With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise.

中文翻译：

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大规模物种分布模型的数据集成。

随着所收集的生物多样性数据的数量和类型的增加，需要找到方法来组合这些不同的来源，以提供有关物种潜力和时空分布的凝聚性总结。最近，基于模型的数据集成已成为通过以保留每个数据集优势的方式组合数据集来实现此目的的一种手段。我们介绍了一种使用点过程模型进行数据集成的灵活方法，该方法提供了一种跨生态货币进行换算的便捷方法。我们重点介绍了基于数据集成的大型生态模型的最新示例，并概述了概念和技术挑战以及随之而来的机遇。