Dugongs are marine mammals sometimes known as sea cows. Like cows they graze on grass – in this case seagrass! But unlike cows they are considered threatened globally, mainly due to loss of habitat, as well as to entanglement in debris from human activities such as fishing, and shark nets put in place to protect swimmers. Seagrass meadows are destroyed by industrial development such as gas mining, as well as by runoff from agriculture and other land-based environmental degradation.
A single dugong can eat up to 40 kg of seagrass in a day.
The Western Australian Marine Science Institution has been collecting data about dugongs off the coast of the Kimberley in Northern WA. We have been provided with the raw data from its sightings trips. In this project students will learn how dugong sightings are conducted, and develop the data-science skills needed to make the sightings data useful for analysis.
The cultural practices of Aboriginal and Torres Strait Islander peoples have sustainably managed the coastal waters of Australia for thousands of years. Traditional ecological knowledge considers the impact of community practices on the environment to ensure that the organism populations within the ecosystems are not detrimentally affected. CSIRO scientists are working with Traditional Owners to conserve dugong populations.
This lesson was devised by Linda McIver, Australian Data Science Education Institute.
Explain that scientists need to collect data about the dugong to manage its habitat and understand its numbers and movements.
Provide students with background information describing how scientific aerial surveys are conducted from a plane.
Ask students to think about the survey approach and list three stages at which errors might occur, from the start of a flight through to analysis of the data.
How accurate do you think this survey is at identifying the number of dugongs in an area? Give three reasons why the surveyors might miss some dugongs.
Raise the point that from this aerial survey the scientists want to know many dugongs are in the area. The data they can actually collect is how many dugongs they can see. It is not quite the same. This is called ‘proxy data’ because it’s a proxy (substitute) for the data we actually want. Given that scientists know this data is inaccurate, why do they continue to collect it?
Provide students with the career profile poster of Marlee Hutton, a scientist leading the aerial survey team. Use the poster to inspire students to consider a STEM career by highlighting the scientist’s background and interests, a snapshot of their current role and the pathways they have taken to enter their profession. Marlee Hutton is an indigenous research scientist working for CSIRO. Career profile: Marlee Hutton, CSIRO research scientist
Dugong numbers are difficult to track, so sometimes aerial surveys are run to see how many dugongs can be spotted in an area. These surveys are done from a small plane with four observers, two on each side of the plane. For each trip there is also an environmental observer who records changes in cloud coverage (which affects glare and what you can see), as well as changes in water surface, such the number of waves and whitecaps there are, as these also affect the ability to see.
Each observer has a headset and their voices are recorded, so that they don’t have to look away from the windows to write anything down. A GPS recording is also synced with the voice recorder, so that each time a sighting is made the GPS coordinates are known.
For each sighting, the observers note four points – what type of animal (for example dolphin, turtle, dugong), how many, are they on or below the surface (below makes them harder to spot and identify), and a measure of the turbidity of the water from 1 to 4:
Survey flights are typically four hours long. When the survey is complete the data from the audio recordings is entered into a database.
Provide students with a copy of the Dugong Sightings Data spreadsheet. This contains the data from survey flights in September and October 2015.
Make a duplicate of the original sheet. Label the original and the duplicate so you know which one to work in. You might like to call them ‘original’ and ‘working copy’, or similar.
Ensure students make a copy of the original data sheet and work on a copy. This avoids losing data on the off-chance the data is accidentally changed or lost during analysis. Systems like Google Drive that keep version histories make this a little less important, but things can still go wrong!
Ask students to look through the column headers of the data. Ask: Which ones do you understand the meaning of, and which do you need more information on before you can understand? Have students make a glossary of the headers, leaving the ones they don’t understand blank.
Using the dugong data glossary provided, now go through the glossary as a class and have students fill in the headers they don’t understand. Students will also need to check the headers they think they do understand, to make sure they haven’t made false assumptions!
Scroll down the length of the data to get a feel for how far it goes. Note the dates of the trips, how many observations there seem to be per trip, and which columns have data for every entry, which ones only have data for some, and whether there are any columns that aren’t used at all.
Scroll across the data until you find the column labelled ‘reliability’. This is where observations are labelled when observers think their sightings might not be wholly reliable.
When there is no label observers are confident about what they saw.
‘Probable’ is taken as a genuine sighting, and ‘guess’ is not used.
Raise questions for inquiry. These may include:
Sort and filter the data to answer questions of interest.
Encourage students to use evidence towards any claims made about dugongs. Ask students how they found out answers to particular questions. Use these opportunities for formative assessment.
Optional advanced exercise: Write a Python program that will find the sightings of dugongs in the csv file (spreadsheet). Then transfer the ones not labelled ‘guess’ to a new file, so that the new file will contain all of the data from each row listing dugong sightings – except for the guesses. Calculate how many of each type of animal were sighted on each day.
Plot the following data on a printout of the worksheet Northern Australia map developed by seaturtle.org.
*Note latitude and longitude reduced to one decimal place for the purpose of this activity.
Have your students visualise data using online mapping software. Two options include Google My Maps and NationalMap.
This spreadsheet contains spatial data. Spatial data is geographic information about the Earth and its features. A set of latitude (lat) and longitude (lon) coordinates pinpoints a specific location on Earth. The sightings of animal types are identified at particular lat and lon coordinates.
Data visualisation is the visual representation of information and data. In this example we are using mapping software to visually represent the location of marine animals sighted during an aerial study. Colour can be a useful way to distinguish between different animal types plotted on the online map, but using patterns such as dots or hatching, either instead of colour or added to it, makes the representation more accessible.
Make a text graph using an ‘x’ for each animal, to show its relative number. For example, if there were 10 turtles, 3 dolphins, and 1 dugong, your Xs would look like this:
Modify your program so that the Xs all start at the same position on the line, to make comparisons easier, for example:
If there are too many Xs for one animal to fit on one line, adapt your program so that each X represents 10 animals (or part thereof). So you have one X for 1–10, two for 11–20, 3 for 21–30, and so on.
Students may use an integrated development environment (IDE) such as repl instead of downloading Python software.
Explore this example program created in Python using the repl IDE. The program imports two libraries and links a csv and answers the question: How many of each animal type were spotted?
Ask students to find three online examples of infographics or visualisations that present information effectively. Discuss any limitations and cases where they found it a challenge to understand the information.
Make sure the images students choose are licensed for reuse, by selecting ‘Advanced Search’ from the settings menu in the search and choosing ‘free to use, share, or modify’ from the Licensing drop-down menu.
An infographic is a display that contains both text and visual representations of data. The visual representations, also known as visualisations, might be as simple as a line or bar chart, or as complex as the data journalism you can see in this ABC news piece about the census.