Data integration is a statistical modeling approach that incorporates multiple data sources within a unified analytical framework. Macrosystems ecology – the study of ecological phenomena at broad scales, including interactions across scales – increasingly employs data integration techniques to expand the spatiotemporal scope of research and inferences, increase the precision of parameter estimates, and account for multiple sources of uncertainty in estimates of multiscale processes. We highlight four common analytical challenges to data integration in macrosystems ecology research: data scale mismatches, unbalanced data, sampling biases, and model development and assessment. We explain each problem, discuss current approaches to address the issue, and describe potential areas of research to overcome these hurdles. Use of data integration techniques has increased rapidly in recent years, and given the inferential value of such approaches, we expect continued development and wider application across ecological disciplines, especially in macrosystems ecology.

中文翻译：

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应对数据集成挑战，以跨规模链接生态过程

数据集成是一种统计建模方法，可以将多个数据源合并到一个统一的分析框架中。宏观系统生态学-广泛尺度上的生态现象研究，包括跨尺度的相互作用-越来越多地采用数据集成技术来扩大研究和推论的时空范围，提高参数估计的精度，并考虑到多尺度估计中的多种不确定性来源流程。我们重点介绍了宏观系统生态学研究中数据集成面临的四个常见分析挑战：数据规模不匹配，数据不平衡，抽样偏差以及模型开发和评估。我们将解释每个问题，讨论解决该问题的当前方法，并描述克服这些障碍的潜在研究领域。