Climate change is rapidly affecting the seasonal timing of spatial demographic processes. Consequently, resource managers require information from models that simultaneously measure seasonal, interannual, and spatial variation. We present a spatio-temporal model that includes annual, seasonal, and spatial variation in density and then highlight two important uses: (i) standardizing data that are spatially unbalanced within multiple seasons and (ii) identifying interannual changes in seasonal timing (phenology) of population processes. We demonstrate these uses with two contrasting case studies: three bottom trawl surveys for yellowtail flounder (Limanda ferruginea) in the Northwest Atlantic Ocean from 1985 to 2017 and pelagic tows for copepodite stage 3+ copepod (Calanus glacialis/marshallae) densities in the eastern Bering Sea from 1993 to 2016. The yellowtail analysis illustrates how data from multiple surveys can be used to infer density hot spots in an area that is not sampled one or more surveys. The copepod analysis assimilates seasonally unbalanced samples to estimate an annual index of the seasonal timing of copepod abundance and identifies a positive correlation between this index and cold-pool extent. We conclude by discussing additional potential uses of seasonal spatio-temporal models and emphasize their ability to identify climate-driven shifts in the seasonal timing of fish movement and ecosystem productivity.

中文翻译：

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用于索引标准化和物候研究的时空模型的季节性和年际变化

气候变化正在迅速影响空间人口统计过程的季节性时机。因此，资源管理器需要来自模型的信息，这些模型可以同时测量季节，年际和空间变化。我们提出了一个时空模型，其中包括密度的年度，季节和空间变化，然后重点介绍了两个重要用途：（i）标准化多个季节内空间上不平衡的数据，以及（ii）确定季节时间的年际变化（物候学）人口过程。我们通过两个对比案例研究证明了这些用途：1985年至2017年在西北大西洋对黄尾比目鱼（Limanda ferruginea）进行了三个海底拖网调查，以及3d + copepod（Calanus glacialis / marshallae）的浮游丝束1993年至2016年白令海东部的密度）。黄尾鱼分析说明了如何使用多项调查的数据来推断未采样一项或多项调查的区域中的密度热点。pe足类动物分析会同化季节不平衡的样本，以估计co足类足动物丰盛季节的年度指数，并确定该指数与冷水池范围之间的正相关关系。最后，我们讨论了季节性时空模型的其他潜在用途，并强调了它们在鱼类运动和生态系统生产力的季节性变化中识别气候驱动的变化的能力。