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  1. Abstract

    Plants with the C4photosynthesis pathway typically respond to climate change differently from more common C3-type plants, due to their distinct anatomical and biochemical characteristics. These different responses are expected to drive changes in global C4and C3vegetation distributions. However, current C4vegetation distribution models may not predict this response as they do not capture multiple interacting factors and often lack observational constraints. Here, we used global observations of plant photosynthetic pathways, satellite remote sensing, and photosynthetic optimality theory to produce an observation-constrained global map of C4vegetation. We find that global C4vegetation coverage decreased from 17.7% to 17.1% of the land surface during 2001 to 2019. This was the net result of a reduction in C4natural grass cover due to elevated CO2favoring C3-type photosynthesis, and an increase in C4crop cover, mainly from corn (maize) expansion. Using an emergent constraint approach, we estimated that C4vegetation contributed 19.5% of global photosynthetic carbon assimilation, a value within the range of previous estimates (18–23%) but higher than the ensemble mean of dynamic global vegetation models (14 ± 13%; mean ± one standard deviation). Our study sheds insight on the critical and underappreciated role of C4plants in the contemporary global carbon cycle.

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  2. Abstract

    Long-term soil CO2emission measurements are necessary for detecting trends and interannual variability in the terrestrial carbon cycle. Such records are becoming increasingly valuable as ecosystems experience altered environmental conditions associated with climate change. From 2013 to 2021, we continuously measured soil CO2concentrations in the two dominant high elevation forest types, mixed conifer and aspen, in the upper Colorado River basin. We quantified the soil CO2flux during the summer months, and found that the mean and total CO2flux in both forests was related to the prior winter’s snowfall and current summer’s rainfall, with greater sensitivity to rainfall. We observed a decline in surface soil CO2production, which we attributed to warming and a decrease in amount and frequency of summer rains. Our results demonstrate strong precipitation control on the soil CO2flux in mountainous regions, a finding which has important implications for carbon cycling under future environmental change.

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  3. Abstract

    Stable isotope ratios of H (δ2H), O (δ18O), and C (δ13C) 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 ofδ13C of net ecosystem exchange (NEE) as well asδ2H andδ18O 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 aboutNEEandLHfluxes as wind speed observations. Such information from isotope measurements, however, is largely unique. Generally,δ13C provides more information aboutLHas aridity increases or mean annual precipitation decreases.δ2H provides more information aboutLHas temperatures or mean annual precipitation decreases, and also provides more information aboutNEEas 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.

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  4. Free, publicly-accessible full text available August 1, 2024
  5. Abstract

    Climate change projections provided by global climate models (GCM) are generally too coarse for local and regional applications. Local and regional climate change impact studies therefore use downscaled datasets. While there are studies that evaluate downscaling methodologies, there is no study comparing the downscaled datasets that are actually distributed and used in climate change impact studies, and there is no guidance for selecting a published downscaled dataset. We compare five widely used statistically downscaled climate change projection datasets that cover the conterminous USA (CONUS): ClimateNA, LOCA, MACAv2-LIVNEH, MACAv2-METDATA, and NEX-DCP30. All of the datasets are derived from CMIP5 GCMs and are publicly distributed. The five datasets generally have good agreement across CONUS for Representative Concentration Pathways (RCP) 4.5 and 8.5, although the agreement among the datasets vary greatly depending on the GCM, and there are many localized areas of sharp disagreements. Areas of higher dataset disagreement emerge over time, and their importance relative to differences among GCMs is comparable between RCP4.5 and RCP8.5. Dataset disagreement displays distinct regional patterns, with greater disagreement in △Tmax and △Tmin in the interior West and in the North, and disagreement in △P in California and the Southeast. LOCA and ClimateNA are often the outlier dataset, while the seasonal timing of ClimateNA is somewhat shifted from the others. To easily identify regional study areas with high disagreement, we generated maps of dataset disagreement aggregated to states, ecoregions, watersheds, and forests. Climate change assessment studies can use the maps to evaluate and select one or more downscaled datasets for their study area.

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  6. Understanding and predicting the relationship between leaf temperature ( T leaf ) and air temperature ( T air ) is essential for projecting responses to a warming climate, as studies suggest that many forests are near thermal thresholds for carbon uptake. Based on leaf measurements, the limited leaf homeothermy hypothesis argues that daytime T leaf is maintained near photosynthetic temperature optima and below damaging temperature thresholds. Specifically, leaves should cool below T air at higher temperatures (i.e., > ∼25–30°C) leading to slopes <1 in T leaf / T air relationships and substantial carbon uptake when leaves are cooler than air. This hypothesis implies that climate warming will be mitigated by a compensatory leaf cooling response. A key uncertainty is understanding whether such thermoregulatory behavior occurs in natural forest canopies. We present an unprecedented set of growing season canopy-level leaf temperature ( T can ) data measured with thermal imaging at multiple well-instrumented forest sites in North and Central America. Our data do not support the limited homeothermy hypothesis: canopy leaves are warmer than air during most of the day and only cool below air in mid to late afternoon, leading to T can / T air slopes >1 and hysteretic behavior. We find that the majority of ecosystem photosynthesis occurs when canopy leaves are warmer than air. Using energy balance and physiological modeling, we show that key leaf traits influence leaf-air coupling and ultimately the T can / T air relationship. Canopy structure also plays an important role in T can dynamics. Future climate warming is likely to lead to even greater T can , with attendant impacts on forest carbon cycling and mortality risk. 
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  7. Abstract

    The National Ecological Observatory Network (NEON) provides open-access measurements of stable isotope ratios in atmospheric water vapor (δ2H, δ18O) and carbon dioxide (δ13C) at different tower heights, as well as aggregated biweekly precipitation samples (δ2H, δ18O) across the United States. These measurements were used to create the NEON Daily Isotopic Composition of Environmental Exchanges (NEON-DICEE) dataset estimating precipitation (P; δ2H, δ18O), evapotranspiration (ET; δ2H, δ18O), and net ecosystem exchange (NEE; δ13C) isotope ratios. Statistically downscaled precipitation datasets were generated to be consistent with the estimated covariance between isotope ratios and precipitation amounts at daily time scales. Isotope ratios in ET and NEE fluxes were estimated using a mixing-model approach with calibrated NEON tower measurements. NEON-DICEE is publicly available on HydroShare and can be reproduced or modified to fit user specific applications or include additional NEON data records as they become available. The NEON-DICEE dataset can facilitate understanding of terrestrial ecosystem processes through their incorporation into environmental investigations that require daily δ2H, δ18O, and δ13C flux data.

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  8. Summary

    Evolutionary history plays a key role driving patterns of trait variation across plant species. For scaling and modeling purposes, grass species are typically organized into C3vs C4plant functional types (PFTs). Plant functional type groupings may obscure important functional differences among species. Rather, grouping grasses by evolutionary lineage may better represent grass functional diversity.

    We measured 11 structural and physiological traitsin situfrom 75 grass species within the North American tallgrass prairie. We tested whether traits differed significantly among photosynthetic pathways or lineages (tribe) in annual and perennial grass species.

    Critically, we found evidence that grass traits varied among lineages, including independent origins of C4photosynthesis. Using a rigorous model selection approach, tribe was included in the top models for five of nine traits for perennial species. Tribes were separable in a multivariate and phylogenetically controlled analysis of traits, owing to coordination of important structural and ecophysiological characteristics.

    Our findings suggest grouping grass species by photosynthetic pathway overlooks variation in several functional traits, particularly for C4species. These results indicate that further assessment of lineage‐based differences at other sites and across other grass species distributions may improve representation of C4species in trait comparison analyses and modeling investigations.

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  9. Abstract

    Extant conifer species may be susceptible to rapid environmental change owing to their long generation times, but could also be resilient due to high levels of standing genetic diversity. Hybridisation between closely related species can increase genetic diversity and generate novel allelic combinations capable of fuelling adaptive evolution. Our study unravelled the genetic architecture of adaptive evolution in a conifer hybrid zone formed betweenPinus strobiformisandP. flexilis. Using a multifaceted approach emphasising the spatial and environmental patterns of linkage disequilibrium and ancestry enrichment, we identified recently introgressed and background genetic variants to be driving adaptive evolution along different environmental gradients. Specifically, recently introgressed variants fromP. flexiliswere favoured along freeze-related environmental gradients, while background variants were favoured along water availability-related gradients. We posit that such mosaics of allelic variants within conifer hybrid zones will confer upon them greater resilience to ongoing and future environmental change and can be a key resource for conservation efforts.

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