Effect of changing in weather conditions on Eastern Mediterranean coastal lagoon fishery

Abstract

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 -≈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.