Latest updates to the goFlux R package: new functionalities and improvements

The goFlux R package was designed as an all-inclusive flux calculation tool to calculate chamber GHG fluxes. goFlux allows for an easy import of raw data directly into R from a variety of instruments; simplifies identification of start and end times of individual flux measurements; quality checks the results based on objective criteria that goes beyond simply using R²; and provides the user with a recommendation for the best flux estimate.

In the last year, goFlux has been improved with new functionalities and additional ongoing improvements, including auto.ID for an automatic selection of the observation window (no more clicking!), iso.comp for isotopic composition determination of different isotope ratios (¹³C, ¹⁵N or ¹⁸O), crop.meas for additional pre-processing of data after import (e.g., adding a deadband or cropping measurements), and auto.deadband for an automatic detection of the best deadband per measurement. Furthermore, goFlux is now compatible with a larger selection of instruments from LI-COR, LGR, GAIA2TECH, Gasmet, Picarro, Aeris, PP-Systems, Earthbound Scientific Ltd, Healthy Photon, Eosense, and PRI-ECO. Finally, goFlux now integrates new functionalities for reproducible calculation of GHG fluxes from static or floating chamber measurements in aquatic ecosystems, also accounting for ebullition events and separating total fluxes into diffusive and ebullitive components.

Further improvements are being made to the automatic selection of the best flux estimate using the function best.flux. In goFlux, a central element is to constrain the maximal curvature allowed due to non-linearity, by using the parameter of kappa-max (k), first introduced in Hüppi et al. (2018). The advantage of the k parameter is that it is based on objective metrics of instrument precision and chamber specific dimensions and applying k essentially avoids excessive flux overestimation, especially for noisy or small fluxes which often appear in chamber-based applications. Similar to the kappa-max parameter, we are developing the kappa-min parameter, which indicates a minimum threshold under which the best flux estimate will automatically default to the non-linear model.

In summary, goFlux is meant to be “student proof”, meaning that no extensive knowledge or experience is needed for data import and pre-processing in R, and selecting the best flux estimate (linear or non-linear models). This poster presentation will highlight new functionalities and improvements made to the goFlux R package in the last year, as well as ongoing developments, that have been made to improve the "user-friendliness" of the package. Come see us at our poster session to discuss new features you would like to see added in the future!

For more information, visit our webpage: https://qepanna.quarto.pub/goflux/