Code and data for 'Towards an annual carbon balance of biological soil crusts: parametric equations and neural networks to model gas exchange and net primary productivity'

We present two methods to model the physiological response, specifically CO2 gas exchange rates of biocrusts as a function of soil moisture, temperature and light intensity. The models are a parametric equation with optimized fitting parameters, and an artificial neural network model. Both methods are applied to two types of biocrusts, a cyanobacteria- and a lichen-dominated biocrust, using laboratory measurements of CO2 gas exchange rates as training data. Or models achieve very good agreement with independent test data and permit detailed insights into the physiological response of biocrusts to environmental conditions. As the models are not mechanistic, they can easily be applied to other organisms or environmental parameters in a similar fashion. We also demonstrate how such models can be used alongside field measurements of micrometeorological conditions in order to calculate the net primary productivity of biocrusts in specific locations.