Fish populations respond to environmental change with daily, monthly, and annual time delays, depending on each species life cycle. However, these delays are rarely included in Generalized Additive Models (GAMs) for species distribution that uses time-series data. Therefore, the predictions of these models entirely rely on assumptions of immediate fish response to oceanic factors. Spatial autocorrelation is also an issue for GAMs because datasets of fish occurrence usually exhibit this property, and though it has been progressively considered for modeling, it is still frequently ignored. These problems cause low model performance, unstable predictions and more importantly, wrong conclusions for fisheries management. We built and applied Generalized Additive Models with spatial terms and delayed effects of oceanic covariates (SDE-GAMs) to investigate model performance and prediction power for the spatiotemporal distribution of the skipjack (Katsuwonus pelamis), a species of commercial importance across the Exclusive Economic Zone (EEZ) of the Colombian Pacific Ocean. We used satellite-derived Surface Sea Temperature (SST), Sea Level Anomaly (SLA), and Chlorophyll-a (CHLA) as predictors for the Catch Per Unit of Effort (CPUE), considering monthly delayed covariate effects and spatial terms at intra-annual cycles. We evaluated performance improvement of SDE-GAMs compared to that of traditional GAMs (T-GAMs: only immediate covariate effects) and spatial GAMs (S-GAMs: immediate covariate effects plus spatial terms). The model performance of SDE-GAMs was on average 25.4% higher, while its prediction error was on average 43% lower. One, two and three-month delayed SST effects were the primary drivers of CPUE throughout the intra-annual cycle across the EEZ. SDE-GAMs were able to predict both general patterns and smaller details of the spatiotemporal distribution of skipjack, capturing sub-regional differentiation with high importance for management and decision making.

鱼类种群对环境变化的响应每天，每月和每年都有时间延迟，具体取决于每个物种的生命周期。但是，这些延迟很少包含在使用时间序列数据进行物种分布的通用加性模型（GAM）中。因此，这些模型的预测完全依赖于鱼类对海洋因素立即做出反应的假设。空间自相关也是GAM的一个问题，因为鱼类发生的数据集通常显示此属性，尽管已逐渐考虑将其用于建模，但仍经常被忽略。这些问题导致模型性能低下，预测不稳定，更重要的是导致渔业管理结论错误。泡菜），这是横跨哥伦比亚太平洋专属经济区（EEZ）的一种具有商业重要性的物种。考虑到每月延迟的协变量效应和空间内部的空间条件，我们使用了卫星衍生的地表海水温度（SST），海平面异常（SLA）和叶绿素a（CHLA）作为预报单位努力量（CPUE）的指标。年度周期。我们评估了SDE-GAM与传统GAM（T-GAM：仅立即协变量效应）和空间GAM（S-GAM：直接协变量效应加空间项）相比，性能的提高。SDE-GAM的模型性能平均提高25.4％，而其预测误差平均降低43％。在整个专属经济区的年内周期中，一，两个和三个月的SST延迟效应是CPUE的主要驱动力。