根据优先取样的生物探测器模拟海洋温度
Modelling ocean temperatures from bio-probes under preferential sampling
摘要：
在过去的25年里，从动物身上安装的传感器（生物探针）收集的数据量有了很大的增加，这些传感器通常用于研究动物的行为或环境。在这里，我们专注于后者的一个例子，其中的兴趣是在海面温度（SST），并从安装在南印度洋的象海豹传感器的测量。我们表明，标准的地质统计模型可能是不可靠的这种类型的数据，由于动物访问的区域可能取决于SST的可能性。这种现象在文献中被称为优先采样，如果忽略，它可能会影响得到的空间预测和参数估计。关于这一专题的研究大多局限于监测点等固定取样地点。这份手稿的主要贡献是将这种方法扩展到通过感兴趣区域移动的设备获得的观测结果，就像标记的海豹一样。更具体地说，我们提出了一个灵活的框架，优先采样字段的推理，其中生成采样位置的过程是随机的，并随着时间的推移通过一个2维空间移动。我们的模拟研究证实，从优先抽样模型得到的预测是更可靠的，当这种现象存在时，他们比较非常好的标准时，没有优先抽样。最后，我们注意到，我们的SST数据分析的结论可能会发生很大变化，当我们在模型中纳入优先抽样。
英文摘要：
In the last 25 years there has been an important increase in the amount of data collected from animal-mounted sensors (bio-probes), which are often used to study the animals' behaviour or environment. We focus here on an example of the latter, where the interest is in sea surface temperature (SST), and measurements are taken from sensors mounted on Elephant Seals in the Southern Indian ocean. We show that standard geostatistical models may not be reliable for this type of data, due to the possibility that the regions visited by the animals may depend on the SST. This phenomenon is known in the literature as preferential sampling, and, if ignored, it may affect the resulting spatial predictions and parameter estimates. Research on this topic has been mostly restricted to stationary sampling locations such as monitoring sites. The main contribution of this manuscript is to extend this methodology to observations obtained by devices that move through the region of interest, as is the case with the tagged seals. More specifically, we propose a flexible framework for inference on preferentially sampled fields, where the process that generates the sampling locations is stochastic and moving over time through a 2-dimensional space. Our simulation studies confirm that predictions obtained from the preferential sampling model are more reliable when this phenomenon is present, and they compare very well to the standard ones when there is no preferential sampling. Finally, we note that the conclusions of our analysis of the SST data can change considerably when we incorporate preferential sampling in the model.
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