USU Teaching Documentation: Dossiers from the Mentoring Program

The nation\u27s land grant institutions were founded on the principle of access for the general public to the knowledge gained through research and creative activity fostered in higher education. Central to our access mission is our dedication to teaching and learning that is informed by research and discovery, both of which must result, at least in part, from our engagement with our external constituents. That teaching and learning informs our research and vice versa; our research informs and aids in our teaching mission. This work, compiled by Professors Maria Luisa Spicer-Escalante and Cathy Ferrand Bullock, is focused on how the best, highly informed teaching is accomplished when done in an intentional manner. That intentional process helps the best university educators thoughtfully build their teaching story in an organized manner. Educators think about how they can successfully reach and engage their appropriate student audiences (or mentees), what they hope to accomplish, and how they intend to accomplish their goals. Further, as learning outcomes are identified and established, first-rate methods for course design, content inclusion, and continuous improvement can be outlined. Those of us who follow these intentional principles may then detail our growth and success along the way as teachers in the development of documents that tell our stories. Undoubtedly, the ability to clearly document and articulate that story will help academic personnel add to their tenure and promotion preparation in a very meaningful way. But as or even more important is the opportunity to describe these journeys with all the efforts, large and small, of improving their product in terms of learning outcomes and student growth and success. The nuggets of wisdom compiled by Professors Spicer-Escalante and Bullock, in USU Teaching Documentation: Dossiers from the Mentoring Program, will help teachers across the board from the new lecturer or assistant professor to the experienced professor dive into their teaching programs and find ways to continuously experiment and refine their approaches to our critically important student audiences. Good luck, teach on, and successfully document some of the most important work you all do! Frank Galey Executive Vice President and Provost Utah State University 2019https://digitalcommons.usu.edu/ua_faculty/1000/thumbnail.jp

An Electrochemical Study of Frustrated Lewis Pairs: A Metal-free Route to Hydrogen Oxidation

[Image: see text] Frustrated Lewis pairs have found many applications in the heterolytic activation of H(2) and subsequent hydrogenation of small molecules through delivery of the resulting proton and hydride equivalents. Herein, we describe how H(2) can be preactivated using classical frustrated Lewis pair chemistry and combined with in situ nonaqueous electrochemical oxidation of the resulting borohydride. Our approach allows hydrogen to be cleanly converted into two protons and two electrons in situ, and reduces the potential (the required energetic driving force) for nonaqueous H(2) oxidation by 610 mV (117.7 kJ mol(–1)). This significant energy reduction opens routes to the development of nonaqueous hydrogen energy technology

Mate-Finding as an Overlooked Critical Determinant of Dispersal Variation in Sexually-Reproducing Animals

Dispersal is a critically important process in ecology, but robust predictive models of animal dispersal remain elusive. We identify a potentially ubiquitous component of variation in animal dispersal that has been largely overlooked until now: the influence of mate encounters on settlement probability. We use an individual-based model to simulate dispersal in sexually-reproducing organisms that follow a simple set of movement rules based on conspecific encounters, within an environment lacking spatial habitat heterogeneity. We show that dispersal distances vary dramatically with fluctuations in population density in such a model, even in the absence of variation in dispersive traits between individuals. In a simple random-walk model with promiscuous mating, dispersal distributions become increasingly ‘fat-tailed’ at low population densities due to the increasing scarcity of mates. Similar variation arises in models incorporating territoriality. In a model with polygynous mating, we show that patterns of sex-biased dispersal can even be reversed across a gradient of population density, despite underlying dispersal mechanisms remaining unchanged. We show that some widespread dispersal patterns found in nature (e.g. fat tailed distributions) can arise as a result of demographic variability in the absence of heterogeneity in dispersive traits across the population. This implies that models in which individual dispersal distances are considered to be fixed traits might be unrealistic, as dispersal distances vary widely under a single dispersal mechanism when settlement is influenced by mate encounters. Mechanistic models offer a promising means of advancing our understanding of dispersal in sexually-reproducing organisms

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie

A trait-based approach for predicting species responses to environmental change from sparse data: how well might terrestrial mammals track climate change?

Estimating population spread rates across multiple species is vital for projecting biodiversity responses to climate change. A major challenge is to parameterise spread models for many species. We introduce an approach that addresses this challenge, coupling a trait-based analysis with spatial population modelling to project spread rates for 15&nbsp;000 virtual mammals with life histories that reflect those seen in the real world. Covariances among life-history&nbsp;traits are estimated from an extensive terrestrial mammal data set using Bayesian inference. We elucidate the relative roles of different life-history traits in driving modelled spread rates, demonstrating that any one alone will be a poor predictor. We also estimate that around 30% of mammal species have potential spread rates slower than the global mean velocity of climate change. This novel trait-space-demographic modelling approach has broad applicability for tackling many key ecological questions for which we have the models but are hindered by data availability