Comment on bg-2022-179

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Biocrusts are a worldwide phenomenon, contributing substantially to ecosystem functioning. Their growth and survival depend on multiple environmental factors, including climatic conditions. While the physiological responses of biocrusts to individual environmental factors have been examined in laboratory experiments, the relative importance of these factors along climatic gradients is largely unknown. Moreover, it is not fully understood how acclimation of biocrusts may alter the relative impacts of certain factors. We aim here at determining the relative effects of environmental factors on biocrusts along climatic gradients, using the carbon balance of biocrust organisms as a measure of their performance. Additionally, we explore the role that seasonal acclimation plays in the carbon balance of biocrusts. We applied a data-driven mechanistic model at six study sites along a climatic gradient to simulate the annual carbon balance of biocrusts dominated by different lichen and moss species. Furthermore, we performed several sensitivity analyses to investigate the relative importance of driving factors, thereby including the impacts of acclimation. Our modeling approach suggests substantial effects of light intensity and relative humidity in temperate regions, while air temperature has the strongest impact at alpine sites. In drylands, ambient CO2 concentration and also the amount of rainfall are important drivers of the carbon balance of biocrusts. Seasonal acclimation is a key feature, mostly in temperate regions, affecting biocrust functioning. We conclude that climate change, which may lead to warmer and, in some regions, drier air, will potentially have large effects on long-term carbon balances of biocrusts at global scale. Moreover, we highlight the key role of seasonal acclimation, which suggests that the season and timing of collecting and monitoring biocrusts should be given additional consideration in experimental investigations, especially when measurements are used as the basis for quantitative estimates and forecasts.