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We show that coadsorbed oxygen atoms have a dramatic influence on the CO desorption dynamics from Ru(0001). In contrast to the precursor-mediated desorption mechanism on Ru(0001), the presence of surface oxygen modifies the electronic structure of Ru atoms such that CO desorption occurs predominantly via the direct pathway. This phenomenon is directly observed in an ultrafast pump-probe experiment using a soft x-ray free-electron laser to monitor the dynamic evolution of the valence electronic structure of the surface species. This is supported with the potential of mean force along the CO desorption path obtained from density-functional theory calculations. Charge density distribution and frozen-orbital analysis suggest that the oxygen-induced reduction of the Pauli repulsion, and consequent increase of the dative interaction between the CO 5 sigma and the charged Ru atom, is the electronic origin of the distinct desorption dynamics. Ab initio molecular dynamics simulations of CO desorption from Ru(0001) and oxygen-coadsorbed Ru(0001) provide further insights into the surface bond-breaking process.
Increasing evidence shows that anthropogenic climate change is affecting biodiversity. Reducing or stabilizing greenhouse gas emissions may slow global warming, but past emissions will continue to contribute to further unavoidable warming for more than a century. With obvious signs of difficulties in achieving effective mitigation worldwide in the short term at least, sound scientific predictions of future impacts on biodiversity will be required to guide conservation planning and adaptation. This is especially true in Mediterranean type ecosystems that are projected to be among the most significantly affected by anthropogenic climate change, and show the highest levels of confidence in rainfall projections. Multiple methods are available for projecting the consequences of climate change on the main unit of interest - the species - with each method having strengths and weaknesses. Species distribution models (SDMs) are increasingly applied for forecasting climate change impacts on species geographic ranges. Aggregation of models for different species allows inferences of impacts on biodiversity, though excluding the effects of species interactions. The modelling approach is based on several further assumptions and projections and should be treated cautiously. In the absence of comparable approaches that address large numbers of species, SDMs remain valuable in estimating the vulnerability of species. In this review we discuss the application of SDMs in predicting the impacts of climate change on biodiversity with special reference to the species-rich South West Australian Floristic Region and South African Cape Floristic Region. We discuss the advantages and challenges in applying SDMs in biodiverse regions with high levels of endemicity, and how a similar biogeographical history in both regions may assist us in understanding their vulnerability to climate change. We suggest how the process of predicting the impacts of climate change on biodiversity with SDMs can be improved and emphasize the role of field monitoring and experiments in validating the predictions of SDMs.