Photodegradation leads to increased carbon dioxide losses from terrestrial organic matter

Abstract CO 2 production in terrestrial ecosystems is generally assumed to be solely biologically driven while the role of abiotic processes has been largely overlooked. In addition to microbial decomposition, photodegradation – the direct breakdown of organic matter (OM) by solar irradiance – has been found to contribute to litter mass loss in dry ecosystems. Previous small‐scale studies have shown that litter degradation by irradiance is accompanied by emissions of CO 2 . However, the contribution of photodegradation to total CO 2 losses at ecosystems scales is unknown. This study determined the proportion of the total CO 2 losses caused by photodegradation in two ecosystems: a bare peatland in New Zealand and a seasonally dry grassland in California. The direct effect of solar irradiance on CO 2 production was examined by comparing daytime CO 2 fluxes measured using eddy covariance (EC) systems with simultaneous measurements made using an opaque chamber and the soil CO 2 gradient technique, and with night‐time EC measurements under the same soil temperature and moisture conditions. In addition, a transparent chamber was used to directly measure CO 2 fluxes from OM caused by solar irradiance. Photodegradation contributed 19% of the annual CO 2 flux from the peatland and almost 60% of the dry season CO 2 flux from the grassland, and up to 62% and 92% of the summer mid‐day CO 2 fluxes, respectively. Our results suggest that photodegradation may be important in a wide range of ecosystems with exposed OM. Furthermore, the practice of partitioning daytime ecosystem CO 2 exchange into its gross components by assuming that total daytime CO 2 losses can be approximated using estimates of biological respiration alone may be in error. To obtain robust estimates of global ecosystem–atmosphere carbon transfers, the contribution of photodegradation to OM decomposition must be quantified for other ecosystems and the results incorporated into coupled carbon–climate models.