Improving ecological data science with workflow management software

Pressing environmental research questions demand the integration of increasingly diverse and large-scale ecological datasets as well complex analytical methods, which require specialized tools resources. Computational training for evolutionary sciences has become more abundant accessible over past decade, but tool development outpaced availability training. Most scripted analyses focuses on individual analysis steps in one script rather than creating a pipeline, where modular functions comprise an ecosystem interdependent steps. Although current computational creates excellent starting place, linear styles scripting can risk becoming labor- time-intensive less reproducible by often requiring manual execution. Pipelines, however, be easily automated or tracked software to increase efficiency reduce potential errors. Ecology evolution would benefit from techniques that these risks managing pipelines modular, readily parallelizable format with clear documentation dependencies. Workflow management (WMS) aid reproducibility, intelligibility pipelines. To date, WMS adoption ecology been slow. We discuss benefits challenges implementing illustrate its use through case study targets r package further highlight workflow automation, dependency tracking improved clarity reviewers. requires familiarity function-oriented programming careful planning advanced applications pipeline sharing, investment will enable access impart transferable computing skills facilitate data science at large scales.