Fluxible: an R package to calculate ecosystem gas fluxes from closed loop chamber systems in a reproducible and automated workflow

Gas fluxes measurements are widely used when assessing the impact of global change drivers on key aspects of ecosystem dynamics. In particular, gas fluxes help estimate the carbon balance of an ecosystem and the impacts of global changes. Ecosystem gas fluxes are typically calculated from field-measured gas concentrations over time using a linear, quadratic or exponential model and manually selecting good quality data. This approach is time consuming and prone to bias that can be amplified in further analyses, as well as presenting major reproducibility issues. The lack of a reproducible and bias-free approach creates challenges when combining global change studies to make biome and landscape scale comparisons.

The Fluxible R package aims to fill this critical gap with a workflow that removes individual evaluation of each flux, reduces risk of bias, and makes the process reproducible. Users set data quality standards and selection parameters as function arguments that are applied to the entire dataset. The current version of Fluxible provides flux calculation for a closed loop chamber system using linear, quadratic or exponential models. The latest update also includes an automated segmentation tool to process data from a leaky setup such as with flux tents, where leakage cannot be assumed negligible. This automated and fully reproducible segmentation tool is a major upgrade as it allows the use of the Fluxible workflow in setups that are prone to leaks or other disturbances that previously had to be taken care of manually.

The package runs the calculations automatically without prompting the user to make decisions, and provides plots for visual check and a quality summary of the dataset at the end of the process. These outputs make it easier to process large flux datasets and to integrate the package into a reproducible workflow. Using the Fluxible R package makes the workflow reproducible, increases compatibility across studies, and is more time efficient.