Challenges and opportunities for comparative studies of survival rates: An example with male pinnipeds.

Survival rates are a central component of life-history strategies of large vertebrate species. However, comparative studies seldom investigate interspecific variation in survival rates with respect to other life-history traits, especially for males. The lack of such studies could be due to the challenges associated with obtaining reliable datasets, incorporating information on the 0-1 probability scale, or dealing with several types of measurement error in life-history traits, which can be a computationally intensive process that is often absent in comparative studies. We present a quantitative approach using a Bayesian phylogenetically controlled regression with the flexibility to incorporate uncertainty in estimated survival rates and quantitative life-history traits while considering genetic similarity among species and uncertainty in relatedness. As with any comparative analysis, our approach makes several assumptions regarding the generalizability and comparability of empirical data from separate studies. Our model is versatile in that it can be applied to any species group of interest and include any life-history traits as covariates. We used an unbiased simulation framework to provide "proof of concept" for our model and applied a slightly richer model to a real data example for pinnipeds. Pinnipeds are an excellent taxonomic group for comparative analysis, but survival rate data are scarce. Our work elucidates the challenges associated with addressing important questions related to broader ecological life-history patterns and how survival-reproduction trade-offs might shape evolutionary histories of extant taxa. Specifically, we underscore the importance of having high-quality estimates of age-specific survival rates and information on other life-history traits that reasonably characterize a species for accurately comparing across species.

An Experiential-Learning Lesson to Encourage Teamwork and Healthy Practices.

Many careers require individuals to work together as a team. However, group work or teamwork is often met with resistance in an academic setting, and students can struggle to learn the skills associated with collaboration. I present a lesson that has the overarching goal of helping students practice and learn to make healthy lifestyle choices in the context of experiential learning. Additional learning objectives are to help students understand the physiological processes related to heart disease, practice effective prevention of this medical ailment, and encourage teamwork. Students received a risk score calculated from a randomized combination of hypothetical lifestyle characteristics that affect heart disease risk. Students then spent time outside of class gaining points against their assigned risk score by engaging in specific healthy lifestyle choices outlined on their score sheet. In unannounced pretests and posttests, students showed significant learning gains related to the physiological mechanisms and preventative agents of heart disease. Practicing proper diet, physical activity, and teamwork during adolescence and early adulthood contributes to the integration of these essential healthy habits throughout adulthood. Many students reported that collaboration among the group was a key component in overcoming the challenges associated with completing heart disease prevention actions on their score sheets. The lesson promotes creativity and the building of a support network to lower their risk scores, and it provides them with the opportunity to synthesize information and evaluate their performance in the activity.

Evaluating the predictability of distance race performance in NCAA cross country and track and field from high school race times in the United States.

Successful recruiting for collegiate track & field athletes has become a more competitive and essential component of coaching. This study aims to determine the relationship between race performances of distance runners at the United States high school and National Collegiate Athletic Association (NCAA) levels. Conditional inference classification tree models were built and analysed to predict the probability that runners would qualify for the NCAA Division I National Cross Country Meet and/or the East or West NCAA Division I Outdoor Track & Field Preliminary Round based on their high school race times in the 800 m, 1600 m, and 3200 m. Prediction accuracies of the classification trees ranged from 60.0 to 76.6 percent. The models produced the most reliable estimates for predicting qualifiers in cross country, the 1500 m, and the 800 m for females and cross country, the 5000 m, and the 800 m for males. NCAA track & field coaches can use the results from this study as a guideline for recruiting decisions. Additionally, future studies can apply the methodological foundations of this research to predicting race performances set at different metrics, such as national meets in other countries or Olympic qualifications, from previous race data.