Energetic trade-offs in migration decision-making, reproductive effort and subsequent parental care in a long-distance migratory bird.

Migratory species trade-off long-distance movement with survival and reproduction, but the spatio-temporal scales at which these decisions occur are relatively unknown. Technological and statistical advances allow fine-scale study of animal decision-making, improving our understanding of possible causes and therefore conservation management. We quantified effects of reproductive preparation during spring migration on subsequent breeding outcomes, breeding outcomes on autumn migration characteristics and autumn migration characteristics on subsequent parental survival in Greenland white-fronted geese ().

Associations between rurality and regional differences in sociodemographic factors and the 1918-20 influenza and 2020-21 COVID-19 pandemics in Missouri counties: An ecological study.

This study compares pandemic experiences of Missouri's 115 counties based on rurality and sociodemographic characteristics during the 1918-20 influenza and 2020-21 COVID-19 pandemics. The state's counties and overall population distribution have remained relatively stable over the last century, which enables identification of long-lasting pandemic attributes. Sociodemographic data available at the county level for both time periods were taken from U.

Excursions in the Bayesian treatment of model error.

Advances in observational and computational assets have led to revolutions in the range and quality of results in many science and engineering settings. However, those advances have led to needs for new research in treating model errors and assessing their impacts. We consider two settings.

An illustration of model agnostic explainability methods applied to environmental data.

Historically, two primary criticisms statisticians have of machine learning and deep neural models is their lack of uncertainty quantification and the inability to do inference (i.e., to explain what inputs are important).

Artificial Intelligence to Aid Glaucoma Diagnosis and Monitoring: State of the Art and New Directions.

Recent developments in the use of artificial intelligence in the diagnosis and monitoring of glaucoma are discussed. To set the context and fix terminology, a brief historic overview of artificial intelligence is provided, along with some fundamentals of statistical modeling. Next, recent applications of artificial intelligence techniques in glaucoma diagnosis and the monitoring of glaucoma progression are reviewed, including the classification of visual field images and the detection of glaucomatous change in retinal nerve fiber layer thickness.