In this study we describe the rapid feather vibrations that peacocks use during courtship. These vibrations – at a rate of about 26 Hz on average – represent a substantial mechanical and metabolic challenge for the birds, especially given that they are performed using a massive array of feathers with widely varying lengths.
A peacock shows his stuff. His train feathers range from 10 cm to > 150 cm in length, and the whole thing weighs about 300 g. Photo by Roslyn Dakin.
We recorded high speed videos of peacocks displaying in the field. We also used lab experiments to test whether the peacocks move their feathers at resonance (which would be an efficient strategy), and to understand how the colourful eyespots can remain so steady during these vibrations. One surprising result was that the peacocks with the longest trains actually used slightly higher vibration frequencies overall – making their displays a greater challenge to perform. The next step is to understand how these feather motions influence the iridescent colour patterns as viewed by the peahens (the females), and ultimately, the hens’ choice of a mate.
Media coverage has been great – here are a few of my favourites:
Here is the poster we presented at SICB Portland last week on the biomechanics of peacock displays (click to enlarge):
I think it turned out pretty well, although I’m not sure it could stand alone without an interpreter.
We had a constant stream of awesome visitors. My coauthor Suzanne brought feathers and a model peacock to demonstrate what we were talking about – brilliant! We also had a touchscreen mounted to the left of the poster to display the supplemental videos, but to my surprise we didn’t use it much. It was too slow to load for every new visitor, although it did come in handy for people who wanted an in-depth look. I realize now that videos should really be integrated spatially with the poster content. This could be done if whole display was a touchscreen, for example.
One of the highlights of the meeting was seeing how folks in Stacey Combes’ lab are tracking the movements of individual bees by gluing tiny QR codes onto the bees’ backs (the codes are automatically recognized on video of the bees entering and exiting their hives by tracking software). Another highlight was Ken Dial’s talk about the influence of predation on the development of flight in nestling birds. Portland had lots of good food and drink and exciting views of 1000s of crows roosting late at night downtown.
Thanks to Owen, Suzanne, Jim and Bob for such a fun project!
June 2013 was bad for tree swallows. At the Queen’s University Biological Station, over 90% of nests failed as a result of persistent cold, rainy weather.
This happened to be the same year we were conducting an experiment on the hormonal mechanisms of parental care in these birds. The bad weather made for a disastrous field season. Just a couple of weeks in, and we were turning up cold lifeless chicks in nearly every nest. The upside was that it led to some potential insights into the way stress hormones and tough weather conditions interact. My coauthors Jenny Ouyang and Ádám Lendvai were invited to write an excellent blog post about it here:
It was remarkable how closely the nest failure rates tracked the fluctuating air temperature. This could be caused by a couple of factors, with a major one being that tree swallows rely on flying insects to feed their young, and the ability of insects to fly depends on temperature. Persistent cold weather means that parent tree swallows cannot find enough food to support their offspring.
The corticosterone hormone implants made the treatment birds more susceptible to faster brood mortality, even during benign weather. It should be noted that the implants were deployed before the bad weather struck, and we would not have performed this experiment if we had known in advance that this would be such a tough year! Hopefully, though, the results provide some insight into the role of stress hormones as mediators of a sensitive period in the life history of these birds.
My friend Terry Beech is running for parliament in the Burnaby North-Seymour riding. Charlie and I are helping with his campaign – Charlie is his campaign manager, and I’m part of his team of volunteers. It’s shaping up to be an exciting three-way race between Terry (the Liberal), Mike Little (Conservative), and Carol Baird-Ellan (NDP). The press has highlighted Burnaby North-Seymour as a “riding to watch”. Go Terry!
One of the highlights was attending a local candidates debate last week.
The results of the Reproducibility Project – a very cool endeavour to repeat a bunch of published studies in psychology – came out this week . The authors (a team of psychologists from around to world) found that they were able to successfully replicate the results of 39 out of 100 studies, leaving 61% unreplicated. This seems like an awful lot of negatives, but the authors argue that it’s more or less what you’d expect. A good chunk of published research is wrong, because of sampling error, experimenter bias, an emphasis on publishing surprising findings that turn out to be false, or more than one of the above. No one study can ever represent the truth – nor is it intended to. The idea is that with time and collective effort, scientific knowledge progresses towards certainty.
This is from a session I did with the UBC R Study Group. Loops can be convenient for applying the same steps to big/distributed datasets, running simulations, and writing your own resampling/bootstrapping analyses. Here are some ways to make them faster.
1. Don’t grow things in your loops.
2. Vectorize where possible. i.e. pull things out of the loop.
3. Do less work in the loop if you can.
Frame-blending is a great way to illustrate animal behaviour and other things that change over time. This got me thinking about ways to animate time series data. In R, the animation package has lots of options, but you can also build your own just by plotting over the same device window. If you save each iteration in a loop, the resulting images can be used as frames in a video or gif.
Click the image to see a larger version
Here is an example using recordings that track hummingbirds flying in our tunnel here at UBC. This animation shows a bird’s eye view of 50 flights by 10 birds. In half of the flights (the red ones), the birds had horizontal stripes on their left side and vertical stripes on their right, and the other half (blue) had the reverse. The subtle difference between the red and blue trajectories (red ones tend to have more positive y values) shows that on average, birds tend to deviate away from vertical stripes, and towards horizontal ones. The histogram that builds up on the right side of the figure shows the mean lateral (y) position for each trajectory as it finishe
The Los Angeles Arboretum is one of the most beautiful places I have been. Where else can you see six species of hummingbird zooming from perch to flower, an Asian red-whiskered bulbul nesting beside a dancing peacock, with noisy flocks of parakeets commuting overhead? Even the introduced species (on this list, all but the hummingbirds) are beautiful.
So it was fitting right after I got back from LA to read the news that 30 new, never-before-seen species of flies were just discovered in the city. Read more about it here. And how did the discoverers identify these flies as unique? By the gnarly shapes and bristles of their genitalia. These traits can help define species in other groups too, like bats and primates.
Photo by Kelsey Bailey, LA Natural History Museum BioSCAN project.
I’ve been going to a graduate class in science communication this semester. Doug taught us the rule that if you’re using a bar graph, the y axis must start at 0. Otherwise you end up with trickery like this:
Hlynsky uses frame-blending to great effect, to give you a sense of overall motion trajectories. When he turns his lens on animals, the results are both beautiful (see fruit flies paint a still life here), and an exciting way to visualize huge amounts of data. It’s got me thinking I could use this method to illustrate the 100s of hummingbird flights in our latest experiment here at UBC in a single animation.
My learning curve with the statistical software R has been a long one, but one of the steepest and most exciting times was learning how to write functions and loops. Suddenly I could do all kinds of things that used to seem impossible. Since then, I’ve learned to avoid for loops whenever possible. Why? Because doing things serially is slow. With R, you can almost always reduce a big loop to just few lines of vectorized code.
But there’s one situation where I can’t avoid the dreaded for loop. Recently, I learned how to make for loops run 100s of times faster in these situations.
In between field work, I’ve been making a lot of videos lately – mostly for my students in the summer course in Ecology and the Environment. But my latest creation is entirely different: it’s for the upcoming American Ornithologists’ Union (read: bird nerd) conference.
It features slow-motion clips of peacocks vibrating their train feathers during their courtship displays. I used a special high-speed camera to film this behaviour at 210 frames per second – it was incredibly difficult to do, because the high-speed camera requires that you get really close, and males only perform the vibration when a female is nearby (and not a human one!). In the end, I was able to coax some hungry peahens practically into my lap by slowly doling out the treats. This allowed me to film males displaying at the females from just a couple of feet away.
From these videos, I estimated that peacocks vibrate their eyespot feathers at a rate of 25 Hz (i.e., the feathers move back and forth a whopping 25 times each second). That’s incredibly fast, but it’s hardly record breaking for birds. For instance, Teresa Feo and Chris Clark recently showed that hummingbirds vibrate their tail feathers at a rate of more than 80 Hz to produce a buzzy trill-like sound during their display dives. However, the hummingbirds do it passively, I believe.
Other birds are also making the news these days for their choreographic skills. Anastasia Dalziell and her coauthors at the Australian National University have shown that superb lyrebirds actually coordinate song and dance during their remarkable courtship displays.
My guest post for my university’s School of Graduate Studies blog is up! (You can read it here.) The inspiration was a new radio podcast that we have in the works on research here at Queen’s – scientific and otherwise. I’ve been working on the concept with Vee, an English PhD, and Savita, an undergraduate student who is keen to make top-notch radio documentaries.
I wrote the blog post to try to drum up some interest in being a subject of the radio show, but I hope it has a few nuggets of advice for those finishing and/or considering grad school as well.
There’s no question that broadly speaking, big brains are smart. Take humans, for instance: our brains weigh in at about 3 pounds on average, nearly four times the size of the brains of chimpanzees (whose brains weigh in at less than a pound apiece).
What’s less clear is why. There are a number of theories: maybe intelligence evolved to give us a competitive edge in foraging, or maybe it helped us keep track of increasingly complex social interactions. Ideally, we’d like a theory to explain the evolution of intelligence broadly, so researchers have tried to these hypotheses across multiple species (for instance, comparing relative brain size and social group size among hoofed mammals like horses and deer1).
But brain size alone – even when scaled as a proportion of overall body size – is not an ideal measure of intelligence. The trouble is that small animals often have considerably higher brain-to-body mass ratios – ant brains, for instance, can weigh nearly 15% of their total body mass (the equivalent of a 20 pound human head!), and mice have about the same brain-to-body mass ratio as we do. So how can we study brain evolution, when even primates span a 3000-fold difference in body size (comparing a gray mouse lemur and a gorilla)?
Enter the encephalization quotient, or EQ, a measure of brain size relative to what we would predict, given that there is a curved relationship between brain size and body size (allometry is the technical term for this). It’s the best yardstick we have for the evolution of intelligence. Until now, that is.