Showing posts with label behaviour. Show all posts
Showing posts with label behaviour. Show all posts

Friday, May 31, 2013

An introduction to our animal movement studies



Movement studies

Animal movement is a very large and interesting topic. In the past, most scientists were interested in describing movement of an animal let’s say from point A to point B. This is now known as dispersal (translocation) of an animal. Dispersal usually answers two main questions in animal ecology which are WHERE and WHEN. 

When I started working with marine animals I got very interested in describing the “movements” of white sharks in Mossel Bay. We put a simple acoustic transmitter on a white shark and followed it for several days (our longest track has been 107 hours continuously). This method allows us to look at dispersal and activity space. The following graphs have been recently published by our team in Environmental Biology of Fishes (2013: 96, pp.881-894), and show the activity spaces of several sharks within Mossel Bay.

As you can see Seal Island, Hartenbos, Kleinbrak and Grootbrak are focal areas in Mossel Bay. 

Can you find out possible reasons for that? 
For answers you can look through some of our publications within the following links:



A unique problem with active manual tracking of marine animals from a boat is that we work on a 2 dimensional field while the animal moves within a 3 dimensional environment. We therefore decided to incorporate transmitters with depth sensors into our tagging protocol. This allows, for instance, calculations of speed (as rate of movement: distance travelled divided by the time used to move that distance) in not just two, but three dimensions, which are more realistic estimations of the true speed of an animal. We were able to cluster different white shark behaviours in different areas of Mossel Bay, based on obviously position, but also swimming depth, swimming linearity and swimming speed. We differentiated behavioural types such as “resting” (swimming very slow at the bottom, sometimes almost drifting with current) from hunting for fish (“fishing”: swimming up and down -from the surface to the bottom in a yo-yo pattern-, back and forth –in a very localised area-, quite fast), from hunting for sea lions (swimming at a certain favourable depth, back and forth, quite fast), from travelling (swimming in a more sporadic yo-yo pattern, at medium speed) etc... This helps in answering the HOW part of the animal movement study.

Why do you think travelling for a white shark can not be too fast? 3 hints: metabolism, thermoregulation and energy expenditure.

In the same way that this novel branch of research has evolved, we soon realised that knowing the WHEN, WHERE and HOW, was not enough for us. We struggled to further develop our understanding of these movement processes with just simple description, so we started looking around at possible other avenues to get a better understanding of animal movement. We realised that a big effort was put into trying to characterise and model movement patterns. Animals can move for several reasons such as searching for food (hunger), searching for possible mates (passing own genes to the next generation), avoiding being preyed upon (fear), avoiding being harassed (stress), searching for better conditions  in terms of habitat or in terms of space (i.e. overpopulation). Models such as random walks and Levy flights are attempts to look at underlying parameters which could help in understanding the biggest questions in animal movement studies which is WHY an animal moves. For instance if you were an albatross you may search randomly an area for food (random walk model) and if you don’t find anything you may want to move 'quite a bit' and start searching for food in another area (Levy flight model).

Recently a new branch of animal movement studies has gone even further. As usual in science when you answer a question at least two more questions open in front of you: that is not a negative consequence but rather an exciting opportunity for us to grow as scientists. If we understand the WHY next we might want to know WHO. Do all specimens behave in the same pattern? Are some individuals more prone to certain kind of behaviours? Individuals may respond differently to different stimuli because of different threshold for those stimuli. So individual may have intrinsic traits which govern dispersal: usually age, gender, reproductive status, social status (in social animals), and morphology; but what about personality. I can already hear the comments from several ‘old style’ scientists: “Animals do not have personalities! They are not humans”. Actually I have had this thought for a long time based on what I can see while working with white sharks on the field: some white sharks are more aggressive (“playful”) whereas others are more skittish (“shy”). Even further, the same white sharks can change behaviour in two consecutive days (what I call ‘moods’). Without going into too much detail, while mood changes can be the consequence of changes in the environment that supports that animal. The so called ‘personality’ types are something well known in scientific literature. Cote et al. (2010) studying mosquitofish assessed that some individuals, identified as more asocial (bold) than the population norm, tended to disperse greater distances, but that was not all: mosquitofish from populations characterised as more bold overall, also dispersed more often regardless of their individual personality type. Reale et al. (2007) assessed individual behaviour over five fixed axes of personality: boldness, exploration, activity, aggressiveness and sociability. 

Can you guess which type of personality in mosquitofish would be the one more efficient in finding food in environments with scarce food supply?  

Now why I am talking about all this? What if we were able to distinguish individuals by using, for instance, their interest in using wide areas (explorers) vs. individuals paying more attention to the environment they move within (sitters)? You will find out with the next blog about movement studies.

Enrico Gennari

Tuesday, January 8, 2013

The Ups and Downs (or Side to Sides) of Shark Research

If you’re reading this blog, chances are you’re well versed in the fate of the modern shark. Shark fin mafia, extinction, 38 million, ecosystem collapse, blah, blah, blah; there are a lot of buzzwords being thrown around by active and armchair conservationists alike. I don’t want to start any arguments so we’ll take a middle-of-the-road approach and just agree that these species are important, threatened, in need of much further research.

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