fluxible: An R package to process ecosystem gas fluxes from closed-loop chambers in an automated and reproducible way

Article accepted in Methods in Ecology and Evolution describing the R package fluxible for automated processing of ecosystem gas flux data.

Authors

Gaudard, J.

Telford, R. J.

Chacon-Labella, J.

Dawson, H. R.

Enquist, B. J.

Töpper, J. P.

Trepel, J.

Vandvik, V.

Baumane, M.

Birkeli, K.

Holle, M. J. M.

Hupp, J. R.

Satriawan, T. W.

Halbritter, A. H.

Paul Efren Santos-Andrade

Published

December 31, 2024

Abstract

Measuring ecosystem gas fluxes is fundamental for understanding ecosystem carbon and greenhouse gas dynamics. However, raw chamber data require extensive processing, and heterogeneous workflows reduce transparency and comparability across studies. The fluxible R package provides a fully reproducible and automated workflow to process gas concentration time-series from closed-loop chambers into analysis-ready flux estimates. The package implements multiple regression models (linear, quadratic, exponential), objective model-quality diagnostics, visual inspection tools, and flexible unit conversion supporting different chamber configurations. We validate fluxible using independent datasets and compare it with existing software, showing strong agreement and improved transparency. The package facilitates harmonised analysis of CO2, CH4 and N2O fluxes and integration into Open Science pipelines.

Journal article (Open Access)

Abstract

Measuring ecosystem gas fluxes quantifies how resources are allocated in ecosystems and is essential for predicting future global warming. Fluxes measured with closed-loop chambers require extensive post-processing, which is often inconsistent among studies. The fluxible R package provides an automated and reproducible workflow to convert raw gas concentration data into quality-checked flux estimates. It includes modules for metadata matching, model fitting, quality assessment, plotting, and flux calculation, supporting linear, quadratic and exponential models. We validate fluxible using LI-COR datasets and demonstrate high correlation with SoilFluxPro outputs. The workflow improves transparency, comparability and integration with Open Science practices.