Enhanced statistical models will aid conservation of killer whales and
other species

Date:
January 17, 2022
Source:
University of British Columbia
Summary:
Retrieving an accurate picture of what a tagged animal does as it
journeys through its environment requires statistical analysis,
especially when it comes to animal movement, and the methods
statisticians use are always evolving to make full use of the
large and complex data sets that are available. A recent study by
researchers at the Institute for the Oceans and Fisheries (IOF)
and the UBC department of statistics has taken us a step closer to
understanding the behaviours of northern resident killer whales
by improving statistical tools useful for identifying animal
behaviours that can't be observed directly.



FULL STORY ========================================================================== Ecologists need to understand wild animal behaviours in order to conserve species, but following animals around can be expensive, dangerous,
or sometimes impossible in the case of animals that move underwater or
into areas we can't reach easily.


========================================================================== Scientists turned to the next best thing: bio-logging devices that can
be attached to animals and capture information about movement, breathing
rate, heart rate, and more.

However, retrieving an accurate picture of what a tagged animal does
as it journeys through its environment requires statistical analysis, especially when it comes to animal movement, and the methods statisticians
use are always evolving to make full use of the large and complex data
sets that are available.

A recent study by researchers at the Institute for the Oceans and
Fisheries (IOF) and the UBC department of statistics has taken us a step
closer to understanding the behaviours of northern resident killer whales
by improving statistical tools useful for identifying animal behaviours
that can't be observed directly.

"The thing we really tackled with this paper was trying to get at some
of those fine-scale behaviours that aren't that easy to model," said
Evan Sidrow, a doctoral student in the department of statistics and the
study's lead author.

"It's a matter of finding behaviours on the order of seconds -- maybe
10 to 15 seconds. Usually, it's a matter of a whale looking around, and
then actively swimming for a second to get over to a new location. We are trying to observe fleeting behaviours, like a whale catching a fish."
The research team improved a statistical tool that is based on what
is called a hidden Markov model, which is helpful for unlocking the
mysteries hidden inside animal movement datasets.



========================================================================== "Traditional hidden Markov models break down at very fine scales,"
Sidrow said.

"That's because there's structure in the data you can't capture using the
basic type of hidden Markov model. We're trying to capture it with this
model - - we're trying to account for this 'wigglyness' that a traditional hidden Markov model wouldn't be able to account for." In other words,
now that tags can collect data almost continuously, researchers are
left with an immense number of data points taken fractions of seconds
apart, and traditional Markov models and statistical methods struggle
to interpret such high-frequency information -- hence the need for the
more advanced Markov model proposed in the study.

Using the enhanced hidden Markov models, the team found some undiscovered northern resident killer whale behaviours. The whale they used to develop
the model preferred to save energy by gliding through the water when
making deep dives, and when it was closer to the surface, it moved more actively, accelerating faster and 'fluking' its tail more often.

Understanding these diving patterns will be crucial for whale conservation because it will help researchers learn how much energy the whales require
to sustain themselves.

And the method's applications extend far beyond whale movement data,
according to Sidrow.



==========================================================================
"It could be applied to pretty much any animal movement data," he
said. "If you're tagging animals and you want to understand fine-scale behaviours, then this method that could be useful -- even for things
like the flapping of birds' wings." It could even prove useful in areas outside of ecology, such as determining when machines are likely to break
by classifying when the parts inside of them are vibrating abnormally.

The work is one of the first steps on the road to fully understanding why southern resident killer whales are not faring as well as their northern counterparts, according to Dr. Marie Auger-Me'the', senior author of
the study and an assistant professor in the department of statistics
and the Institute for the Oceans and Fisheries.

"Using our methods to detect when the animals are catching prey and
to model their energy expenditure will be key to understanding the
differences between these neighbouring whale populations," she said.

The next goal is to understand when the whales are capturing prey and
applying the models to both northern and southern resident killer whale populations to see how they are behaving differently.

"The paper offers many 'building block' solutions that can be used
together or independently," Dr. Auger-Me'the' said. "In essence,
we are providing a toolbox to researchers using high-frequency
movement data, and other similar high- frequency time series." ========================================================================== Story Source: Materials provided by University_of_British_Columbia. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Evan Sidrow, Nancy Heckman, Sarah M. E. Fortune, Andrew W. Trites,
Ian
Murphy, Marie Auger‐Me'the'. Modelling multi‐scale,
state‐switching functional data with hidden Markov
models. Canadian Journal of Statistics, 2021; DOI: 10.1002/cjs.11673 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220117093007.htm

--- up 6 weeks, 2 days, 7 hours, 13 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)