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Drylands cover ca. 40% of the land surface and are hypothesised to play a major role in the global carbon cycle, controlling both long-term trends and interannual variation. These insights originate from land surface models (LSMs) that have not been extensively calibrated and evaluated for water-limited ecosystems. We need to learn more about dryland carbon dynamics, particularly as the transitory response and rapid turnover rates of semi-arid systems may limit their function as a carbon sink over multi-decadal scales. We quantified aboveground biomass carbon (AGC; inferred from SMOS L-band vegetation optical depth) and gross primary productivity (GPP; from PML-v2 inferred from MODIS observations) and tested their spatial and temporal correspondence with estimates from the TRENDY ensemble of LSMs. We found strong correspondence in GPP between LSMs and PML-v2 both in spatial patterns (Pearson’s r = 0.9 for TRENDY-mean) and in inter-annual variability, but not in trends. Conversely, for AGC we found lesser correspondence in space (Pearson’s r = 0.75 for TRENDY-mean, strong biases for individual models) and in the magnitude of inter-annual variability compared to satellite retrievals. These disagreements likely arise from limited representation of ecosystem responses to plant water availability, fire, and photodegradation that drive dryland carbon dynamics. We assessed inter-model agreement and drivers of long-term change in carbon stocks over centennial timescales. This analysis suggested that the simulated trend of increasing carbon stocks in drylands is in soils and primarily driven by increased productivity due to CO 2 enrichment. However, there is limited empirical evidence of this 50-year sink in dryland soils. Our findings highlight important uncertainties in simulations of dryland ecosystems by current LSMs, suggesting a need for continued model refinements and for greater caution when interpreting LSM estimates with regards to current and future carbon dynamics in drylands and by extension the global carbon cycle. 

Electrostatic interactions drive molecular assembly and organization in the plasma membrane. Specific protein-lipid interactions, however, are difficult to resolve. Here we report on a unique approach to investigate these interactions with time-resolved fluorescence spectroscopy. The experiments were performed on a model membrane system consisting of a supported lipid bilayer with an asymmetric distribution of PIP2 in the upper leaflet of the bilayer. The bilayer also contained nickel-chelating lipids that bind to a histidine-tagged peptide of interest. Both the peptide and the lipid were labeled with orthogonal fluorescent probes, so that diffusion and binding could be measured with two-color, pulsed-interleaved excitation fluorescence cross-correlation spectroscopy (PIE-FCCS). Our PIE-FCCS data showed significant lipid-peptide cross-correlation between PIP2 lipids and membrane-bound cationic peptides. Cross-correlation is a direct indication of lipid-peptide binding and complexation. Together with mobility data, we quantified the degree of binding, which offers new insight into this class of lipid-peptide interactions. Overall, this is the first report of lipid-peptide cross-correlation by FCCS, and provides a new route to quantifying the interactions between proteins and lipid membranes, a key interface in cell signaling.