Chickadees are hard to take photos of



I think I’ve been in Canada almost two months now. It’s hard to say as the initial turmoil of moving to a new country slowly changes into everyday routine. This generally involves getting up, trying to do some science until some time in the evening and drinking far too much tea.

I’ve met my study species properly now. A few weekends ago we went for a walk at a place called Muddy Lake (currently frozen and definitely not muddy). When walking along the paths you will generally be followed by a cloud of chickadees, who live in hope that you will be one of the many people that feed them. They whiz around your head, dancing from branch to branch, waiting for the food to be provided. If you DO have food, they will quite cheerfully eat out of your hand.


Despite their tameness taking a photo of them in a more natural setting is challenging, due to their dislike of sitting still for more than a second.


As well as having met my study species properly, I’ve also been working to get to grips with both social network analysis and the study of personality in animals. I had a passing interest in both of these topics before, but now I’m having to rapidly learn about how these analyses are done.


Chickadees are highly social and tend to move about in small flocks. We have information about which birds were with which other birds at feeders.This is generated by special feeders which can identify individual birds fitted with RFID readers.

Picture1An RFID feeder, photo by Teri Jones

We also have information about how certain birds reacted to personality tests as well as which birds are dominant and which are subordinate. There are quite a few interesting questions we could answer by bringing these datasets together. Wrangling the various files together is and working out how to analyse them is the main thing I’m currently dealing with. This involves spending a lot of time shuffling data about in R. I’m also trying to automate and improve some of the workflow for generating this type of data.

We’ve  had a few quite heavy snowfalls. This was my building the day after a particularly severe one:


and this was the window to our office on the same day as the sun was steadily blotted out:


A few weeks ago I gave a departmental seminar on my ENTIRE PHD. This involved talking for longer than I ever had before, which made it very easy to lose track of time. I was extremely afraid of speaking for too long and boring everyone to death.

Picture2So many slides!

I felt it went quite well. My main mistake was towards the end. I glanced at a clock (the WRONG clock) as it turned out and was convinced I’d gone overtime. I sped up for the last few slides (which is unfortunate as they are the most interesting) and then apologised at the end for speaking for so long. As it turned out, I’d looked at the wrong clock and came in under time. At least it left lots of time for the many questions, which hopefully indicate that people are interested in what I spent about four years of life doing.

I have been finding time to try and do some more CANADIAN things. For example, I’ve now tried Poutine which according to Wikipedia is Canada’s national food. It’s basically chips, cheese and gravy. I think that undersells it, it’s warm, tasty, cheesy, potatoey goo.


I also ambled up to Winterlude (a winter festival thing held in Ottawa), where the international ice sculpture championship:IMG_0759OWL


This included a demonstration of ice carving, which is apparently mostly done with powertools now!


I clearly need to find some more CANADIAN things to do. Someone mentioned something called Nanaimo bars..


Gemeinsame Nahrungssuche bei Krähenscharben

headerOr, my new paper:

Social foraging European shags: GPS tracking reveals birds from neighbouring colonies have shared foraging grounds

has been published.

From the German translation of my title and abstract I have learnt that the German for shag is Krähenscharben. This paper based on my first year of fieldwork and data from the FAME project is now available from Journal of Ornithology! The paper looks at the movement and behaviour of shags foraging in the Isles of Scilly using high resolution GPS data, showing that most individuals forage in the same areas within the islands.

After the tale of woe that was my first year of fieldwork, it’s nice to see the data collected finally producing something tangible. The paper also uses some of my rafting dataset , which I hope to release a more detailed study of at some point in the future.

There is a full text version on Researchgate at some point in the future.

As stated at the bottom of the article, many people helped with this: A big thankyou to Richard Bufton, David Evans, Liz Mackley and the volunteers who assisted with data collection. Thanks to Vicky Heaney for population data and advice! Finally thanks to everyone at the Isles of Scilly wildlife trust for their invaluable assistance and for permission to work on the islands!

EDIT: Link to journal now fixed and full text now available on researchgate.

So what do you do after you hand in your thesis anyway?

As much as I’d like the answer to this question to be “Take a massive holiday”, the actual answer is somewhat more complicated.

10929903_10152925026312054_7107168219422032774_nCooking bacon on a beach is Thing You Can Do after handing in

The pace can definitely slow down a bit. Days off can be taken. Staying in the office till midnight becomes less critical. I can now respond to invitations to things with something other than “Sorry, writing!”. I’ve also been able to attend a few statistics workshops, which are definitely going to help with the work mentioned below. However inevitably I was drawn back into the office to try and do some more science..

Post thesis I can take stock and look back at the chapters so as to work out how best to turn them into papers. My lit review surprised me by suddenly coming out online. My tracking work received some comments as I was writing up, and I spent the week after my hand in dealing with those comments. Other chapters will require some more work for them to reach their full potential.

shagdiveOne thing I keep returning to is my collective behaviour chapter. I did quite a lot of work on this over the last year and a half, so I’m very keen to try and develop this further. The main thing I’m interested in looking into more are the different diving rules animals might follow. Previously I’ve modelled this as a discrete process with 3 simple mutually exclusive rules.  I’d like to investigate the individual parameters such as the probability of diving depending time and distance from a previous dive, or the likelihood of diving when a dive is directly in front of the bird compared to behind it.


So with help from Colin Torney from our department of applied mathematics, we’re developing a new model (looking at social diving parameters!), using a new statistical method (bayesian!) in a new programming language (python!). So I’m learning many new things, which is also a good idea in a post-thesis world.

Of course I’m also having to delve back into old data files so as to reorganise them for the new model to read..


(The other thing you do after handing in your thesis is wait on tenterhooks for your thesis defence date. Don’t panic!)

Then and now


So I have had something published! It’s a literature review discussing how information can contribute to the formation and maintenance of animal colonies. For comparison, on the left is a literature review I wrote in my third year of my undergraduate degree, about four years ago. As was the case with me doing a project on shags in the isles of Scilly in my second year, this piece of work now seems strangely prescient.

You can find my review here on Biological Reviews.

This actually appeared on the internet last Thursday, but I had no idea of this until someone I’d cited contacted me about it. This work was first submitted for publication in June last year (though it was one of the first things I started writing back when I initially began my PhD). A couple of iterations later (and not at the dates given on the website!) here we are.

I think their presentation is definitely better than my attempt at doing columns.

ASAB in Winter..

I have just returned from the Association for Animal Behaviour (ASAB) winter meeting. I’ve always enjoyed this mini-conference, which is traditionally held over two days in December, at the ZSL in London. It’s a nice way to meet up with others in your field and generally find out what people are working on. You also get to go round London Zoo for free during the lunchbreak. Over the years, London in Winter, lots of behavioural talks, walks around the zoo and meeting other behavioural ecologists in London pubs have become the main things I associate with the onset of Christmas.

B3_8-HpIEAARy89Photo from ASAB Winter 2014 twitter account

While I’d attended this meeting a few times, I’d never actually presented anything. This was to change this year. This year’s theme was “Individuals in groups” which fits my work rather well. So I applied to present a talk, and got accepted.

We got to London the night before and got to the conference bright and early for the first day of the meeting. It was great to see some familiar faces and to catch up with old friends. Getting there somewhat early was a good idea as it turned out there was a much larger turnout than anticipated, with some 250 people attending. Many ended up sitting on the floor or standing at the back.

After the first few talks one of the organisers stood up.

“The bad news is, there isn’t enough wine for everyone.”

There was a collective indrawn breath as 250 behavioural ecologists prepared to riot.

“The good news is, we have ordered some more!”

Cue cheering.

B4FXUaVIcAAloqjPhoto from ASAB Winter 2014 twitter account

On the second day, I was speaking.

This talk has evolved over the course of the year as the work came together. I first gave a sort-of prototype of it while in a haze of painkillers back at Easter ASAB. At this point I had just got the video analysis code sort of working and knew sort of where I was going in terms of the models I would use. After I returned from fieldwork later in the year I got to work refining the video analysis,building a set of tools so as to eventually analyse all the sequences I have. I also got to work on writing the code for the simulation models that I would later compare to the real data. I presented my initial results from this work, based on a selection of my video sequences at ISBE in New York.

Since then I’ve got round to analysing all the sequences I have. This took far longer than I expected, but simulations are now being compared to my complete dataset rather than a subset.

simcompareExample of comparing data from a real raft to that from a simulated raft

I also completely rebuilt the way diving and surfacing worked in the simulation. This obviously complicated matters. There are now nine different combinations of dive and surface rules to deal with, which also required rewriting the code that searches for the best fitting model.

Other bits of the talk had stayed exactly the same, such as the introduction. I should REALLY know it well by now. Nevertheless, I was still incredibly nervous on the morning I was to be speaking. I was in the middle of the morning session, so infinitely preferable to being one of the last talks of the day as I had been before.

The annoying thing about this is that when speaking, it’s generally nervousness that will cause you to slip up, mangle your words or generally forget what it is your talking about. The precise things you’re nervous about happening. It’s a self perpetuating cycle. You’re nervous about messing up due to nervousness. The anticipation is agonising as you sit there trying to enjoy the talks before yours (which was pretty easy in this case as they were great, a lovely distraction!)

tumblr_m9xr5uEXdT1rdq725o1_1280I’d been reading Dune the night before, so was probably mumbling the litany against fear to myself.

When it was my turn (12:00 on the dot) I had to do a computer change over, switching the laptop on the podium from a Mac to a PC and then plugging in the projector. Amazingly I managed not to drop anything expensive. Once I had done this, I was handed a microphone. Oh dear, something else to worry about. Especially as I rather like to wave my arms about as I talk. I was going to have to be careful not to hurl it into the crowd or deafen everyone with feedback.

Then I looked up.

The view through Julian-cam looked something like this (blurriness included no doubt)

B4A1OHvIAAAEsMf.jpg largePhoto from ASAB Winter 2014 twitter account

It was an audience of 200+ people. Admittedly I’d known this already, but standing in front of them all rather rammed the point home. I was introduced and began speaking.

10858388_10100273233232435_2563350174951693748_nMe in the middle of my talk, explaining the surfacing rules. Photo credit: Richard Woods, who also spoke

I think it went well. As I mentioned before I find it hard to judge how a talk is going while I’m actually doing it. I improvised a sort-of joke at the expense of my study species. I wasn’t thrown by the new slides I’d added. I did go “arg” at one point and start a sentence again, but everyone was too polite to notice. Results from my attempts to read the facial expression of my supervisor to see how it was going were inconclusive.

The general reception seemed positive though. The questions were fairly straight-forward, with some questions about other aspects of the study (if there was a difference between behaviour depending on whether the birds were diving over rocks or sand. I said the words “sandy bottom” but nobody smirked too much.

Later I was shown some of the live-tweeting that had gone on during my talk, which was nice (though several were from people I know). I suppressed a snigger when sandy bottoms were also retweeted.


I could now relax and enjoy the remaining talks. Afterwards several people came up and spoke to me about my talk and my work, which is always fun. Not having a microphone in my hand anymore, I could wave my hands around as much as I liked.

This was a great conference focused on one of the topics I’m most interested in, with loads of fantastic talks. Getting out of the office for a few days to mix with other people working on similar (and yet different!) things to me is always great.

Back to work now I suppose!

More virtual birds

I have returned to my simulated rafts of shags. As well as general code refinements, I have also completely overhauled the way they dive and surface.

The dive rules have been in place for a while. They are as follows:


1. The virtual bird will dive wherever and whenever it wants, without paying any attention to what other birds are doing. Given that my hypothesis is that birds are using the diving behaviour of others to make their own diving decisions, this is the null model.

2. Birds are more likely to dive if another bird dived or surfaced recently. How likely they are to dive depends on how close the other bird was in space and time.

3. Similar to 2. but birds only have a limited cone of vision. They will not be away of a dive going on behind them for example.


Previously I only had one rule for surfacing: birds would surface at a random time after they had dived, within a circle of a random radius centred around where they initially dived . The distance they were allowed to travel was constrained by how long they’d been underwater. They were also more likely to surface in proximity to other birds, so as to maintain the cohesion we see in real birds.

Just before ISBE, my supervisor suggested that actually we probably want to try out a variety of surface rules to try a few different scenarios. This required a complete rewrite of the simulation, but this was a good opportunity for me to merge all my various dive rules into one piece of code too. Though there was quite a bit of hair pulling involved, I eventually ended up with a simple unified function.

The surfacing rules birds can now obey are as follows:


1. Surface completely randomly in time and space. Virtual birds can reappear at any time within a circle of random radius centred around their dive, with the maximum radius depending on how long a bird was underwater. They don’t care if they are near another bird on the surface or not.

2. Same as rule 1, but birds are more likely to surface closer to another bird. The time they stay underwater is still random, which still controls the maximum possible distance they can travel underwater.

3. Similar to 2. with an important addition: A bird is more likely to surface in close proximity (in time and space) to a bird it dived in close proximity to (in time and space). This is to attempt to simulate birds following each other more closely underwater.

Of course. rewriting my code so that I can switch between which rules a simulation is using just by changing a number in my simulation uses was somewhat tricky, but eventually I hammered out all the bugs. Unifying all my code like this will make the selection of a best fitting model much simpler.

Back to Reality

When I eventually got back into the office after my trip to the USA, I turned on my computer and stared at it for a while, trying to remember how it worked.

Once the basics of how to operate a computer came back to me, I took a long hard look at where I am in my PhD. My basic thought process was something like this:

  • I have some stuff.
  • I need to write about this stuff.
  • Most of this stuff is going to require the doing of additional stuff before it is in a state where I can write about this stuff.
  • I REALLY need to write about this stuff.
  • I wonder where I have put all this stuff?

By which I mean I need to consolidate a few years worth of data, that in the past has been exported, rather haphazardly, to various folders in my dropbox. I also probably need to redo some statistics to include additional data collected this year. I then need to try and hammer this out into writing that people other than myself can understand and find interesting.

Let the gathering of Excel files commence.