Climate change shows itself in many different ways on marine life. The fishery is also a part of marine life and affected by climate change-driven weather conditions directly or indirectly. In the present study, relationships between commercial species (grey mullet -$\approx$90% of total capture- and gilthead seabream) that were captured from lagoon traps in Köyceğiz lagoon (Turkey) and local weather conditions were analysed. The machine learning method Random Forests (RF) was used to pre-select the model predictors. RF results showed that while temperature-related parameters, cloudy days, and wind speed were the most effective parameters, precipitation-parameters were the least important parameters for these two species catch. Generalized linear models (GLMs) were applied to each fish species with the best pre-selected parameters, with the resulting equation being used for future prediction of the two fish species. Future prediction of predictors was calculated by monthly autoregressive integrated moving average (ARIMA) and 20th/80th percentile intervals were used as the scenarios. Simulations showed that an increase in some weather parameters (wind speed, seawater temperature, maximum air temperature, cloudy days) lead to an increase in grey mullet and (wind speed) gilthead seabream catch. Models proved that the impact of the weather parameters differs for those two targeted fish species although they live in the same environment. We recommend that individual fish species (and/or catch) should be used in the models, not the whole fish yield. Moreover, the model can also be used for non-commercial species in ecosystem-based studies. Changes in weather parameters due to climate change should be monitored to make proper decision on fishery management.

气候变化以许多不同的方式影响海洋生物。渔业也是海洋生物的一部分，直接或间接受到气候变化驱动的天气条件的影响。在本研究中，商业物种（鲻鱼 -$\approx$对从 Köyceğiz 泻湖（土耳其）的泻湖陷阱捕获的捕获和金头鲷总数的 90% 和当地天气条件进行了分析。机器学习方法随机森林 (RF) 用于预选模型预测器。RF 结果表明，虽然与温度相关的参数、阴天和风速是最有效的参数，但降水参数是这两种捕捞的最不重要的参数。将广义线性模型 (GLM) 应用于具有最佳预选参数的每种鱼类，所得方程用于未来预测两种鱼类。预测变量的未来预测是通过每月自回归综合移动平均 (ARIMA) 计算的，并且使用 20th/80th 百分位区间作为场景。模拟显示，某些天气参数（风速、海水温度、最高气温、阴天）的增加导致鲻鱼和（风速）金头鲷的捕获量增加。模型证明，尽管这两种目标鱼类生活在相同的环境中，但天气参数的影响是不同的。我们建议模型中应使用个别鱼类（和/或渔获量），而不是整条鱼的产量。此外，该模型还可用于基于生态系统的研究中的非商业物种。应监测因气候变化引起的天气参数变化，以做出正确的渔业管理决策。模型证明，尽管这两种目标鱼类生活在相同的环境中，但天气参数的影响是不同的。我们建议模型中应使用个别鱼类（和/或渔获量），而不是整条鱼的产量。此外，该模型还可用于基于生态系统的研究中的非商业物种。应监测因气候变化引起的天气参数变化，以做出正确的渔业管理决策。模型证明，尽管这两种目标鱼类生活在相同的环境中，但天气参数的影响是不同的。我们建议模型中应使用个别鱼类（和/或渔获量），而不是整条鱼的产量。此外，该模型还可用于基于生态系统的研究中的非商业物种。应监测因气候变化引起的天气参数变化，以做出正确的渔业管理决策。