Abstract
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.
Highlights
Addressing data integration challenges to link ecological processes across scales Elise F
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 review the most common statistical challenges related to data integration in macrosystems ecology and discuss ways in which they can be overcome
Summary
Addressing data integration challenges to link ecological processes across scales Elise F Zipkin1,2*, Erin R Zylstra, Alexander D Wright, Sarah P Saunders, Andrew O Finley, Michael C Dietze, Malcolm S Itter, and Morgan W Tingley. Combining disvan et al 2014; LaSorte et al 2018; Saunders et al 2019b) Such parate data sources to create increasingly complex models programs can provide vast amounts of data to inform species may not always result in improved inferences if the necessary distributions, relative abundance, and phenology, but collection assumptions are untenable or too restrictive, data on one efforts are often focused near urban areas, roads, or other loca- or more aspects of the system are severely limited, or the tions with high human population densities A wide array of strategies have been proposed to account to validate model fit may not be feasible for integrated macfor sampling biases, depending on the amount and type of data rosystems analyses because of data limitations and/or logistical
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