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Military personnel are at an increased risk of shoulder injuries due to training and deployment demands, however, there is a lack of information on the tactical athlete's upper extremity profile. Therefore, the purpose of this study was to examine shoulder musculoskeletal characteristics, including range of motion (ROM), strength, and function, and the relationships between these measures in Marine Corps Forces Special Operations Command (MARSOC) personnel without history of shoulder injury.

Effective conservation of rare species requires an understanding of how potential threats affect population dynamics. Unfortunately, information about population demographics prior to threats (i.e., baseline data) is lacking for many species. Perturbations, caused by climate change, disease, or other stressors can lead to population declines and heightened conservation concerns. Boreal toads (Anaxyrus boreas boreas) have undergone rangewide declines due mostly to the amphibian chytrid fungus Batrachochytrium dendrobatidis(Bd), with only a few sizable populations remaining in the southern Rocky Mountains, USA, that are disease‐free. Despite the apparent region‐wide occurrence of Bd, our focal populations in central Colorado were disease free over a 14‐year capture‐mark‐recapture study until the recent discovery of Bd at one of the sites. We used recapture data and the Pradel reverse‐time model to assess the influence of environmental and site‐specific conditions on survival and recruitment. We then forecast changes in the toad populations with 2 growth models; one using an average lambda value to initiate the projection, and one using the most recent value to capture potential effects of the incursion of disease into the system. Adult survival was consistently high at the 3 sites, whereas recruitment was more variable and markedly low at 1 site. We found that active season moisture, active season length, and breeding shallows were important factors in estimating recruitment. Population growth models indicated a slight increase at 1 site but decreasing trends at the 2 other sites, possibly influenced by low recruitment. Insight into declining species management can be gained from information on survival and recruitment and how site‐specific environmental factors influence these demographic parameters. Our data are particularly useful because they provide baseline data on demographics in populations before a disease outbreak and enhance our ability to detect changes in population parameters potentially caused by the disease. © 2016 The Wildlife Society.

Context:The recent increase in athlete size, particularly in football athletes of all levels, coupled with the increased health risk associated with obesity warrants continued monitoring of body composition from a health perspective in this population. Equations developed to predict percentage of body fat (Fat) have been shown to be population specific and might not be accurate for football athletes.Objective:To develop multiple regression equations using standard anthropometric measurements to estimate dual-energy x-ray absorptiometry Fat (DEXAFat) in collegiate football players.Design:Controlled laboratory study.Patients and Other Participants:One hundred fifty-seven National Collegiate Athletic Association Division IA football athletes (age 20 ± 1 years, height 185.6 ± 6.5 cm, mass 103.1 ± 20.4 kg, DEXAFat 19.5 ± 9.1) participated.Main Outcome Measure(s):Participants had the following measures: (1) body composition testing with dual-energy x-ray absorptiometry; (2) skinfold measurements in millimeters, including chest, triceps, subscapular, midaxillary, suprailiac, abdominal (SFAB), and thigh; and (3) standard circumference measurements in centimeters, including ankle, calf, thigh, hip (AHIP), waist, umbilical (AUMB), chest, wrist, forearm, arm, and neck. Regression analysis and fit statistics were used to determine the relationship between DEXAFat and each skinfold thickness, sum of all skinfold measures (SFSUM), and individual circumference measures.Results:Statistical analysis resulted in the development of 3 equations to predict DEXAFat: model 1, (0.178 • AHIP) (0.097 • AUMB) (0.089 • SFSUM) − 19.641; model 2, (0.193 • AHIP) (0.133 • AUMB) (0.371 • SFAB) − 23.0523; and model 3, (0.132 • SFSUM) 3.530. The R2values were 0.94 for model 1, 0.93 for model 2, and 0.91 for model 3 (for all, P< .001).Conclusions:The equations developed provide an accurate way to assess DEXAFat in collegiate football players using standard anthropometric measures so athletic trainers and coaches can monitor these athletes at increased health risk due to increased size.