Summary In sexually reproducing animals, male and female reproductive strategies often conflict [1]. In some species, males use aggression to overcome female choice [2, 3], but debate persists over the extent to which this strategy is successful. Previous studies of male aggression toward females among wild chimpanzees have yielded contradictory results about the relationship between aggression and mating behavior [4-11]. Critically, however, copulation frequency in primates is not always predictive of reproductive success [12]. We analyzed a 17-year sample of behavioral and genetic data from the Kasekela chimpanzee (Pan troglodytes schweinfurthii) community in Gombe National Park, Tanzania, to test the hypothesis that male aggression toward females increases male reproductive success. We examined the effect of male aggression toward females during ovarian cycling, including periods when the females were sexually receptive (swollen) and periods when they were not. We found that, after controlling for confounding factors, male aggression during a female’s swollen periods was positively correlated with copulation frequency. However, aggression toward swollen females was not predictive of paternity. Instead, aggression by high-ranking males toward females during their nonswollen periods was positively associated with likelihood of paternity. This indicates that long-term patterns of intimidation allow high-ranking males to increase their reproductive success, supporting the sexual coercion hypothesis. To our knowledge, this is the first study to present genetic evidence of sexual coercion as an adaptive strategy in a social mammal.

The proliferation of vast quantities of available datasets that are large and complex in nature has challenged universities to keep up with the demand for graduates trained in both the statistical and the computational set of skills required to effectively plan, acquire, manage, analyze, and communicate the findings of such data. To keep up with this demand, attracting students early on to data science as well as providing them a solid foray into the field becomes increasingly important. We present a case study of an introductory undergraduate course in data science that is designed to address these needs. Offered at Duke University, this course has no prerequisites and serves a wide audience of aspiring statistics and data science majors as well as humanities, social sciences, and natural sciences students. We discuss the unique set of challenges posed by offering such a course, and in light of these challenges, we present a detailed discussion into the pedagogical design elements, content, structure, computational infrastructure, and the assessment methodology of the course. We also offer a repository containing all teaching materials that are opensource, along with supplementary materials and the R code for reproducing the figures found in the article.

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