. Entropy, 2024, 26(1): 43-58.
    [9]
    SAOWAKOON S, SAOWAKOON K, JUTAGATE A, et al. Growth and feeding behavior of fishes in organic rice-fish systems with various species combinations[J]. Aquac Rep, 2021, 20: 100663. doi: 10.1016/j.aqrep.2021.100663
    [10]
    da SILVEIRA E L, SEMMAR N, CARTES J E, et al. Methods for trophic ecology assessment in fishes: a critical review of stomach analyses[J]. Rev Fish Sci Aquac, 2020, 28(1): 71-106. doi: 10.1080/23308249.2019.1678013
    [11]
    GÜL G, DEMIREL N. Ontogenetic shift in diet and trophic role of Raja clavata inferred by stable isotopes and stomach content analysis in the Sea of Marmara[J]. J Fish Biol, 2022, 101(3): 560-572. doi: 10.1111/jfb.15123
    [12]
    JIANG R J, YANG F, CHEN F, et al. Assessing trophic interactions among three tuna species in the Solomon Islands based on stomach contents and stable isotopes[J]. Front Mar Sci, 2022, 9: 961990. doi: 10.3389/fmars.2022.961990
    [13]
    VARELA J L, INTRIAGO K M, FLORES J C, et al. Feeding habits of juvenile yellowfin tuna (Thunnus albacares) in Ecuadorian waters assessed from stomach content and stable isotope analysis[J]. Fish Res, 2017, 194: 89-98. doi: 10.1016/j.fishres.2017.05.017
    [14]
    BROWNSCOMBE J W. Telemetry-based spatial-temporal fish habitat models for fishes in an urban freshwater harbour[J]. Hydrobiologia, 2023, 850(8): 1779-1800. doi: 10.1007/s10750-023-05180-z
    [15]
    BROWNSCOMBE J W, SHIPLEY O N, GRIFFIN L P, et al. Application of telemetry and stable isotope analyses to inform the resource ecology and management of a marine fish[J]. J Appl Ecol, 2022, 59(4): 1110-1121. doi: 10.1111/1365-2664.14123
    [16]
    RUBIN A M, SEEBACHER F. Feeding frequency does not interact with BPA exposure to influence metabolism or behaviour in zebrafish (Danio rerio)[J]. Physiol Behav, 2024, 273: 114403. doi: 10.1016/j.physbeh.2023.114403
    [17]
    曾令清, 付成, 奚露, 等. 鳊幼鱼能量代谢和个性行为的个体变异及表型关联[J]. 生态学报, 2017, 37(14): 4807-4816.
    [18]
    彭敏锐, 郑雪丽, 李平, 等. 温度和重复测定对秦岭细鳞鲑快速启动反应, 游泳性能及力竭后代谢特征的影响[J]. 生态学报, 2021, 41(6): 2505-2514.
    [19]
    YEUNG C, COPEMAN L A, MATTA M E, et al. Latitudinal variation in the growth and condition of juvenile flatfishes in the Bering Sea[J]. Estuar Coast Shelf S, 2021, 258: 107416. doi: 10.1016/j.ecss.2021.107416
    [20]
    MORALES M M, FIECHTER J, FIELD J C, et al. Development and application of a bioenergetics growth model for multiple early life stages of an ecologically important marine fish[J]. Ecol Model, 2024, 488: 110575. doi: 10.1016/j.ecolmodel.2023.110575
    [21]
    MULLER E B, LIKA K, NISBET R M, et al. Regulation of reproductive processes with dynamic energy budgets[J]. Funct Ecol, 2019, 33(5): 819-832. doi: 10.1111/1365-2435.13298
    [22]
    STAVRAKIDIS-ZACHOU O, PAPANDROULAKIS N, LIKA K. A bioenergetics approach to modelling tolerance limits under acute thermal stress in farmed finfish[J]. Front Mar Sci, 2023, 10: 1173358. doi: 10.3389/fmars.2023.1173358
    [23]
    RAPOSO A I G, SOARES F, NOBRE A, et al. Development of dynamic growth and body composition models for Nile tilapia (Oreochromis niloticus): an exploratory approach to protein and energy metabolism[J]. Aquaculture, 2024, 578: 740032. doi: 10.1016/j.aquaculture.2023.740032
    [24]
    ALSHDAIFAT N F F, TALIB A Z, OSMAN M A. Improved deep learning framework for fish segmentation in underwater videos[J]. Ecol Inform, 2020, 59: 101121. doi: 10.1016/j.ecoinf.2020.101121
    [25]
    DAN W, BAI M J, LI H J, et al. Modified kinetic energy feature-based graph convolutional network for fish appetite grading using time-limited data in aquaculture[J]. Front Mar Sci, 2022, 9: 1021688. doi: 10.3389/fmars.2022.1021688
    [26]
    KONNERT G D P, GERRITS W J J, GUSSEKLOO S W S, et al. Balancing protein and energy in Nile tilapia feeds: a meta-analysis[J]. Rev Aquac, 2022, 14(4): 1757-1778. doi: 10.1111/raq.12671
    [27]
    SERRANO E, LEFILLANCA J K, CARRASCO J, et al. Evaluation of Andean lupin (Lupinus mutabilis) seed meal as a dietary component on growth performance, feed utilization, nutrient digestibility, and liver histology of rainbow trout (Oncorhynchus mykiss) juveniles[J]. Aquac Rep, 2024, 34: 101919. doi: 10.1016/j.aqrep.2024.101919
    [28]
    POLITIKOS D, SOMARAKIS S, TSIARAS K P, et al. Simulating anchovy's full life cycle in the northern Aegean Sea (eastern Mediterranean): a coupled hydro-biogeochemical-IBM model[J]. Prog Oceanogr, 2015, 138: 399-416. doi: 10.1016/j.pocean.2014.09.002
    [29]
    MOLTÓ V, PALMER M, OSPINA-ÁLVAREZ A, et al. Projected effects of ocean warming on an iconic pelagic fish and its fishery[J]. Sci Rep-UK, 2021, 11(1): 1-11. doi: 10.1038/s41598-020-79139-8
    [30]
    邓悦, 郑一琛, 常剑波. 利用Ecopath模型评价鲢鳙放养对千岛湖生态系统的影响[J]. 生态学报, 2022, 42(16): 6853-6862.
    [31]
    PAPANTONIOU G, ZERVOUDAKI S, ASSIMAKOPOULOU G, et al. Ecosystem-level responses to multiple stressors using a time-dynamic food-web model: the case of a re-oligotrophicated coastal embayment (Saronikos Gulf, E Mediterranean)[J]. Sci Total Environ, 2023, 903: 165882. doi: 10.1016/j.scitotenv.2023.165882
    [32]
    GEARY W L, BODE M, DOHERTY T S, et al. A guide to ecosystem models and their environmental applications[J]. Nat Ecol Evol, 2020, 4(11): 1459-1471. doi: 10.1038/s41559-020-01298-8
    [33]
    RODRIGUEZ-PEREZ A, TSIKLIRAS A C, GAL G, et al. Using ecosystem models to inform ecosystem-based fisheries management in Europe: a review of the policy landscape and related stakeholder needs[J]. Front Mar Sci, 2023, 10: 1196329. doi: 10.3389/fmars.2023.1196329
    [34]
    COLL M, BELLIDO J M, PENNINO M G, et al. Retrospective analysis of the pelagic ecosystem of the Western Mediterranean Sea: drivers, changes and effects[J]. Sci Total Environ, 2024, 907: 167790. doi: 10.1016/j.scitotenv.2023.167790
    [35]
    HAMZA F, M A, VALSALA V, et al. A bioenergetics model for seasonal growth of Indian oil sardine (Sardinella longiceps) in the Indian west coast[J]. Ecol Model, 2021, 456: 109661. doi: 10.1016/j.ecolmodel.2021.109661
    [36]
    HEINICHEN M, MCMANUS M C, LUCEY S M, et al. Incorporating temperature-dependent fish bioenergetics into a Narragansett Bay food web model[J]. Ecol Model, 2022, 466: 109911. doi: 10.1016/j.ecolmodel.2022.109911
    [37]
    罗玉莲, 庞旭, 刘蒨莹, 等. 中华倒刺鲃幼鱼能量代谢特征个体差异及生态关联[J]. 水生生物学报, 2022, 46(6): 820-825.
    [38]
    史东杰, 王文峰, 李文通, 等. 5种光周期对锦鲤生长、能量收支及生物钟基因表达的影响[J]. 水生生物学报, 2022, 46(5): 664-670.
    [39]
    聂国兴, 邓大鹏, 闫潇. Apelin 与鱼类摄食[J]. 水产学报, 2020, 44(11): 1926-1934.
    [40]
    王书磊, 周宇, 朱雪梅. 几种因子对鱼类能量收支影响的研究进展[J]. 中国渔业质量与标准, 2012, 2(4): 61-67.
    [41]
    SILVA-GARAY L, LOWE C G. Effects of temperature and body-mass on the standard metabolic rates of the round stingray, Urobatis halleri (Cooper, 1863)[J]. J Exp Mar Biol Ecol, 2021, 540: 151564. doi: 10.1016/j.jembe.2021.151564
    [42]
    SCHULTE P. The effects of temperature on aerobic metabolism: towards a mechanistic understanding of the responses of ectotherms to a changing environment[J]. J Exp Biol, 2015, 218: 1856-1866. doi: 10.1242/jeb.118851
    [43]
    WEN B, JIN S R, CHEN Z Z, et al. Plasticity of energy reserves and metabolic performance of discus fish (Symphysodon aequifasciatus) exposed to low-temperature stress[J]. Aquaculture, 2017, 481: 169-176. doi: 10.1016/j.aquaculture.2017.09.002
    [44]
    PHAM L P, NGUYEN M V, JORDAL A E O, et al. Metabolic rates, feed intake, appetite control, and gut transit of clownfish Amphiprion ocellaris exposed to increased temperature and limited feed availability[J]. Comp Biochem Phys A, 2022, 274: 111318. doi: 10.1016/j.cbpa.2022.111318
    [45]
    庞旭, 付世建, 刘小红, 等. 中华倒刺鲃能量代谢和热耐受特征的体重效应[J]. 生态学报, 2020, 40(16): 5814-5821.
    [46]
    ZHANG Z, YANG Z W, DING N, et al. Effects of temperature on the survival, feeding, and growth of pearl gentian grouper (female Epinephelus fuscoguttatus × male Epinephelus lanceolatus)[J]. Fish Sci, 2018, 84(2): 399-404. doi: 10.1007/s12562-017-1163-4
    [47]
    IINO Y, KITAGAWA T, ABE T K, et al. Effect of food amount and temperature on growth rate and aerobic scope of juvenile chum salmon[J]. Fish Sci, 2022, 88(3): 397-409. doi: 10.1007/s12562-022-01599-w
    [48]
    TOMIYAMA T, KUSAKABE K, OTSUKI N, et al. Ontogenetic changes in the optimal temperature for growth of juvenile marbled flounder Pseudopleuronectes yokohamae[J]. J Sea Res, 2018, 141: 14-20. doi: 10.1016/j.seares.2018.07.010
    [49]
    CARRILLO-LONGORIA J A, GAYLORD G, ANDREWS L, et al. Effect of temperature on growth, survival, and chronic stress responses of Arctic grayling juveniles[J]. T Am Fish Soc, 2024, 153(1): 3-22. doi: 10.1002/tafs.10453
    [50]
    刘梅, 原居林, 练青平, 等. 不同流速对流水槽大口黑鲈生长性能、抗氧化能力、能量代谢及组织结构的影响[J]. 水生生物学报, 2023, 47(1): 25-36.
    [51]
    PALSTRA A P, ROQUE A, KRUIJT L, et al. Physiological effects of water flow induced swimming exercise in seabream Sparus aurata[J]. Front Physiol, 2020, 11: 610049. doi: 10.3389/fphys.2020.610049
    [52]
    代忠义, 高勤峰, 高永刚, 等. 不同水流速度对虹鳟生长性能、呼吸代谢及免疫相关酶活性的影响[J]. 中国海洋大学学报(自然科学版), 2023(3): 133-140.
    [53]
    SCHAKMANN M, KORSMEYER K E. Fish swimming mode and body morphology affect the energetics of swimming in a wave-surge water flow[J]. J Exp Biol, 2023, 226(6): jeb244739. doi: 10.1242/jeb.244739
    [54]
    汤保贵, 陈秀灿, 胡长圣, 等. 流速对黑鲷幼鱼游泳行为及运动生理的影响[J]. 水生生物学报, 2023, 47(12): 1993-2002.
    [55]
    TAN J H, LIU Z B, WANG Y, et al. Analysis of movements and behavior of bighead carps (Hypophthalmichthys nobilis) considering fish passage energetics in an experimental vertical slot fishway[J]. Animals, 2022, 12(13): 1725-1737. doi: 10.3390/ani12131725
    [56]
    YANG M, GAO J, KE H, et al. Transcriptome-based analysis of the response mechanism of leopard coralgrouper liver at different flow velocities[J]. Fishes, 2022, 7(5): 279-290. doi: 10.3390/fishes7050279
    [57]
    CASTEJÓN-SILVO I, TERRADOS J, NGUYEN T, et al. Increased energy expenditure is an indirect effect of habitat structural complexity loss[J]. Funct Ecol, 2021, 35(10): 2316-2328. doi: 10.1111/1365-2435.13876
    [58]
    WU Y B, REN X, ZHOU J H, et al. Feed intake, growth, feed utilization, body composition and waste output of juvenile hybrid bream at different feeding frequencies[J]. Aquac Nutr, 2019, 25(2): 292-297. doi: 10.1111/anu.12852
    [59]
    MCLEAN S, PERSSON A, NORIN T, et al. Metabolic costs of feeding predictively alter the spatial distribution of individuals in fish schools[J]. Curr Biol, 2018, 28(7): 1144-1149. doi: 10.1016/j.cub.2018.02.043
    [60]
    BU X Y, LIAN X Q, ZHANG Y, et al. Effects of feeding rates on growth, feed utilization, and body composition of juvenile Pseudobagrus ussuriensis[J]. Aquac Int, 2017, 25(5): 1821-1831. doi: 10.1007/s10499-017-0156-8
    [61]
    MENGISTU S B, MULDER H A, BENZIE J A H, et al. A systematic literature review of the major factors causing yield gap by affecting growth, feed conversion ratio and survival in Nile tilapia (Oreochromis niloticus)[J]. Rev Aquac, 2020, 12(2): 524-541. doi: 10.1111/raq.12331
    [62]
    POUIL S, KERNÉIS T, QUILLET E, et al. Isogenic lines of rainbow trout (Oncorhynchus mykiss) as a tool to assess how growth and feeding behaviour are correlated to feed efficiency in fish[J]. Aquaculture, 2023, 577: 739904. doi: 10.1016/j.aquaculture.2023.739904
    [63]
    FORSGREN E, AMUNDSEN T, BORG Å A, et al. Unusually dynamic sex roles in a fish[J]. Nature, 2004, 429(6991): 551-554. doi: 10.1038/nature02562
    [64]
    KARJALAINEN J, URPANEN O, KESKINEN T, et al. Phenotypic plasticity in growth and fecundity induced by strong population fluctuations affects reproductive traits of female fish[J]. Ecol Evol, 2016, 6(3): 779-790. doi: 10.1002/ece3.1936
    [65]
    HORNE C R, HIRST A G, ATKINSON D. Selection for increased male size predicts variation in sexual size dimorphism among fish species[J]. P Roy Soc B-Biol Sci, 2020, 287(1918): 20192640.
    [66]
    PAULY D, OZYNSKI C C, STERGIOU K I. Interactions between growth, sex, reproduction and activity levels in control and fast-growing strains of Nile tilapia, Oreochromis niloticus[J]. Fish Centre Res Rep, 2017, 25(1): 12-30.
    [67]
    PAULY D. Female fish grow bigger: let's deal with it[J]. Trends Ecol Evol, 2019, 34(3): 181-182. doi: 10.1016/j.tree.2018.12.007
    [68]
    CYR B A D, TVEITEN H, VANDENBERG G W, et al. Characterization of the growth rate of adult wolffishes Anarhichas minor and A. lupus: is avoidance of paternal care at the origin of the expression of a sexual size dimorphism?[J]. Aquaculture, 2018, 497: 24-31. doi: 10.1016/j.aquaculture.2018.07.028
    [69]
    ABOU ANNI I S, BIANCHINI A, BARCAROLLI I F, et al. Salinity influence on growth, osmoregulation and energy turnover in juvenile pompano Trachinotus marginatus Cuvier 1832[J]. Aquaculture, 2016, 455: 63-72. doi: 10.1016/j.aquaculture.2016.01.010
    [70]
    SEALE A P, BREVES J P. Endocrine and osmoregulatory responses to tidally-changing salinities in fishes[J]. Gen Comp Endocr, 2022, 326: 114071. doi: 10.1016/j.ygcen.2022.114071
    [71]
    HUANG J S, LI H J, GUO Z X, et al. Identification and expression analysis of cobia (Rachycentron canadum) liver-related miRNAs under hypoxia stress[J]. Fish Physiol Biochem, 2021, 47(6): 1951-1967. doi: 10.1007/s10695-021-01017-5
    [72]
    MESSINA-HENRÍQUEZ S, AGUIRRE Á, BROKORDT K, et al. Swimming performance and physiological responses of juvenile Cojinoba Seriolella violacea in hypoxic conditions[J]. Aquaculture, 2022, 548: 737560. doi: 10.1016/j.aquaculture.2021.737560
    [73]
    TROIA M J, PERKIN J S. Can fisheries bioenergetics modelling refine spatially explicit assessments of climate change vulnerability?[J]. Conserv Physiol, 2022, 10(1): coac035. doi: 10.1093/conphys/coac035
    [74]
    SCHIETTEKATTE N M D, BRANDL S J, CASEY J M, et al. Biological trade-offs underpin coral reef ecosystem functioning[J]. Nat Ecol Evol, 2022, 6(6): 701-708. doi: 10.1038/s41559-022-01710-5
    [75]
    MCLEAN M. Functional trade-offs in fish communities[J]. Nat Ecol Evol, 2022, 6(6): 669-670. doi: 10.1038/s41559-022-01706-1
    [76]
    SPURGEON J J, PEGG M A. Juvenile growth of a macrohabitat generalist tied to hydrological character of large-river system[J]. Freshwater Biol, 2017, 62(2): 291-302. doi: 10.1111/fwb.12867
    [77]
    PHANG S C, STILLMAN R A, CUCHEROUSSET J, et al. FishMORPH: an agent-based model to predict salmonid growth and distribution responses under natural and low flows[J]. Sci Rep-UK, 2016, 6(1): 29414. doi: 10.1038/srep29414
    [78]
    LESTER R E, POLLINO C A, CUMMINGS C R. Testing an environmental flow-based decision support tool: evaluating the fish model in the Murray Flow Assessment Tool[J]. Environ Modell Softw, 2019, 111: 72-93. doi: 10.1016/j.envsoft.2018.09.012
    [79]
    RHOADES S J, CALDWELL T J, MCBAIN S, et al. Trout bioenergetics as a process-based tool to estimate ecological risk in a regulated river[J]. J Am Water Resour As, 2024, 60(2): 287-304. doi: 10.1111/1752-1688.13173
    [80]
    CHAHID A, N'DOYE I, MAJORIS J E, et al. Model predictive control paradigms for fish growth reference tracking in precision aquaculture[J]. J Process Contr, 2021, 105: 160-168. doi: 10.1016/j.jprocont.2021.07.015
    [81]
    ZHANG B, SHEN J, XIONG J H, et al. Influence of solution and operating conditions on the treatment of aquaculture wastewater using direct contact membrane distillation: ammonia rejection and membrane fouling[J]. Chem Eng J, 2023, 478: 147326. doi: 10.1016/j.cej.2023.147326
    [82]
    刘晓娟, 罗伟, 王春芳, 等. 运用生物能量学模型预测草鱼生长, 饲料需求和污染排放[J]. 水产学报, 2018, 42(6): 950-967.
    [83]
    MA H J, YE J Y, HUANG H, et al. Growth, nutrient retention, wastes output and nonspecific immunity of juvenile snakehead Channa argus in response to dietary zinc levels[J]. Aquac Res, 2022, 53(17): 6090-6097. doi: 10.1111/are.16082
    • Related Articles

  • Related Articles

    [1]ZHANG Yuqing, GUO Huayang, ZHANG Nan, LIU Baosuo, ZHU Kecheng, XIAN Lin, ZHU Tengfei, ZHANG Dianchang. Path analysis of partial growth-related traits for wild large yellow croaker (Larimichthys crocea) of different geographical populations[J]. South China Fisheries Science, 2024, 20(3): 112-119. DOI: 10.12131/20230203
    [2]ZHENG Haohao, YANG Xiaoming, ZHU Jiangfeng. Environmental impact mechanism of skipjack tuna fishery in Western and Central Pacific Ocean based on Multi-scale Geographical Weighted Regression Model (MGWR)[J]. South China Fisheries Science, 2023, 19(5): 1-10. DOI: 10.12131/20230014
    [3]FANG Wei, ZHOU Shengjie, ZHAO Wang, YANG Rui, HU Jing, YU Gang, MA Zhenhua. Correlation and path analysis of morphological traits to body mass of juvenile Thunnus albacores[J]. South China Fisheries Science, 2021, 17(1): 52-58. DOI: 10.12131/20200158
    [4]HAN Peiwu, LI Nan, FANG Zhou, CHEN Xinjun. Heterogeneity of mantle length-body mass relationship in different Ommastrephes bartramii populations based on linear mixed model[J]. South China Fisheries Science, 2020, 16(6): 12-20. DOI: 10.12131/20200117
    [5]WANG Xiaochen, LÜ Binbin, XING Juanjuan, LI Dan, WANG Caining, REN Shengjie. Induced flow speed and its relationship to body length of two Schizothoracinae fishes in upper reaches of Yellow River[J]. South China Fisheries Science, 2020, 16(4): 47-53. DOI: 10.12131/20190249
    [6]LI Jie, YAN Lei, CHEN Sen, YANG Bingzhong, ZHANG Peng. Study on sinking performance of falling-net based on different weights[J]. South China Fisheries Science, 2016, 12(5): 16-22. DOI: 10.3969/j.issn.2095-0780.2016.05.003
    [7]GUO Huayang, CHEN Mingqiang, WANG Yu, JIANG Shigui, ZHANG Dianchang, YANG Qibin, LI Youning, ZHU Lichun, WU Kaichang. Correlation and path analysis of main economic traits of wild population of Trachycardium flavum[J]. South China Fisheries Science, 2013, 9(2): 1-8. DOI: 10.3969/j.issn.2095-0780.2013.02.001
    [8]LI Laihao, WANG Guochao, HAO Shuxian, YANG Xianqing, CEN Jianwei, HUANG Hui. Discrimination of tilapia at different frozen storage time by electronic nose[J]. South China Fisheries Science, 2012, 8(4): 1-6. DOI: 10.3969/j.issn.2095-0780.2012.04.001
    [9]LIU Qun, WANG Yanjun. Preliminary study of the tuning methods in fisheries virtual population analysis (VPA)[J]. South China Fisheries Science, 2007, 3(1): 1-6.
    [10]WANG Yingbin, LIU Qun. An elementary study of impacts of error structure on the estimation of fish natural mortality coefficient using cohort analysis (CA) model[J]. South China Fisheries Science, 2006, 2(3): 7-15.

Catalog

    Get Citation
    PDF
    XML
    Article views (526) PDF downloads (42) Cited by()
    Related

    Supported by: Beijing Renhe Information Technology Co., Ltd.  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return