In ecology we may find scenarios where the same phenomenon (species occurrence, species abundance, etc.) is observed using two different types of samplers. For instance, species data can be collected from scientific surveys with a completely random sample pattern, but also from opportunistic sampling (e.g., whale or bird watching fishery commercial vessels), in which observers tend to look for a specific species in areas where they expect to find it. Species Distribution Models (SDMs) are a widely used tool for analyzing this kind of ecological data. Specifically, we have two models available for the above data: an independent model (IM) for the data coming from a complete random sampler and a dependent model (DM) for data from opportunistic sampling. In this work, we propose a sequential Bayesian procedure to connect these two models through the update of prior distributions. Implementation of the Bayesian paradigm is done through the integrated nested Laplace approximation (INLA) methodology, a good option to make inference and prediction in spatial models with high performance and low computational costs. This sequential approach has been evaluated by simulating several scenarios and comparing the results of sharing information from one model to another using different criteria. Our main results imply that, in general, it is better to share information from the independent (completely random) to the dependent model than the alternative way. However, it depends on different factors such as the spatial range or the spatial arrangement of sampling locations.

翻译：在生态学中，我们可能会遇到使用两种不同类型采样器观测同一现象（如物种出现、物种丰度等）的场景。例如，物种数据既可通过完全随机采样模式的科学调查收集，也可来自机会性采样（如观鲸、观鸟或渔业商船），其中观察者倾向于在期望发现特定物种的区域进行搜寻。物种分布模型（SDMs）是分析此类生态数据的常用工具。具体而言，针对上述数据我们有两种可用模型：针对完全随机采样数据建立的独立模型（IM）和针对机会性采样数据建立的依赖模型（DM）。本研究提出了一种序贯贝叶斯方法，通过先验分布的更新将这两个模型联系起来。贝叶斯范式的实现采用了集成嵌套拉普拉斯近似（INLA）方法，该方法能够以高性能和低计算成本对空间模型进行推断与预测。我们通过模拟多种场景并采用不同标准评估模型间信息共享的效果来验证该序贯方法。主要结果表明：总体而言，信息从独立模型（完全随机）向依赖模型传递的效果优于反向传递。但该结论依赖于空间范围或采样点空间布局等不同因素。