Planned missing data design: stronger inferences, increased research efficiency and improved animal welfare in ecology and evolution

Abstract

O_LIEcological and evolutionary research questions are increasingly requiring the integration of research fields along with larger datasets to address fundamental local and global scale problems. Unfortunately, these agendas are often in conflict with limited funding and a need to balance animal welfare concerns.\nC_LIO_LIPlanned missing data design (PMDD), where data are randomly and deliberately missed during data collection, is a simple and effective strategy to working under greater research constraints while ensuring experiments have sufficient power to address fundamental research questions. Here, we review how PMDD can be incorporated into existing experimental designs by discussing alternative design approaches and evaluating how data imputation procedures work under PMDD situations.\nC_LIO_LIUsing realistic examples and simulations of multilevel data we show how a variety of research questions and data types, common in ecology and evolution, can be aided by using a PMDD with data imputation procedures. More specifically, we show how PMDD can improve statistical power in detecting effects of interest even with high levels (50%) of missing data and moderate sample sizes. We also provide examples of how PMDD can facilitate improved animal welfare and potentially alleviate research costs and constraints that would make endeavours for integrative research challenging.\nC_LIO_LIPlanned missing data designs are still in their infancy and we discuss some of the difficulties in their implementation and provide tentative solutions. Nonetheless, data imputation procedures are becoming more sophisticated and more easily implemented and it is likely that PMDD will be an effective and powerful tool for a wide range of experimental designs, data types and problems in ecology and evolution.\nC_LI