Associate Professor
Department of Integrative Biology
Ecology, Evolution, and Behavior Program Director
Michigan State University
Abstract
Emerging data integration approaches 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. 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. Multi-scaled 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. I will highlight these benefits by showcasing a research case study examining the recent decline of monarch butterflies across eastern North America. I will also present some of the key ongoing challenges of integrated modeling that are exacerbated in broad and multi-scale research such data scale mismatches, unbalanced data, sampling biases, and model development and assessment. Use of data integration techniques has increased rapidly in recent years. Given the inferential value of such approaches, we should expect sustained development and wider application across ecological disciplines.
