Where’s the academic in service-learning?

Research has focused on the impact of service-learning on student development “as a whole person”, on assisting community in solving social challenges and issues, on civic education and even leadership and critical thinking. On a more practical level, emphasis has been put on identifying community, social and other worldly challenges to which the university and its students can contribute to their resolutions, via service-learning programs. This workshop, while not neglecting the elements noted thus far, focuses on designing service-learning offerings in meeting established academic objectives, be that in the humanities, social science, physical or life science. The Workshop facilitator has had over 20 years of experience in developing service-learning as part of the undergraduate liberal arts and science curriculum at a large comprehensive state university. Join the Workshop and see how your specific academic objectives can be fulfilled through service-learning. 2

Cross boarders and reap beyond the obvious outcomes

What borders are we talking about, and how might we be enriched by crossing these borders? Answers to these questions can broaden our horizons with reference to the borders that we need to cross in order to develop and institutionalized Service-Learning. Furthermore, identification of outcomes from Service-Learning beyond the obvious is essential to integrate Service-Learning into the academic curricula. We know that Service-Learning yield student learning, but is it worth academic credits? We know community service is performed, but what else

Genetic analysis of the Arabidopsis TIR1/AFB auxin receptors reveals both overlapping and specialized functions

© 2020, Prigge et al. The TIR1/AFB auxin co-receptors mediate diverse responses to the plant hormone auxin. The Arabidopsis genome encodes six TIR1/AFB proteins representing three of the four clades that were established prior to angiosperm radiation. To determine the role of these proteins in plant development we performed an extensive genetic analysis involving the generation and characterization of all possible multiply-mutant lines. We find that loss of all six TIR1/AFB proteins results in early embryo defects and eventually seed abortion, and yet a single wild-type allele of TIR1 or AFB2 is sufficient to support growth throughout development. Our analysis reveals extensive functional overlap between even the most distantly related TIR1/AFB genes except for AFB1. Surprisingly, AFB1 has a specialized function in rapid auxin-dependent inhibition of root growth and early phase of root gravitropism. This activity may be related to a difference in subcellular localization compared to the other members of the family

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

COSORE: A community database for continuous soil respiration and other soil‐atmosphere greenhouse gas flux data

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil‐to‐atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS), is one of the largest carbon fluxes in the Earth system. An increasing number of high‐frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open‐source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long‐term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS, the database design accommodates other soil‐atmosphere measurements (e.g. ecosystem respiration, chamber‐measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

Year-Round Transpiration Dynamics Linked With Deep Soil Moisture in a Warm Desert Shrubland

Ecohydrological processes in semiarid shrublands and other dryland ecosystems are sensitive to discrete pulses of precipitation. Anticipated changes in the frequency and magnitude of precipitation events are expected to impact the spatial and temporal distribution of soil moisture in these drylands, thereby impacting their ecohydrological processes. Recent field studies have shown that in dryland ecosystems, transpiration dynamics and plant productivity are largely a function of deep soil moisture available after large precipitation events, regardless of where the majority of plant roots occur. However, the strength of this relationship and how and why it varies throughout the year remains unclear. We present eddy covariance, soil moisture, and sap flow measurements taken over an 18‐month period in conjunction with an analysis of biweekly precipitation, shallow soil, deep soil, and stem stable water isotope samples from a creosotebush‐dominated shrubland ecosystem at the Santa Rita Experimental Range in southern Arizona. Within the context of a hydrologically defined two‐layer conceptual framework, our results support that transpiration is associated with the availability of deep soil moisture and that the source of this moisture varies seasonally. Therefore, changes in precipitation pulses that alter the timing and magnitude of the availability of deep soil moisture are expected to have major consequences for dryland ecosystems. Our findings offer insights that can improve the representation of drylands within regional and global models of land surface atmosphere exchange and their linkages to the hydrologic cycle