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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.

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Abstract

During this presentation, Joe will outline why a researcher might choose to analyze their complex data sets with Bayesian inference, over frequentist (i.e., more conventional) approaches. Joe will show a brief example of conducting a multiple linear regression and ranking predictor influence, using the statistical programming language Stan, which he will interface with R packages `rstan` and `rstanarm`.

Bio

Joe is an Assistant Professor in SICCS, and his research focuses on building and testing mathematical models that represent how environmental heterogeneities influence the spread of infectious diseases in wildlife and human populations. For example, one NSF-funded project seeks to understand the interactions between climate change and infectious disease in explaining long-term stability of ectotherm populations. One NIH-funded project is building software to implement and test spatial models of infectious disease transmission of humans, to increase the standardization and fidelity of model forecasts. Joe received his BA in Environmental Sciences from WashU in St. Louis, got his PhD in Ecology & Evolutionary Bio at Univ of Colorado-Boulder, and then did a postdoc at Univ of Chicago, and one at Univ of California-Santa Barbara before coming to NAU in 2018.