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Sustainable development of Antarctic krill environmental resources based on system dynamics

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Antarctic krill mainly inhabit the Antarctic Ocean, not far from Antarctica, especially the Weddell Sea, where krill is dense. Marine fisheries have reached new levels, but the topic of sustainable use of marine fishery resources is far from reaching the required levels. In order to study the sustainable development of the Antarctic krill environment, this paper studies the living environment and applicability of Antarctic krill based on system dynamics, and provides some references for the sustainable development of marine resources. Mentioned the use of case analysis method, literature analysis method and other methods to collect data, build a Model, and read and analyse a large number of related literatures through the literature survey method. The experimental results proved that the salinity has a significant effect on the survival rate of Antarctic krill (p < 0.05). When the salinity is 34, the molting frequency reaches its maximum value, which is 70 %. It is concluded that the ability of Antarctic krill to adapt to gradual changes in salinity is stronger than that of sudden changes in salinity, and the suitable salinity for survival is 30-42. With 34 as the basic salinity, when the salinity rises within a certain range, the molting rate of krill will increase, and as the salinity decreases, the molting rate will gradually decrease. This shows that improving the environmental resources of Antarctic krill is an effective method for improving salinity.
Rocznik
Strony
471--485
Opis fizyczny
Bibliogr. 23 poz., rys., tab., wykr.
Twórcy
autor
  • East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China
autor
  • School of Economics and Management, Dalian University of Science and Technology, Liaoning 116052, China
autor
  • East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China
  • East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China
autor
  • East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China
Bibliografia
  • [1] Peng D, Yang Q, Yang HJ, Liu H, Zhu Y, Mu Y. Analysis on the relationship between fisheries economic growth and marine environmental pollution in China's coastal regions. Sci Total Environ. 2020;713:136641.1-136641.9. DOI: 10.1016/j.scitotenv.2020.136641.
  • [2] Cheung W, Reygondeau G, Frlicher TL. Large benefits to marine fisheries of meeting the 1.5°C global warming target. Science. 2016;354(6319):1591-4. DOI: 10.1126/science.aag2331.
  • [3] Chen YY, Zheng WZ, Li WB, Huang YM. The robustness and sustainability of port logistics systems for emergency supplies from overseas. J Advanced Transportation. 2020; DOI: 10.1155/2020/8868533.
  • [4] Hart P. Global atlas of marine fisheries: a critical appraisal of catches and ecosystem impacts. J Fish Biol. 2017;91(6):1750-1. DOI: 10.1111/jfb.13501.
  • [5] Essington TE, Ciannelli L, Heppell SS, Levin PS, Mcclanahan TR, Micheli F, et al. Empiricism and modeling for marine fisheries: advancing an interdisciplinary science. Ecosystems. 2017;20(2):1-8. DOI: 10.1007/s10021-016-0073-0.
  • [6] Grubljesic T, Coelho PS, Jaklic J. The shift to socio-organizational drivers of business intelligence and analytics acceptance. J Organizational End User Computing. 2019;31(2):37-64. DOI: 10.4018/JOEUC.2019040103.
  • [7] Lozano A, Heinen JT. Identifying drivers of collective action for the co-management of coastal marine fisheries in the gulf of Nicoya, Costa Rica. Environ Manage. 2016;57(4):759-69. DOI: 10.1007/s00267-015-0646-2.
  • [8] Tsai SB, Riga S, Lin YC, Quan C. Discussing measurement criteria and competitive strategies of green suppliers from a green law perspective. Proc Institution Mechanical Engineers. Part B: J Manufacture. 2015;229(S1):135-45. DOI: 10.1177/0954405414558740.
  • [9] Hughes TP, Cameron DS, Chin A, Connolly SR, Day JC, Jones GP, et al. A critique of claims for negative impacts of Marine Protected Areas on fisheries. Ecol Applications. 2016;26(2):637-41. DOI: 10.1890/15-0457.
  • [10] Ertor-Akyazi P. Contesting growth in marine capture fisheries: the case of small-scale fishing cooperatives in Istanbul. Sust Sci. 2020;15(1):45-62. DOI: 10.1007/s11625-019-00748-y.
  • [11] Pita P, Villasante S. The building of a management system for marine recreational fisheries in Galicia (NW Spain). Ocean Coastal Manage. 2019;169:191-200. DOI: 10.1016/j.ocecoaman.2018.12.027.
  • [12] Asina FNU, Brzonova I, Kozliak E, Kubatova A, Ji Y. Models for forecasting growth trends in renewable energy. Renew Sust Energy Rev. 2017;77:1169-78. DOI: 10.1016/j.rser.2017.03.098.
  • [13] David RR, Rodriguez J, Malak DA, Nastasi A, Hernandez P. Marine protected areas and fisheries restricted areas in the Mediterranean: assessing ”actual” marine biodiversity protection coverage at multiple scales. Marine Policy. 2016;64:24-30. DOI: 10.1016/j.marpol.2015.11.006.
  • [14] Kitouni I, Benmerzoug D, Lezzar F. Smart agricultural enterprise system based on integration of internet of things and agent technology. J Organizational End User Computing. 2018;30(4):64-82. DOI: 10.4018/JOEUC.2018100105.
  • [15] Marszałek M, Kowalski Z, Makara A. The possibility of contamination of water-soil environment as a result of the use of pig slurry. Ecol Chem Eng S. 2019;26(2):313-30. DOI: 10.1515/eces-2019-0022.
  • [16] Biswas S, Devi D, Chakraborty M. A hybrid case based reasoning model for classification in internet of things (iot) environment. J Organizational End User Computing. 2018;30(4):104-22. DOI: 10.4018/JOEUC.2018100107
  • [17] Bakker YW, Koning JD, Tatenhove JV. Resilience and social capital: the engagement of fisheries communities in marine spatial planning. Marine Policy. 2019;99:132-9. DOI: 10.1016/j.marpol.2018.09.032.
  • [18] Ibrahim M, El H, Sherif B, Mohamed E, Reham RM. Improved feature selection model for big data analytics. IEEE Access. 2020;8(1):66989-7004. DOI: 10.1109/ACCESS.2020.2986232.
  • [19] Lv Z, Li X, Lv H, Xiu W. BIM Big data storage in Webvrgis. IEEE Trans Industrial Informatics. 2019. DOI: 10.1109/TII.2019.2916689.
  • [20] Xu XZ, Zhang N, Zhou Y, Wang Y, Wang ZH. The effects of NaI, KBr and KI salts on the vapor-liquid equilibrium of H2O+CH3OH system. Frontiers Chemistry. 2020. DOI:10.3389/fchem.2020.00192.
  • [21] Sim K, Yang J, Lu W, Gao X. MaD-DLS: Mean and deviation of deep and local similarity for image quality assessment. IEEE Trans Multimedia. 2020;1-1. DOI: 10.1109/TMM.2020.3037482.
  • [22] Sołowski G, Konkol I, Cenian A. Perspectives of hydrogen production from corn wastes in Poland by means of dark fermentation. Ecol Chem Eng S. 2019;26(2):255-63. DOI: 10.1515/eces-2019-0031.
  • [23] Pavelkova A, Stejskall V, Voloscukova O, Nosek J. Cost-effective remediation using microscale ZVI: comparison of commercially available products. Ecol Chem Eng S. 2020;27(2):211-24. DOI: 10.2478/eces-2020-0014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fd653cb7-bced-4327-9493-f84454a52cd9
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