Calibration Strategies for Detecting Macroscale Patterns in NEON Atmospheric Carbon Isotope Observations

Carbon fluxes in terrestrial ecosystems and their response to environmental change are a major source of uncertainty in the modern carbon cycle. The National Ecological Observatory Network (NEON) presents the opportunity to merge eddy covariance (EC)-derived fluxes with CO2 isotope ratio measurements to gain insights into carbon cycle processes. Collected continuously and consistently across >40 sites, NEON EC and isotope data facilitate novel integrative analyses. However, currently provisioned atmospheric isotope data are uncalibrated, greatly limiting ability to perform cross-site analyses. Here, we present two approaches to calibrating NEON CO2 isotope ratios, along with an R package to calibrate NEON data. We find that calibrating CO2 isotopologues independently yields a lower δ13C bias (<0.05‰) and higher precision (<0.40‰) than directly correcting δ13C with linear regression (bias: <0.11‰, precision: 0.42‰), but with slightly higher error and lower precision in calibrated CO2 mole fraction. The magnitude of the corrections to δ13C and CO2 mole fractions vary substantially by site, underscoring the need for users to apply a consistent calibration framework to data in the NEON archive. Post-calibration data sets show that site mean annual δ13C correlates negatively with precipitation, temperature, and aridity, but positively with elevation. Forested and agricultural ecosystems exhibit larger gradients in CO2 and δ13C than other sites, particularly during the summer and at night. The overview and analysis tools developed here will facilitate cross-site analysis using NEON data, provide a model for other continental-scale observational networks, and enable new advances leveraging the isotope ratios of specific carbon fluxes. © 2021. The Authors.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Calibration Strategies for Detecting Macroscale Patterns in NEON Atmospheric Carbon Isotope Observations

Carbon fluxes in terrestrial ecosystems and their response to environmental change are a major source of uncertainty in the modern carbon cycle. The National Ecological Observatory Network (NEON) presents the opportunity to merge eddy covariance (EC)-derived fluxes with CO2 isotope ratio measurements to gain insights into carbon cycle processes. Collected continuously and consistently across >40 sites, NEON EC and isotope data facilitate novel integrative analyses. However, currently provisioned atmospheric isotope data are uncalibrated, greatly limiting ability to perform cross-site analyses. Here, we present two approaches to calibrating NEON CO2 isotope ratios, along with an R package to calibrate NEON data. We find that calibrating CO2 isotopologues independently yields a lower δ13C bias (<0.05‰) and higher precision (<0.40‰) than directly correcting δ13C with linear regression (bias: <0.11‰, precision: 0.42‰), but with slightly higher error and lower precision in calibrated CO2 mole fraction. The magnitude of the corrections to δ13C and CO2 mole fractions vary substantially by site, underscoring the need for users to apply a consistent calibration framework to data in the NEON archive. Post-calibration data sets show that site mean annual δ13C correlates negatively with precipitation, temperature, and aridity, but positively with elevation. Forested and agricultural ecosystems exhibit larger gradients in CO2 and δ13C than other sites, particularly during the summer and at night. The overview and analysis tools developed here will facilitate cross-site analysis using NEON data, provide a model for other continental-scale observational networks, and enable new advances leveraging the isotope ratios of specific carbon fluxes. © 2021. The Authors.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

A Comprehensive Review Of The Tourism Forecasting Literature

Major Reports of Tourism Forecasting Studies Are Classified by Forecasting Method Employed, Reviewed, Compared, and Critiqued. a Partial Meta-Analysis is Used to Attempt to Gauge Relative Accuracy of the Various Approaches Used in Differing Situations. Summary Statements on the Relative Usefulness of the Different Methods in Specific Forecasting Situations Are Provided. Some Conclusions Are Drawn to Aid Future Conduct and Reporting of Tourism Forecasting Reports. -Author