Confronting Missing Ecological Data in the Age of Pandemic Lockdown

• Main Authors: Thomas J. Hossie, Jenilee Gobin, Dennis L. Murray
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-08-01
• Series: Frontiers in Ecology and Evolution
• Subjects: data missingness; data analysis; imputation; missingness mechanisms; data gap; full information maximum likelihood
• Online Access: https://www.frontiersin.org/articles/10.3389/fevo.2021.669477/full

The COVID-19 pandemic profoundly affected research in ecology and evolution, with lockdowns resulting in the suspension of most research programs and creating gaps in many ecological datasets. Likewise, monitoring efforts directed either at tracking trends in natural systems or documenting the environmental impacts of anthropogenic activities were largely curtailed. In addition, lockdowns have affected human activity in natural environments in ways that impact the systems under investigation, rendering many widely used approaches for handling missing data (e.g., available case analysis, mean substitution) inadequate. Failure to properly address missing data will lead to bias and weak inference. Researchers and environmental monitors must ensure that lost data are handled robustly by diagnosing patterns and mechanisms of missingness and applying appropriate tools like multiple imputation, full-information maximum likelihood, or Bayesian approaches. The pandemic has altered many aspects of society and it is timely that we critically reassess how we treat missing data in ecological research and environmental monitoring, and plan future data collection to ensure robust inference when faced with missing data. These efforts will help ensure the integrity of inference derived from datasets spanning the COVID-19 lockdown and beyond.