Baboon Reproduction: Linking Sexual Swellings, Estrogen, and Mating Behavior

Project Summary

Female baboons occasionally exhibit large swellings on their behinds. Although these ‘sexual swellings’ may evoke disgust from human on-lookers, they provide important information to group members about a female’s reproductive state. To figure out what these sexual swellings mean and whether male baboons notice, we need to look at the data.  

Themes and Categories
Year
2020

This data expedition explores the relationship between female baboon sexual swellings, female estrogen concentrations, and male mating success. The expedition uses long-term data collected on wild baboons by the Amboseli Baboon Research Project. After learning background information about baboon social lives and reproduction, students generate testable predictions for two hypotheses about baboon reproduction. Students then learn how to use the popular R packages dplyr and ggplot2 to calculate descriptive statistics about the dataset. Finally, students perform data visualization to understand and explore patterns in animal mating behavior and sexual signals.

Learning objectives

  • Learn basics of exploratory data analysis (descriptive statistics, generating plots) in R
  • Learn basics of popular R packages dplyr and ggplot2
  • Increase understanding of association between hormones and mating behavior
  • Increase science literary skills (e.g. generating predictions, interpreting results)

Course Materials

Workflow

The lesson started with a brief Powerpoint presentation to introduce the class to basic information on baboon sociality and reproduction. At the end of the Powerpoint, students were introduced to 2 key hypotheses about baboon reproduction that they then explored using R. The class was divided into small groups where students worked together to propose possible predictions to test these hypotheses (and filled out the first section of the provided worksheet) before seeing the provided predictions.

Students then worked through the provided R script and accompanying dataset to test these predictions. Students with prior experience with R were able to skip ahead by following instructions on the R script, while most of the class worked through the script step-by-step with guidance from instructors. The course instructors walked the students through most of the script, then let students work independently to complete Data Visualization Part 2. Students filled out the worksheet as they went along.

At the end of the R script, students ultimately replicated Figure 1 from Gesquiere et al. 2007. This figure is included as the final slide of the Powerpoint presentation. The end of the class session was used to interpret the figure and discuss how it relates to the 2 project hypotheses.

Student level

This lesson is designed for undergraduate students who have little to no exposure to R or other programming software. It could be easily adjusted for students who are familiar with R or other programming software. This lesson takes about 75 minutes to complete.

The dataset and the Amboseli Baboon Research Project

The dataset for this expedition is a subset of the long-term database of the Amboseli Baboon Research Project, a project co-directed by Drs. Jeanne Altmann, Susan Alberts, Beth Archie, and Jenny Tung. The Amboseli Baboon Research Project has collected demographic, behavioral, genetic, and endocrinological data on a population of wild baboons since 1971 in order to study questions related to animal behavior, life history, behavioral ecology, genetics, and physiology. The project’s database is managed by Jake Gordon at Duke University and Niki Learn at Princeton University.

The unit of analysis for this dataset is a fecal estrogen sample from a cycling1 female. For each fecal sample (n = 843), 6 variables are recorded:

  1. female - identity of the female baboon
  2. cyle_day - day of her reproductive cycle
  3. estrogen - fecal estrogen concentration
  4. swelling_size - sexual swelling size2
  5. alpha_consort - whether or not the female consorted3 with an alpha male4 on that day
  6. nonalpha_consort - whether or not the female consorted with a non-alpha male on that day

This dataset includes data from 93 female baboons, with approximately 10 fecal estrogen samples per female. Minor differences between this dataset and the the dataset used in Gesquiere et al. 2007 are due to small, incremental changes in the database over time.

Footnotes

1 cycling: sexually mature but not pregnant or lactating
2 female yellow baboons exhibit exaggerated sexual swellings (an enlargement/engorgement of the genital and perineal skin) around ovulation
3 consortship: a period in which a male mate-guards a female. Virtually all matings and conceptions occur during consorts

Graduate students: Ali Galezo and Emily Levy
Course: Mechanisms of Animal Behavior (BIOLOGY 268D)

Downloads

Related Projects

This data expedition focused on the mechanisms animals use to orient using environmental stimuli, the methods that scientists use to test hypotheses about orientation, and the statistical methods used with circular orientation data. Students collected their own data set during the class period, performed hypothesis testing on their data using circular statistics in R, and aggregated their data to formally test the hypothesis that isopods orient with light using an RShiny online application.

This exercise served as a capstone to a series of four class sessions on orientation and navigation, where students read primary scientific literature that used circular statistics in their methods. This data exercise was used to give students the opportunity to collect their own data, discover why linear statistics wouldn’t be sufficient to analyze them, and then implement their own analysis. The goal of this course was to give students a better understanding of circular statistics, with hands-on application in forming and testing a hypothesis.

In this two-day, virtual data expedition project, students were introduced to the APIM in the context of stress proliferation, linked lives, the spousal relationship, and mental and physical health outcomes.

Stress proliferation is a concept within the stress process paradigm that explains how one person’s stressors can influence others (Thoits 2010). Combining this with the life course principle of linked lives explains that because people are embedded in social networks, stress not only can impact the individual but can also proliferate to people close to them (Elder Jr, Shanahan and Jennings 2015). For example, one spouse’s chronic health condition may lead to stress-provoking strain in the marital relationship, eventually spilling over to affect the other spouse’s mental health. Additionally, because partners share an environment, experiences, and resources (e.g., money and information), as well as exert social control over each other, they can monitor and influence each other’s health and health behaviors. This often leads to health concordance within couples; in other words, because individuals within the couple influence each other’s health and well-being, their health tends to become more similar or more alike (Kiecolt-Glaser and Wilson 2017, Polenick, Renn and Birditt 2018). Thus, a spouse’s current health condition may influence their partner’s future health and spouses may contemporaneously exhibit similar health conditions or behaviors.

However, how spouses influence each other may be patterned by the gender of the spouse with the health condition or exhibiting the health behaviors. Recent evidence suggests that a wife’s health condition may have little influence on her husband’s future health conditions, but that a husband’s health condition will most likely influence his wife’s future health (Kiecolt-Glaser and Wilson 2017).

Stats/Sociology major Mitchelle Mojekwu joined Neuroscience majors Kassie Hamilton and Zineb Jaidi in a ten-week exploration of data relevant to an upcoming public school zone redistricting in Durham County. Using information acquired from the General Social Survey and the US Census, the team applied modern mathematical and statistical methods for generating proposed redistricting plans, with the aim of providing decision-makers with information they can use to produce school districts that are equitable and reflective of the Durham County student population.

View the team's project poster here

Watch the team's final presentation on Zoom:

 

Faculty Lead: Greg Herschlag

Project Manager: Bernard Coles