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Abstract Sensitivity of ecosystem productivity to climate variability is a critical component of ecosystem resilience to climate change. Variation in ecosystem sensitivity is influenced by many variables. Here we investigate the effect of bedrock lithology and weathering products on the sensitivity of ecosystem productivity to variation in climate water deficit using Bayesian statistical models. Two thirds of terrestrial ecosystems exhibit negative sensitivity, where productivity decreases with increased climate water deficit, while the other third exhibit positive sensitivity. Variation in ecosystem sensitivity is significantly affected by regolith porosity and permeability and regolith and soil thickness, indicating that lithology, through its control on water holding capacity, exerts important controls on ecosystem sensitivity. After accounting for effects of these four variables, significant differences in sensitivity remain among ecosystems on different rock types, indicating the complexity of bedrock effects. Our analysis suggests that regolith affects ecosystem sensitivity to climate change worldwide and thus their resilience. 

Abstract Despite its far-reaching implications for conservation and natural resource management, little is known about the color of environmental noise, or the structure of temporal autocorrelation in random environmental variation, in streams and rivers. Here, we analyze the geography, drivers, and timescale-dependence of noise color in streamflow across the U.S. hydrography, using streamflow time series from 7504 gages. We find that daily and annual flows are dominated by red and white spectra respectively, and spatial variation in noise color is explained by a combination of geographic, hydroclimatic, and anthropogenic variables. Noise color at the daily scale is influenced by stream network position, and land use and water management explain around one third of the spatial variation in noise color irrespective of the timescale considered. Our results highlight the peculiarities of environmental variation regimes in riverine systems, and reveal a strong human fingerprint on the stochastic patterns of streamflow variation in river networks.