Historically
we begin this research with direct (we say in
situ) observations of the study species in their natural environment. Think
Jane Goodall and her Gombe River Chimpanzees. From here we progress to higher
order analyses with multidisciplinary syntheses and hypothesis testing (test
tubes and bubbly liquids). The marine environment however, presents several
unique challenges to this fundamental task. It’s bloody huge, often
inaccessible, and the best visibility of 30 metres is but a fraction of some
shark home ranges than can span thousands of kilometres. This is perhaps why we
still know very little about many of the oceans inhabitants.
Fortunately
though you are reading this from your shiny new iPad and we have made several
advances in technology since Jane Goodall was out in the
field. We can place tags on sharks that transmit their location to your very
screen and find out just how they like to spend their weekends. As scientists,
this allows us to shift our perspective from that of an outside observer and
onto the animal itself. We remove the need to actually see the animal, and in
turn remove many of those limitations to observation that I listed before.
This
answers the ‘When’ and the ‘Where,’ but increasingly, scientists want to know
the ‘Why,’ the ‘What,’ and the ‘How.’ So we put sensors on those tags. A
current estimation identifies about 24 different types of available sensors
measuring anything from temperature to heartbeat.
One
such sensor, the accelerometer, allows for the detection of very fine scale
movement. You’ve got one in your iPad and it is what detects the tilting and
shaking of the screen when you play Temple Run. In the very same way, the
accelerometers we place on sharks measure their tail movements and posture.
My
work here at Oceans Research has been a ground truthing exercise whereby we
test the ability of these accelerometers to measure shark tail beats, and from
there extract specific swimming behaviours. Now that I’m into the final
analyses before writing up the dissertation, I can show you how this is done.
After
some manipulation, this is what I get from the sway axis of the accelerometer
(the side to side movements). Essentially each peak and trough pair corresponds
to one complete tail stroke cycle. So in the beginning we see some steady
swimming, followed by a sweeping turn, some more steady swimming, and so on
until the very end where we the shark stops moving al together before quickly
resuming movement in a quick burst of
speed. And that’s pretty much it! By taking a little bit of physics into
account, we can remodel the movement of the shark, even making some behavioural
inferences, without ever having witnessed the animal.
Unfortunately
science isn’t quite that simple and we have to ‘test’ hypotheses with
‘statistics,’ but that’s my problem, not yours. What’s that you say? My humour
and brilliance has got you curious? Oh go on you! Fine, here’s a bit more…
Here
is a comparison of several different methods for extracting those behaviours.
At the bottom, marked ‘video’ is our direct observation, an ethogram, of the
animal that will serve as the truth, against which we will compare all others.
Each coloured bar indicates when a specific behaviour was observed. Marked
‘accel’ are the behaviours that I have manually observed from the accelerometer
time series. The next one, marked ‘Eth’ are the behaviours extracted by an
autonomous k-means clustering algorithm of the accelerometer record. Special
software has analysed the data and grouped them together. Lastly, is the same
analysis performed with some training. So in this case I have given the
computer a set of criteria to use for each clustering before it automatically
groups them together.
You
can see clear differences in the efficacy of each method. Not immediately
apparent however is the work involved in each method. Manually annotating the
accelerometer record by hand is very time consuming compared with the split-second
analysis performed by modern computers. So which method would you choose? Each
has its own advantages and disadvantages, ups and down (side to sides). Leave a
comment and tell me what you think! Here’s one thing to consider; we are
comparing everything to a visual record, but who’s to say we can see
everything? Perhaps the accelerometer can pick our subtleties not visible to
the human observer. Food for thought!
For
your viewing pleasure, here’s a short video demonstrating the ability of the
accelerometer to identify specific tail beats. https://www.youtube.com/watch?v=zUfk3arpleU
