skip to main content

Search for: All records

Creators/Authors contains: "Lofton, Mary E."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available August 1, 2023
  2. Free, publicly-accessible full text available July 1, 2023
  3. Near-term, ecological forecasting with iterative model refitting and uncertainty partitioning has great promise for improving our understanding of ecological processes and the predictive skill of ecological models, but to date has been infrequently applied to predict biogeochemical fluxes. Bubble fluxes of methane (CH 4 ) from aquatic sediments to the atmosphere (ebullition) dominate freshwater greenhouse gas emissions, but it remains unknown how best to make robust near-term CH 4 ebullition predictions using models. Near-term forecasting workflows have the potential to address several current challenges in predicting CH 4 ebullition rates, including: development of models that can be applied across time horizons and ecosystems, identification of the timescales for which predictions can provide useful information, and quantification of uncertainty in predictions. To assess the capacity of near-term, iterative forecasting workflows to improve ebullition rate predictions, we developed and tested a near-term, iterative forecasting workflow of CH 4 ebullition rates in a small eutrophic reservoir throughout one open-water period. The workflow included the repeated updating of a CH 4 ebullition forecast model over time with newly-collected data via iterative model refitting. We compared the CH 4 forecasts from our workflow to both alternative forecasts generated without iterative model refitting and a persistencemore »null model. Our forecasts with iterative model refitting estimated CH 4 ebullition rates up to 2 weeks into the future [RMSE at 1-week ahead = 0.53 and 0.48 log e (mg CH 4 m −2 d −1 ) at 2-week ahead horizons]. Forecasts with iterative model refitting outperformed forecasts without refitting and the persistence null model at both 1- and 2-week forecast horizons. Driver uncertainty and model process uncertainty contributed the most to total forecast uncertainty, suggesting that future workflow improvements should focus on improved mechanistic understanding of CH 4 models and drivers. Altogether, our study suggests that iterative forecasting improves week-to-week CH 4 ebullition predictions, provides insight into predictability of ebullition rates into the future, and identifies which sources of uncertainty are the most important contributors to the total uncertainty in CH 4 ebullition predictions.« less
    Free, publicly-accessible full text available December 1, 2022
  4. Free, publicly-accessible full text available January 2, 2023
  5. Free, publicly-accessible full text available November 20, 2022
  6. In freshwater lakes and reservoirs, climate change and eutrophication are increasing the occurrence of low-dissolved oxygen concentrations (hypoxia), which has the potential to alter the variability of zooplankton seasonal dynamics. We sampled zooplankton and physical, chemical and biological variables (e.g., temperature, dissolved oxygen, and chlorophyll a) in four reservoirs during the summer stratified period for three consecutive years. The hypolimnion (bottom waters) of two reservoirs remained oxic throughout the entire stratified period, whereas the hypolimnion of the other two reservoirs became hypoxic during the stratified period. Biomass variability (measured as the coefficient of the variation of zooplankton biomass) and compositional variability (measured as the community composition of zooplankton) of crustacean zooplankton communities were similar throughout the summer in the oxic reservoirs; however, biomass variability and compositional variability significantly increased after the onset of hypoxia in the two seasonally-hypoxic reservoirs. The increase in biomass variability in the seasonally-hypoxic reservoirs was driven largely by an increase in the variability of copepod biomass, while the increase in compositional variability was driven by increased variability in the dominance (proportion of total crustacean zooplankton biomass) of copepod taxa. Our results suggest that hypoxia may increase the seasonal variability of crustacean zooplankton communities.