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 sampling 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 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: a geostatistical model (GM) for the data coming from a complete random sampler and a preferential model (PM) for data from opportunistic sampling. Integration of information coming from different sources can be handled via expert elicitation and integrated models. We focus here in a sequential Bayesian procedure to connect 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. The procedure has also been exemplified with a real dataset. Our main results imply that, in general, it is better to share information from the independent (completely random) to the preferential 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，物种数据可以通过完全随机样本模式的科学采样获取，也可通过机会性采样（如鲸类或鸟类观测、渔业商船）收集，后者中观测者倾向于在预期发现特定物种的区域进行观测。物种分布模型（SDMs）是分析这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：用于完全随机采样数据的空间统计模型（GM）和用于机会性采样数据的偏好模型（PM）。不同来源信息的整合可通过专家启发和集成模型实现。本文聚焦于通过先验分布更新的顺序贝叶斯方法来连接两个模型。贝叶斯范式的实现采用集成嵌套拉普拉斯近似（INLA）方法，该方法以高性能和低计算成本进行空间模型推断与预测。通过模拟多种场景并使用不同准则比较模型间信息共享的结果，对该顺序方法进行了评估。该流程也通过实际数据集进行了实例验证。主要结果表明：通常从独立（完全随机）模型向偏好模型共享信息的效果优于反向传递。然而，该结论取决于空间范围、采样点空间布局等不同因素。