مقاله علمی – پژوهشی:‌ چشم‌انداز هوش مصنوعی و یادگیری ماشین در علوم شیلاتی Perspective of artificial intelligence (AI) and machine learning (ML) in fisheries science 1 1 یکپارچه‌سازی هوش مصنوعی و فناوری‌های نوین در علوم شیلات، تحولی اساسی در روش‌های مدیریت منابع دریایی ایجاد کرده است. در مطالعه حاضر، پیشرفت‌های اخیر در روش‌شناسی‌ هوش مصنوعی، از جمله یادگیری عمیق و رویکردهای سنتی یادگیری ماشین و کاربردهای آنها در شناسایی ماهی، نظارت بر جمعیت، مدیریت پایدار و ارزیابی ذخایر را تحلیل کرده است. یافته‌ها نشان می‌دهد که فناوری‌های هوش مصنوعی ابزارهای قدرتمندی برای مقابله با چالش‌های پیچیده آتی در شیلات جهانی ارائه می‌دهند که از جمله می‌توان به بهبود دقت شناسایی گونه‌ها، افزایش کیفیت ارزیابی ذخایر، کاهش صید ضمنی و مبارزه با ماهیگیری غیرقانونی اشاره نمود. با این‌حال، تحقق پتانسیل کامل هوش مصنوعی در مدیریت شیلات مستلزم رفع چالش‌های موجود در دسترسی به داده‌ها، حساسیت مدل‌ها و موانع فناوری است. مطالعه حاضر، نقشه راهی برای ادغام مسئولانه فناوری‌های هوش مصنوعی در مدیریت شیلات به‌ویژه در ایران ارائه می‌دهد و هدف آن پشتیبانی از شیوه‌های مؤثرتر و پایدارتر در مواجهه با چالش‌های پیچیده زیست‌محیطی و اجتماعی-اقتصادی است. 2 Introduction The integration of artificial intelligence and emerging technologies into fisheries science has fundamentally transformed marine resource management approaches (Bradley et al., 2019; Ebrahimi et al., 2021). This field has evolved from foundational object-oriented modeling approaches (Bousquet et al., 1994) to sophisticated expert systems such as CANOFISH and ProTuna, which have enhanced management decision accuracy by 85% (Alagappan and Kumaran, 2013). Currently, the integration of emerging technologies including satellite image processing, smart sensor networks (WSN), and deep learning algorithms has created a new paradigm in sustainable marine resource management (Lu et al., 2024). Global fisheries face significant challenges, as FAO reports indicate 94% of aquatic resources are in two distinct states: 60% in full exploitation and 34% at levels beyond biological sustainability (Kumar et al., 2024; Stroe, 2024). This situation is directly related to illegal, unreported, and unregulated fishing (IUU), which accounts for 20-35% of global catch and causes annual economic damages of $10-23.5 billion (Samy-Kamal, 2022; Grey, 2023; Lubchenco and Haugan, 2023). Fishing activities impact not only target species but also non-target species and biodiversity (Liang and Pauly, 2017), while socioeconomic factors contribute additional complexity to fisheries management (Phillipson and Symes, 2013). This review examines technological advancements in fisheries management from 2004-2024, focusing on machine learning developments in conjunction with traditional management approaches. The investigation addresses how artificial intelligence has improved management efficiency, what implementation challenges exist across different contexts, and what frameworks are necessary for sustainable integration of AI in global fisheries management. Methodology This study applies a systematic review methodology that comprises both quantitative and qualitative methods to examine the implementation and effectiveness of AI technologies in fisheries management. The research procedure was a three-phase structured method that started with a full-fledged literature search in the most important scientific databases including the Web of Science, Scopus, and Google Scholar, with a time frame of 2004 to 2024. Then, the investigation proceeded with the analysis of technical reports from international organizations such as FAO and the World Bank to gain an understanding of the practical aspects of the project, as well as broad analyses of case studies from both developed and developing countries to observe real-world implementations and problems. Data analysis included statistical evaluation of the implementation results via comparisons of success rates over different areas and thematic analysis of the implementation challenges. The main point is, studies brought about objective evaluation of the technology impact among the different locations. Results Artificial intelligence and emerging technologies have demonstrated significant contributions to fisheries management. In monitoring applications, empirical studies show that machine learning applied to fish species identification from images has achieved 95% accuracy (Silva et al., 2022). Additionally, the integration of Automatic Identification System (AIS) and Vessel Monitoring System (VMS) data has led to a 40% improvement in marine spatial planning (Thoya et al., 2021; Lu et al., 2024). Recent research demonstrates that deep learning models in early detection of environmental threats have accuracy above 90% (Fei et al., 2023), while advanced radar technologies in monitoring wildlife interactions and fishing activities have shown remarkable efficiency (Navarro-Herrero, 2024). The scalability of these solutions has been enhanced through the development of open-source frameworks, enabling traditional fisheries to benefit from advanced technologies (Silva et al., 2022). Implementation challenges span technical domains, with data standardization issues prominent; socioeconomic barriers, which vary significantly between regions; and regulatory constraints, characterized by adaptation delays. Discussion and conclusion The transformative potential of artificial intelligence in fisheries management requires balanced consideration of technical, socioeconomic, and institutional factors for successful implementation. Studies have shown that the integration of remote sensing data with AIS can effectively monitor IUU fishing activities, particularly in regions with limited monitoring capacity (Kurekin et al., 2019). Smart technology implementation in aquaculture has led to significant efficiency improvements through IoT systems and smart sensors, demonstrating the economic value of AI integration (Lan et al., 2022). Local ecological knowledge (LEK) complements scientific data by providing deeper understanding of marine ecosystems (Silvano and Valbo‐Jørgensen, 2008). Successful examples include identification of causes for fish population decline (Dey et al., 2019) and bycatch management (Cazé et al., 2022). The scalable framework for fish image collection and annotation proposed by Silva et al. (2022) demonstrates how technology can be made accessible across different contexts. Three principal directions for future development are identified: standardization of integration protocols, capacity development in developing regions, and adaptive regulatory frameworks. Future initiatives should address implementation barriers, develop comprehensive training programs, and establish regulatory frameworks that facilitate innovation while ensuring sustainable resource management. Conflict of Interest The authors declare that there is no conflict of interest in this research work. Acknowledgment We sincerely thank the Office of Vice Chancellor for Research and Artemia and Aquaculture Research Institute of Urmia University for the kind support. 1 36 2024/11/19 1403/8/29 2025/04/30 1404/2/10 آذین احمدی Azin Ahmadi دانشگاه گیلان Iran azin.ahmadi6@gmail.com علی حقی وایقان Ali Haghi Vayghan دانشگاه ارومیه Iran a.haghi@urmia.ac.ir Artificial Intelligence (AI) Machine Learning (ML) Fisheries Resource fish and fisheries Sustainable Management هوش مصنوعی (AI) یادگیری ماشین(ML) شیلات مدیریت پایدار Abangan, A., Kopp, D. and Faillettaz, R., 2023. Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity. Frontiers in Marine Science, vol. 10. DOI:10.3389/fmars.2023.1010761##Aeberhard, W.H., Flemming, J.M. and Nielsen, A., 2018. Review of state-space models for fisheries science. Annual Review of Statistics and Its Application, vol. 5, no. 1, pp. 215-235. DOI: 10.1146/annurev-statistics-031017-100427##Aghilinejhad, S.M., Gorgin, S., van Uhm, D., Joolaie, R., Ghorbani, R., Paighambari, S.Y., Mohammadi, J. and Jalali, A., 2018. What are the drivers of the occurrence of illegal fishing and conservation barriers of sturgeons in the Caspian Sea?. Aquatic Conservation: Marine and Freshwater Ecosystems, vol. 28, no. 3, pp. 690-701. DOI: 10.1002/aqc.2897##Ahmad, M., Al-Shayea, N., Tang, X., Jamal, A., Al-Ahmadi, H.M. and Ahmad, F., 2020. Predicting the pillar stability of underground mines with random trees and c4.5 decision trees. Applied Sciences, vol. 10, no. 18, pp. 6486. DOI: 10.3390/app10186486##Ahmed, A., Masri, N., Abu Sultan, Y., Akkila, A.N., Almasri, A., Mahmoud, A.Y., Zaqout, I. and Abu-Naser, S.S., 2019. Knowledge-based systems survey. International Journal of Academic Engineering Research, vol. 3, no. 7, pp. 1-22.##Akoğlu, E., 2021. Exploring the dynamics of small pelagic fish catches in the Marmara Sea in relation to changing environmental and bio-optical parameters. Turkish Journal of Zoology, vol. 45, no. 3, pp. 257-265. DOI: 10.3906/zoo-2012-12##Alagappan, M. and Kumaran, M., 2013. Application of expert systems in fisheries sector. Research Journal of Animal, Veterinary and Fishery Sciences, vol. 1, no. 8, pp. 19-30##Alsharabi, N., Ktari, J., Frikha, T., Alayba, A., Alzahrani, A.J., Jadi, A. and Hamam, H., 2024. Using blockchain and AI technologies for sustainable, biodiverse, and transparent fisheries of the future. Journal of Cloud Computing, vol. 13, no. 1, pp. 135. DOI: 10.1186/s13677-023-00595-4##Armsworth, P.R., Block, B.A., Eagle, J. and Roughgarden, J., 2010. The economic efficiency of a time–area closure to protect spawning bluefin tuna. Journal of Applied Ecology, vol. 47, no. 1, pp. 36-46. DOI: 10.1111/j.1365-2664.2009. 01738.x##Astles, K.L. and Cormier, R., 2018. Implementing sustainably managed fisheries using ecological risk assessment and bowtie analysis. Sustainability, vol. 10, no. 10, pp. 3659. DOI: 10.3390/su10103659##Azimi, M., Bagheri, M., Khoshnamvand, H., Abdoli, A. and Ahmadzadeh, F., 2023. The Silent Threat of a Non-native Oligochaete Species in Irans freshwater: Assessment of the Diversity and Origin of Eiseniella tetraedra (Savigny, 1826) and its Response to Climate Change. DOI:10.21203/rs.3.rs-2959416/v1##Bagheri, M., Azimi, M., Khoshnamvand, H., Abdoli, A. and Ahmadzadeh, F., 2023. The threat of a non-native oligochaete species in Irans freshwater: assessment of the diversity and origin of Eiseniella tetraedra (Savigny, 1826) and its response to climate change. Biology Open, vol. 12, no. 12, pp. bio060180. DOI: 10.1242/bio.060180##Barnes, M.A. and Turner, C.R., 2016. The ecology of environmental DNA and implications for conservation genetics. Conservation Genetics, vol. 17, no. 1, pp. 1-17. DOI: 10.1007/s10592-015-0775-4##Barnett, A., Jaine, F.R.A., Bierwagen, S.L., Lubitz, N., Abrantes, K., Heupel, M.R. and Currey-Randall, L.M., 2024. From little things big things grow: enhancement of an acoustic telemetry network to monitor broad-scale movements of marine species along Australias east coast. Movement Ecology, vol. 12. DOI: 10.1186/s40462-023-00431-z##Barnett, L., Jacobsen, N., Thorson, J. and Cope, J., 2019. Realizing the potential of trait‐based approaches to advance fisheries science. Fish and Fisheries. DOI: 10.1111/faf.12429##Bartholomew, D.C., Mangel, J.C., Alfaro-Shigueto, J., Pingo, S., Jimenez, A. and Godley, B.J., 2018. Remote electronic monitoring as a potential alternative to on-board observers in small-scale fisheries. Biological Conservation, vol. 219, pp. 35-45. DOI: 10.1016/j.biocon.2018.01.003##Beaufort, L.B.d., Sedki, K. and Fontenelle, G., 2015. Inference reasoning on fishers' knowledge using bayesian causal maps. Ecological Informatics, vol. 30, pp. 345-355. DOI: 10.1016/j.ecoinf.2015.06.005##Begossi, A. and Salivonchyk, S.V., 2019. Integrating science and citizen science: the dusky grouper (epinephelus marginatus) sustainable fishery of Copacabana, Rio de Janeiro, Brazil.##Beijbom, O., Edmunds, P.J., Kline, D.I., Mitchell, B.G. and Kriegman, D., 2012. Automated annotation of coral reef survey images. IEEE Conference on Computer Vision and Pattern Recognition. DOI: 10.1109/cvpr.2012.6247798##Bermejo, S., Monegal, B. and Cabestany, J., 2007. Fish age categorization from otolith images using multi-class support vector machines. Fisheries Research, vol. 84, no. 2, pp. 247-253. DOI: 10.1016/j.fishres.2006.11.028##Beyan, C. and Browman, H.I., 2020. Setting the stage for the machine intelligence era in marine science. ICES Journal of Marine Science, vol. 77, no. 4, pp. 1267-1273. DOI: 10.1093/icesjms/fsaa084##Bitunjac, I., Jajac, N. and Katavić, I., 2016. Decision support to sustainable management of bottom trawl fleet. Sustainability, vol. 8, no. 3, pp. 204. DOI: 10.3390/su8030204##Blincow, K.M. and Semmens, B.X., 2022. The effect of sea surface temperature on the structure and connectivity of species landings interaction networks in a multispecies recreational fishery. Canadian Journal of Fisheries and Aquatic Sciences, vol. 79, no. 7, pp. 1109-1119. DOI: 10.1139/cjfas-2021-0195##Bond, T., McLean, D.L., Prince, J., Taylor, M.D. and Partridge, J.C., 2022. Baited remote underwater video sample less site attached fish species along a subsea pipeline compared to a remotely operated vehicle. Marine and Freshwater Research, vol. 73, no. 7, pp. 915-930. DOI: 10.1071/mf21300##Bousquet, F., Cambier, C. and Morand, P., 1994. Distributed artificial intelligence and object-oriented modelling of a fishery. Mathematical and Computer Modelling, vol. 20, no. 8, pp. 97-107. DOI: 10.1016/0895-7177(94)90172-4##Bradley, D., Merrifield, M., Miller, K.M., Lomonico, S., Wilson, J.R. and Gleason, M., 2019. Opportunities to improve fisheries management through innovative technology and advanced data systems. Fish and Fisheries, vol. 20, no. 3, pp. 564-583. DOI: 10.1111/faf.12361##Breed, G.A., Matthews, C.J.D., Marcoux, M., Higdon, J.W., LeBlanc, B., Petersen, S.D. and Ferguson, S.H., 2017. Sustained disruption of narwhal habitat use and behavior in the presence of arctic killer whales. Proceedings of the National Academy of Sciences, vol. 114, no. 10, pp. 2628-2633. DOI: 10.1073/pnas.1611707114##Brooks, J.L., Chapman, J.M., Barkley, A.N., Kessel, S.T., Hussey, N.E., Hinch, S.G. and Nguyen, V.M., 2019. Biotelemetry informing management: case studies exploring successful integration of biotelemetry data into fisheries and habitat management. Canadian Journal of Fisheries and Aquatic Sciences, vol. 76, no. 7, pp. 1238-1252. DOI: 10.1139/cjfas-2017-0530##Campo Ruiz, I., 2024. Controlling the environment with artificial intelligence risks intensifying social inequalities and colonization. Open Research Europe, vol. 4, pp. 16. DOI: 10.12688/openreseurope.15876.1.##Carbotte, S.M., OHara, S., Stocks, K., Clark, P.D., Stolp, L., Smith, S.R. and Holik, J., 2022. Rolling deck to repository: supporting the marine science community with data management services from academic research expeditions. Frontiers in Marine Science, vol. 9. DOI: 10.3389/fmars.2022.856838##Cardiec, F., et al., 2020. "Too big to ignore": A feasibility analysis of detecting fishing events in Gabonese small-scale fisheries. PLOS ONE, vol. 15, no. 6, pp. e0234091. DOI: 10.1371/journal.pone.0234091##Carrella, E., Powers, J., Saul, S., Bailey, R.M., Payette, N., Vert-pre, K.A. and Madsen, J.K., 2024. Rejection sampling and agent-based models for data limited fisheries. Frontiers in Marine Science, vol. 11. DOI: 10.3389/fmars.2024.1300845##Casado, M.R., Palma, M. and Leinster, P., 2020. Autonomous systems for the environmental characterization of lagoons. Lagoon Environments Around the World - A Scientific Perspective. DOI: 10.5772/intechopen.90851##Casas, L. and Saborido-Rey, F., 2023. A review of an emerging tool to estimate population parameters: the close-kin mark-recapture method. Frontiers in Marine Science, vol. 10, pp. 1087027. DOI: 10.3389/fmars.2023.1087027##Cazé, C., Réveillas, J., Danto, A. and Mazé, C., 2022. Integrating fishers knowledge contributions in marine science to tackle bycatch in the Bay of Biscay. Frontiers in Marine Science, vol. 9. DOI: 10.3389/fmars.2022.832099##Chang, C.C., Ubina, N.A., Cheng, S.C., Lan, H.Y., Chen, K.C. and Huang, C.C., 2022. A two-mode underwater smart sensor object for precision aquaculture based on AIoT technology. Sensors, vol. 22, no. 19, pp. 7603. DOI: 10.3390/s22197603##Chen, Y., He, G., Yin, R., Zheng, K. and Wang, G., 2022. Comparative study of marine ranching recognition in multi-temporal high-resolution remote sensing images based on deeplab-v3+ and u-net. Remote Sensing, vol. 14, no. 22, pp. 5654. DOI: 10.3390/rs14225654##Cheng, X., Zhang, F., Chen, X. and Wang, J., 2023. Application of artificial intelligence in the study of fishing vessel behavior. Fishes, vol. 8, no. 10, pp. 516. DOI: 10.3390/fishes8100516##Cheung, W.W.L., Jones, M.C., Lam, V.W.Y., Miller, D., Ota, Y., Teh, L. and Sumaila, U.R., 2016. Transform high seas management to build climate resilience in marine seafood supply. Fish and Fisheries, vol. 18, no. 2, pp. 254-263. DOI: 10.1111/faf.12209##Choudhury, T., Aryan, A., Mahdi, H.F., Arunachalaeshwaran, V.R. and Sarkar, T., 2023. CNN-based freshness grading of Mourala fish (Amblypharyngodon mola). In Intelligent Sustainable Systems: Selected Papers of WorldS4 2022, vol. 2, pp. 495-509. Singapore: Springer Nature Singapore.##Close, C. and Hall, G.E., 2006. A GIS-based protocol for the collection and use of local knowledge in fisheries management planning. Journal of Environmental Management, vol. 78, no. 4, pp. 341-352. DOI: 10.1016/j.jenvman.2005.04.027##Cooke, S.J., Fulton, E.A., Sauer, W.H.H., Lynch, A.J., Link, J.S., Koning, A.A. and Gillanders, B.M., 2023. Towards vibrant fish populations and sustainable fisheries that benefit all: learning from the last 30 years to inform the next 30 years. Reviews in Fish Biology and Fisheries, vol. 33, no. 2, pp. 317-347. DOI: 10.1007/s11160-022-09740-9##Corrales, X., Katsanevakis, S., Coll, M., Heymans, J.J., Piroddi, C., Ofir, E. and Gal, G., 2019. Advances and challenges in modelling the impacts of invasive alien species on aquatic ecosystems. Biological Invasions, vol. 22, no. 3, pp. 907-934. DOI: 10.1007/s10530-019-02160-0##Cupp, A., Fritts, A., Brey, M., Woodley, C., Smith, D., Cornish, M., McGovern, A., Simmonds, R. and Jackson, N., 2023. Application of the Technology Readiness Levels Framework to Natural Resource Management Tools. Fisheries, vol. 48, no. 11, pp. 474-479. DOI: 10.1002/fsh.10924##Daley, T.T. and Leaf, R.T., 2019. Age and growth of Atlantic chub mackerel (Scomber colias) in the Northwest Atlantic. Journal of Northwest Atlantic Fishery Science, vol. 50. DOI: 10.2960/J.v50.m719##Davoodi, F. and Raisi, A., 2019. Effects of adult sex ratio on larvae number and survival of fry in Xiphophorus maculatus (Cyprinodontiformes: Poeciliidae). Journal of Animal Diversity, vol. 1, no. 2, pp. 83-89. DOI: 10.29252/jad.2019.1.2.4##Dey, S., Choudhary, S.K., Dey, S., Deshpande, K.S. and Kelkar, N., 2019. Identifying potential causes of fish declines through local ecological knowledge of fishers in the Ganga River, eastern Bihar, India. Fisheries Management and Ecology, vol. 27, no. 2, pp. 140-154. DOI: 10.1111/fme.12394##DeYoreo, M. and Kottas, A., 2018. Modeling for dynamic ordinal regression relationships: an application to estimating maturity of rockfish in California. Journal of the American Statistical Association, vol. 113, no. 521, pp. 68-80. DOI: 10.1080/01621459.2017.1323641##Ding, Q., Cao, J. and Chen, X., 2019. Stock assessment of the western winter-spring cohort of Ommastrephes bartramii in the northwest Pacific Ocean using a Bayesian hierarchical DeLury model based on daily natural mortality during 2005-2015. Scientia Marina, vol. 83, no. 2, pp. 155. DOI: 10.3989/scimar.04835.12A##Doll, J.C. and Jacquemin, S.J., 2019. Bayesian model selection in fisheries management and ecology. Journal of Fish and Wildlife Management, vol. 10, no. 2, pp. 691-707. DOI: 10.3996/082018-jfwm-077##Dowling, N.G., Wilson, J., Rudd, M., Babcock, E.A., Caillaux, M., Cope, J.M. and Victor, S., 2016. FishPath: A decision support system for assessing and managing data- and capacity- limited fisheries. Assessing and Managing Data-Limited Fish Stocks.##Dupont, C., Wussah, A., Malo, S., Thiare, O., Niass, F., Pham, C. and Cousin, P., 2018. Low-cost IoT solutions for fish farmers in Africa. IST-Africa Week Conference, pp. 1-8.##Ebrahimi, S.H., Ossewaarde, M.R. and Need, A., 2021. Smart fishery: a systematic review and research agenda for sustainable fisheries in the age of AI. Sustainability, vol. 13, no. 11, pp. 6037. DOI: 10.3390/su13116037##Embke, H.S., Rypel, A.L., Carpenter, S.R., Sass, G.G., Ogle, D.H., Cichosz, T.A. and Zanden, M.J.V., 2019. Production dynamics reveal hidden overharvest of inland recreational fisheries. Proceedings of the National Academy of Sciences, vol. 116, no. 49, pp. 24676-24681. DOI: 10.1073/pnas.1913196116##English, G., Lawrence, M.J., McKindsey, C.W., Lacoursière‐Roussel, A., Bergeron, H., Gauthier, S. and Trudel, M., 2024. A review of data collection methods used to monitor the associations of wild species with marine aquaculture sites. Reviews in Aquaculture, vol. 16, no. 3, pp. 1160-1185. DOI: 10.1111/raq.12856##Er, M.J., Chen, J., Zhang, Y. and Gao, W., 2023. Research challenges, recent advances, and popular datasets in deep learning-based underwater marine object detection: a review. Sensors, vol. 23, no. 4, pp. 1990. DOI: 10.3390/s23041990##Fablet, R. and Le Josse, N., 2005. Automated fish age estimation from otolith images using statistical learning. Fisheries Research, vol. 72, no. 2-3, pp. 279-290. DOI: 10.1016/j.fishres.2004.10.008##FAO, 2020. The State of World Fisheries and Aquaculture - Sustainability in Action. Food and Agriculture Organization of the United Nations, Rome.##FAO, 2022. Digital Aquaculture Library for the CARICOM Report. Appendices 4 and 8. FAO Subregional Office for the Caribbean (Bridgetown).##Farchadi, N., Welch, H., Braun, C.D., Allyn, A.J., Bograd, S.J., Brodie, S. and Lewison, R.L., 2024. Marine heatwaves redistribute pelagic fishing fleets. Fish and Fisheries, vol. 25, no. 4, pp. 602-618. DOI: 10.1111/faf.12821##Farr, E.R., Stoll, J.S. and Beitl, C.M., 2018. Effects of fisheries management on local ecological knowledge. Ecology and Society, vol. 23, no. 3. DOI: 10.5751/es-10344-230315##Fei, Y., Yang, S., Huang, M., Wu, X., Yang, Z., Zhao, J. and Yuan, S., 2023. Evaluating Suitability of Fishing Areas for Squid-Jigging Vessels in the Northwest Pacific Ocean Derived from AIS Data. Fishes, vol. 8, no. 10, pp. 530. DOI: 10.3390/fishes8100530##Feng, R., Xu, J., Jin, K., Xu, L., Liu, Y., Chen, D. and Chen, L., 2023. An automatic deep learning bowhead whale whistle recognizing method based on adaptive SWT: applying to the Beaufort Sea. Remote Sensing, vol. 15, no. 22, pp. 5346. DOI: 10.3390/rs15225346##Fernandes-Salvador, J.A., Oanta, G.A., Olivert-Amado, A., Goienetxea, I., Ibaibarriaga, L., Aranda, M. and Research for PECH Committee, 2020. Artificial Intelligence and the fisheries sector. European Parliament, Policy Department for Structural and Cohesion Policies, Brussels.##Ferrà, C., Tassetti, A.N., Armelloni, E.N., Galdelli, A., Scarcella, G. and Fabi, G., 2020. Using AIS to attempt a quantitative evaluation of unobserved trawling activity in the Mediterranean Sea. Frontiers in Marine Science, vol. 7. DOI: 10.3389/fmars.2020.00233##Ferrette, B.L.D.S., Domingues, R.R., Rotundo, M.M., Miranda, M.P., Bunholi, I.V., De Biasi, J.B. and Mendonça, F.F., 2019. DNA barcode reveals the bycatch of endangered batoids species in the southwest Atlantic: implications for sustainable fisheries management and conservation efforts. Genes, vol. 10, no. 4, pp. 304. DOI: 10.3390/genes10040304##Fisheries Statistical Yearbook, Iranian Area., 2018. Fisheries Administration, Council of Agriculture, Executive Tehran.Iran. 20 p (In Persian)##Fischer, V., Bennion, R.F., Foffa, D., MacLaren, J.A., McCurry, M.R., Melstrom, K.M. and Bardet, N., 2022. Ecological signal in the size and shape of marine amniote teeth. Proceedings of the Royal Society B, vol. 289, no. 1982, pp. 20221214. DOI: 10.1098/rspb.2022.1214##Fitzgerald, D.B., Perez, M.H.S., Sousa, L.M., Gonçalves, A.P., Py-Daniel, L.R., Lujan, N.K., Zuanon, J., Winemiller, K.O. and Lundberg, J.G., 2018. Diversity and community structure of rapids-dwelling fishes of the Xingu River: Implications for conservation amid large-scale hydroelectric development. Biological Conservation, vol. 222, pp. 104-112. DOI: 10.1016/j.biocon.2018.04.002##Føre, M., Frank, K., Norton, T., Svendsen, E., Alfredsen, J.A., Dempster, T. and Berckmans, D., 2018. Precision fish farming: A new framework to improve production in aquaculture. Biosystems Engineering, vol. 173, pp. 176-193. DOI: 10.1016/j.biosystemseng.2017.10.014##Franco, B.C., Defeo, O., Piola, A.R., Barreiro, M., Yang, H., Ortega, L. and Möller, O.O., 2020. Climate change impacts on the atmospheric circulation, ocean, and fisheries in the southwest South Atlantic Ocean: a review. Climatic Change, vol. 162, no. 4, pp. 2359-2377. DOI: 10.1007/s10584-020-02783-6##Friedrich, S., Antes, G., Behr, S., Binder, H., Brannath, W., Dumpert, F. and Friede, T., 2021. Is there a role for statistics in artificial intelligence? Advances in Data Analysis and Classification, pp. 1-24. DOI: 10.1007/s11634-021-00455-6##Froese, R., Winker, H., Coro, G., Palomares, M.L., Tsikliras, A.C., Dimarchopoulou, D., Touloumis, K., Demirel, N., Vianna, G., Scarcella, G. and Schijns, R., 2023. New developments in the analysis of catch time series as the basis for fish stock assessments: The CMSY++ method. Acta Ichthyologica et Piscatoria, vol. 53, pp. 173-189. DOI: 10.3897/aiep.53.e111621##Froese, R., Winker, H., Coro, G., Palomares, M.L.D., Tsikliras, A.C., Dimarchopoulou, D., Touloumis, K., Demirel, N., Vianna, G., Scarcella, G. and Schijns, R., 2021. Catch time series as the basis for fish stock assessments: the CMSY++ method. Frontiers in Marine Science.##Fu, Y., Deng, J., Wang, H., Comber, A., Yang, W., Wu, W., You, S., Lin, Y. and Wang, K., 2021. A new satellite-derived dataset for marine aquaculture areas in Chinas coastal region. Earth System Science Data, vol. 13, no. 5, pp. 1829-1842. DOI: 10.5194/essd-13-1829-2021##Fujii, I., Okochi, Y. and Kawamura, H., 2021. Promoting cooperation of monitoring, control, and surveillance of IUU fishing in the Asia-Pacific. Sustainability, vol. 13, no. 18, pp. 10231. DOI: 10.3390/su131810231##Fujita, R., Cusack, C., Karasik, R. and Takade-Heumacher, H., 2018. Designing and Implementing Electronic Monitoring Systems for Fisheries: A Supplement to the Catch Share Design Manual. Environmental Defense Fund, San Francisco.##Fulton, E.A., Smith, A.D.M., Smith, D.C. and Johnson, P., 2014. An integrated approach is needed for ecosystem-based fisheries management: insights from ecosystem-level management strategy evaluation. PLoS ONE, vol. 9, no. 1, pp. e84242. DOI: 10.1371/journal.pone.0084242##Fulton, S., López-Sagástegui, C., Weaver, A.H., Fitzmaurice-Cahluni, F., Galindo, C.R., Melo, F.J.F. and Torres-Bahena, E., 2019. Untapped potential of citizen science in Mexican small-scale fisheries. Frontiers in Marine Science, vol. 6. DOI: 10.3389/fmars.2019.00517##Garlock, T., Anderson, J.L., Asche, F., Camp, E.V., Chu, J., Lorenzen, K. and Vannuccini, S., 2022. Global insights on managing fishery systems for the three pillars of sustainability. Fish and Fisheries, vol. 23, no. 4, pp. 899-909. DOI: 10.1111/faf.12668##Gautama, D.A., Susanto, H., Riyanto, M., Wahju, R.I., Osmond, M. and Wang, J.H., 2022. Reducing sea turtle bycatch with net illumination in an Indonesian small-scale coastal gillnet fishery. Frontiers in Marine Science, vol. 9. DOI: 10.3389/fmars.2022.927164##Gervasi, C.L., Karnauskas, M., Rios, A., Santos, R.O., James, W.R., Rezek, R.J. and Rehage, J.S., 2023. Rapid approach for assessing an unregulated fishery using a series of data‐limited tools. Marine and Coastal Fisheries, vol. 15, no. 5, pp. e10270. DOI: 10.1002/mcf2.10270##Girard, P. and Du Payrat, T., 2017. An inventory of new technologies in fisheries. The Green Growth and Sustainable Development (GGSD) Forum.##Gordon, J.Y., Beaudreau, A.H., Saas, E.M. and Carothers, C., 2022. Engaging formal and informal institutions for stewardship of rockfish fisheries in the Gulf of Alaska. Marine Policy, vol. 143, pp. 105170. DOI: 10.1016/j.marpol.2022.105170##Grey, B., 2023. An investigation into computational methods for classifying fishing vessels to identify illegal, unreported and unregulated fishing activity.##Gu, S., Deng, Y., Wang, P., Li, C., Shi, D. and Wang, S., 2023. Assessing riverine fish community diversity and stability by eDNA metabarcoding. Ecological Indicators, vol. 157, pp. 111222. DOI: 10.1016/j.ecolind.2023.111222##Gundelund, C., Venturelli, P.A., Hartill, B., Hyder, K., Olesen, H.J. and Skov, C., 2021. Evaluation of a citizen science platform for collecting fisheries data from coastal sea trout anglers. Canadian Journal of Fisheries and Aquatic Sciences, vol. 78, no. 11, pp. 1576-1585. DOI: 10.1139/cjfas-2020-0364##Guobao, X., Shen, W. and Wang, X., 2014. Applications of wireless sensor networks in marine environment monitoring: a survey. Sensors, vol. 14, no. 9, pp. 16932-16954. DOI: 10.3390/s140916932##Haghi Vayghan, A., Ray, A., Mondal, S. and Lee, M.A., 2024. Modeling of swordtip squid (Uroteuthis edulis) monthly habitat preference using remote sensing environmental data and climate indices. Frontiers in Marine Science, vol. 11, pp. 1329254. DOI: 10.3389/fmars.2024.1329254##Haghi Vayghan, A. and Ghanbarzadeh, M., 2022. Estimation of Fisheries Reference Points for (Scomberomorus guttatus Bloch and Schneider, 1801) Using the Catch-Maximum Sustainable Yield (CMSY) and the Bayesian Surplus Production (BSM) Models in the Southern Waters of Iran (Persian Gulf and Oman Sea). Journal of Fisheries, vol. 75, no. 1, pp. 31-47. DOI: 10.22059/jfisheries.2021.326498.1268 (In Persian)##Haghi Vayghan, A., 2021. Distribution modeling of bigeye tuna (Thunnus obesus Lowe, 1839), using satellite derived environmental variables in Indian Ocean. Iranian Journal of Applied Ecology, vol. 9, no. 4, pp. 1-14. DOI: 10.47176/ijae.9.4.12831 (In Persian)##Haq, K.P.R.A. and Harigovindan, V.P., 2022. Water quality prediction for smart aquaculture using hybrid deep learning models. IEEE Access, vol. 10, pp. 60078-60098. DOI: 10.1109/access.2022.3180431##Hashemi, S., 2024. Relationship of water physicochemical factors with white thread fish Holothuria leucospilota (Brandt, 1835) biomass in the northern waters of the Oman Sea. Sarhad Journal of Agriculture, vol. 40, no. 1. DOI: 10.17582/journal.sja/2024.40.1.192.201##Hastie, G.D., Wu, G., Moss, S., Jepp, P., Macaulay, J., Lee, A. and Gillespie, D., 2019. Automated detection and tracking of marine mammals: a novel sonar tool for monitoring effects of marine industry. Aquatic Conservation: Marine and Freshwater Ecosystems, vol. 29, no. S1, pp. 119-130. DOI: 10.1002/aqc.3103##Hastings, A., Gaines, S.D. and Costello, C., 2017. Marine reserves solve an important bycatch problem in fisheries. Proceedings of the National Academy of Sciences, vol. 114, no. 34, pp. 8927-8934. DOI: 10.1073/pnas.1705169114##Hindarto, D., 2023. Comparative analysis VGG16 vs MobileNet performance for fish identification. International Journal Software Engineering and Computer Science (IJSECS), vol. 3, no. 3, pp. 270-280. DOI: 10.35870/ijsecs.v3i3.1763##Hou, G., Zhang, H., Wang, J., Chen, Y. and Lin, J., 2021. Stock assessment of 19 Perciformes in the Beibu Gulf, China, using a length-based Bayesian biomass method. Frontiers in Marine Science, vol. 8. DOI: 10.3389/fmars.2021.718042##Jafari, O., Ebrahimi, M., Hedayati, S.A.A., Zeinalabedini, M., Poorbagher, H., Nasrolahpourmoghadam, M. and Fernandes, J.M., 2022. Integration of morphometrics and machine learning enables accurate distinction between wild and farmed common carp. Life, vol. 12, no. 7, pp. 957. DOI: 10.3390/life12070957##James, M., Mendo, T., Jones, E.L., Orr, K., McKnight, A. and Thompson, J., 2018. AIS data to inform small scale fisheries management and marine spatial planning. Marine Policy, vol. 91, pp. 113-121. DOI: 10.1016/j.marpol.2018.02.012##Jamnia, A.R., Keikha, A.A., Ahmadpour, M., Cissé, A.A. and Rokouei, M., 2018. Applying Bayesian population assessment models to artisanal, multispecies fisheries in the northern Mokran Sea, Iran. Nature Conservation, vol. 28, pp. 61-89. DOI: 10.3897/natureconservation.28.25002##Jannot, J.E., Ward, E.J., Somers, K.A., Feist, B.E., Good, T.P., Lawson, D. and Carretta, J.V., 2021. Using Bayesian models to estimate humpback whale entanglements in the United States west coast sablefish pot fishery. Frontiers in Marine Science, vol. 8. DOI: 10.3389/fmars.2021.624729##Joo, R., et al., 2013. Hidden Markov Models: The best models for forager movements? PLOS ONE, vol. 8, no. 8, pp. e71246. DOI: 10.1371/journal.pone.0071246##Joutsijoki, H. and Juhola, M., 2011. Comparing the one-vs-one and one-vs-all methods in benthic macroinvertebrate image classification. International Workshop on Machine Learning and Data Mining in Pattern Recognition, pp. 399-413. DOI: 10.1007/978-3-642-23199-5_30##Jurado-Molina, J., García-Meléndez, J.J. and Cortes-Salgado, M., 2021. Development of a stochastic bioeconomic model for the red octopus fishery on the Yucatan Peninsula: implications for management. Ciencias Marinas, vol. 47, no. 4. DOI: 10.7773/cm. v47i4.3213##Karr, K., Fujita, R., Carcamo, R., Epstein, L., Foley, J.R., Fraire-Cervantes, J.A. and Kritzer, J.P., 2017. Integrating science-based co-management, partnerships, participatory processes and stewardship incentives to improve the performance of small-scale fisheries. Frontiers in Marine Science, vol. 4. DOI: 10.3389/fmars.2017.00345##Karras, A., Karras, C., Sioutas, S., Makris, C., Katselis, G., Hatzilygeroudis, I. and Tsolis, D., 2023. An integrated GIS-based reinforcement learning approach for efficient prediction of disease transmission in aquaculture. Information, vol. 14, no. 11, pp. 583. DOI: 10.3390/info14110583##Keskar, A., Raghavan, R., Kumkar, P., Padhye, A. and Dahanukar, N., 2017. Assessing the sustainability of subsistence fisheries of small indigenous fish species: fishing mortality and exploitation of hill stream loaches in India. Aquatic Living Resources, vol. 30, pp. 13. DOI: 10.1051/alr/2017012##Kim, S. and Kim, H., 2023. Long-term spatiotemporal oceanographic data from the northeast Pacific Ocean: 1980–2022 reconstruction based on the Korea Oceanographic Data Center (KODC) dataset. Data, vol. 8, no. 12, pp. 175. DOI: 10.3390/data8120175##Kindong, R., Gao, C., Pandong, N.A., Ma, Q., Tian, S., Wu, F. and Sarr, O., 2020. Stock status assessments of five small pelagic species in the Atlantic and Pacific oceans using the length-based Bayesian estimation (LBB) method. Frontiers in Marine Science, vol. 7. DOI: 10.3389/fmars.2020.00592##Konovalov, D.A., Saleh, A., Bradley, M., Sankupellay, M., Marini, S. and Sheaves, M., 2019. Underwater fish detection with weak multi-domain supervision. International Joint Conference on Neural Networks (IJCNN), pp. 1-8. DOI: 10.1109/ijcnn.2019.8851907##Kumar, R., Dash, G., M. Muktha, Sasikumar, G., U. Ganga, Joe Kizhakudan, S. and Gopalakrishnan, A., 2024. Assessment of marine fish stocks within Indias exclusive economic zone: status report 2022. Indian Journal of Fisheries, vol. 71, no. 1.##Kurekin, A.A., Loveday, B.R., Clements, D.O., Quartly, G.D., Miller, P.I., Wiafe, G. and Agyekum, K.A., 2019. Operational monitoring of illegal fishing in Ghana through exploitation of satellite earth observation and AIS data. Remote Sensing, vol. 11, no. 3, pp. 293. DOI: 10.3390/rs11030293##Kylili, K., Hadjistassou, C. and Artusi, A., 2020. An intelligent way for discerning plastics at the shorelines and the seas. Environmental Science and Pollution Research, vol. 27, no. 34, pp. 42631-42643. DOI: 10.1007/s11356-020-10387-x##Kyriakou, C., Christodoulou, S.E. and Dimitriou, L., 2021. Spatial roadway condition-assessment mapping utilizing smartphones and machine learning algorithms. Transportation Research Record: Journal of the Transportation Research Board, vol. 2675, no. 9, pp. 1118-1126. DOI: 10.1177/03611981211006725##Lan, H.Y., Ubina, N.A., Cheng, S.C., Lin, S.S. and Huang, C.T., 2022. Digital twin architecture evaluation for intelligent fish farm management using modified analytic hierarchy process. Applied Sciences, vol. 13, no. 1, pp. 141. DOI: 10.3390/app13010141##Lapeyrolerie, M., Chapman, M., Norman, K. and Boettiger, C., 2022. Deep reinforcement learning for conservation decisions. Methods in Ecology and Evolution, vol. 13, no. 11, pp. 2649-2662. DOI: 10.1111/2041-210x.13974##Le, V.C., Phan, H.M. and Nguyen, K.H., 2024. The fabrication and testing drifting buoys of marine data collection. Vietnam Journal of Marine Science and Technology, vol. 24, no. 1, pp. 33-45.##Lee, B., Suhartono, S., Lim, A. and Ahn, S.K., 2021. Forecasting world tuna catches with ARIMA-spline and ARIMA-neural networks models. Walailak Journal of Science and Technology (WJST), vol. 18, no. 17.##Li, J., Xu, W., Deng, L., Xiao, Y., Han, Z.J. and Zheng, H., 2022. Deep learning for visual recognition and detection of aquatic animals: a review. Reviews in Aquaculture, vol. 15, no. 2, pp. 409-433. DOI: 10.1111/raq.12743##Li, X., Xiao, Y., Su, F., Wu, W. and Zhou, L., 2021. AIS and VBD data fusion for marine fishing intensity mapping and analysis in the northern part of the South China Sea. ISPRS International Journal of Geo-Information, vol. 10, no. 5, pp. 277. DOI: 10.3390/ijgi10050277##Lim, L.W.K., 2024. Implementation of artificial intelligence in aquaculture and fisheries: deep learning, machine vision, big data, internet of things, robots and beyond. Journal of Computational and Cognitive Engineering, vol. 3, no. 2, pp. 112-118. DOI: 10.48550/arXiv.2401.09231##Lindkvist, E., Pellowe, K., Alexander, S., ONeill, E., Finkbeiner, E., Giron‐Nava, A., González-Mon, B., Johnson, A., Pittman, J., Schill, C., Wijermans, N., Bodin, Ö., Gelcich, S. and Glaser, M., 2022. Untangling social–ecological interactions: A methods portfolio approach to tackling contemporary sustainability challenges in fisheries. Fish and Fisheries, vol. 23, pp. 1202-1220. DOI: 10.1111/faf.12678##Liu, J., Wu, D., Hellevik, C.C. and Wang, H., 2023. Plastopol: a collaborative data-driven solution for marine litter detection and monitoring. IEEE International Conference on Industrial Technology (ICIT), pp. 1-6. DOI: 10.1109/icit58465.2023.10142474##Liu, Q., Zhang, Y., Wu, H., Peng, W., Zhang, X., Chang, F. and Zhang, H., 2020. A review and perspective of eDNA application to eutrophication and HAB control in freshwater and marine ecosystems. Microorganisms, vol. 8, no. 3, pp. 417. DOI: 10.3390/microorganisms8030417##Lowerre-Barbieri, S.K., Catalán, I.A., Frugård Opdal, A. and Jørgensen, C., 2019. Preparing for the future: integrating spatial ecology into ecosystem-based management. ICES Journal of Marine Science, vol. 76, no. 2, pp. 467-476. DOI: 10.1093/icesjms/fsy209##Lu, H., Li, H., Liu, T., Fan, Y., Yuan, Y., Xie, M. and Qian, X., 2019. Simulating heavy metal concentrations in an aquatic environment using artificial intelligence models and physicochemical indexes. Science of the Total Environment, vol. 694, pp. 133591. DOI: 10.1016/j.scitotenv.2019.133591##Lu, J., Wang, Y., Zhang, Y. and Tang, J., 2024. A Metric-Based Few-Shot Learning Method for Fish Species Identification with Limited Samples. Animals, vol. 14, no. 5, pp. 755. DOI: 10.3390/ani14050755##Lubchenco, J. and Haugan, P.M., 2023. Illegal, unreported and unregulated fishing and associated drivers. The Blue Compendium, pp. 553-591.##Mackintosh, A., Hill, G., Costello, M.J., Jueterbock, A. and Assis, J., 2023. Modeling aquaculture suitability in a climate change future. Oceanography. DOI: 10.5670/oceanog.2023.207##Mahendra, K. and Oktavia, T., 2024. Identifying Fishing Trip Behavior from Vessel Monitoring System (VMS) Data Using Machine Learning Models.##Mahmood, M.A. and Ali, Q., 2023. How can blue economy contribute to inclusive growth and ecosystem resources in Asia? A comparative analysis of economic, environmental, and social indicators among 19 Asian cooperation dialogue members. Preprints. DOI: 10.1111/fwb.14111##Makki, T., Mostafavi, H., Matkan, A.A., Aghighi, H., Valavi, R., Chee, Y.E. and Teimori, A., 2023. Impacts of climate change on the distribution of riverine endemic fish species in Iran, a biodiversity hotspot region. Freshwater Biology, vol. 68, no. 6, pp. 1007-1019. DOI: 10.1038/s41598-023-41465-y##Makki, T., Mostafavi, H., Matkan, A.A., Valavi, R., Hughes, R.M., Shadloo, S., Aghighi, H., Abdoli, A., Teimori, A., Eagderi, S. and Coad, B.W., 2023. Predicting climate heating impacts on riverine fish species diversity in a biodiversity hotspot region. Scientific Reports, vol. 13, no. 1, pp. 14347.##Malde, K., Handegard, N.O., Eikvil, L. and Salberg, A.B., 2020. Machine intelligence and the data-driven future of marine science. ICES Journal of Marine Science, vol. 77, no. 4, pp. 1274-1285. DOI: 10.1093/icesjms/fsz057##Malik, J. and Kristiana, H., 2021. Sustainability strategy for small-scale fisheries management: case study in Semarang City coastal, Indonesia. Journal of Tropical Fisheries Management, vol. 5, no. 2, pp. 83-90.##Mallet, D. and Pelletier, D., 2014. Underwater video techniques for observing coastal marine biodiversity: a review of sixty years of publications (1952–2012). Fisheries Research, vol. 154, pp. 44-62. DOI: 10.1016/j.fishres.2014.01.019##Marini, S., Fanelli, E., Sbragaglia, V., Azzurro, E., Río, J.D. and Aguzzi, J., 2018. Tracking fish abundance by underwater image recognition. Scientific Reports, vol. 8, no. 1. DOI: 10.1038/s41598-018-32089-8##McClanahan, T.R. and Azali, M.K., 2020. Improving sustainable yield estimates for tropical reef fisheries. Fish and Fisheries, vol. 21, no. 4, pp. 683-699. DOI: 10.1111/faf.12454##Meng, C., Liu, X., Zhao, J., Sun, J., Lian, G., Chen, T. and Wang, W., 2022. Fish feeding intensity assessment in aquaculture: a new audio dataset AFFIA3K and a deep learning algorithm. IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP). DOI: 10.1109/mlsp53980.2022.9943304##Michelin, M., Elliott, M., Bucher, M., Zimring, M. and Sweeney, M., 2018. Catalyzing the growth of electronic monitoring in fisheries. The Nature Conservancy and California Environmental Associates, 64 pp.##Michielsens, C. and Cave, J.D., 2019. In-season assessment and management of salmon stocks using a Bayesian time–density model. Canadian Journal of Fisheries and Aquatic Sciences, vol. 76, no. 7, pp. 1073-1085. DOI: 10.1139/cjfas-2018-0213##Midway, S.R., Ostrowski, A.D., West, L., Hernandez, M.F. and Robertson, M., 2018. Improved growth estimates for Lethrinus harak: measuring increments, adjusting ages, and fitting flexible growth models. Fishes, vol. 3, no. 3, pp. 31. DOI: 10.3390/fishes3030031##Mohale, H.P., Narsale, S.A., Kadam, R.V., Prakash, P., Sheikh, S., Mansukhbhai, C.R., Kirtikumar, P.B. and Baraiya, R., 2024. Artificial Intelligence in Fisheries and Aquaculture: Enhancing Sustainability and Productivity. Archives of Current Research International, vol. 24, no. 3, pp. 106-123. DOI: 10.9734/acri/2024/v24i3650##Morgera, E. and Ntona, M., 2017. Linking small-scale fisheries to international obligations on marine technology transfer. SSRN Electronic Journal. DOI: 10.2139/ssrn.3015751##Moullec, F., Velez, L., Verley, P., Barrier, N., Ulses, C., Carbonara, P. and Shin, Y., 2019. Capturing the big picture of Mediterranean marine biodiversity with an end-to-end model of climate and fishing impacts. Progress in Oceanography, vol. 178, pp. 102179. DOI: 10.1016/j.pocean.2019.102179##Munif, M.M. and Ahmad, M.I., 2024. Enhanced Ant Colony System for Water Quality Rules Classification in Smart Aquaculture. IEEE Symposium on Wireless Technology and Applications (ISWTA), pp. 216-220. DOI: 10.1109/iswta58016.2024.10419231##Murphy, H.M. and Jenkins, G.P., 2010. Observational methods used in marine spatial monitoring of fishes and associated habitats: a review. Marine and Freshwater Research, vol. 61, no. 2, pp. 236-252. DOI: 10.1071/mf09068##Natesan, B., Liu, C., Ta, V. and Liao, R., 2023. Advanced robotic system with keypoint extraction and YOLOv5 object detection algorithm for precise livestock monitoring. Fishes, vol. 8, no. 10, pp. 524. DOI: 10.3390/fishes8100524##Navarro Herrero, L., 2024. Unravelling seabird-fishery dynamics through bird-borne technologies.##Nguyen, V.M., Young, N., Brownscombe, J.W. and Cooke, S.J., 2019. Collaboration and engagement produce more actionable science: quantitatively analyzing uptake of fish tracking studies. Ecological Applications, vol. 29, no. 6. DOI: 10.1002/eap.1943##Núñez, R.A.C., Ojeda-Ruíz, M.Á., Flores-Irigollen, A., Marín-Monroy, E.A., Ibañez-Lucero, M. and Sánchez-Ortíz, C., 2021. Focused small-scale fisheries as complex systems using deep learning models. Latin American Journal of Aquatic Research, vol. 49, no. 2, pp. 342-353. DOI: 10.3856/vol49-issue2-fulltext-2565##Nur, S., Nurdjaman, S., Cahya, B.D.P.P. and Al-Ghifari, K.H.D., 2021. Integrating sentinel-2 spectral-imagery and field data of seagrass coverage with species identification in the coastal of Riau Islands, Indonesia. Borneo Journal of Marine Science and Aquaculture (BJoMSA), vol. 5, no. 2, pp. 78-82. DOI: 10.51200/bjomsa.v5i2.2710##Ojea, E., Fontán, E., Fuentes-Santos, I. and Bueno-Pardo, J., 2021. Assessing countries social-ecological resilience to shifting marine commercial species. Scientific Reports, vol. 11, no. 1. DOI: 10.1038/s41598-021-93843-z##Paradinas, I., Illian, J., Alonso-Fernändez, A., Pennino, M. and Smout, S., 2023. Combining fishery data through integrated species distribution models. ICES Journal of Marine Science. DOI: 10.1093/icesjms/fsad159##Pata, P.R. and Yñiguez, A.T., 2021. Spatial planning insights for Philippine coral reef conservation using larval connectivity networks. Frontiers in Marine Science, vol. 8. DOI: 10.3389/fmars.2021.638062##Pelletier, D., Roos, D., Bouchoucha, M., Schohn, T., Roman, W., Gonson, C. and Reecht, Y., 2021. A standardized workflow based on the STAVIRO unbaited underwater video system for monitoring fish and habitat essential biodiversity variables in coastal areas. Frontiers in Marine Science, vol. 8, pp. 689280. DOI: 10.3389/fmars.2021.689280##Pereira, I., Peixoto, U.I., Medeiros‐Leal, W., Casal-Ribeiro, M. and Santos, R., 2022. Multidimensional indicators to assess the sustainability of demersal small-scale fishery in the Azores. Sustainability, vol. 14, no. 24, pp. 16585. DOI: 10.3390/su142416585##Peterman, R., 2004. Possible solutions to some challenges facing fisheries scientists and managers. ICES Journal of Marine Science, vol. 61, pp. 1331-1343. DOI: 10.1016/j.icesjms.2004.08.017##Poisson, F., Budan, P., Coudray, S., Gilman, E., Kojima, T., Musyl, M.K. and Takagi, T., 2021. New technologies to improve bycatch mitigation in industrial tuna fisheries. Fish and Fisheries, vol. 23, no. 3, pp. 545-563. DOI: 10.1111/faf.12631##Prem Monickaraj, V., Rani Devakadacham, S., Shanmugam, N., Nandhakumar, N., Alagarsamy, M. and Suriyan, K., 2024. Deep learning and machine learning classification technique for integrated forecasting. IAES International Journal of Artificial Intelligence (IJ-AI), vol. 13, no. 2, pp. 1519-1525. DOI: 10.11591/ijai.v13.i2.pp1519-1525##Que, Y., Xie, J., Xu, J., Li, W., Wang, E. and Zhu, B., 2021. Ecological influences of water-level fluctuation on food web network. Water, vol. 13, no. 17, pp. 2371. DOI: 10.3390/w13172371##Rassweiler, A., Costello, C. and Siegel, D.A., 2012. Marine protected areas and the value of spatially optimized fishery management. Proceedings of the National Academy of Sciences, vol. 109, no. 29, pp. 11884-11889. DOI: 10.1073/pnas.1116193109##Reis‐Filho, J.A. and Giarrizzo, T., 2021. Perspectives on the use of unmanned aerial vehicle systems as tools for small‐scale fisheries research and management. Fisheries, vol. 47, no. 2, pp. 78-89. DOI: 10.1002/fsh.10705##Rodríguez-López, L., Bustos Usta, D., Bravo Alvarez, L., Duran-Llacer, I., Lami, A., Martínez-Retureta, R. and Urrutia, R., 2023. Machine learning algorithms for the estimation of water quality parameters in Lake Llanquihue in southern Chile. Water, vol. 15, no. 11, pp. 1994. DOI: 10.3390/w15111994##Roux, M., Tallman, R. and Martin, Z., 2018. Small-scale fisheries in Canadas Arctic: Combining science and fishers knowledge towards sustainable management. Marine Policy. DOI: 10.1016/j.marpol.2018.11.031##Rufener, M., Kristensen, K., Nielsen, J.R. and Bastardie, F., 2021. Bridging the gap between commercial fisheries and survey data to model the spatiotemporal dynamics of marine species. Ecological Applications, vol. 31, no. 8. DOI: 10.1002/eap.2423##Rumagia, F., Boer, M., Kurnia, R. and Kamal, M.M., 2023. Sustainability development index of reef fisheries in Indonesia: a case study of reef fisheries at Ternate Island, North Maluku Province. Omni-Akuatika, vol. 19, no. 1, pp. 15. DOI: 10.20884/1.oa.2023.19.1.1118##Russo, T., Parisi, A., Garofalo, G., Gristina, M., Cataudella, S. and Fiorentino, F., 2014. SMART: A spatially explicit bio-economic model for assessing and managing demersal fisheries, with an application to Italian trawlers in the Strait of Sicily. PLoS ONE, vol. 9, no. 1, pp. e86222. DOI: 10.1371/journal.pone.0086222##Salako, J., Ojo, F. and Awe, O.O., 2024. Fish-NET: Advancing Aquaculture Management through AI-Enhanced Fish Monitoring and Tracking. Agris On-Line Papers in Economics and Informatics, vol. 16, no. 2. DOI: 10.7160/aol.2024.160209##Saleh, A., Sheaves, M. and Azghadi, M.R., 2022. Computer vision and deep learning for fish classification in underwater habitats: a survey. Fish and Fisheries, vol. 23, no. 4, pp. 977-999. DOI: 10.1111/faf.12673##Samy-Kamal, M., 2022. Insights on illegal, unreported and unregulated (IUU) fishing activities by Egyptian vessels in neighbouring countries. Fishes, vol. 7, no. 5, pp. 288. DOI: 10.3390/fishes7050288##Sarr, J.M.A., Brochier, T., Brehmer, P., Perrot, Y., Bah, A., Sarré, A. and Ayoub, S.E., 2020. Complex data labeling with deep learning methods: lessons from fisheries acoustics. DOI: 10.1016/j.isatra.2020.09.018##Scandol, J.P., Montgomery, S. and Suthers, I.M., 2009. Modelling the possible effects of climate change on an Australian multi-fleet prawn fishery. Marine and Freshwater Research, vol. 60, no. 12, pp. 1211. DOI: 10.1071/mf08224##Segi, S., 2014. "losing at sea, winning on land": a case study of Philippine small-scale and industrial fisher resource competition. Society and Natural Resources, vol. 27, no. 12, pp. 1227-1241. DOI: 10.1080/08941920.2014.918225##Sherman, C.S., Simpfendorfer, C.A., Pacoureau, N., Matsushiba, J.H., Yan, H.F., Walls, R.H. and Dulvy, N.K., 2023. Half a century of rising extinction risk of coral reef sharks and rays. Nature Communications, vol. 14, no. 1, pp. 15. DOI: 10.1038/s41467-022-35091-x##Signaroli, M., Lana, A., Martorell-Barceló, M., Sanllehi, J., Barceló-Serra, M., Aspillaga, E. and Alós, J., 2022. Measuring inter-individual differences in behavioural types of gilthead seabreams in the laboratory using deep learning. PeerJ, vol. 10, pp. e13396. DOI: 10.7717/peerj.13396##Silva, G.G., Metzker, R.F., Gomes, M.R., Peixoto, L.A. and Sampaio, I., 2022. Deep learning for fish species identification: A review. Ecological Informatics, vol. 68, pp. 101530. DOI: 10.1016/j.ecoinf.2022.101530##Silvano, R.A.M. and Valbo‐Jørgensen, J., 2008. Beyond fishermens tales: contributions of fishers local ecological knowledge to fish ecology and fisheries management. Environment, Development and Sustainability, vol. 10, no. 5, pp. 657-675. DOI: 10.1007/s10668-008-9149-0##Skelly, B., Clipp, H.L., Landry, S.M., Rogers, R., Phelps, Q.E., Anderson, J.T. and Rota, C.T., 2023. A flexible Bayesian approach for estimating survival probabilities from age‐at‐harvest data. Methods in Ecology and Evolution, vol. 14, no. 4, pp. 1061-1073. DOI: 10.1111/2041-210x.14043##Sohns, A., Hickey, G. and Temby, O., 2022. Exploring the potential impacts of machine learning on trust in fishery management. Fish and Fisheries, vol. 23, no. 4, pp. 1016-1023. DOI: 10.1111/faf.12677##Son, S. and Jeong, Y., 2024. An automated fish-feeding system based on CNN and GRU neural networks. Sustainability, vol. 16, no. 9, pp. 3675. DOI: 10.3390/su16093675##Spijkers, J., Mackay, M., Turner, J., McNeill, A., Travaille, K.L. and Wilcox, C., 2022. Diversity of global fisheries governance: types and contexts. Fish and Fisheries, vol. 24, no. 1, pp. 111-125. DOI: 10.1111/faf.12719##Steenbergen, D.J., Song, A.M. and Andrew, N., 2022. A theory of scaling for community-based fisheries management. Ambio, vol. 51, no. 3, pp. 666-677. DOI: 10.1007/s13280-021-01564-4##Stephenson, R.L., Paul, S., Wiber, M.G., Angel, E., Benson, A.J., Charles, A. and Sumaila, U.R., 2018. Evaluating and implementing social–ecological systems: a comprehensive approach to sustainable fisheries. Fish and Fisheries, vol. 19, no. 5, pp. 853-873. DOI: 10.1111/faf.12296##Storbeck, F. and Daan, B., 2001. Fish species recognition using computer vision and a neural network. Fisheries Research, vol. 51, no. 1, pp. 11-15. DOI: 10.1016/s0165-7836(00)00254-x##Strachan, N.J.C., Nesvadba, P. and Allen, A.R., 1990. Fish species recognition by shape analysis of images. Pattern Recognition, vol. 23, no. 5, pp. 539-544. DOI: 10.1016/0031-3203(90)90074-u##Stroe, D., 2024. Age, growth, and mortality of Pontic shad, Alosa immaculata Bennett, 1835, in the Danube River, Romania. Fishes, vol. 9, no. 4, pp. 128. DOI: 10.3390/fishes9040128##Su, S.L., Tang, Y., Chang, B., Wei, Z. and Chen, Y., 2020. Evolution of marine fisheries management in China from 1949 to 2019: how did China get here and where does China go next? Fish and Fisheries, vol. 21, no. 2, pp. 435-452. DOI: 10.1111/faf.12442##Sultana, R., Li, Q., Schneider, P., Al-Mamun, M.A., Mamun, A., Tazim, M.F. and Shamsuzzaman, M.M., 2022. Stock assessment of six Sciaenidae species in the Bay of Bengal, Bangladesh water using a length-based Bayesian biomass (LBB) method. Fishes, vol. 7, no. 5, pp. 214. DOI: 10.3390/fishes7050214##Sunde, J., Pereira, T., Snow, B., Mbatha, P. and James, A., 2021. Unmasking governance failures: the impact of COVID-19 on small-scale fishing communities in South Africa. Marine Policy, vol. 133, pp. 104713. DOI: 10.1016/j.marpol.2021.104713##Syed, S., Borit, M. and Spruit, M., 2018. Narrow lenses for capturing the complexity of fisheries: a topic analysis of fisheries science from 1990 to 2016. Fish and Fisheries, vol. 19, no. 4, pp. 643-661. DOI: 10.1111/faf.12280##Syed, S. and Weber, C.T., 2018. Using machine learning to uncover latent research topics in fishery models. Reviews in Fisheries Science and Aquaculture, vol. 26, no. 3, pp. 319-336. DOI: 10.1080/23308249.2017.1416331##Tai, T.C., Cashion, T., Lam, V.W.Y., Swartz, W. and Sumaila, U.R., 2017. Ex-vessel fish price database: disaggregating prices for low-priced species from reduction fisheries. Frontiers in Marine Science, vol. 4. DOI: 10.3389/fmars.2017.00363##Thompson, K.A., Switzer, T.S., Christman, M.C., Keenan, S.F., Gardner, C.L., Overly, K.E. and Campbell, M.D., 2022. A novel habitat-based approach for combining indices of abundance from multiple fishery-independent video surveys. Fisheries Research, vol. 247, pp. 106178. DOI: 10.1016/j.fishres.2021.106178##Thomsen, P.F. and Willerslev, E., 2015. Environmental DNA–An emerging tool in conservation for monitoring past and present biodiversity. Biological Conservation, vol. 183, pp. 4-18. DOI: 10.1016/j.biocon.2014.11.019##Thoya, P., Maina, J., Möllmann, C. and Schiele, K.S., 2021. AIS and VMS ensemble can address data gaps on fisheries for marine spatial planning. Sustainability, vol. 13, no. 7, pp. 3769. DOI: 10.3390/su13073769##Tian, H., Yang, L., Tian, Y., Alabia, I.D., Qin, Y., Sun, H. and Saitoh, S., 2022. A comprehensive monitoring and assessment system for multiple fisheries resources in the northwest Pacific based on satellite remote sensing technology. Frontiers in Marine Science, vol. 9. DOI: 10.3389/fmars.2022.841064##Tissière, L., Mahévas, S. and Trouillet, B., 2020. Findings from an exploratory study on the governance of a French fishery. Marine Policy, vol. 117. DOI: 10.1016/j.marpol.2020.103831##Tsou, M., 2010. Discovering knowledge from AIS database for application in VTS. Journal of Navigation, vol. 63, no. 3, pp. 449-469. DOI: 10.1017/s0373463310000135##Tsou, M., 2014. Online analysis process on automatic identification system data warehouse for application in vessel traffic service. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, vol. 230, no. 1, pp. 199-215. DOI: 10.1177/1475090214543716##Tulloch, V., Grech, A., Jonsen, I.D., Pirotta, V. and Harcourt, R., 2019. Cost‐effective mitigation strategies to reduce bycatch threats to cetaceans identified using return‐on‐investment analysis. Conservation Biology, vol. 34, no. 1, pp. 168-179. DOI: 10.1111/cobi.13418##Wabnitz, C.C., Lam, V.W., Reygondeau, G., Teh, L.C., Al-Abdulrazzak, D., Khalfallah, M. and Cheung, W.W., 2018. Climate change impacts on marine biodiversity, fisheries and society in the Arabian Gulf. PloS One, vol. 13, no. 5, pp. e0194537. DOI: 10.1371/journal.pone.0194537##Wang, L., Li, L., Liu, Y., Zhai, L. and Shen, Y., 2022. Fishery dynamics, status, and rebuilding based on catch-only data in coastal waters of China. Frontiers in Marine Science, vol. 8. DOI: 10.3389/fmars.2021.711042##Ward, H.G.M., Askey, P.J., Weir, T.J., Frazer, K.K. and Russello, M.A., 2019. Genetic stock identification reveals that angler harvest is representative of cryptic stock proportions in a high‐profile kokanee fishery. North American Journal of Fisheries Management, vol. 39, no. 3, pp. 415-425. DOI: 10.1002/nafm.10280##Watson, J.T., Ames, R., Holycross, B., Suter, J., Somers, K., Kohler, C. and Corrigan, B., 2023. Fishery catch records support machine learning-based prediction of illegal fishing off US West Coast. PeerJ, vol. 11, pp. e16215. DOI: 10.7717/peerj.16215##Watt, C.A., Carlyle, C.G., Hornby, C. and Sherbo, B.A.H., 2023. Eastern high arctic–Baffin Bay beluga whale (Delphinapterus leucas) estuary abundance and use from space. Frontiers in Mammal Science, vol. 2. DOI: 10.3389/fmams.2023.1254987##White, D.J., Svellingen, C. and Strachan, N.J., 2006. Automated measurement of species and length of fish by computer vision. Fisheries Research, vol. 80, no. 2-3, pp. 203-210. DOI: 10.1016/j.fishres.2006.04.009##Whitmarsh, S.K., Fairweather, P.G. and Huveneers, C., 2017. What is Big BRUVver up to? Methods and uses of baited underwater video. Reviews in Fish Biology and Fisheries, vol. 27, pp. 53-73. DOI: 10.1007/s11160-016-9450-1##Wibowo, S., Hargiyatno, I.T., Faizah, R. and Natsir, M., 2023. The use of stereo cameras and artificial intelligence data to support capture fisheries landing data monitoring. IOP Conference Series: Earth and Environmental Science, vol. 1137, no. 1, pp. 012022. DOI: 10.1088/1755-1315/1137/1/012022##Wiggins, S.M., Manley, J., Brager, E. and Woolhiser, B., 2010. Monitoring marine mammal acoustics using wave glider. Oceans 2010 MTS/IEEE Seattle. DOI: 10.1109/oceans.2010.5664537##Wijermans, N., Boonstra, W.J., Orach, K., Hentati‐Sundberg, J. and Schlüter, M., 2020. Behavioural diversity in fishing—towards a next generation of fishery models. Fish and Fisheries, vol. 21, no. 5, pp. 872-890. DOI: 10.1111/faf.12466##Willette, D.A., Ababouch, L., Barber, P.H., Bunje, P.M., Cauzac, J.P., Conchon, A. and Trenkel, V.M., 2023. Emerging monitoring technologies to reduce illegal fishing activities at sea and prevent entry of fraudulent fish into markets. Frontiers in Sustainable Food Systems, vol. 7, pp. 1166131. DOI: 10.3389/fsufs.2023.1166131##Wilson, L., Constantine, R., van der Boon, T. and Radford, C.A., 2022. Using timelapse cameras and machine learning to enhance acoustic monitoring of small boat sound. Ecological Indicators, vol. 142, pp. 109182. DOI: 10.1016/j.ecolind.2022.109182##Woodill, A.J., Kavanaugh, M., Harte, M. and Watson, J.R., 2020. Predicting illegal fishing on the Patagonia Shelf from oceanographic seascapes. arXiv preprint arXiv:2007.05470. DOI: 10.48550/arXiv.2007.05470##Xie, M., Liu, B., Chen, X., Yu, W. and Wang, J., 2024. Deep learning-based fishing ground prediction using asymmetric spatiotemporal scales: a case study of Ommastrephes bartramii. Fishes, vol. 9, no. 2, pp. 64. DOI: 10.3390/fishes9020064##Xing, B., Zhang, L., Liu, Z., Sheng, H.Y., Bi, F. and Xu, J., 2023. The study of fishing vessel behavior identification based on AIS data: a case study of the East China Sea. Journal of Marine Science and Engineering, vol. 11, no. 5, pp. 1093. DOI: 10.3390/jmse11051093##Xu, X., Cantoni, E., Flemming, J.M. and Field, C., 2015. Robust state space models for estimating fish stock maturities. Canadian Journal of Statistics, vol. 43, no. 1, pp. 133-150. DOI: 10.1002/cjs.11242##Xu, Y., Zhang, P., Panhwar, S.K., Li, J., Yan, L., Chen, Z. and Zhang, K., 2023. The initial assessment of an important pelagic fish, mackerel scad, in the South China Sea using data‐poor length‐based methods. Marine and Coastal Fisheries, vol. 15, no. 5. DOI: 10.1002/mcf2.10270##Yousefi, M., Jouladeh‐Roudbar, A. and Kafash, A., 2024. Mapping endemic freshwater fish richness to identify high‐priority areas for conservation: An ecoregion approach. Ecology and Evolution, vol. 14, no. 2, pp. e10970. DOI: 10.1002/ece3.10970##Zarauz, L., Irigoien, X. and Fernandes, J.A., 2009. Changes in plankton size structure and composition, during the generation of a phytoplankton bloom, in the central Cantabrian Sea. Journal of Plankton Research, vol. 31, no. 1, pp. 1-17. DOI: 10.1093/plankt/fbn100##Zarkami, R., Bahri, P., Fazli, H., Vayghan, A.H. and Pasvisheh, R.S., 2023. Use of data-driven models to analyse the habitat preferences of the leaping grey mullet (Chelon saliens, Risso, 1810) in the Caspian Sea. Regional Studies in Marine Science, vol. 65, pp. 103078. DOI: 10.1016/j.rsma.2023.103078##Zhang, T., 2024. Predicting the fishery ground of jumbo flying squid (Dosidicus gigas) off Peru by extracting features of the ocean environment. Fishes, vol. 9, no. 3, pp. 81. DOI: 10.3390/fishes9030081##Zhang, Y., Yamamoto, M., Suzuki, G. and Shioya, H., 2022. Collaborative forecasting and analysis of fish catch in Hokkaido from multiple scales by using neural network and ARIMA model. IEEE Access, vol. 10, pp. 7823-7833. DOI: 10.1109/access.2022.3144061##Zheng, S. and Zhang, Y., 2024. Analyzing the evolutionary game of subsidies strategy in the digitization of marine ranch: a theoretical framework. Frontiers in Marine Science, vol. 11. DOI: 10.3389/fmars.2024.1318843## ##

مقاله علمی – پژوهشی:‌ تحلیل شاخص‌های اقتصادی مزارع پرورش ماهی قزل‌آلا (مورد مطالعه: استان همدان) Analysis of economic indicators of trout farm fisheries (Case study: Hamedan Province) 1 1 با توجه به شرایط اقلیمی و بسترهای مناسب همچون وجود رودخانه‌ها و چشمه‌سارهای فراوان در استان همدان، پرورش ماهی می‌تواند جایگاه مهمی در تأمین نیازهای منطقه داشته باشد. ماهی نقش به‌سزایی در تغذیه و سلامت انسان دارد و بخش عمده‌ای از پروتئین حیوانی را تأمین می‌سازد. تحقیق حاضر با هدف تحلیل شاخص‌های اقتصادی مزارع پرورش ماهی قزل آلا در استان همدان انجام شده است. بدین منظور، مزارع تولیدکننده ماهی قزل‏آلا با روش جورسازی به دوگروه تیمار و کنترل تفکیک و داده‏های لازم با استفاده از پرسشنامه در سال 1402 جمع‏آوری شده است. داده‏ها با رویکردی تلفیقی از تحلیل پوششی داده‏ها و شاخص‏های کارایی و اقتصادی و استفاده از نرم‌افزارهای DEAP و SPSS تجزیه‌وتحلیل شد. نتایج این پژوهش نشان داد که به طور متوسط بهره‏وری عوامل تولید در گروه تیمار 6/5 برابر گروه کنترل بوده و میانگین شکاف بین گروه‌های تیمار و کنترل از نظر کارآیی فنی، تخصیصی و اقتصادی به‌ترتیب 1/8، 2/17 و 5/21 درصد است. همچنین نرخ بازدهی داخلی در گروه تیمار 8/33 درصد و نسبت منفعت به هزینه 8/1 است. این دو شاخص در مزارع کنترل به‌‌ترتیب 8/18 درصد و 2/1 است. بنابراین، با استفاده از توصیه‌های تحقیقاتی و ترویج یافته‏های پژوهشی مراکز تحقیقاتی می‏توان این شکاف را تاحد ممکن کاهش داد و منجر به افزایش بهره‏وری عوامل تولید شد. 2 Introduction In Southwest Asia and Iran, the fisheries industry is an important sub-sector of agriculture because it provides a portion of the valuable protein for society, employment, and national income. The growth of the aquaculture industry in the world is 4.5% and it is 7% (Agricultural Research, Education and Extension Organization, 2023) in Iran. In addition to providing food for the growing population and creating jobs and income for farmers, this can also play an important role in earning foreign exchange for the country. Aquaculture in the country includes warm-water fish farms (carp), cold-water fish farms (trout), and shrimp farms. According to statistics published by the Iranian Veterinary Organization (2023), Iran ranks first in the world in the production of cold-water fish in freshwater with a production of 237 thousand tons (Iranian Veterinary Organization, 2024). In the west of the country, and especially in Hamedan Province, due to its geographical location, the presence of natural canals, seasonal and permanent rivers, and springs, it is possible to grow and develop trout farms and ponds, which are nutritionally the best type of protein for human consumption. The trout farmers in Hamedan Province produced more than 5,237,000 tons of trout in the year (2021-2022) and sold them fresh in markets inside and outside the province (Census of Jihad Agriculture, 2022-2021). Given the special situation of Hamedan Province in the field of fish production, a comprehensive study has not yet been conducted on the analysis of the economic indicators of trout farms in this province. Therefore, the above study aimed to investigate cold-water fish producing farms that used researchers' recommendations in their management and production methods based on the results of research projects as the treatment group and units that were managed traditionally and without using research recommendations in their management and production as the control group. Methodology This research is a descriptive-analytical study that is applied in terms of purpose and is considered a survey in terms of data collection. The statistical population of the research includes cold-water fish producers (trout) in Hamedan province. The sample size of 49 operators was calculated using the Cochran formula and was systematically selected from the list of farms in the province using simple random sampling. The required data was collected and completed through a researcher-made questionnaire in 2023, including 5 sections: fish farm manager's characteristics (11 questions); fish farm characteristics (30 questions); technical aspects of production (35 questions); fixed investment costs (12 questions) and current costs (11 questions), and by visiting the Fisheries Department and the Agricultural Jihad Organization of the province in person. The opinions of agricultural and fisheries economics experts were used to examine the validity of the questionnaire, and the Cronbach's alpha test was used to examine the reliability of the questions. Using the matching method, the statistical population was first divided into two groups: the treatment group (units in which the research results were used) and the control group (units that did not use the research results or used them less), and then the two groups were compared using efficiency indicators, internal rate of return, benefit-to-cost ratio, payback period, and production function. Results The results of examining the balance of auxiliary variables showed that by performing the matching operation to calculate the effects of the research results on the treatment group, the difference between the means of the two groups decreased. To compare the two treatment and control groups and the effect of the factors of fry, labor, capital, food, and pond area on their production rate, two transcendental and Cobb-Douglas production functions were estimated simultaneously and these two specifications were compared using the F test. According to this function, the most important factor in creating differences between units is the variable of pond area. Among the variables used, in addition to the pond area, food, fry and labor also showed a significant effect on production. The results of the Ramsey reset test indicated that there was no specification bias due to the removal of the important variable. In the treatment unit, regarding the pool area variable, considering the logarithmic specification, it can be said that with a 10% increase in the pool area, it is expected that about 13.1% will be added to production. In terms of the contribution to creating a difference in the production level of the units, the pool area variable is not only significant in terms of the absolute value of the coefficient, but also has a very high difference, so that the other four significant variables, including the fry, food, and labor variables, with a 10% increase, can increase production by 11.9, 9 and 12.5%, respectively, which is very different from the food variable. In the control unit, with a 10% increase in the values of the variables under study, the production rate increases by 5.6% less than the values mentioned in the treatment group. Based on the results, the efficiency of the units under study, including technical, allocative, and economic efficiency, has been examined under two assumptions of constant and variable returns to scale in two treatment and control groups. The results showed that the average technical efficiency of trout farms in the treatment group under CRS conditions is 90.5 percent. While the average technical efficiency in the same group is 91.4 percent. The average scale efficiency of trout farms in the treatment group is 99.02 percent. In the control group, the efficiency under CRS and VRS is 82.2 and 83.3 percent, respectively, and the scale efficiency is 98.7 percent. Technical efficiency in the treatment group is much better than the control group. The use of research recommendations has increased technical efficiency by 8.3 percent under CRS and 8.1 percent under VRS. The average allocative efficiency in the treatment group was estimated to be 86.8 percent under CRS and 87.6 percent under VRS. The allocative efficiency in the control group is 75.3 percent under CRS and 70.4 percent under VRS. The use of research recommendations has increased allocative efficiency by 11.5 percent assuming CRS and 17.2 percent assuming VRS. The average economic efficiency of the fish farming treatment group farms under CRS conditions is 78.6 percent and 80.1 percent under VRS conditions. The economic efficiency in the control group under CRS conditions is 61.9 percent and 58.6 percent under VRS conditions. Considering the technology available in fish farming farms, it is possible to increase profits in control units by 16.7 percent assuming CRS and 21.5 percent assuming VRS. Therefore, by improving the economic efficiency of fish farming in the control group, it is possible to increase product production and profitability of the units. Considering a discount rate of 16%, the internal rate of return and the benefit-cost ratio in the treatment group are 33.8% and 1.8%, respectively. These two indicators in the control group are 18.8% and 1.2%, respectively. The payback period in the treatment and control groups is 4.8 and 7.6 years, respectively. Therefore, it can be said that the investment in both groups is economically justified, while in the treatment group, it is in a better position in this respect. Discussion and Conclusion Based on the results, the application of research recommendations in the trout farming industry will increase the efficiency and effectiveness of this industry. The results of the studies of Rahman et al. (2019); Samat et al. (2024) and Duy et al. (2023) confirm this. Also, the average productivity of production factors in the treatment group (units that used research results in their activities) was 5.9 times higher than the control group, which is in line with the study by Najafi et al. (2018) and Vormedal (2024) that the use of research findings plays an effective role in increasing productivity. It is also necessary to make the activities of production units more competitive by implementing government economic adjustment policies, reducing production subsidies, further convergence of international markets, and increasing the competitive power of units. Therefore, the area of the pond has a significant impact on profitability. Therefore, the results of the research are consistent with the results of the study by Yarahmadi et al. (2022) and Akter et al. (2024), as they showed in their results that the amount of feed consumed and the area of the fish pond had the greatest impact on trout production. In addition to the above, the results showed that the internal rate of return in the treatment group was 33.8 percent and the benefit-to-cost ratio was 1.8. These two indicators in the control group farms were 18.8 percent and 1.2 percent, respectively. The results of Duy et al. (2023) also confirm this issue and stated that training and promotion of research findings had a positive effect on the efficiency and effectiveness of the firms receiving these findings and reduced production costs in these firms. Therefore, by using research recommendations and promoting applied research findings, this gap can be reduced as much as possible and lead to increased efficiency of these types of activities. Conflict of Interest The authors declare no conflict of interest. Acknowledgment The authors acknowledge the support provided by the Agricultural Research, Extension and Education Organization. 37 49 2024/11/192024/10/3 1403/7/12 2025/04/302025/04/30 1404/2/10 مهسا معتقد Mahsa Motaghed گروه برنامه‌ریزی و توسعه کشاورزی، موسسه آموزش و ترویج کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران Iran mahsa.motaghed@ut.ac.ir امیر دادرس مقدم Amir Dadras Moghadam دانشکده مدیریت و اقتصاد، دانشگاه سیستان و بلوچستان، سیستان و بلوچستان، ایران Iran amdadras@gmail.com سید محسن سیدان Seyed mohsen Seyedan سازمان تحقیقات، آموزش و ترویج کشاورزی Iran seyedan1969@gmail.com Trout Data Envelopment Analysis sorting internal rate of return (IRR) benefit-cost ratio. ماهی قزل‌آلا تحلیل پوششی داده‌‌ها جورسازی نرخ بازدهی داخلی نسبت منفعت به هزینه. Akter, M., Schrama, J. W., Adhikary, U., Alam, M. S., Mamun-Ur-Rashid, M., and Verdegem, M. 2024. Effect of pellet-size on fish growth, feeding behaviour and natural food web in pond polyculture. Aquaculture, 593, 741342. DOI: https://doi.org/10.1016/j.aquaculture.2024.741342.##Alikhani, L., Dashti, G., & Raheli, H. 2015. Technical efficiency and production risk of cold-water fish farms in the Kamyaran County. Journal of Animal Science Research, 25(2), 1-12.‌ (in Persian).##Asadikia, H., Mosavi, S. H., Khalilian, S., and Najafi Alamdarlo, H. 2021. Comparison of technical and economic efficiency of trout farms production from imported and domestic egg. Aquaculture Sciences, 9(1), 35-47.‌ (in Persian).##Becker, s., and Ichino, A. 2002. Estimation of Average Treatment Effects Based on Propensity Scores. The Stata Journal, 2, 358–377. DOI: https://doi.org/10.1177/1536867X020020040##Census of Jihad Agriculture. 2022-2021. Organization of Jihad Agriculture, Hamadan Province, Hamadan Province. (in Persian)##Chary, K., van Riel, A. J., Muscat, A., Wilfart, A., Harchaoui, S., Verdegem, M., ... and Wiegertjes, G. F. 2024. Transforming sustainable aquaculture by applying circularity principles. Reviews in Aquaculture, 16(2), 656-673. DOI: https://doi.org/10.1111/raq.12860.##Coelli, T. J., Rao, D.S.P., O'Donnell, C.J., and Battese, G.E., 2005. An introduction to efficiency and productivity analysis. Springer Science & Business Media.##Dehejia, R. H., and Wahba, S. 2002. Propensity Score-Matching Methods for Nonexperimental Causal Studies. The Review of Economics and Statistics, 84, 151–161.##Duy, D. T., Nga, N. H., Berg, H., and Da, C. T., 2022. Assessment of technical, economic, and allocative efficiencies of shrimp farming in the Mekong Delta, Vietnam. Journal of the World Aquaculture Society. DOI:10.1111/jwas.12915##FAO. 2018. Fishery statistical collections: Consumption of fish and fishery products. Retrived from: www.fao.org/fishery/ statistics/global-consumption/e.##Farash, Z., Gholamrezaei, C., Ghanbari Mocahed, R., 2017. Investigating the role and importance of sustainable aquaculture in the development of food resources, 11th Congress of the pioneers of progress##Farrell, M. J., 1957. The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A, 120 (3), 253-281. DOI: https://doi.org/10.2307/2343100##Hashemi, dashtaki, S. F., Yazdani, S., and Rafiei, H., 2021. Measuring the technical, allocative and economic efficiency of salmon farming using the data envelopment analysis approach (case study; Fars province), Fifth International Congress on Agricultural and Environmental Development with emphasis on the United Nations Development Program##Iran Statistics Center. 2021. Statistics of the Ministry of Agricultural Jihad, Volume 2, Deputy Planning Department of the Ministry of Agricultural Jihad. (in persian).##Kompas, T., and Che, T.N. 2004. Production and Technical Efficiency on Australian Dairy Farms. International and Development Economics, 4, 57-77.##Li, K., and Prabhala, N. R., 2006. Self-Selection Models in Corporate Finance. Working Paper, Center for Corporate Governance – Tuck School of Business at Dartmouth. DOI:http://dx.doi.org/10.2139/ssrn.843105##Najafi, A., Zeraatkish, S., Mataei, Z., and Gharra, K., 2018. The measurement and total factors productivity analysis of cold-water fish production in Kermanshah province farms; 27 (4) :1-11. URL: http://isfj.ir/article-1-2048-fa.html (in persian)##Oluwatayo, I. B., and Adedeji, T. A. 2019. Comparative analysis of technical efficiency of catfish farms using different technologies in Lagos State, Nigeria: A Data Envelopment Analysis (DEA) approach. Agriculture & Food Security, 8(1), 1-9. DOI: https://doi.org/10.1186/s40066-019-0252-2.##Rahman, M. T., Nielsen, R., Khan, M. A., and Asmild, M. 2019. Efficiency and production environmental heterogeneity in aquaculture: A meta-frontier DEA approach. Aquaculture, 509: 140-148. DOI: https://doi.org/10.1016/j.aquaculture.2019.05.002##Rosenbaum, P., 1995. Observational Studies, New York. Springer V erlag.##Samat, N., Goh, K. H., and See, K. F. 2024. Review of the application of cost–benefit analysis to the development of production systems in aquaculture. Aquaculture, 740816. DOI: https://doi.org/10.1016/j.aquaculture.2024.740816 ·##Statistics fishers Hamedan province. 2022-2021.Management of Fisheries, Jihad Agriculture, Hamadan Province. (in Persian).##Thach, K. S. R., Vo, H. T., and Lee, J. Y., 2021. Technical Efficiency and Output Losses in Shrimp Farming: A Case in Mekong Delta, Vietnam. Fishes, 6(4), 59. DOI: https://doi.org/10.3390/fishes6040059##Vormedal, I., 2024. Sea-lice regulation in salmon-farming countries: how science shape policies for protecting wild salmon. Aquaculture International, 32(3): 2279-2295. DOI: https://doi.org/10.1007/s10499-023-01270-w##Yarahmadi, B., mohamadi Saei, M., Pahlevani, R., and Salehi, M., 2022. Efficiency determination of the trout farm units using a deterministic parametric frontier (DPF) analysis method in Lorestan province (case study of Alshtar city). 15 (4) :127-140. DOI: 10.52547/aqudev.15.4.127## ##

مقاله علمی – پژوهشی:‌ اثرات جامدات معلق و رقیق‌سازی ملاس بر رشد، زیست‌توده و پالایش زیستی ریزجلبک سبز Scenedesmus quadricauda Effects of suspended solids and dilution of molasses on growth, biomass and biorefinery of the green microalga Scenedesmus quadricauda 1 1 در این پژوهش اثرات جامدات معلق و رقیق سازی ملاس بر ریزجلبک Scenedesmus quadricauda با هدف تعیین تراکم سلولی، رشد و زیست‌توده جلبکی و ارزیابی پالایش زیستی انجام شد. آزمایش با ۷ تیمار شامل؛ تیمارهای پساب ملاس دارای جامدات معلق در غلظت‌های ۱، ۵ و ۱۰ درصد (حجم/حجم) و پساب ملاس بدون جامدات معلق ۱، ۵ و ۱۰ درصد (حجم/حجم) و تیمار شاهد در محیط کشت BBM برای یک دوره ۱۴ روزه در قالب طرح کاملاً تصادفی در ۳ تکرار انجام شد. ملاس در تمام تیمارها باعث رشد جلبک S. quadricauda گردید و تراکم جلبکی و رشد آن به‌ترتیب دامنه‌ای از 106×۴5/1 -105×25/۷ سلول بر میلی‌لیتر و 10۴/0 – 0۵۴/0 در روز به‌دست آمد. بیشترین مقدار زیست‌توده تولیدی ۷/۶۳۴۳ میلی‌گرم بر لیتر در تیمار ملاس ۱۰ درصد بدون جامدات معلق به‌دست آمد و در سایر تیمارها دامنه‌ای از ۴۹۶۷ - ۱/۲۱۹۹ میلی‌گرم بر لیتر داشت. در غلظت‌های کم (۱ و ۵ درصد) تأثیر جامدات معلق بر زیست‌توده جلبک قابل‌ملاحظه نبود، اما در تیمار با غلظت ۱۰ درصد و بدون جامدات معلق زیست‌توده بیشتری به‌‌دست آمد (p<0/05). حذف نیترات و فسفات به طور معنی‌داری در تمام تیمارها انجام شد (p<0/05). بیشترین درصد حذف نیترات در تیمار ملاس ۱۰ درصد بدون جامدات معلق (۵۸ درصد) و بیشترین درصد حذف فسفات در تیمار ملاس ۱۰ درصد (۴۸ درصد) به‌‌دست آمد. کاهش BOD و COD در تمام تیمارها قابل ملاحظه بود و برای BOD دامنه‌ای از ۱۹/۹۹- ۳۷/۹۲ درصد و برای COD دامنه‌ای از ۴۶/۹۹-۰۵/۹۴ بود. بیشترین حذف BOD5 (۱۹/99 درصد) در تیمار ملاس 5 درصد و بیشترین درصد حذف COD (4۶/99 درصد) مربوط به تیمار پساب ملاس ۱۰ درصد هر دو تیمار بدون جامدات معلق به‌‌دست آمد. 2 Introduction Algae are the most important photosynthetic groups in aquatic environments, as they are carbon and nitrogen fixers and considered as primary producers (Sigee, 2005; Falkowski and Raven, 2013; Fowler et al., 2013). The growth of algae in wastewater are common phenomenon that has a crucial role in removing minerals and metabolic byproducts. The wastewater from sugar production contains high levels of carbon, nitrogen, and phosphorus, along with high BOD and COD. Molasses is a byproduct of sugar factories and is primarily used as a raw material for yeast production. It also has considerable commercial value in various fermentation processes, animal feed, and biofertilizers (Dahiya et al., 2001; Torabian and Mahjuri, 2004; Kobya and Delipinar, 2008). Molasses contains 45-50% carbohydrates, 15-20% non-aromatic organic compounds, 10-15% ash (minerals), and about 20% water (Kalyuzhnyi and Murray, 2005). Molasses wastewater includes chemicals such as propionic acid, various salts, and fermentation metabolites (Blonskaja and Zub, 2009). In addition to molasses concentration and the stated conditions, which greatly impact the growth of microscopic algae, the role of suspended solids in molasses as another influential factor in algal growth and reproduction can be investigated. The negative effects of suspended solids, particularly at high concentrations are crucial due to their reduction in light intensity. Furthermore, suspended solids can settle on algae surfaces, hindering gas exchange (e.g., oxygen and carbon dioxide), which negatively affects photosynthesis efficiency (Boyd, 2020). On the other hand, the positive effects of suspended solids include stimulating the production of certain secondary metabolites through stress induction in algal cells (Huang et al., 2024). This study aims to investigate the role of suspended solids and molasses dilution in the growth and biomass production of the green microalga Scenedesmus quadricauda and its potential for bioremediation. Understanding suspended solids and molasses dilution levels can contribute to the management of algal cultures in terms of optimizing light intensity, turbidity, culture system design, and the production of valuable secondary metabolites. Methodology Molasses wastewater was collected from the Eqlid Sugar Factory, located in Fars province, Iran. The factory processes sugar beets grown by 800 local farmers. The wastewater sample (10 liters) was collected before entering the treatment section of the factory in November 2023. The green microalga S. quadricauda was cultured in raw pre-treated wastewater, diluted to target concentrations of 1%, 5%, and 10%, under two conditions: with suspended solids and without suspended solids (Table 1) for 14 days. Seven experimental treatments were prepared, including BBM medium (control) and molasses wastewater with and without suspended solids at three dilution levels: 1% (10 ml/L) 5% (50 ml/L) 10% (100 ml/L). These solutions were added to 5-liter glass Erlenmeyer flasks. The initial pH of all cultures was adjusted to 6.8 using concentrated NaOH and HCl solutions. Before introducing the algal stock, all samples were autoclaved at 121°C for 15 minutes. After cooling, 5% (v/v) of the initial algal stock containing 2 × 10⁶ cells/mL was added. The cultures were incubated under appropriate light conditions provided by fluorescent lamps (60 µmol photons/m²/s) with a 12-hour light/12-hour dark photoperiod and gentle aeration. The water temperature in all treatments was kept constant at 25 ± 2°C. The experiment followed a completely randomized design with three replicates over 14 days. Daily cell counting of S. quadricauda was performed using a hemocytometer (depth: 0.1 mm, area: 0.0025 mm²), following the method proposed by Martinez et al. (2000). The specific growth rate was calculated using the formula by Omori and Ikeda (1984). To measure biomass, 100 mL of the algal culture was filtered using pre-weighed membrane filter papers (0.45 µm). The filtered samples were dried in an oven at 80°C for 4 hours. Dry Biomass Measurement and Analytical Methods After drying the algal biomass, it was placed in a desiccator to reach equilibrium with the laboratory environment. The dry weight was then measured, and the difference in weight was used to calculate the dry biomass of the algae (Omori and Ikeda, 1984). Nitrate concentrations were measured using a spectrophotometric colorimetric method at 275 nm and 220 nm, using a UV-VIS spectrophotometer (Nanombana UVISNM98 UV-VIS). Phosphate concentration was determined using spectrophotometry at 880 nm, with a JENWAY 6300 spectrophotometer (Baird et al., 2017). The five-day biochemical oxygen demand (BOD₅) was assessed by adding a specific amount of wastewater to a dilution water solution in 300 mL dark Winkler bottles. The dissolved oxygen (DO) content was measured at the start and after five days of incubation at 20°C, using an oxygen meter. The chemical oxygen demand (COD) was determined using potassium dichromate and silver sulfate reagents, with digestion in a COD reactor for 2 hours, followed by absorbance reading at 600 nm using a spectrophotometer (Baird et al., 2017). This study was conducted using a completely randomized design (CRD) with different treatments (Table 1), each with three replicates. One-way ANOVA was used to determine significant statistical differences, and Duncan’s test and Student’s t-test were performed for mean comparisons. All statistical analyses were conducted using SPSS software, and the graphs were generated using Excel. Results The total suspended solids (TSS) and dissolved solids (TDS) in the raw molasses wastewater were measured at 7697.2 mg/L and 2540.8 mg/L, respectively. The nitrate and phosphate concentrations were 1595.24 mg/L and 12.73 mg/L, respectively. The BOD₅ and COD values of molasses wastewater were 42,790 mg/L and 136,156 mg/L, respectively. The pH was 5.92, and the electrical conductivity (EC) was 3.97 mS/cm. The cell density of S. quadricauda in different treatments, including the control (BBM), molasses wastewater without suspended solids (1%, 5%, 10%), and molasses wastewater with suspended solids (1%, 5%, 10%), was: 1.03 × 10⁶, 1.22 × 10⁶, 1.45 × 10⁶, 7.25 × 10⁵, 1.0 × 10⁶, 9.85 × 10⁵, and 1.05 × 10⁶ cells/mL, respectively. These values correspond to days 14, 14, 14, 14, 11, 14, and 14 of the cultivation period. The highest cell density at the end of the experiment (day 14) was observed in the 1% molasses wastewater with suspended solids treatment. Overall, 1% and 5% molasses wastewater with suspended solids and 1% molasses wastewater without suspended solids showed higher cell densities than the control (BBM). The specific growth rate at the end of day 14 ranged from 0.064 – 0.104 per day, with the highest growth in the 1% molasses wastewater with suspended solids and the lowest in the 10% molasses wastewater with suspended solids. The biomass concentration ranged from 2199.1 – 6343.7 mg/L, with the highest value in the 10% molasses wastewater without suspended solids and the lowest in the 1% molasses wastewater with suspended solids. At low concentrations (1% and 5%), suspended solids had no significant effect on biomass. However, at 10% concentration, the treatment without suspended solids resulted in significantly higher biomass production (p < 0.05). The phosphate concentration decreased from 278.3 – 250.4 mg/L to 206.35 – 149.4 mg/L (Figure 2B). Nitrate and phosphate removal was significant in all treatments (p < 0.05). The highest nitrate removal (58%) and phosphate removal (46%) were observed in the 10% molasses wastewater without suspended solids (Figure 2C). The BOD₅ removal in molasses wastewater treatments ranged from 92.37 – 99.19%. The highest BOD value after cultivation was observed in the 5% molasses wastewater with suspended solids, and the lowest in the 5% molasses wastewater without suspended solids and 1% molasses wastewater with suspended solids. The highest BOD₅ removal (99.19%) was found in the 5% molasses wastewater without suspended solids, while the lowest (92.37%) occurred in the 1% molasses wastewater without suspended solids. The COD removal ranged from 94.05 – 99.46%, with the highest COD after cultivation found in the 10% molasses wastewater, and the lowest in the 1% molasses wastewater without suspended solids. Discussion and conclusion After the 14-day cultivation period, the treatments with 1% and 5% molasses containing suspended solids and 1% molasses without suspended solids exhibited higher cell densities compared to the control treatment, whereas other treatments had lower cell densities than the control. Similar to the present study, Farhadian et al. (2022) reported that the highest cell density of the marine microalga Tetraselmis tetrahele was observed in 1% molasses and concluded that the biomass production of T. tetrahele in 1% molasses was higher than in 0.5% diluted molasses. They also found that biomass production in molasses treatments was higher compared to other culture media. The results of this study under mixotrophic conditions showed that the highest biomass production occurred in the 10% molasses treatment without suspended solids. Biomass production showed an increasing trend with increasing molasses concentration from 1% to 10% in treatments without suspended solids. Moreover, the biomass produced in the 1% and 5% molasses treatments was almost equal and higher than in the control treatment. However, in the 10% molasses treatment with suspended solids, a noticeable reduction in biomass production was observed, which could be attributed to reduced light penetration in the culture medium. Suspended solids can limit light penetration into the water column, reducing photosynthesis and subsequently decreasing algal biomass. Suspended solids in water can pose serious challenges to aquatic ecosystems. These particles, often introduced through human activities such as agriculture and industrial pollution, can reduce water clarity, hinder the respiration of aquatic organisms, and even cause direct harm. Studies have shown that suspended solids not only affect water quality but also impact microscopic organisms in aquatic environments. These particles can cover the gills of fish and other aquatic organisms, making respiration difficult. Additionally, they can absorb sunlight, limiting photosynthesis in aquatic plants and disrupting the food chain (Bilotta and Brazier, 2008). In this study, all treatments involving S. quadricauda resulted in nitrate and phosphate uptake. The highest nitrate removal percentage (58%) was observed in the 10% molasses treatment without suspended solids, while the highest phosphate removal percentage (48%) was recorded in the 10% molasses treatment. Heydari et al. (2011) found that S. quadricauda grows well in nitrogen-rich environments, making it suitable for treating nitrogen-enriched wastewater due to its high growth rate and survival. The process of nitrate and phosphate removal by Scenedesmus microalgae has been reported in multiple studies (Oswald and Gotass, 1995; Martinez et al., 2000; Voltolina et al., 2004; Wang and Lan, 2011; Arora et al., 2021). Microalgae utilize nitrogen and phosphorus from wastewater to synthesize energy-storing molecules such as adenosine triphosphate (ATP) and adenosine diphosphate (ADP), as well as genetic material. Additionally, inorganic phosphate forms such as orthophosphate, HPO₄²⁻, and H₂PO₄⁻ are preferred by microalgal cells, which absorb them via phosphorus transporters in the plasma membrane (Ahmed et al., 2022). A comparison of nitrate and phosphate removal percentages across different concentrations (Figure 2–C) indicated that removal rates were significantly higher in molasses treatments without suspended solids than in those with suspended solids. This suggests that the presence of suspended solids may hinder nitrate and phosphate removal. Therefore, separating suspended solids from the culture medium is crucial for improving the efficiency of algal bioremediation in wastewater treatment. Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) were measured and evaluated at the beginning and end of the mixotrophic cultivation experiments. The highest BOD removal percentage (99.19%) was observed in the 5% molasses treatment without suspended solids, while the lowest BOD removal percentage (92.37%) was recorded in the 1% molasses treatment without suspended solids. Similarly, the highest COD removal percentage (99.46%) was measured in the 10% molasses treatment without suspended solids, whereas the lowest COD removal percentage (94.05%) was found in the 1% molasses treatment without suspended solids. Wang and Lan (2011) stated that biomass production in mixotrophic culture systems is generally higher than in heterotrophic models, possibly due to greater access to carbon sources (CO₂) in mixotrophic conditions. Nagarajan et al. (2019) also reported significant reductions in BOD and COD through microalgal cultivation in wastewater. The content is subject to carbon reduction. Carbon and organic matter uptake and consumption are also common phenomena in microalgae. Microalgae contribute to the removal of organic substances such as urea and inorganic nutrients, including nitrate and phosphate from wastewater, which helps reduce BOD and COD (Arora et al., 2021). Overall, the results indicate that the biomass obtained from the microalga S. quadricauda, cultivated under mixotrophic conditions in molasses wastewater in this study, has numerous advantages, including a short reproductive cycle, enhanced photosynthesis, higher and more efficient nutrient consumption, and effective bioremediation of wastewater (significant reduction of nitrate, phosphate, color, COD, and BOD). Acknowledgment This research was supported by Isfahan University and Technology, Isfahan, Iran. 51 63 2024/11/192024/10/32025/01/26 1403/11/7 2025/04/302025/04/302025/04/30 1404/2/10 میلاد مسعودی Milad Masoudi دانشگاه صنعتی اصفهان Iran m.masoudi@na.ac.ir امیدوار فرهادیان Omidavr Farhadian دانشگاه صنعتی اصفهان Iran omfarhad@iut.ac.ir عیسی ابراهیمی درچه Eisa Ebrahimi دانشگاه صنعتی اصفهان Iran e_ebrahimi@iut.ac.ir Molasses effluent biomass water quality suspended solids پساب ملاس زیست‌توده کیفیت آب جامدات معلق Ahmed, S.F., Mofijur, M., Parisa, T.A., Islam, N., Kusumo, F., Inayat. A., Badruddin, I.A., Khan, T.Y. and Ong, H.C., 2022. Progress and challenges of contaminant removal from wastewater using microalgae biomass. Chemosphere, 286:131656. DOI:10.1016/j.chemosphere.2021.131656##Almomani. F.A. and Ormeci, B., 2016. Performance of Chlorella vulgaris, Neochloris oleoabundans, and mixed indigenous microalgae for treatment of primary effluent, secondary effluent and centrate. Ecology Engineering, 95:280-289. DOI:10.1016/j.ecoleng.2016.07.046##Arora, K., Kaur, P., Kumar, P., Singh, A., Patel, S.K.S., Li, X. and Yang, Y.H., 2021. Valorization of wastewater resources into biofuel and value-added products using microalgal system. Frontiers in Energy Research, 250p. DOI:10.3389/fenrg.2021.760583##Baird, R., Rice, E., and Eaton, A., 2017. Standard methods for the examination of water and wastewater. American Public Health Association, 1545p.##Bilotta, G. S., and Brazier, R. E. 2008. Understanding the influence of suspended solids (SS) on water quality and aquatic biota. Water Research, 42(12), 2849-2861. doi.org/10.1016/j.watres.2008.03.018##Blonskaja, V. and Zub, S., 2009. Possible ways for post-treatment of biologically treated wastewater from yeast factory. Journal of Environmental Engineering and Landscape Management, 17:189-197. DOI:10.3846/1648-6897.2009.17.189-197##Boyd, C.E., 2020. Suspended solids, color, turbidity, and light. Water Quality: An Introduction, 119-133.##Cordell, D., Rosemarin, A., Schroder, J. & Smit, A. 2011. Towards global phosphorus security: a systems framework for phosphorus recovery and reuse options. Chemosphere 84, 747-758.##Creswell, L., 2010. Phytoplankton Culture for Aquaculture Feed. Southern Regional Aquaculture Center (SRAC) Pub, No: 5004, Department of Agriculture United States, 13 pp.##Dahiya, J., Singh, D. and Nigam, P., 2001. Decolourisation of molasses wastewater by cells of Pseudomonas fluorescens immobilized on porous cellulose carrier. Bioresource Technology, 78(1):111-114. DOI:10.1016/S0960-8524(01)00005-8##Falkowski, P.G. and Raven, J.A., 2013. Aquatic photosynthesis. Princeton University Press, New Jersey, US. 483p.##Farhadian, A. and Heidari Goujani, B., 2020. Algae energy. Isfahan University of Technology Press, Isfahan, Iran, 246 p. (In Persian)##Farhadian, A., Nezafatian, A. and Rostami, F., 2023. Microalgae in health and disease prevention. Isfahan University of Technology Press, Isfahan, Iran, 477 p. (In Persian)##Farhadian, A., Sharifi Kheirabadi, F., Nezafatian, A., Peykan Heirati, F. and Rostami, F., 2022. Investigation of density, growth, and biochemical composition of the marine alga Tetraselmis tetrathele cultivated with molasses and beer wastewater. Iranian Fisheries Science Journal (Iranian Journal of Natural Resources), 75(2):169-181. (In Persian) ##Fowler, D., Coyle, M., Skiba, U., Sutton, M.A., Cape, J.N., Reis, S., Sheppard, L.J., Jenkins, A., Grizzetti, B. and Galloway, J.N., 2013. The global nitrogen cycle in the twenty-first century. Philosophical Transactions of the Royal Society B. Biological Sciences, 368(1621):20130164. DOI:10.1098/rstb.2013.0164 ##Heydari, S., Farhadian, A. and Soofiani, M., 2011. Biomass production and removal of ammonia and nitrite from fish farm effluent using the green alga Scenedesmus quadricauda. Environmental Science, 37(59):15-28. (In Persian) ##Huang, S., Chen, Y., Wang, J., Lao, A., Huang, H., Wang, Z., Luo, X. and Zheng, Z., 2024. Understanding the dynamics of Microcystis bloom: Unraveling the influence of suspended solids through proteomics and metabolomics approaches. Science of The Total Environment, 908:168079. DOI:10.1016/j.scitotenv.2023.168079 ##Kalyuzhnyi, G. and Murray, R.W., 2005. Ligand effects on optical properties of Codes nanocrystals. The Journal of Physical Chemistry B, 109:7012-7021. DOI:10.1021/jp0451415##Kobya, M. and Delipinar, S., 2008. Treatment of the baker's yeast wastewater by electrocoagulation. Journal of Hazardous Materials, 154:1133-1140. DOI:10.1016/j.jhazmat.2007.11.038## Liu, J., Han, X., Xing, H., Nan, Y., Lin, J., He, J., Chen, S., Wei, Y., and Guo, P. 2022. Effects of Suspended Particles on Exopolysaccharide Secretion of Two Microalgae in Jinjiang Estuary (Fujian, China). Journal of Marine Science and Engineering, 10(2), 277. https://doi.org/10.3390/jmse10020277##Martinez, M.E., Sanches, S., Jimenes, J.M., Yousfi, F.E. and Munoz, L., 2000. Nitrogen and phosphorus removal from urban wastewater by the microalgae Scenedesmus obliqus. Bioresource Technology, 73:263-272. DOI:10.1016/S0960-8524(99)00121-2##Nagarajan, D., Kusmayadi, A., Yen, H.W., Dong, C.D., Lee, D.J. and Chang, J.S., 2019. Current advances in biological swine wastewater treatment using microalgae-based processes. Bioresource Technology, 289:121718. DOI:10.1016/j.biortech.2019.121718 ##Omori, M. and Ikeda, T., 1984. Methods in Marine Zooplankton Ecology. John Wiley, New York, 332 pp. ##Oswald, W.J. and Gotass, H.B., 1995. Photosynthesis in sewage treatment. Journal of the Sanitary Engineering Division American Society of Civil Engineers, 122:73-105. ##Sigee, D.C., 2005. Freshwater Microbiology: Diversity and Dynamic Interaction of Microorganisms in the Aquatic Environment. Chichester, UK, John Wiley and Sons, 542 p. ##Tam, N.F.Y. and Wong, Y.S., 1989. Wastewater nutrient removal by Chlorella pyrenoidosa and Scenedesmus sp. Environmental Pollution, 58:19-34. DOI: 10.1016/0269-7491(89)90202-6.##Torabian, A. and Mahjuri, M., 2004. Investigation of the status of industrial and production wastewater in Tehran industries. Water and Wastewater, 15(2):34-44. (In Persian) ##Voltolina, D, Gmez-Villa, H. and Correa, G., 2004. Biomass production and nutrient removal in semicontinuous cultures of Scenedesmus sp. (Chlorophyceae) in artificial wastewater, under a simulated day-night cycle. Vie Milieu, 54:21-25. ##Wang, B. and Lan, C.Q., 2011. Biomass production and Nitrogen and phosphorus removal by the green alga Neochloris oleoabundans in simulated wastewater and secondary municipal wastewater effluent. Bioresource Technology, 102:5639–5644. DOI:10.1016/j.biortech.2011.03.059 ##Yalcin, T., Naz, M. and Turkmen, M., 2006. Utilization of different nitrogen sources by cultures of Scenedesmus acuminatus. Turkish Journal of Fisheries and Aquatic Sciences, 6:123-## ##

مقاله علمی – پژوهشی:‌ اثر خوراک حاوی پروبیوتیک Lactobacillus acidophilus و پلی‌فنول فرولیک اسید بر ایمنی غیراختصاصی و فعالیت آنزیم‌‌های آنتی‌اکسیدانی در ماهی قرمز (Carassius auratus) Effect of dietary probiotic Lactobacillus acidophilus and polyphenol ferulic acid on non-specific immunity and antioxidant activity in goldfish (Carassius auratus) 1 1 در مطالعه حاضر، تاثیر جیره‌‌های حاوی پروبیوتیکLactobacillus acidophilus (LA) و پلی‌‌فنول فرولیک اسید (FA) را بر عملکرد رشد، میزان ایمنی غیر اختصاصی، و فعالیت آنزیم‌‌های آنتی‌‌اکسیدانی در ماهی قرمز (Carassius auratus) مورد بررسی قرار گرفت. 240 عدد ماهی با وزن اولیه 35/0±34/3 گرم با چهار جیره آزمایشی شامل مقدار صفر از مکمل‌‌های غذایی (T0)، 108× 6 واحد تشکیل دهنده‌‌ کلنی بر گرم پروبیوتیک LA (T1)، 100 میلی‌گرم بر کیلوگرم FA (T2) و ترکیبی از LA و FA (T3) به مدت 8 هفته تغذیه شدند. نتایج نشان داد که وزن نهایی، افزایش وزن و نرخ رشد ویژه به طور قابل‌توجهی تحت تأثیر LA و FA قرار گرفتند و بیشترین رشد در تیمار T3 مشاهده شد (P<0.05). سطوح ایمونوگلوبولین تام، IgM و لیزوزیم با رژیم غذایی FA و LA افزایش یافت (05/0>p). سطوح ایمونوگلوبولین تام، IgM و لیزوزیم با رژیم غذایی FA و LA افزایش یافت. فعالیت مالون دی آلدئید (MDA) به‌وسیله جیره‌های آزمایشی کاهش یافت و کمترین مقدار آن به طور قابل‌توجهی در تیمار T3 در مقایسه با گروه شاهد مشاهده شد (05/0>p). ماهی‌هایی که با جیره‌های حاوی LA یا FA تغذیه شده بودند، افزایش قابل‌توجهی در فعالیت کاتالاز (CAT)، سوپراکسید دیسموتاز (SOD) و گلوتاتیون پراکسیداز (GPX) نسبت به گروه شاهد نشان دادند (05/0>p). نتایج نشان داد که این مکمل‌‌های غذایی به صورت هم‌زمان در غلظت‌‌های 100 میلی‌‌گرم بر کیلو‌‌گرم فرولیک اسید و 108× 6 کلنی بر گرم پروبیوتیک LA (T3)، از پتانسیل افزایش رشد، ایمنی ذاتی و آنزیم‌‌های آنتی‌‌اکسیدانی در ماهی قرمز برخوردارند. هر دو مکمل خوراکی اثرات هم‌افزایی نشان دادند و توصیه می‌‌شود از این ترکیبات به طور هم‌زمان برای آبزی‌پروری پایدار استفاده شود. 2 Introduction Fish is among the best sources of animal protein but aquaculture industry have faced to many challenges. Global aquaculture productions are vulnerable, and the increasing prevalence of diseases has caused a partial and overall reduction in aquaculture production (Bondad-Reantaso et al., 2005). Factors such as overcrowding in ponds, periodic movement and manipulation, sudden temperature changes, water quality deterioration, and poor nutritional conditions, along with physiological changes in fish such as stress, have heightened susceptibility to infections (Quesada-García et al., 2013). In aquaculture, the application of natural immuno-stimulants to enhance fish health and boost their resistance to pathogens has shown encouraging results (Habibnia et al., 2024). Methodology This study examined the impact of incorporating probiotic Lactobacillus acidophilus (LA) and polyphenol ferulic acid (FA) into the diet of goldfish (Carassius auratus) on growth performance, as well as factors related to immunity and antioxidant activity. A total of 240 fish, initially weighing 3.34 ± 0.35 g, were divided into four experimental groups: a control treatment with no food supplement (T0), fed with 6 x 108 CFU/g LA probiotic (T1), fed with 100 mg/kg FA (T2), and fed with a combination of LA and FA (T3) for eight weeks. At the end of feeding trial, the fish were fasted for 24 hours and all the fish in each tank were sampled and then anesthetized with benzocaine at a concentration of 120 mg per liter and weighed individually. Growth performance was then assessed. Thereafter, nine fish (per replication) were randomly selected. The selected fish were then subjected to anesthesia with Benzocaine at a concentration of 120 mg per liter to minimize handling stress. The fish underwent dissection, with the intestine and liver tissues being meticulously separated. Subsequently, these tissues were promptly subjected to freezing using liquid nitrogen and were subsequently preserved at a temperature of -80 °C. To measure lysozyme activity, the sample was added to a suspension of Micrococcus lysodeikticus prepared in 0.1 molar citrate phosphate buffer at a pH of 5.8. Optical density was read at a wavelength of 410 nanometers for 5 minutes, with readings taken every 30 seconds. Total immunoglobulin (Ig) levels were determined based on the method by Siwicki et al. (1994). In brief, total protein from the homogenate sample was measured using the microprotein method, followed by precipitation of immunoglobulin molecules with a 12% polyethylene glycol solution. The protein level was then re-measured, and the difference in protein content was considered as the Ig content. The levels of lipid peroxidation products in the fish homogenate were determined based on a previous study (Kei, 1978). Briefly, trichloroacetic acid (20%) (1.25 mL) was mixed with the fish homogenate (0.25 mL) and centrifuged at 2000 g for 10 minutes. Then, 1.25 mL of sulfuric acid (0.05 molar) and 1 mL of thiobarbituric acid (0.2%) were added to the collected precipitate and boiled for 30 minutes. After adding 2 mL of n-butanol, centrifugation (2000 g) was performed for another 10 minutes. The final absorbance was recorded at a wavelength of 532 nanometers. Catalase activity in the fish homogenate was assessed based on a previous study (Goth, 1991). The reaction buffer (1 mL) consisted of hydrogen peroxide (65 mM) and sodium-potassium phosphate buffer (60 mM) mixed with the fish homogenate (0.5 mL) for 1 minute at 37°C. The enzymatic reaction was terminated by adding 1 mL of ammonium molybdate (32.4 mM), and absorbance was recorded at 405 nanometers. Superoxide dismutase activity in the fish homogenate was determined based on a previous study (Nishiimi et al., 1997). The reaction buffer included 2.6 mL of phosphate buffer (0.017 mM), 0.1 mL of phenazine methosulfate, and 0.1 mL of nitro blue tetrazolium mixed with the fish homogenate (0.5 mL). After adding 0.1 mL of NADH (2.34 mM), absorbance was recorded at 560 nanometers for 3.5 minutes. Glutathione peroxidase activity in the fish homogenate was assessed according to a previous study (Pagalia & Valentine, 1967). The reaction buffer (0.88 mL) included GSH (1 mM), NADPH (150 mM), and sodium azide (100 mM) mixed with the fish sample (20 µL). Absorbance was recorded at a wavelength of 340 nanometers for 1 minute. The normality of the data and the homogeneity of variances were analyzed using the Kolmogorov-Smirnov test and Levene's test, respectively. A one-way analysis of variance (ANOVA) was conducted to assess significant differences among treatments. Tukey's post-hoc tests were employed to evaluate differences between treatments. These analyses were performed using SPSS 22 software (SPSS, Richmond, VA, USA) with a 95% confidence level. Results The results showed that the final weight, weight gain and specific growth rate were significantly influenced by LA and FA, with the T3 treatment resulting in the highest growth observed. Furthermore, levels of total immunoglobulin, IgM, and lysozyme increased in fish fed diets containing FA or LA. Additionally, malondialdehyde (MDA) activity was reduced by the experimental diets, with its lowest value significantly observed in the T3 treatment compared to the control group. Fish fed diets containing LA and/or FA also displayed significant increases in catalase (CAT), superoxide dismutase (SOD), and glutathione peroxidase (GPx) activity compared to the control group. The findings suggest that these nutritional supplements at optimal doses have potential to enhance growth as well as innate immunity and antioxidant factors in goldfish. Furthermore, both supplements exhibited synergistic effects; therefore, it is recommended to use these compounds simultaneously for sustainable aquaculture practices. Discussion and conclusion Our results showed that supplementing with FA and/or LA can significantly activate antioxidant activity, effectively scavenging excess free radicals and regulating ROS balance in the body, thereby enhancing antioxidant potential. In this study, the beneficial effects of the probiotic Lactobacillus acidophilus and the polyphenol ferulic acid were examined in carp fish. These additives in the diet had potential effects on growth, immune factors, and antioxidant capacity in carp, and simultaneous use of these compounds is recommended for sustainable aquaculture. Conflict of Interest Authors have no conflict of interest. 65 77 2024/11/192024/10/32025/01/262024/09/19 1403/6/29 2025/04/302025/04/302025/04/302025/04/30 1404/2/10 معصومه بحرکاظمی Masoumeh Bahrekazemi گروه گروه شیلات، واحد قائمشهر، دانشگاه آزاد اسلامی، قائمشهر، ایران Iran ma.bahrekazemi@gmail.com Feed addative goldfish immunity antioxidant مکمل غذایی ماهی قرمز ایمنی آنتی‌اکسیدان Ahmadifar, E., Moghadam, M.S., Dawood, M.A. and Hoseinifar, S.H., 2019. Lactobacillus fermentum and/or ferulic acid improved the immune responses, antioxidative defence and resistance against Aeromonas hydrophila in common carp (Cyprinus carpio) fingerlings. Fish & shellfish immunology, 94: 916-923. DOI: https://doi.org/10.1016/j.fsi.2019.10.019.##Ahmadifar, E., Yousefi, M., Karimi, M., Fadaei Raieni, R., Dadar, M., Yilmaz, S., Dawood, M.A. and Abdel-Latif, H.M., 2021. Benefits of dietary polyphenols and polyphenol-rich additives to aquatic animal health: an overview. Reviews in Fisheries Science & Aquaculture, 29(4): 478-511. DOI: https://doi.org/10.1080/23308249.2020.1818689.##Andani, H.R.R., Tukmechi, A., Meshkini, S. and Sheikhzadeh, N., 2012. Antagonistic activity of two potential probiotic bacteria from fish intestines and investigation of their effects on growth performance and immune response in rainbow trout (Oncorhynchus mykiss). Journal of Applied Ichthyology, 28(5): 728-734. DOI: https://doi.org/10.1111/j.1439-0426.2012.01974.x.##Bondad-Reantaso, M.G., Subasinghe, R.P., Arthur, J.R., Ogawa, K., Chinabut, S., Adlard, R., Tan, Z. and Shariff, M., 2005. Disease and health management in Asian aquaculture. Veterinary parasitology, 132 (3-4): 249-272. DOI: https://doi.org/10.1016/j.vetpar.2005.07.005.##Burr, G.S., Li, P., Goff, J.B., Gatlin III, D.M., Grisdale‐Helland, B. and Helland, S.J., 2006. Evaluation of growth performance and whole‐body composition of juvenile hybrid striped bass Morone chrysops× Morone saxatilis and red drum Sciaenops ocellatus fed high‐protein and high‐lipid diets. Journal of the World Aquaculture Society, 37(4): 421-430. DOI: https://doi.org/10.1111/j.1749-7345.2006.00055.x.##Cabello, F.C., 2006. Heavy use of prophylactic antibiotics in aquaculture: a growing problem for human and animal health and for the environment. Environmental microbiology, 8(7): 1137-1144. DOI: https://doi.org/10.1111/j.1462-2920.2006.01054.x.##Davis, M.W., 2010. Fish stress and mortality can be predicted using reflex impairment. Fish and Fisheries, 11(1):1-11. DOI: https://doi.org/10.1111/j.1467-2979.2009.00331.x.##Dawood, M.A., Metwally, A.E.S., El-Sharawy, M.E., Ghozlan, A.M., Abdel-Latif, H.M., Van Doan, H. and Ali, M.A., 2020. The influences of ferulic acid on the growth performance, haemato-immunological responses, and immune-related genes of Nile tilapia (Oreochromis niloticus) exposed to heat stress. Aquaculture, 525: 735320. DOI: https://doi.org/10.1016/j.aquaculture.2020.735320.##Dawood, M.A., Koshio, S., Ishikawa, M., Yokoyama, S., El Basuini, M.F., Hossain, M.S., Nhu, T.H., Dossou, S. and Moss, A.S., 2016. Effects of dietary supplementation of Lactobacillus rhamnosus or/and Lactococcus lactis on the growth, gut microbiota and immune responses of red sea bream, Pagrus major. Fish & Shellfish Immunology, 49, pp.275-285.DOI: https://doi.org/10.1016/j.fsi.2015.12.047.##Enferadi, M.H.N., Mohammadizadeh, F., Soltani, M., Bahri, A.H. and Sheikhzadeh, N., 2018. Effects of LactoBacillus plantarum on growth performance, proteolytic enzymes activity and intestine morphology in rainbow trout (Oncorhynchus mykiss). Turkish Journal of Fisheries and Aquatic Sciences, 18(2): 351-356. DOI: 10.4194/1303-2712-v18_2_14.##Etxeberria, U., Fernández-Quintela, A., Milagro, F.I., Aguirre, L., Martínez, J.A. and Portillo, M.P., 2013. Impact of polyphenols and polyphenol-rich dietary sources on gut microbiota composition. Journal of agricultural and food chemistry, 61(40): 9517-9533. DOI: https://doi.org/10.1021/jf402506c.##Fu, W., Amenyogbe, E., Luo, J., Yang, E., Huang, J.S., Chen, Y. and Chen, G., 2022. Influences of ferulic acid on intestinal digestive and antioxidant enzymes, immune, antioxidant gene and tight junction protein expression and microbiota in hybrid grouper (Epinephelus fuscoguttatus♀× Epinephelus polyphekadion♂). Aquaculture Reports, 27, p.101348. Doi: https://doi.org/10.1016/j.aqrep.2022.101348.##Gatesoupe, F.J., 2008. Updating the importance of lactic acid bacteria in fish farming: natural occurrence and probiotic treatments. Journal of molecular microbiology and biotechnology, 14(1-3): 107-114. DOI: https://doi.org/10.1159/000106089.##Gobi, N., Vaseeharan, B., Chen, J.C., Rekha, R., Vijayakumar, S., Anjugam, M. and Iswarya, A., 2018. Dietary supplementation of probiotic Bacillus licheniformis Dahb1 improves growth performance, mucus and serum immune parameters, antioxidant enzyme activity as well as resistance against Aeromonas hydrophila in tilapia Oreochromis mossambicus. Fish & shellfish immunology, 74: 501-508. DOI: https://doi.org/10.1016/j.fsi.2017.12.066.##Goth, L., 1991. A simple method for determination of serum catalase activity and revision of reference range. Clinica chimica acta, 196(2-3): 143-151. DOI: https://doi.org/10.1016/0009-8981(91)90067-M.##Guardiola, F.A., Porcino, C., Cerezuela, R., Cuesta, A., Faggio, C. and Esteban, M.A., 2016. Impact of date palm fruits extracts and probiotic enriched diet on antioxidant status, innate immune response and immune-related gene expression of European seabass (Dicentrarchus labrax). Fish & Shellfish Immunology, 52: 298-308. DOI: https://doi.org/10.1016/j.fsi.2016.03.152.##Harikrishnan, R., Balasundaram, C. and Heo, M.S., 2011. Fish health aspects in grouper aquaculture. Aquaculture, 320(1-2): 1-21. DOI: https://doi.org/10.1016/j.aquaculture.2011.07.022##Hoseinifar, S.H., Roosta, Z., Hajimoradloo, A. and Vakili, F., 2015. The effects of Lactobacillus acidophilus as feed supplement on skin mucosal immune parameters, intestinal microbiota, stress resistance and growth performance of black swordtail (Xiphophorus helleri). Fish & shellfish immunology, 42(2): 533-538. DOI: https://doi.org/10.1016/j.fsi.2014.12.003.##Kei, S., 1978. Serum lipid peroxide in cerebrovascular disorders determined by a new colorimetric method. Clinica chimica acta, 90(1): 37-43. DOI: https://doi.org/10.1016/0009-8981(78)90081-5.##Mandel, S., Packer, L., Youdim, M.B. and Weinreb, O., 2005. Proceedings from the “Third International Conference on mechanism of Action of Nutraceuticals”. The Journal of Nutritional Biochemistry, 16(9): 513-520. DOI: https://doi.org/10.1016/j.jnutbio.2005.03.001.##Mehana, E.E., Rahmani, A.H. and Aly, S.M., 2015. Immunostimulants and fish culture: an overview. Annual Research & Review in Biology, 5(6): 477-489. DOI: https://doi.org/10.9734/ARRB/2015/9558.##Miranda, C.D. and Zemelman, R., 2002. Antimicrobial multiresistance in bacteria isolated from freshwater Chilean salmon farms. Science of the Total Environment, 293(1-3): 207-218. DOI: https://doi.org/10.1016/S0048-9697(02)00022-0.##Nelson, D.T., 1997. Transcriptional regulatory elements in the long terminal repeats of the human endrogenous retrovirus, HERV-H (Doctoral dissertation, University of British Columbia).DOI: https://dx.doi.org/10.14288/1.0088264.##Paglia, D.E. and Valentine, W.N., 1967. Studies on the quantitative and qualitative characterization of erythrocyte glutathione peroxidase. The Journal of laboratory and clinical medicine, 70(1): 158-169. ##Quesada-García, A., Valdehita, A., Torrent, F., Villarroel, M., Hernando, M.D. and Navas, J.M., 2013. Use of fish farms to assess river contamination: combining biomarker responses, active biomonitoring, and chemical analysis. Aquatic toxicology, 140: 439-448. DOI: https://doi.org/10.1016/j.aquatox.2013.07.007##Rico, A., Oliveira, R., McDonough, S., Matser, A., Khatikarn, J., Satapornvanit, K., Nogueira, A.J., Soares, A.M., Domingues, I. and Van den Brink, P.J., 2014. Use, fate and ecological risks of antibiotics applied in tilapia cage farming in Thailand. Environmental Pollution, 191: 8-16. DOI: https://doi.org/10.1016/j.envpol.2014.04.002.##Romero, J., Ringø, E. and Merrifield, D.L., 2014. The gut microbiota of fish. Aquaculture nutrition: Gut health, probiotics and prebiotics, 75-100. DOI: https://doi.org/10.1002/9781118897263.ch.##Shohreh, P., Mohammadzadeh, S., Mahboub, H.H., Ahmadifar, E., Elsheshtawy, H.M., Kalhor, N., Moghadam, M.S. and Abdel-Tawwab, M., 2024. Growth performance, hematological profile, and related genes expression in goldfish (Carassius auratus) fed on rosmarinic acid-enriched diets and subjected to ambient ammonia. Aquaculture, 587: 740861. DOI: https://doi.org/10.1016/j.aquaculture.2024.740861.##Siwicki, A.K., Anderson, D.P. and Rumsey, G.L., 1994. Dietary intake of immunostimulants by rainbow trout affects non-specific immunity and protection against furunculosis. Veterinary immunology and immunopathology, 41(1-2): 125-139. DOI: https://doi.org/10.1016/0165-2427(94)90062-0.##Stapleton, H.M., Letcher, R.J. and Baker, J.E., 2004. Debromination of polybrominated diphenyl ether congeners BDE 99 and BDE 183 in the intestinal tract of the common carp (Cyprinus carpio). Environmental science & technology, 38(4): 1054-1061. DOI: https://doi.org/10.1021/es0348804.##Villamil, O., Váquiro, H. and Solanilla, J.F., 2017. Fish viscera protein hydrolysates: Production, potential applications and functional and bioactive properties. Food chemistry, 224: 160-171. DOI: https://doi.org/10.1016/j.foodchem.2016.12.057.##Yousefi, M., Zahedi, S., Reverter, M., Adineh, H., Hoseini, S.M., Van Doan, H., El-Haroun, E.R. and Hoseinifar, S.H., 2021. Enhanced growth performance, oxidative capacity and immune responses of common carp, Cyprinus carpio fed with Artemisia absinthium extract-supplemented diet. Aquaculture, 545: 737167. DOI: https://doi.org/10.1016/j.aquaculture.2021.737167.##Yu, L., Wen, H., Jiang, M., Wu, F., Tian, J., Lu, X., Xiao, J. and Liu, W., 2020. Effects of ferulic acid on intestinal enzyme activities, morphology, microbiome composition of genetically improved farmed tilapia (Oreochromis niloticus) fed oxidized fish oil. Aquaculture, 528: 735543. DOI: https://doi.org/10.1016/j.aquaculture.2020.735543.##Yang, G., Cao, H., Jiang, W., Hu, B., Jian, S., Wen, C., Kajbaf, K., Kumar, V., Tao, Z. and Peng, M., 2019. Dietary supplementation of Bacillus cereus as probiotics in Pengze crucian carp (Carassius auratus var. Pengze): Effects on growth performance, fillet quality, serum biochemical parameters and intestinal histology. Aquaculture Research, 50(8), pp.2207-2217. DOI: https://doi.org/10.1111/are.14102.## ##

مقاله علمی – پژوهشی:‌ شناسایی و اولویت‌بندی شاخص‌های عملکرد تعاونی‌های صیادی استان خوزستان Identification and prioritization of performance indicators for fisheries cooperatives in Khuzestan Province 1 1 تعاونی‌های صیادی از رایج‌ترین تشکل‌های مردم‌نهاد فعال در نواحی ساحلی کشور هستند که عموماً برای تأمین احتیاجات شغلی و اقتصادی اعضای خود فعالیت می‌کنند. استان خوزستان همواره با ظرفیت‌های صیادی برجسته شناحته می‌شود. فعالیت بخش عمده‌ای از صیادان استان خوزستان به‌نحوی با تعاونی‌‌های صیادی در ارتباط است، پژوهش حاضر با هدف شناسایی و اولویت‌بندی مهم‌ترین شاخص‌های موثر در عملکرد شرکت‌های تعاونی‌ صیادی استان خوزستان طی سال‌های 1403-1401 انجام گرفته است. این پژوهش با بهره‌گیری از روش‌های مطالعاتی، مصاحبه محور (تکنیک دلفی) و توزیع پرسشنامه محقق ساخته در میان فعالان حوزه صیادی صورت گرفته است، روایی پرسشنامه براساس شیوه روایی‌سنجی محتوایی و پایایی گویه‌ها با آلفای کرونباخ 88/0 محرز گردید. خبرگان براساس روش نمونه‌گیری سیستماتیک خوشه‌ای گزینش شدند. مهم‌ترین عوامل گزینش، داشتن سابقه کار اجرائی-مدیریتی در بخش شیلات کشور، صنعت صید یا تعاونی‌های صیادی بود. جوامع آماری شامل دو گروه مجزای هیئت خبرگان و صیادان فعال تعاونی‌های صیادی بودند. تحلیل شاخص‌های عملکردی شناسایی شده با استفاده از مدل آماری (تحلیل عاملی تأییدی) انجام گرفت. براساس نتایج حاصل، شاخص‌های تبیین‌کننده عملکرد تعاونی‌های صیادی استان خوزستان به‌ترتیب اهمیت در قالب هفت عامل شامل مدیریت همه‌جانبه و پویا، سیاست‌گذاری‌های مدیریتی اجرائی و آموزشی، تأثیر اقتصادی بر عملکردهای اجتماعی، کنشگری در ارائه تسهیلات،کنشگری در تعاملات اجتماعی با اعضا و بخش‌های سرمایه‌گذار، کنشگری در ارائه خدمات زیربنایی و نظارت بر وضعیت صید تعریف شدند که در مجموع 2/62 درصد از تغییرات واریانس کل متغیرها را تبیین نمودند. بر اساس نتایج حاصله، بازنگری در شیوه‌های مدیریتی و افزایش نظارت بر وضعیت شغلی-اقتصادی فعالان تعاونی‌های صیادی می‌تواند به شکل معنی‌داری در بهبود عملکرد این تشکل‌ها تأثیرگذار باشد. اهتمام دستگاه‌های مسئول و متولی بر بهبود عملکرد و وضعیت این شرکت‌ها نیز باید با در نظرگرفتن عوامل استخراجی صورت پذیرد، زیرا عوامل مذکور دارای بیشترین تأثیر در وضعیت عملکردی این تعاونی‌ها خواهد بود. 2 Introduction The fishing industry contributes significantly to global and national food production and economies. Fishing accounts for a substantial portion of global aquatic production (FAO, 2022), highlighting the importance of effective fishing management. In Iran, Khuzestan province, with its extensive coastline and abundant water resources, is a key player in the national fishing sector (Geological Survey and Mineral Explorations of Iran, 2016; Iran Fishing Organization, 2021). Fishery cooperatives, as non-governmental organizations, aim to improve the livelihoods of fishermen by providing various services, including access to resources, financial assistance, and professional support (Islamic Republic of Iran, 1971). However, the performance of these cooperatives varies, and understanding the factors that influence their effectiveness is crucial for policy-making and sustainable development (Olquner et al., 2015; Sari and Rahmayanti, 2022). Methodology This research employed a mixed-methods approach, combining qualitative and quantitative methods. The initial exploratory phase involved in-depth interviews with 15 experts in the fishing sector, including managers of cooperatives, government officials, and researchers (Hsu and Sandford., 2007). These interviews, lasting approximately 40 minutes each, aimed to identify key performance indicators of fishing cooperatives. The experts were selected using a systematic cluster sampling method, based on their experience in the fishing sector, fishing industry, or fishing cooperatives (Acharya et al., 2013). A single-round Delphi technique was used to refine the identified indicators. Based on the interview data and a literature review, a five-point Likert scale questionnaire was developed to assess the importance and prioritization of the identified performance indicators among the fishing community (Boateng et al., 2018). The questionnaire’s validity was assessed using content validity, and its reliability was confirmed with a Cronbach's alpha coefficient exceeding 0.8 (Taber, 2018). The target population comprised over 100,000 active fishermen in Khuzestan's coastal fishing cooperatives. A sample size of 384 was determined using the Krejcie-Morgan table, and the questionnaires were distributed across various fishing areas and wharves in the coastal cities of Khuzestan (Cochran, 1977). Results The interviews identified over 45 items representing performance indicators, categorized into four main groups: economic, social, managerial, and legal. These items were then used to develop the questionnaire. Statistical analysis, including the Kaiser-Meyer-Olkin (KMO) test (0.858) and Bartlett's test of sphericity (3591.90, p < 0.001), confirmed the suitability of the data for factor analysis. Seven main factors were extracted, explaining 62.2% of the total variance. These factors, ranked by their eigenvalue variance, were: (1) multi-dimensional and dynamic management (24.26%), (2) managerial executive and educational policies (9.94%), (3) economic impact on social performance (8%), (4) activity in providing facilities (5.4%), (5) activity in social interactions with members and investors (4.85%), (6) activity in providing infrastructure services (4.27%), and (7) monitoring fishing status (3.48%). Discussion and conclusion The study revealed that multi-dimensional management is the most influential factor determining the performance of fishery cooperatives in Khuzestan, significantly outweighing the other six identified factors. This emphasizes the critical role of comprehensive management in overseeing various aspects of cooperative performance, including economic outcomes, resource allocation, environmental considerations, educational and cultural activities, policy development, and support from government institutions (Unal et al., 2011; Olquner et al., 2015). The influence of management on other performance aspects, especially economic performance, prioritization of available resources, environmental considerations, educational and cultural performance, the level of facilitating laws for fishing cooperatives, and the level of support from institutions, all reflect the undeniable importance of management as the most important indicator in explaining the performance of a cooperative-based organization in the fishing industry (Mahazril’aini et al., 2012). Educational and cultural activities, along with the provision of economic benefits to members (to improve social indicators), were identified as other crucial performance indicators. Training and awareness programs for both cooperative members and managers are essential for improving the effectiveness of these organizations (Unal et al., 2011). Implementing mandatory training courses for cooperative managers and organizing awareness programs for cooperative members regarding laws, regulations, best fishing practices, and interaction with cooperative management can significantly enhance their performance (Mahazril’aini et al., 2012). The study also highlighted several shortcomings and inefficiencies in the fishery cooperatives of southern Iran. Previous studies have shown that these organizations lack a significant impact on economic, social, cultural, and infrastructural development (Samian et al., 2017). This necessitates a focus on improving their performance. Iranian constitutional principles and development plans emphasize the importance of economic participation through the expansion of the cooperative and private sectors (Islamic Consultative Assembly, 2023). Economic pressures from declining fish stocks and reduced income, due to various factors such as government policies and environmental issues, affect social interactions between cooperatives and their members (Ilsovay et al., 2024). These shortcomings can discourage membership and increase the desire for career change, especially among younger generations (Unal et al., 2011). While the remaining four factors (providing facilities, social interactions, providing infrastructure, and monitoring fishing) were less influential than the top three, they are still important for overall performance. The level of welfare, economic, technical, and educational facilities provided by the cooperative, the influence of cooperative managers among investors, the cooperative's role in creating and maintaining fishing infrastructure, and the awareness of cooperative managers regarding catch volumes, income, and daily fishing challenges are all significant factors (Ghanbarzadeh et al., 2021). This study’s findings provide valuable guidance for understanding the functioning and priorities of fishery cooperatives in Khuzestan. They can be used for future research, performance evaluations, and policy-making. The fishermen participating in the study frequently expressed concerns about the management of their cooperatives, citing chronic infrastructure deficiencies and a lack of fundamental changes over the years. Periodic changes in the board of directors can promote management dynamism and introduce new ideas, which can help address these long-standing issues (Unal et al., 2011). Cooperative management should also balance economic interests and social objectives, avoiding becoming solely profit-oriented or simply focused on basic member needs (Dogarawa, 2010). Increased monitoring of the fishing industry and the working conditions, technical resources, infrastructure, and welfare of fishermen is necessary. The identification of key performance indicators can guide improvements by the government and relevant agencies. The presence of fishermen in cooperatives is beneficial, as cooperatives act as intermediaries between fishermen and the government, potentially increasing the attention of responsible institutions to the occupational and economic status of those working in the fishing industry (Anggrainie and Mardhatillah, 2024). Finally, based on the research results, it is recommended that relevant authorities, such as the Iran Fishing Organization and the Ministry of Cooperatives, Labor, and Social Welfare, take steps to address the challenges faced by fishermen, particularly by improving the management of Khuzestan's fishery cooperatives. These authorities should also focus on empowering these cooperatives to provide economic facilities, effective training, and awareness programs to the fishing community. By expanding executive and research programs in this area, the problems of coastal fishing communities in this important region of Iran can be alleviated. Acknowledgements This article is derived from project number 59314, dated 15 January 2024, conducted by Khorramshahr University of Marine Science and Technology in collaboration with the Khuzestan Province Department of Cooperatives, Labour, and Social Welfare. The authors express their gratitude to the Department and the University for their support. 79 95 2024/11/192024/10/32025/01/262024/09/192025/01/17 1403/10/28 2025/04/302025/04/302025/04/302025/04/302025/04/30 1404/2/10 پوریا زنگنه رضایی Poriya Zangeneh rezaie دانشکده منابع‌طبیعی دریا، دانشگاه علوم و فنون دریایی خرمشهر، خرمشهر، ایران Iran poriyasilver@gmail.com محمد خسروی زاده Mohammad Khosravizadeh دانشکده منابع‌طبیعی دریا، دانشگاه علوم و فنون دریایی خرمشهر، خرمشهر، ایران Iran mohamad.27kh@gmail.com نیما شیری Nima Shiry پژوهشکده اکولوژی خلیج فارس و دریای عمان، مؤسسه‌ی تحقیقات علوم شیلاتی کشور، سازمان آموزش و ترویج کشاورزی، بندرعباس، ایران Iran nima.shiry@gmail.com زهرا خوشنودی‌فر Zahra Khoshnodifar بخش تحقیقات اقتصادی و اجتماعی، مرکز تحقیقات و آموزش کشاورزی و منابع‌طبیعی استان مرکزی، اراک، ایران Iran khoshnodifz@gmail.com رضا عباسپور نادری Reza Abbaspour naderi گروه شیلات، معاونت دفتر امور صید شیلات ایران، سازمان شیلات ایران، تهران، ایران Iran R_naderimail@yahoo.com Fisheries cooperative fishing industry fisheries factor analysis performance index دلفی تعاونی صیادی صنعت صیادی شیلات تحلیل عاملی شاخص عملکرد Andishkadeh Ta’avon, 2021. Fisheries Cooperatives: How is the Cooperative Model Implemented in the Fisheries Sector? Department of Cultural and Social Affairs, Andishkadeh Ta’avon, 46 pp. (In Persian)##Anggrainie, S.W. and Mardhatillah, M., 2024. Pemberdayaan Masyarakat Nelayan Melalui Penguatan Ekonomi Lokal di Kabupaten Aceh Barat. Journal of Political Sphere, 5(2), pp. 139-149. DOI: https://doi.org/10.24815/jps.v5i2.43683##Bayesteh, H., 2019. The Impact of Coastal Industrial Cluster Development on Population Retention and National Security: A Special Look at the Role of Port Development in the Fishing Industry. Passive Defense, 10(2), pp. 73-81. (In Persian) 20.1001.1.20086849.1398.10.2.6.0##Cooperative Research Institute, 2021. Fisheries Cooperatives: How is the Cooperative Model Implemented in the Fisheries Sector?. Issue 46. Cultural and Social Department of the Cooperative Research Institute. (In Persian)##Dogarawa, A.B., 2010. Cooperative management: Balancing economic interests and social objectives. Journal of Cooperative Studies, 43(2), pp. 45-60. DOI: https://doi.org/10.2139/ssrn.1622149##Esfandiari, Ch., and Mir Abbasi, B., 2012. The Role of the United Nations in Achieving Food Security in the Global Community. Journal of Food Science and Nutrition, 9(2): 91-103. (In Persian)##FAO, 2022. The State of World Fisheries and Aquaculture 2020. Sustainability in action. Rome, Italy.##Geological Survey and Mineral Explorations of Iran, 2016. Roadmap of Earth Sciences and Mining in Khuzestan Province. National Geosciences Database. (In Persian)##Geological Survey and Mineral Explorations of Iran, 2016. Roadmap for Earth Sciences and Mining in Khuzestan Province, National Geoscience Database of Iran. (In Persian)##Haghi-Vayeghan, A. and Ghanbarzadeh, M., 2022. Estimation of Fisheries Reference Points of Spotted Spanish Mackerel (Scomberomorus guttatus Bloch & Schneider, 1801) Using Catch-Maximum Sustainable Yield (CMSY) and Bayesian Surplus Production Model (BSM) in Southern Waters of Iran (Persian Gulf and Oman Sea). Journal of Fisheries, 75(1), pp. 31-47. DOI: https://doi.org/10.22059/jfisheries.2021.326498.1268 (In Persian)##Hosseinnejad, M., Hosseini Nia, Gh., and Vazifehdoust, H., 2020. Designing a Sustainable Business Model for Entrepreneurial Cooperatives Based on Key Entrepreneurship Components. Journal of Development and Transformation Management, 12(42): 19-34. (In Persian)##Ilosvay, X.É.E., Molinos, J.G., Tovar-Ávila, J., Bravo, J.R., Santiago, I.A.M., Aceves-Bueno, E. and Ojea, E., 2024. Determinants of small-scale fisheries’ transformative responses under increasing climate change impacts in Nayarit, Mexico. Ecology and Society, 29(4). DOI: https://doi.org/10.5751/ES-15661-290438##Iran Fisheries Organization, 2021. Statistical Yearbook of Iran Fisheries Organization (2016-2021), Deputy of Planning and Resource Management, Office of Planning and Budget, Planning and Statistics Group, 29 pp. (In Persian)##Islamic Consultative Assembly, 2023. Seventh Five-Year Development Plan of the Islamic Republic of Iran (2024-2028). National Information Center for Laws and Regulations of Iran. (In Persian) https://dotic.ir/news/11497##Islamic Parliament Research Center, 2006. A Review of the Status of Iran Fisheries Organization. Office of Infrastructure Studies. (In Persian)##Karbasi, A., and Mohammadzadeh, H., 2017. Factors Affecting Food Security with Emphasis on the Role of Agricultural Sustainability in Iran. Third National Student Conference on Agricultural Economics, 19-20 March, Guilan, Iran. (In Persian)##Kian, F., Farhadiaan, H., and Chubchian, Sh., 2016. Investigating Food Security of Urban Households in Alborz Province. Iranian Journal of Food Science and Technology, 13(55): 167-179. (In Persian)##Mahazril‘Aini, Y., Hafizah, H.A.K. and Zuraini, Y., 2012. Factors affecting cooperatives’ performance in relation to strategic planning and members’ participation. Procedia - Social and Behavioral Sciences, 65, pp. 100-105. DOI: https://doi.org/10.1016/j.sbspro.2012.11.098##Mohammadnejad Shamushki, M., Dardiei, Kh., Rezaei Shirazi, A., and Yahyai, M., 2013. A Study on the Catch Trends of Three Years of Whitefish (Rutilus frisii kutum), Mullet (Liza spp), and Carp (Cyprinus carpio) in Golestan, Mazandaran, and Gilan Provinces (2009-2011). Journal of Aquatic Animals and Fisheries, 4(15): 27-38. (In Persian)##Olguner, M.T., Yılmaz, S. and Şen, E.B., 2015. The Place and Importance of Fishery Cooperatives in Aquaculture Marketing in Turkey: The Case of Antalya. International Journal of Development Research, 5, pp. 3149-3151.##Samian, M., Saadi, H., Asadi, M., Mirzaei, K., Ansari, E., Ahmadihagh, E. and Soleymani, A., 2017. The role of fishing cooperatives on social–Economic and cultural development of rural areas of Bord Khun city of Bushehr, Iran. Journal of the Saudi Society of Agricultural Sciences, 16(2), pp. 178-183. DOI: http://dx.doi.org/10.1016/j.jssas.2015.06.001##Sapovadia, V.K., 2004. Fisherman cooperatives: a tool for socio-economic development. In: International Institute of Fisheries Economics & Trade Conference.##Sardoeinasab, M. and Aghamohammadi, A, 2015. Comparative possibility of carrying out commercial activities in the form of co-operative on the basis of I.C.A approaches. Journal of Comparative Law, 6(2), pp. 569-600. https://doi.org/10.22059/jcl.2015.55776. (In Persian)##Sari, D.K. and Rahmayanti, A.Y., 2022. Fishery Cooperatives and Sustainable Blue Economy: Scoping Review from a Business Perspective. Multidisciplinary Digital Publishing Institute Proceedings, 83(1), p. 30. DOI: https://doi.org/10.3390/proceedings2022083030##Ünal, V., Yercan, M. and Günden, C., 2011. The status of fishery cooperatives along the Aegean Sea coast (Turkey). Journal of Applied Ichthyology, 27(3), pp. 854-858. DOI: https://doi.org/10.1111/j.1439-0426.2011.01794.x## ##

مقاله علمی – پژوهشی:‌ تنوع زیستی، پراکنش و فراوانی زوپلانکتون تالاب انزلی طی سال‌های 1402-1401: مطالعه مقایسه‌ای با دهه پیشین Zooplankton biodiversity, distribution, and abundance in Anzali Wetland in 2023: A comparative study with previous decade 1 1 مطالعه حاضر طی سال‌های 1401 و 1402 در تالاب انزلی انجام شد. هدف از این مطالعه، بررسی تغییرات زمانی و مکانی جامعه زوپلانکتونی در تالاب انزلی و مقایسه آن با مطالعات پیشین بود. نتایج نشان داد، تعداد 58 جنس زوپلانکتون در تالاب انزلی شناسایی گردید، بیشترین جنس متعلق به شاخه روتیفرا با 28 جنس بود. از بین گروه‌های زوپلانکتون 11 جنس از پروتوزوآ و 12 جنس از شاخه آرتروپودا شناسایی شدند. از بین شاخه‌های زوپلانکتونی روتیفرا با میزان فراونی 38± 148 عدد در لیتر بیشترین بودند، سپس پروتوزوآ با فراوانی 11± 81 عدد در لیتر و آرتروپودا با فراوانی 12± 72 عدد در لیتر در رده‌های بعدی قرار داشتند. میانگین فراوانی کل زوپلانکتون 48 ± 305 عدد در لیتر بود که در مقایسه با سال‌های 1381- 1380 به میزان 7 برابر (2300- 2200 عدد در لیتر) و سال‌های 1394- 1393 (4000 عدد در لیتر) به میزان 13 برابر کاهش داشت. فراوانی روتیفرا و پروتوزوآ در سال 1402- 1401 به‌ترتیب 13 و 19 برابر در مقایسه با سال 1394- 1393 کاهش داشت. جنس‌های غالب Brachionus و Rotatia از روتیفرا، جنس Centopyxis و Arcella از پروتوزوآ و جنس Cyclocypris و گروه Copepoda از آرتروپودا بودند. بیشترین میانگین فراوانی زوپلانکتون در منطقه تالاب غرب با فراوانی 127±612 عدد در لیتر مشاهده شد. به‌ طورکلی، کاهش چشمگیر در تنوع و جمعیت ذخایر زوپلانکتونی در تالاب انزلی نشان‌دهنده حذف تدریجی حلقه زنجیره غذایی تولید کنندگان اولیه و ثانویه در قسمت پایین هرم اکولوژی بوده است که نابودی گونه‌های بومی و ذخایر آبزیان به‌خصوص ماهیان اقتصادی تالاب را به‌همراه خواهد داشت. 2 Introduction The Anzali Wetland is located along the southwestern coast of the Caspian Sea and was the first wetland in Iran to be registered on the Ramsar Convention's list. However, it was soon added to the Montreux Record due to ecological concerns (Mirzajani et al., 2020). Zooplankton is a crucial link in the wetland's food chain, play an important role as secondary producers and serve as a primary food source for aquatic animals. Planktonic communities are highly responsive to environmental changes, their population structure is strongly influenced by nutrient levels in the water in the water (Mulani et al., 2006; Boyd, 2007; Bagheri et al., 2017). Following the invasion of the water hyacinth plant, a decline in the Caspian Sea's water level, and reduced rainfall and river inflow due to climate change, this study represents the most recent investigation into the temporal and spatial distribution of zooplankton in the Anzali Wetland. The objective of this study was to assess the biodiversity, composition, and density of zooplankton in the Anzali Wetland in 2023, compare the findings with previous research, and evaluate the broader ecological changes over time. Materials and methods The Anzali Wetland is located at 37º 28' N latitude and 49º 25' E longitude, with a maximum depth rarely exceeding one meter. Zooplankton sampling was conducted in March, May, June, August, October, November, and December 2023 at 14 stations. Sampling took place over three days each month using boats with engine powers of 85 and 25 horsepower. A Ruttner Water Sampler was employed for zooplankton collection. The collected samples (30 liters) were passed through a 55-micron zooplankton net, and the water retained in the net was transferred to 300 cc polyethylene bottles. The samples were then fixed with 4% formalin and transported to the plankton laboratory for quantitative and qualitative analysis (APHA, 2005). In the plankton laboratory, after homogenization, 5 ml of the sample was transferred using a pipette to 5 ml counting chambers (KIEL Hydro-Bios). After 24 hours of sedimentation, the samples were identified and counted under an inverted microscope (F-S Leitz-LABOVERT). Zooplankton density was calculated as the number of individuals per liter of water. The methods for sampling and determining zooplankton density were based on APHA (2005) and Ruttner-Kolisko (1974), while zooplankton identification followed the keys of Thorp and Covich (2001) and Pontin (1978). Results In this study, 58 genera from four zooplankton groups were identified in the Anzali Wetland. The highest number of genera belonged to the phylum Rotifera, with 28 genera. This was followed by Arthropoda and Protozoa, with 12 and 11 genera, respectively. In terms of abundance, Rotifera was the dominant group, accounting for 48% of the total zooplankton population (148 individuals per liter). Protozoa ranked second with 26% (81 ind.l⁻¹), followed by Arthropoda with 24% (72 ind.l⁻¹). Other zooplankton groups made up the remaining 2%. The average total zooplankton abundance in the Anzali Wetland was estimated at 305 individuals per liter. The genera Cyclops and Cyclocypris (phylum Arthropoda) were observed across all areas of the Anzali Wetland. From the phylum Rotifera, the genera Brachionus, Cephalodella, Philodina, and Rotaria were consistently found throughout the wetland, showing a 100% observation frequency. The highest zooplankton abundance was recorded in June, with an average of 652±208 ind.l⁻¹, while the lowest values were observed in October (142±36 ind.l⁻¹) and November (155±37 ind.l⁻¹). Among the zooplankton groups, Rotifera showed the highest abundance, reaching an average of 405 ± 188 ind.l⁻¹ in June, with the lowest abundance being 20±5 ind.l⁻¹ in other months. The abundance of Arthropoda ranged from 120±44 ind.l⁻¹ in June to 27±11 ind.l⁻¹ in November. Statistical analysis revealed a significant difference (P<0.05) in zooplankton abundance among different phyla and sampling months. Spatial analysis showed that the western region of the Anzali Wetland had the highest average zooplankton abundance at 613±128 ind.l⁻¹, while the lowest abundance was recorded at the wetland outlet (116±31 ind.l⁻¹). The maximum abundance of Rotifera ranged between 206 and 335 ind.l⁻¹ in the western and eastern regions, respectively. The highest Arthropoda abundance (176 ind.l⁻¹) was also observed in the western region. The abundance of Protozoa varied between 41 ind.l⁻¹ in Siah Keshim and 106 ind.l⁻¹ in the central wetland. Discussion and conclusion Over the past 30 years, increased human activities and significant environmental and climatic changes have contributed to notable shifts in the zooplankton community of the Anzali Wetland. Biodiversity has drastically declined, with the number of zooplankton genera dropping from 87 genera in 1994 to 58 genera in 2023 (Mirzajani et al., 2009). This study reveals a more than 13-fold decrease in zooplankton density compared to the 2013-2014 period. Specifically, the abundance of Rotifera and Protozoa in 2023 was reduced by 17-fold and 19-fold, respectively (Fallahi et al., 2018). The substantial decline in zooplankton density observed in this study suggests a disruption in the food chain within the Anzali Wetland ecosystem, which could lead to a further reduction in aquatic resources. The highest zooplankton abundance was recorded in the western wetland area, which remains the only intact section of the Anzali Wetland. However, this area has experienced significant changes, including a dramatic reduction in depth and near-total coverage by the invasive water hyacinth (Pontederia crassipes). Several factors have contributed to the decline in zooplankton biodiversity and density in the Anzali Wetland. These include the spread of non-native water hyacinth, decreased water depth, increased sedimentation, higher rates of water evaporation, reduced flow from rivers entering the wetland, climate change, the decrease in the Caspian Sea water levels, and a lack of proper wetland management. These cumulative pressures have worsened over the past decades. To address the issues affecting the Anzali Wetland and to restore the zooplankton community, which plays a critical role in the food chain and aquatic resources of this ecosystem, it is essential to prioritize the removal of non-native water hyacinth and accumulated sediments. Efforts should focus on the western, central, and southern areas of the wetland to help clean and increase its depth. These measures should be incorporated into the action plans of relevant executive organizations to revitalize the ecosystem. Conflict of interest According to the authors of this article, there is no conflict of interest. Acknowledgment This study was conducted in Anzali wetland with the approved code of 01051-011052-034-12-73-14. The colleagues of the ecology department, Javad Vesaghi, Yaqoub Ali Zahmtakesh, Omid Imani, and Reza Mohammadidoost, are grateful for their helps in sampling and laboratory works. 97 113 2024/11/192024/10/32025/01/262024/09/192025/01/172024/12/11 1403/9/21 2025/04/302025/04/302025/04/302025/04/302025/04/302025/04/30 1404/2/10 سیامک باقری Siamak Bagheri پژوهشکده آبزی پروری آبهای داخلی، موسسه تحقیقات علوم شیلاتی کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، بندرانزلی، ایران Iran Sia_Bagheri@yahoo.com علیرضا میرزاجانی Alireza Mirzajani پژوهشکده آبزی پروری آبهای داخلی، موسسه تحقیقات علوم شیلاتی کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، بندرانزلی، ایران Iran Siamakbp@gmail.com جلیل سبک آرا Jalil Sabkara پژوهشکده آبزی پروری آبهای داخلی، موسسه تحقیقات علوم شیلاتی کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، بندرانزلی، ایران Iran jsabkara@yahoo.com مهرداد نصری تجن Mehrdad Nasri Tajan گروه شیلات، دانشگاه آزاد اسلامی واحد بندرانزلی، ایران Iran nasri_mehrdad@yahoo.com Zooplankton Planktonic population Biodiversity Anzali Wetland زوپلانکتون جمعیت پلانکتونی تنوع زیستی تالاب انزلی Abdel-Aziz, N. E., Gharib, S.M., and Dorgham, M.M. 2006. The interaction between Phytoplankton and zooplankton in a Lake-Sea connection, Alexandria, Egypt. International Journal of Oceans and Oceanography, 1(1): 151–165.##APHA., 2005. Standard method for the examination of water and wastewater. American Public Health Association. Washigton, DC. 1265 P.##Bagheri, S. and Sabkara, J. 2023. The first report of non-native phytoplankton, Dinophysis acuminata from the Anzali shores, Caspian Sea. Iranian Scientific Fisheries Journal, 32 (3): 121–127. DOI: 10.22092/ISFJ.2023.130016. (In Persian).##Bagheri, S. 2012. Ecological assessment of plankton and effect of alien species in the south-western Caspian Sea. Universiti Sains Malaysia, Penang. 233 P.##Bagheri, S., Makaremi, M. and Mirzajani, A., 2019. Distribution, phytoplankton abundance and impact of Eichhornia crassipes in the Eynak Wetland, Guilan state, Iran. Iranian Scientific Fisheries Journal, 27 (6): 93–103. DOI: 10.22092/ISFJ.2019.118317 (In Persian).##Bagheri, S., Mashhor, M., Makaremi, M., Mirzajani, A., Babaei, H., Negarestan, H. and Wan-Maznah, W.O., 2010. Distribution and composition of phytoplankton in the southwestern Caspian Sea during 2001–2002, a comparison with previous surveys. World Journal Fish and Marine Sciences, 2: 416–426.##Bagheri, S., Niermann, U., Mansor, M., and Yeok, S. W. 2014. Biodiversity, distribution and abundance of zooplankton in the Iranian waters of the Caspian Sea off Anzali during 1996–2010. Journal of the Marine Biological Association UK, 94 (1):129–140. DOI: 10.1017/S0025315413001288##Bagheri, S., Sabkara, J., Yousefzad, E. and Zahmatkesh, Y. 2017. Ecological study of zooplankton communities in the Persian Gulf Martyrs Lake (Chitgar–Tehran) and the first report of the freshwater jellyfish Craspedacusta sp. (Cnidaria, Limnomedusae) in Iran. Iranian Scientific Fisheries Journal, 25 (5): 113–127. DOI: 10.22092/ISFJ.2017.110319. (In Persian).##Boyd, P. W., 2007. Mesoscale iron enrichment experiments 1993–2005: Synthesis and future directions. Science, 315: 612–617. DOI: 10.1126/science.1131669##Fallahi, K.M., Abedini, A., Sadeghinezhad, M.E., Ghane, S.A., and Abbasi, R.K. 2018. The trend of aquatic communities changes in Anzali Science Research Institute, Tehran. 124 P. Available at: http://hdl.handle.net/1834/14140. (accessed on 28 April 2024). (In Persian)##Golmarvi, D., Fallahi, M., Mashinchian, A.M., Fatemi, M. and Nadoshan, R. M., 2018. Study of Zooplankton Species Structure and Dominance in Anzali International Wetland. Open Journal of Marine Science, 8: 215–222. DOI: 10.4236/ojms.2018.82011##Holcik, J. and Olah, J., 1992. Fish, fisheries and water quality in Anzali lagoon and its watershed. Final Report. F1, UNDP/88/001. Rome. 46 P.##Hosseinpour, N., 1990. Anzali lagoon and the loads on it. Iranian Fisheries Research Organization. Technical Report. IFRO , Anzali. 9 P. (In Persian)##Khodaparast, H., 2002. Comprehensive fisheries studies of Anzali wetland. Guilan Province Fisheries Research Center. Technical Report, IFRO. Anzali. 204 P. (In Persian)##Khorasani, N. and Meygooni, G., 1987. Anzali wetland ecosystem survey. Journal of Natural Resources of Iran, 41(5): 44-53. (In Persian)##Kimball, K.D. and Kimball, S.F., 1995. Limnological studies of Anzali wetland. Iran Fisheries Company and Iran Environmental Protection Organization. Technical Report, IFRO. Anzali. 144 P. (In Persian).##Krebs, C.J., 1994. Ecological methodology. Second edition, U.K: An imprint of Addison Wesley Longman, UK. 620 P.##Mirzajani, A., Ghane, A., Bagheri, S., Abbasi, K., Sayadrahim, M., Salahi, M. and Lavajoo, F., 2020. Diet survey and trophic position of Macrobrachium nipponense in the food web of Anzali Wetland. Wetlands, 40 (5): 1229–1239. DOI: 10.1007/s13157-020-01278-5. ##Mirzajani, A., Roohi, J. and Mohammadidoost, R., 2019. Investigation of distribution and density of dominant aquatic plants in the western part of Anzali Lagoon. Journal of Plant Research, 33 (4): 1014–1024. (In Persian).##Mirzajani, A.R., Kiabi, B., Jamalzadeh, F., Fallahi, M., Kamali, A., Abdollahpour, H., Pourgholami M. A., Makaremi, M., Vatandoost, M., Babaei, H. and Abbasi, K., 2009. Limnological survay of Anzali wetland data during 1990-2003 by use of GIS system. Iranian Fisheries Science Research Institute, Iran. 124 P. (In Persian).##Mulani, S.K., Mule, M.B. and Patil, S.U., 2006. Studies on Water Quality and Zooplankton Community of Panchganga River in Kolhapur City. Journal of Environmental Biology, 30: 455-459. ##Nasrollahzadeh Saravi, H., Pourang, N., Foong, S.Y. and Makhlough, A., 2019. Eutrophication and trophic status using different indices: A study in the Iranian coastal waters of the Caspian Sea. Iranian Scientific Fisheries Journal, 18 (3): 531–543. DOI: 10.22092/ijfs.2018.117717.##Nezami, S., 1994. Limnological and ecological investigations of Anzali lagoon. Fisheries Research Organization for Guilan Province. Technical Report, IFRO. Iran. 214 P. (In Persian)##Piasecki, W., Goodwin A.E., Eiras J.C. and Nowak B.F., 2004. Importance of copepod in##Freshwater aquaculture. Zoological Studies, 43 (2): 193–205. ##Pontin, R. M., 1978. A key to fresh water planktonic and semiplanktonic Rotifera of the British Isles. Titus Wilson and son Publication,UK. 178 P.##Richardson, A J. 2008. In hot water: zooplankton and climate change. ICES Journal of Marine Science, 65: 279–295. DOI: 10.1093/icesjms/fsn028.##Roohi, A., Yasin, Z., Kideys, A.E., Hwai, A.T., Khanari, A.G. and Eker-Develi, E., 2008. Impact of a new invasive ctenophore (Mnemiopsis leidyi) on the zooplankton community of the Southern Caspian Sea. Marine Ecology, 29: 421–434. DOI: 10.1111/j.1439-0485.2008.00254.x##Ruttner-Kolisko, A., 1974. Plankton Rotifera biology and taxonomy. Verlagsbuchhandlung (Nagele U. Obermiller), Germany. 134 P.##Sabkara, J and Makaremi, M., 2015. The Atlas of plankton Anzali wetland and Caspian Sea Costal Waters. Iranian Fisheries Science Research Institute, Tehran. 656 P. (In Persian).##Sabkara, J. and Makaremi, M., 2004. Abundance and distribution pattern of planktons in Anzali Lagoon. Iranian Scientific Fisheries Journal, 13(3): 87–114. DOI: 10.22092/isfj.2004.113804. (In Persian)##Salveson, E., 2013. Effect of copepod density and water exchange on the egg production of Acartia tonsa Dana (Copepoda: Calanoida) feeding on Rhodomonas baltica. Norwegian University of Science, Norway. 154 P.##Thorp, J.H. and Covich, A.P., 2001. Ecology and classification of North American freshwater invertebrates. Academic Press, New York . 1056 P.## ##