Zipkin, Elise F.Zylstra, Erin R.Wright, Alexander D.Saunders, Sarah P.Finley, Andrew O.Dietze, Michael C.Itter, Malcolm S.Tingley, Morgan W.2021-10-222021-10-222021-02E.F. Zipkin, E.R. Zylstra, A.D. Wright, S.P. Saunders, A.O. Finley, M.C. Dietze, M.S. Itter, M.W. Tingley. 2021. "Addressing data integration challenges to link ecological processes across scales." Frontiers in Ecology and the Environment, Volume 19, Issue 1, pp. 30 - 38. https://doi.org/10.1002/fee.22901540-92951540-9309https://hdl.handle.net/2144/43208Data 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.p. 30 - 38en-US© 2021 The Authors. Frontiers in Ecology and the Environment published by Wiley Periodicals LLC on behalf of the Ecological Society of America. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.http://creativecommons.org/licenses/by/4.0/Article10.1002/fee.2290616509