Clupeonella caspia fishing in the Caspian Sea has played a significant role in the regional fisheries economy over the past decades. However, a sharp decline in the stock of this species in recent years due to overfishing, environmental changes, and the introduction of invasive species has posed major challenges to the sustainable management of these stocks. This study employs the Stock Reduction Analysis (SRA) model to assess biomass changes and predict the allowable catch of the Clupeonella caspia in the southern waters of the Caspian Sea. This research utilizes historical catch and biometric data of kilka species over the past two decades and applies the SRA model to analyze stock trends and sustainable harvesting rates. Various management scenarios, including fishing effort control, harvest limitations, and setting catch quotas, have been examined. The findings indicate a declining trend in kilka stock, with improper exploitation threatening the biological and economic sustainability of the industry. A comparison of the results with previous studies reveals an increasing share of Clupeonella caspia in the catch composition and a decrease in anchovy Clupeonella caspia harvests. Furthermore, the simulation of management scenarios suggests that implementing catch restrictions and optimizing fishing effort can play a crucial role in the recovery of this species' stock. Based on the study’s findings, it is essential to develop and implement sustainable fisheries management policies, such as setting allowable catch limits and regulating fishing efforts. Utilizing stock prediction models can assist policymakers in making informed and sustainable decisions. This study recommends continuous monitoring of Clupeonella caspia stocks and periodic assessment of their biological status to maintain ecological balance and ensure the long-term economic sustainability of the fisheries sector.
Khedmati K, Gholizadeh M, Fazli H, Raeisi H. Prediction of Biomass Changes and Catch Rate of the Clupeonella caspia in the Caspian Sea Using the Stock Reduction Analysis (SRA) Method. JAIR 2024; 12 (4) :71-80 URL: http://jair.gonbad.ac.ir/article-1-919-en.html