Data Expeditions: Estimating home ranges using track data in QGIS and RStudio

Project Summary

We introduced students to spatial analysis in QGIS and R using location data from two whale species tagged with satellite transmitters. Students were given satellite tracks from five Cuvier’s beaked whales (Ziphius cavirostris) and five short-finned pilot whales (Globicephala macrorhynchus) tagged off the North Carolina coast. Students then used RStudio to calculate two metrics of these species' spatial ranges: home range (where a species spends 95% of its time) and core range (where a species spends 50% of its time). Next, students used QGIS to visualize the data, producing maps that displayed the whales' tracks and their ranges.

Themes and Categories

Graduate Students: Amanda Lohmann, Ph.D., Student in Ecology, and Ann-Marie Jacoby, Ph.D., Student in Marine Science and Conservation

Faculty: Dr. Andrew Read

Course: "Marine Mammals" (BIOLOGY 376LA; 2018)

Data Description

The dataset consists of series of location points recorded from satellite tags on five short-finned pilot whales and five Cuvier's beaked whales. The whales were tagged off Cape Hatteras, NC between 2014 and 2016 as part of a joint effort between Duke University and the US Navy Marine Species Monitoring Program (Deep Divers and Satellite Tagging Records).

Class Summary

We gave a brief overview of the ARGOS satellite system and issues involved in cleaning up the error-prone data that is recorded by ARGOS satellite tags. We introduced the concept of geographic information system (GIS) software used for geospatial analysis. We discussed the need to project three-dimensional spatial data from the Earth's surface onto two-dimensional planes for analysis in GIS software and explained the importance of consistently using the same projection within a single project.

We then presented the whale track data and explained the concept of home ranges and core ranges. Next, we gave students the instructions sheet, which walked them through calculating home range and core range for both species of whale. The assignment asked students to: use the open-source QGIS program to load satellite-recorded whale locations; project the 3D latitude-longitude coordinates onto a 2D plane; export the projected coordinates into a file that could be loaded with the R programming language; use R to calculate the home range and core range of each species using a method known as kernel density estimation (KDE); transfer the resulting polygons back into QGIS; and create clear, easy-to-read maps in QGIS displaying the whales' tracks and their ranges.


  1. Create two maps in QGIS one for short-finned pilot whales (Globicephala macrorhynchus) and one for Cuvier’s beaked whales (Ziphius cavirostris) displaying the track points, core range, and home range for all animals using kernel density estimations in R.
  2. Descriptively compare pilot whale and Cuvier’s beaked whale home ranges by completing the following:
    1. Describe the two species' home ranges and core ranges.
    2. Compare the two species' home ranges (similarities and differences), and
    3. Give possible reasons as of to why there are similarities and differences between the two species' home ranges. Think about their physiology, diet, behavior, etc., as well as the continental shelf and Gulf Stream.

We expect a paragraph but no more than one page. Please cite the sources you use.

Course Materials

  1. Thorne LH, Foley HJ, Baird RW, Webster DL, Swaim ZT, Read AJ (2017) Movement and foraging behavior of short-finned pilot whales in the Mid-Atlantic Bight: importance of bathymetric features and implications for management. Mar Ecol Prog Ser 584:245-257. 
  2. Stanistreet, JE, Nowacek DP, Baumann-Pickering S, Bell JT, Cholewiak DM, Hildebrand JA, Hodge LEW, Moors-Murphy HB, Van Parijs SM, Read AJ.  2017. Using passive acoustic monitoring to document the distribution of beaked whale species in the western North Atlantic Ocean. Canadian Journal of Fisheries and Aquatic Sciences.


Pilot Whale Tracks

Pilot whale home range and core range

Beaked whale home range and core range

Related Projects

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).

Fluid mechanics is the study of how fluids (e.g., air, water) move and the forces on them. Scientists and engineers have developed mathematical equations to model the motions of fluid and inertial particles. However, these equations are often computationally expensive, meaning they take a long time for the computer to solve.


To reduce the computation time, we can use machine learning techniques to develop statistical models of fluid behavior. Statistical models do not actually represent the physics of fluids; rather, they learn trends and relationships from the results of previous simulation experiments. Statistical models allow us to leverage the findings of long, expensive simulations to obtain results in a fraction of the time. 


In this project, we provide students with the results of direct numerical simulations (DNS), which took many weeks for the computer to solve. We ask students to use machine learning techniques to develop statistical models of the results of the DNS.

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.