We compared trapping vs. traditional strychnine baiting to control pocket gophers in alfalfa fields in southern Utah. Additionally, we compared trapping efficiency of three different pocket gopher traps: Macabee, Cinch, and DK-1. Baiting and trapping resulted in similar levels of pocket gopher activity and reduced pocket gopher activity in study plots (P = 0.02). When comparing the three trap types, Macabee was the most successful at trapping pocket gophers (P = 0.004). Macabee traps were also the most time-efficient (P = 0.02). Trapping success was very low; future work needs to focus on increased study plot size, trapping density, and modifying traps to improve trapping success of larger animals.

The Wild Free-Roaming Horses and Burros Act of 1971 provides federal oversight and protection for feral horses (wild free-roaming; WFR horses) that inhabit designated areas on public lands in the western United States. The Bureau of Land Management (BLM) estimated in 2019 that over 80,000 free-roaming equids inhabited 29 million hectares on 177 designated herd management areas. This population estimate exceeds the designated appropriate management level of 26,785. To provide BLM managers with insights regarding the U.S. public knowledge and perceptions about the management of WFR horses in the U.S., we surveyed the public using an online survey process. We hypothesized that respondents from the western U.S. would be more knowledgeable of the ecology and management of WFR horses, because of their proximity to most of the WFR horses managed in the U.S. We stratified the U.S. into five regions, with the intent to acquire at least 400 responses from each; we met this quota in four of the five regions (n of southwest = 376). Overall, the U.S. public was unknowledgeable about the ecology of WFR horses and legal management options to control their populations. While there were some associations between region, sex, age, income, and each of our questions, the strength of these associations was very weak. Therefore, demographics may not be useful in predicting the level of knowledge of the U.S. public concerning WFR horses. Our results highlight the need for improved outreach and communication efforts regarding the issues and consequences of free-roaming equid management approaches.

Representing the response of soil carbon dynamics to global environmental change requires the incorporation of multiple tools in the development of predictive models. An important tool to construct and test models is the incorporation of bomb radiocarbon in soil organic matter during the past decades. In this manuscript, we combined radiocarbon data and a previously developed empirical model to explore decade-scale soil carbon dynamics in a temperate forest ecosystem at the Harvard Forest, Massachusetts, USA. We evaluated the contribution of different soil C fractions to both total soil CO₂ efflux and microbially respired C. We tested the performance of the model based on measurable soil organic matter fractions against a decade of radiocarbon measurements. The model was then challenged with radiocarbon measurements from a warming and N addition experiment to test multiple hypotheses about the different response of soil C fractions to the experimental manipulations. Our results showed that the empirical model satisfactorily predicts the trends of radiocarbon in litter, density fractions, and respired CO₂observed over a decade in the soils not subjected to manipulation. However, the model, modified with prescribed relationships for temperature and decomposition rates, predicted most but not all the observations from the field experiment where soil temperatures and nitrogen levels were increased, suggesting that a larger degree of complexity and mechanistic relations need to be added to the model to predict short-term responses and transient dynamics.

Over the past 10-20 years, neuroscience witnessed an explosion in the use of non-invasive imaging methods, particularly magnetic resonance imaging (MRI), to study brain structure and function. Simultaneously, with access to MRI in many research institutions, MRI has become an indispensable tool for researchers and veterinarians to guide improvements in surgical procedures and implants and thus, experimental as well as clinical outcomes, given that access to MRI also allows for improved diagnosis and monitoring for brain disease. As part of the PRIMEatE Data Exchange, we gathered expert scientists, veterinarians, and clinicians who treat humans, to provide an overview of the use of non-invasive imaging tools, primarily MRI, to enhance experimental and welfare outcomes for laboratory non-human primates engaged in neuroscientific experiments. We aimed to provide guidance for other researchers, scientists and veterinarians in the use of this powerful imaging technology as well as to foster a larger conversation and community of scientists and veterinarians with a shared goal of improving the well-being and experimental outcomes for laboratory animals.

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N. One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an “intelligent” mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved.

In this Brief Communications Arising Reply, the affiliation for author P. H. Templer was incorrectly listed as 'Department of Ecology & Evolutionary Biology, University of California Irvine, Irvine, California 92697, USA' instead of 'Department of Biology, Boston University, Boston, Massachusetts 02215, USA'. This has been corrected online.

The Ionospheric Connection Explorer, or ICON, is a new NASA Explorer mission that will explore the boundary between Earth and space to understand the physical connection between our world and our space environment. This connection is made in the ionosphere, which has long been known to exhibit variability associated with the sun and solar wind. However, it has been recognized in the 21st century that equally significant changes in ionospheric conditions are apparently associated with energy and momentum propagating upward from our own atmosphere. ICON's goal is to weigh the competing impacts of these two drivers as they influence our space environment. Here we describe the specific science objectives that address this goal, as well as the means by which they will be achieved. The instruments selected, the overall performance requirements of the science payload and the operational requirements are also described. ICON's development began in 2013 and the mission is on track for launch in 2017. ICON is developed and managed by the Space Sciences Laboratory at the University of California, Berkeley, with key contributions from several partner institutions.