Consensus Head Acceleration Measurement Practices (CHAMP): Study Design and Statistical Analysis - Steve Rowson; Jason Mihalik; Jillian Urban; Julianne Schmidt; Steve Marshall; Jaroslaw Harezlak; Brian D. Stemper; Mike McCrea; Jim Funk (Annals of Biomedical Engineering, 2022)
Head impact measurement devices enable opportunities to collect impact data directly from humans to study topics like concussion biomechanics, head impact exposure and its effects, and concussion risk reduction techniques in sports when paired with other relevant data. With recent advances in head impact measurement devices and cost-effective price points, more and more investigators are using them to study brain health questions. However, as the field's literature grows, the variance in study quality is apparent. This brief paper aims to provide a high-level set of key considerations for the design and analysis of head impact measurement studies that can help avoid flaws introduced by sampling biases, false data, missing data, and confounding factors. We discuss key points through four overarching themes: study design, operational management, data quality, and data analysis.

Genomics‐informed delineation of conservation units in a desert amphibian - Brenna R. Forester; Melanie Murphy; Chad Mellison; Jeffrey Petersen; David S. Pilliod; Rachel Van Horne; Jim Harvey; W. Chris Funk (Molecular Ecology, 2022)
Delineating conservation units (CUs, e.g., evolutionarily significant units, ESUs, and management units, MUs) is critical to the recovery of declining species because CUs inform both listing status and management actions. Genomic data have strengths and limitations in informing CU delineation and related management questions in natural systems. We illustrate the value of using genomic data in combination with landscape, dispersal and occupancy data to inform CU delineation in Nevada populations of the Great Basin Distinct Population Segment of the Columbia spotted frog ( Rana luteiventris ). R. luteiventris occupies naturally fragmented aquatic habitats in this xeric region, but beaver removal, climate change and other factors have put many of these populations at high risk of extirpation without management intervention. We addressed three objectives: (i) assessing support for ESUs within Nevada; (ii) evaluating and revising, if warranted, the current delineation of MUs; and (iii) evaluating genetic diversity, effective population size, adaptive differentiation and functional connectivity to inform ongoing management actions. We found little support for ESUs within Nevada but did identify potential revisions to MUs based on unique landscape drivers of connectivity that distinguish these desert populations from those in the northern portion of the species range. Effective sizes were uniformly small, with low genetic diversity and weak signatures of adaptive differentiation. Our findings suggest that management actions, including translocations and genetic rescue, might be warranted. Our study illustrates how a carefully planned genetic study, designed to address priority management goals that include CU delineation, can provide multiple insights to inform conservation action.

The collaborative historical African rainfall model: description and evaluation - Chris Funk; Joel Michaelsen; Jim Verdin; Guleid Artan; Greg Husak; Gabriel Senay; Hussein Gadain; Tamuka Magadazire (International Journal of Climatology, 2003)
In Africa the variability of rainfall in space and time is high, and the general availability of historical gauge data is low. This makes many food security and hydrologic preparedness activities difficult. In order to help overcome this limitation, we have created the Collaborative Historical African Rainfall Model (CHARM). CHARM combines three sources of information: climatologically aided interpolated (CAI) rainfall grids (monthly/0.5° ), National Centers for Environmental Prediction reanalysis precipitation fields (daily/1.875° ) and orographic enhancement estimates (daily/0.1° ). The first set of weights scales the daily reanalysis precipitation fields to match the gridded CAI monthly rainfall time series. This produces data with a daily/0.5° resolution. A diagnostic model of orographic precipitation, VDELB—based on the dot‐product of the surface wind V and terrain gradient (DEL) and atmospheric buoyancy B —is then used to estimate the precipitation enhancement produced by complex terrain. Although the data are produced on 0.1° grids to facilitate integration with satellite‐based rainfall estimates, the ‘true’ resolution of the data will be less than this value, and varies with station density, topography, and precipitation dynamics. The CHARM is best suited, therefore, to applications that integrate rainfall or rainfall‐driven model results over large regions. The CHARM time series is compared with three independent datasets: dekadal satellite‐based rainfall estimates across the continent, dekadal interpolated gauge data in Mali, and daily interpolated gauge data in western Kenya. These comparisons suggest reasonable accuracies (standard errors of about half a standard deviation) when data are aggregated to regional scales, even at daily time steps. Thus constrained, numerical weather prediction precipitation fields do a reasonable job of representing large‐scale diurnal variations. Published in 2003 by John Wiley & Sons, Ltd.