Statistical population reconstruction (SPR) using hunter-supplied, age-at-harvest data provides a flexible framework for estimating population parameters and trends over large geographic areas. Early development of the technique assumed natural survival and the vulnerability coefficients that translate hunter effort into harvest mortality are constant over time. We developed random-effects models that relaxed these assumptions to produce more realistic models for cohort analysis. We compared the statistical performance of fixed-effects and random-effects models and the relative benefits of single-stage and 2-stage estimators of abundance with Monte Carlo simulation studies. In single-stage analyses, initial cohort abundance is estimated directly within the likelihood model. In 2-stage analyses, survival and harvest probabilities are estimated using likelihood conditional on total cohort harvest. Annual abundances are then estimated outside the likelihood using Horvitz-Thompson type estimators. Our simulation results indicate random-effects models conditional on total cohort harvest have low bias and yield asymptotic 95% confidence intervals that have near nominal coverage. The other estimation procedures produced confidence intervals for annual abundance that had far-from-nominal coverage. Our new random-effects, 2-stage model is illustrated using 18 years of age-at-harvest data from an elk (Cervus elaphus) herd in the Lower Peninsula of Michigan. Results indicate the multistage estimation process incorporating random effects provides accurate abundance estimates and confidence interval coverage, and we recommend its use. Ó 2013 The Wildlife Society.

Recently, statistical population models using age-at-harvest data have seen increasing use for monitoring of harvested wildlife populations. Even more recently, detailed evaluation of model performance for long-lived, large game animals indicated that the use of random effects to incorporate unmeasured environmental variation, as well as second-stage Horvitz-Thompson-type estimators of abundance, provided more reliable estimates of total abundance than previous models. We adapt this new modeling framework to small game, age-at-harvest models with only young-of-the-year and adult age classes. Our Monte Carlo simulation results indicate superior model performance for the new modeling framework, evidenced by lower bias and proper confidence interval coverage. We apply this method to male wild turkey harvest in the East Ozarks turkey productivity region, Missouri, USA, where statistical population reconstruction indicates a relatively stationary population for 1996–2010.

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In a laboratory exercise for undergraduate biology majors, students plated bacteria from swabs of their facial skin under conditions that selected for coagulase-negative Staphylococcus; added disks containing the antibiotics penicillin, oxacillin, tetracycline, and erythromycin; and measured zones of inhibition. Students also recorded demographic and lifestyle variables and merged this information with similar data collected from 9,000 other students who had contributed to the database from 2003 to 2011. Minimum inhibitory concentration (MIC) testing performed at the Harborview Medical Center Microbiology Laboratory (Seattle, WA) indicated a high degree of accuracy for student-generated data; species identification with a matrix-assisted laser desorption ionization (MALDI) Biotyper revealed that over 88% of the cells analyzed by students were S. epidermidis or S. capitus. The overall frequency of resistant cells was high, ranging from 13.2% of sampled bacteria resistant to oxacillin to 61.7% resistant to penicillin. Stepwise logistic regressions suggested that recent antibiotic use was strongly associated with resistance to three of the four antibiotics tested (p = 0.0003 for penicillin, p << 0.0001 for erythromycin and tetracycline), and that age, gender, use of acne medication, use of antibacterial soaps, or makeup use were associated with resistance to at least one of the four antibiotics. Furthermore, drug resistance to one antibiotic was closely linked to resistance to the other three antibiotics in every case (all p values << 0.0001), suggesting the involvement of multidrug–resistant strains. The data reported here suggest that citizen science could not only provide an important educational experience for undergraduates, but potentially play a role in efforts to expand antibiotic resistance (ABR) surveillance.