Satellite Tagging Records of Deep Divers off Cape Hatteras, North Carolina

Project Summary

Marine mammals exhibit extreme physiological and behavioral adaptions that allow them to dive hundreds to thousands of meters underwater despite their need to breathe air at the surface. Through the development of new remote monitoring technologies, we are just beginning to understand the mechanisms by which they are able to execute these extreme behaviors. Long- term animal-borne tags can now record location, dive depth, and dive duration and then transmit these data to satellite receivers, enabling remote access to behavior occurring both many kilometers out to sea and several kilometers below the ocean surface. 

Themes and Categories
Year
2017

Graduate Students: Jillian Wisse, University Program in Ecology; Vivienne Foroughirad, Marine Science & Conservation

Faculty: Dr. Andrew Read

Course: Biology of Marine Animals, Biology 376A, ENVIRON 376A & Environ 776A 

This dataset includes 30 satellite-linked tags deployed by Duke Marine Lab researchers between 2014 and 2017 as part of the US Navy Marine Species Monitoring Program. Students used these data to explore comparative diving behavior between two deep-diving species off the North Carolina coast, short-finned pilot whales (Globicephala macrorhynchus) and Cuvier’s beaked whale (Ziphius cavirostris). Beaked whales are the deepest divers of all mammals, with recorded dives of almost 3000 meters, and are especially enigmatic, spending up to 95% of their time below the ocean surface. 

Exercises

  • Pre-process satellite tag data to remove corrupted records and identify gaps.
  • Plot dive profiles for individual pilot whales and beaked whales.
  • Calculate summary statistics for dive depths and durations.
  • Compare plots of dive distributions for each species. 

Techniques

  • R & RStudio – Import data from excel and csv files
  • Parse data and properly format data types
  • Visualize dive data in dive profile plots
  • Calculate summary statistics
  • Plot line graphs and scatterplots 

Downloads

An Introduction to Data Analysis with R (PDF)

Project slides (PDF)

Simulated code

Simulated data set

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).

Sean Fiscus (Math/Econ/EnvEng), Alyssa Shi (Stats), Yamil Lopez-Ruiz (BME/CS), Emmanuel Mokel (Stats/Math) spent ten weeks working with data from CovIdentify, a study that focuses on using wearables to predict and diagnose COVID-19 and the Flu. The team improved the memory efficiency of analytic pipelines, and added capacity to ingest different types of data. This project built upon the work accomplished by the Duke Bass Connections team and the Duke MIDS capstone project.

 

View the team's project poster here

Watch the team's final presentation on Zoom:

 

Project Lead: Jessilyn Dunn