Emerging data integration approaches, such as integrated population models (IPMs) and integrated distribution models (IDMs), incorporate multiple data sources within a unified analytical framework. Such approaches are exceptionally valuable for research conducted at broad extents or across multiple scales as it is rarely possible to estimate all ecological parameters of interest using only a single data source. Interacting and nonlinear processes as well as data limitations can impede multi-scaled research, but data integration offers an opportunity to overcome typical constraints. Multiple data sources can inform various components of the study system that operate at different spatial or temporal scales, providing unique or complementary information on biological patterns and/or processes. Broad-scale studies increasingly use data integration techniques to improve precision of parameter estimates, account for multiple sources of uncertainty, estimate parameters for which no explicit data exists, and produce predictions of future ecosystem states and processes across space and time. As a result of these advantages, data integration has become a powerful approach for expanding the spatiotemporal coverage of research, allowing for ecological inference at unprecedented scales.
Results/Conclusions
After highlighting the many benefits of data integration in ecological research, I will present several key challenges of integrated modeling that are exacerbated in broad and multi-scale research. Specifically, I will introduce and discuss issues related to: data scale mismatches, unbalanced data, sampling biases, and model development and assessment. I will review current approaches and limitations for addressing these inferential hurdles, summarize strategies for integrating data across scales, and conclude with potential avenues of future research. Use of data integration techniques has increased rapidly in recent years, and given the inferential value of such approaches, we should expect sustained development and wider application across ecological disciplines. Continued methodological advancements of data integration models, such as incorporation of a wider set of data types (e.g., citizen science data) and coupled population-environmental models, will allow for expanded applicability within basic and applied ecological research and science focused at the interface of conservation and management.