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Methane dynamics within salt marshes are complex because vegetation types, temperature, oscillating water levels, and changes in salinity and redox conditions influence CH₄production, consumption, oxidation, and emissions. These non‐linear and complex interactions among variables affect the traditionally expected functional relationships and present challenges for interpreting and developing process‐based models. We employed empirical dynamic modeling (EDM) and convergent cross mapping (CCM) as a novel approach for characterizing seasonal/multiday and diurnal CH₄dynamics by inferring causal variables, lags, and interconnections among multiple biophysical variables within a temperate salt marsh using 5 years of eddy covariance data. EDM/CCM is a nonparametric approach capable of quantifying the coupling between variables while determining time scales where variable interactions are the most relevant. We found that gross primary productivity, tidal creek dissolved oxygen, and temperature were important for seasonal/multiday dynamics (rho = 0.73–0.80), while water level was most important for diurnal dynamics during both the growing and dormancy phenoperiods (rho = 0.72 and 0.56, respectively). Lags for the top‐ranked variables (i.e., gross primary productivity, dissolved oxygen, temperature, water level) occurred between 1 and 5 weeks at the seasonal scale and 1–24 hr at the diurnal scale. The EDM had high prediction capabilities for intra‐/inter‐seasonal patterns and annual CH₄sums but had limitations in representing large, infrequent fluxes. Results highlight the importance of non‐linearity, drivers, lag times, and interconnections among multiple biophysical variables that regulate CH₄fluxes in tidal wetlands. This research introduces a novel approach to examining CH₄fluxes, which will aid in evaluating current paradigms in wetlands and other ecosystems.

 

It is widely accepted that active learning and group work generally enhance learning in the statistics classroom, but how should those groups be formed? This study aims to better understand the characteristics of a productive team in the undergraduate introductory statistics course. Specifically, we explore the relationship between the attitudes of a student’s teammates and that student’s academic performance in both individual and group settings. We find moderate evidence that positive teammate attitudes towards statistics are associated with greater improvement from a student’s individual to the team exam score. If we can better understand what combination of student characteristics results in productive teams, instructors can be intentional with how they form groups in the classroom, realizing the full efficacy of active learning. 

A collection of carbon dioxide (CO2), methane (CH4) gas flux and leaf area index (LAI) datasets from a temperate salt-marsh with S. alternifloria vegetation cover (39.09 ˚N, 75.44 ˚W). Measurements were collected from May 2020 through December of 2020 with 2 separate collection times per month for a total of 16 field sampling campaigns. Measurements were performed on 5 plots located within the footprint of an eddy covariance tower which collects ecosystem scale CO2 and CH4 fluxes (Ameriflux Site ID: StJ). Supporting measurements were also performed for leaf-level photosynthesis rates and plot LAI during each chamber campaign. 

The dark and light flux chambers each occupied 1.0 m2 ground area and the vegetation stand was fully enclosed by the chambers. The sediment chambers each occupied 0.025 m2 ground area and the vegetation stand was excluded from the chambers. CO2 and CH4 fluxes from all chambers were measured with a portable greenhouse gas analyzer (LGR  Los Gatos Research, Model 915-0011, San Jose, CA) for approximately 2-3 min after chamber closure. The dark and soil chambers were 100% opaque while the light chamber allowed for 90% transmission of photosynthetically active radiation. Sediment fluxes and leaf-level photosynthesis was measured from 2 points within each of the larger chamber plots. Leaf-level photosynthesis was measured with a  portable photosynthesis meter (Li-6400, Licor, Lincoln, NE) and LAI was measured with a ceptometer (Accupar LP80, Meter Inc, Pullman, WA, USA). All measurements were made consecutively over the span of 2-3 hours with leaf-level measurements preformed first, followed by light chambers, dark chambers, sediment chambers and plot LAI.  

 

Tidal wetlands are comprised of complex interdependent pathways where measurements of carbon exchange are often scale dependent. Common data collection methods (i.e., chambers and eddy covariance) are inherently constrained to different spatial and temporal scales which could generate biased information for applications of carbon accounting, identifying functional relationships and predicting future responses to climate change. Consequently, it is needed to systematically evaluate measurements derived from multiple approaches to identify differences and how techniques complement each other to reconcile interpretations. To accomplish this, we tested ecosystem‐scale eddy covariance with plot‐scale chamber measurements within a temperate salt marsh. We found good agreement (R² = 0.71–0.95) when comparing measurements of CH₄emissions and CO₂exchange but this agreement was dependent upon canopy phenology with discrepancies mainly arising during senescence and dormancy phenophases. The environmental drivers for CH₄and CO₂fluxes were mostly preserved across different measurement techniques, but the number of drivers increases while their individual strength decreases at the ecosystem scale. Empirical upscaling models parameterized with chamber measurements overestimated annual net ecosystem exchange (NEE; 108%) and gross primary production (GPP; 12%) while underestimating ecosystem respiration (Reco; 14%) and CH₄emissions (69%) compared to eddy covariance measurements. Our results suggest that the environmental complexity of CH₄and CO₂fluxes in salt marshes may be underestimated by chamber‐based measurements, and highlights how different techniques are complementary while considering limitations at each level of measurement.

 

The goal of the SunPy project is to facilitate and promote the use and development of community-led, free, and open source data analysis software for solar physics based on the scientific Python environment. The project achieves this goal by developing and maintaining the sunpy core package and supporting an ecosystem of affiliated packages. This paper describes the first official stable release (version 1.0) of the core package, as well as the project organization and infrastructure. This paper concludes with a discussion of the future of the SunPy project.