Commercial landings of sardines are known to show strong year-to-year fluctuations. A key driver is thought to be environmental variability, to which small forage fish are especially sensitive. We examined the utility of including environmental covariates in forecasts for landings of the Indian oil sardine using a long-term time series of quarterly catches. Potentially influential variables examined included precipitation, upwelling intensity, sea surface temperature (SST), and chlorophyll-a concentration. All of these have been shown to be important for oil sardine growth and survival, spawning and/or movement into the nearshore fishing regions. However, improving out-of-sample landings forecasts using environmental covariates has often proven elusive. We tested the inclusion of environmental covariates in forecast models using generalized additive models, which allow for non-linear responses, and dynamic linear models, which allow for time-varying responses. Only two environmental covariates improved out-of-sample prediction: the 2.5-year average regional SST and precipitation over land during June–July. The most significant improvement was with the SST covariate and post-monsoon landings with a 19%–22% reduction in mean-squared prediction error. Models with the second best covariate, monsoon precipitation over land, provided a 4%–8% reduction in prediction error. We also tested large-scale ocean climate teleconnection indices. One, an index of the Atlantic Multidecadal Oscillation, also improved out-of-sample predictions similarly to the multiyear average regional SST. The earth's changing climate is associated with both rapid warming in the Western Indian Ocean and changes to monsoon rainfall patterns. Our work highlights these as key variables that can improve forecasting of oil sardine landings.

中文翻译：

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使用环境协变量改进上岸量预测：印度油沙丁鱼（Sardinella longiceps）的案例研究

众所周知，沙丁鱼的商业上岸量呈现出强烈的逐年波动。一个关键的驱动因素被认为是环境变化，小型饲料鱼对环境变化特别敏感。我们使用季度捕获量的长期时间序列研究了将环境协变量纳入印度油沙丁鱼上岸量预测的效用。检查的潜在影响变量包括降水、上升流强度、海面温度 (SST) 和叶绿素-a 浓度。所有这些都被证明对油沙丁鱼的生长和存活、产卵和/或进入近岸捕鱼区很重要。然而，使用环境协变量改进样本外着陆预测往往难以实现。我们使用允许非线性响应的广义加性模型和允许时变响应的动态线性模型测试了在预测模型中包含环境协变量的情况。只有两个环境协变量改进了样本外预测：2.5 年平均区域海温和 6 月至 7 月的陆地降水。最显着的改进是 SST 协变量和季风后着陆，均方预测误差降低了 19%–22%。具有次佳协变量、陆地季风降水的模型可将预测误差降低 4%–8%。我们还测试了大规模海洋气候遥相关指数。一个是大西洋多年代际振荡指数，与多年平均区域 SST 类似，它也改进了样本外预测。地球' 气候变化与西印度洋的快速变暖和季风降雨模式的变化有关。我们的工作强调这些是可以改进油沙丁鱼上岸预测的关键变量。