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Stable isotope ratios of H ( δ ^2 H ), O ( δ ^18 O), and C ( δ ^13 C) are linked to key biogeochemical processes of the water and carbon cycles; however, the degree to which isotope-associated processes are reflected in macroscale ecosystem flux observations remains unquantified. Here through formal information assessment, new measurements of δ ^13 C of net ecosystem exchange ( NEE ) as well as δ ^2 H and δ ^18 O of latent heat ( LH ) fluxes across the United States National Ecological Observation Network (NEON) are used to determine conditions under which isotope measurements are informative of environmental exchanges. We find all three isotopic datasets individually contain comparable amounts of information about NEE and LH fluxes as wind speed observations. Such information from isotope measurements, however, is largely unique. Generally, δ ^13 C provides more information about LH as aridity increases or mean annual precipitation decreases. δ ^2 H provides more information about LH as temperatures or mean annual precipitation decreases, and also provides more information about NEE as temperatures decrease. Overall, we show that the stable isotope datasets collected by NEON contribute non-trivial amounts of new information about bulk environmental fluxes useful for interpreting biogeochemical and ecohydrological processes at landscape scales. However, the utility of this new information varies with environmental conditions at continental scales. This study provides an approach for quantifying the value adding non-traditional sensing approaches to environmental monitoring sites and the patterns identified here are expected to aid in modeling and data interpretation efforts focused on constraining carbon and water cycles’ mechanisms.

Anthropogenic climate change is projected to drive increases in climate extremes and climate-sensitive ecosystem disturbances such as wildfire with enormous economic impacts. Understanding spatial and temporal patterns of risk to property values from climate-sensitive disturbances at national and regional scales and from multiple disturbances is urgently needed to inform risk management and policy efforts. Here, we combine models for three major climate-sensitive disturbances (i.e., wildfire, climate stress-driven tree mortality, and insect-driven tree mortality), future climate projections of these disturbances, and high-resolution property values data to quantify the spatiotemporal exposure of property values to disturbance across the contiguous United States (US). We find that property values exposed to these climate-sensitive disturbances increase sharply in future climate scenarios, particularly in existing high-risk regions of the western US, and that novel exposure risks emerge in some currently lower-risk regions such as the southeast and Great Lakes regions. Climate policy that drives emissions towards low-to-moderate climate futures avoids large increases in disturbance risk exposure compared to high emissions scenarios. Our results provide an important large-scale assessment of climate-sensitive disturbance risk to property values to help inform land management and climate adaptation efforts.

High air pollution levels are associated with school absences. However, low level pollution impacts on individual school absences are under-studied. Understanding the variability of pollution at individual schools within an urban region could improve school recess decisions, better identify local pollution sources, and improve local economic impact assessments by providing granular information relevant to specific schools. We modelled PM _2.5 and ozone concentrations at 36 schools from July 2015 to June 2018 using data from a dense, research grade regulatory sensor network. We determined exposures and daily absences at each school. We used a generalized estimating equations model to retrospectively estimate rate ratios for association between outdoor pollutant concentrations and school absences. We estimated lost school revenue, productivity, and family economic burden. PM _2.5 and ozone concentrations and absence rates vary across the School District. Pollution exposure was associated with a rate ratio as high as 1.02 absences per μ g m ^−3 and 1.01 per ppb increase for PM _2.5 and ozone, respectively. Significantly, even PM _2.5 and ozone exposure below the air quality index breakpoints for good air quality (

Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes.

Despite overwhelming scientific consensus concerning anthropogenic climate change, many in the non-expert public perceive climate change as debated and contentious. There is concern that two recent high-profile media events—the hacking of the University of East Anglia emails and the Himalayan glacier melt rate presented in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change—may have altered public opinion of climate change. While survey data is valuable for tracking public perception and opinion over time, including in response to climate-related media events, emerging methods that facilitate rapid assessment of spatial and temporal patterns in public interest and opinion could be exceptionally valuable for understanding and responding to these events’ effects. We use a novel, freely-available dataset of worldwide web search term volumes to assess temporal patterns of interest in climate change over the past ten years, with a particular focus on looking at indicators of climate change skepticism around the high-profile media events. We find that both around the world and in the US, the public searches for the issue as ‘global warming,’ rather than ‘climate change,’ and that search volumes have been declining since a 2007 peak. We observe high, but transient spikes of search terms indicating skepticism around the two media events, but find no evidence of effects lasting more than a few months. Our results indicate that while such media events are visible in the short-term, they have little effect on salience of skeptical climate search terms on longer time-scales.