Data integration, the joint statistical analysis of data from different observation platforms, is pivotal for data‐hungry disciplines such as spatial ecology. Pooled data types obtained from the same underlying process, analyzed jointly, can improve both precision and accuracy in models of species distributions and species–habitat associations. However, the integration of telemetry and spatial survey data has proved elusive because of the fundamentally different analytical approaches required by these two data types. Here, “spatial survey” denotes a survey that records spatial locations and has no temporal structure, for example, line or point transects but not capture–recapture or telemetry. Step selection functions (SSFs—the canonical framework for telemetry) and habitat selection functions (HSFs—the default approach to spatial surveys) differ in not only their specifications but also their results. By imposing the constraint that microscopic mechanisms (animal movement) must correctly scale up to macroscopic emergence (population distributions), a relationship can be written between SSFs and HSFs, leading to a joint likelihood using both datasets. We implement this approach using maximum likelihood, explore its estimation precision by systematic simulation, and seek to derive broad guidelines for effort allocation in the field. We find that complementarities in spatial coverage and resolution between telemetry and survey data often lead to marked inferential improvements in joint analyses over those using either data type alone. The optimal allocation of effort between the two methods (or the choice between them, if only one can be selected) depends on the overall effort expended and the pattern of environmental heterogeneity. Examining inferential performance over a broad range of scenarios for the relative cost between the two methods, we find that integrated analysis usually offers higher precision. Our methodological work shows how to integrate the analysis of telemetry and spatial survey data under a novel joint likelihood function, using traditional computational methods. Our simulation experiments suggest that even when the relative costs of the two methods favor the deployment of one field approach over another, their joint use is broadly preferable. Therefore, joint analysis of the two key methods used in spatial ecology is not only possible but also computationally efficient and statistically more powerful.

中文翻译：

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遥测和空间调查数据的联合推理


数据集成，即对来自不同观测平台的数据进行联合统计分析，对于空间生态学等数据密集型学科至关重要。从同一基础过程获得的合并数据类型，联合分析，可以提高物种分布和物种-栖息地关联的模型的精度和准确性。然而，遥测和空间调查数据的集成已被证明是难以捉摸的，因为这两种数据类型所需的分析方法根本不同。在这里，“空间测量”表示记录空间位置且没有时间结构的测量，例如，线或点样带，但不具有捕获-重新捕获或遥测。步长选择函数 （SSF - 遥测的规范框架）和栖息地选择函数 （HSF - 空间调查的默认方法） 不仅在规格上有所不同，而且在结果上也有所不同。通过施加微观机制（动物运动）必须正确扩展到宏观出现（种群分布）的约束，可以在 SSF 和 HSF 之间编写一种关系，从而使用两个数据集得出联合似然。我们使用最大似然实现这种方法，通过系统模拟探索其估计精度，并寻求为该领域的努力分配得出广泛的指导方针。我们发现，遥测和调查数据之间空间覆盖范围和分辨率的互补性通常会导致联合分析的推理比单独使用任何一种数据类型的分析有显着的推理改进。两种方法之间的最佳努力分配（或它们之间的选择，如果只能选择一种方法）取决于所花费的总体努力和环境异质性的模式。 通过检查两种方法之间相对成本的广泛场景中的推理性能，我们发现集成分析通常提供更高的精度。我们的方法工作展示了如何使用传统的计算方法将遥测和空间调查数据的分析整合到一个新的联合似然函数下。我们的模拟实验表明，即使这两种方法的相对成本有利于部署一种现场方法而不是另一种，它们的联合使用也普遍更可取。因此，对空间生态学中使用的两种关键方法进行联合分析不仅是可能的，而且计算效率更高，统计功能更强大。