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Science drinks – 22nd October

A rather low-key science drinks on this occasion, as the mid-semester break meant a number of people are away (including our Fearless Leader, who is swanning about in Finland right now). Stu and I were joined by Claudia, Gregor and Toby to discuss a diverse set of topics, ranging from where best to set up camp on Mull to why I have just ordered a pint of maggots through the internet. We also managed to talk some science, including a general discussion on the peer review process and how it works, as well as why it sometimes doesn’t. Gregor outlined his plans for his project for the statistics module he’s taking, in which he’s hoping to do some work on random forests – a machine learning technique involving ‘forests’ of decision trees that is useful for ‘small n large p‘ problems (that is, problems that are high-dimensional but have a low sample size). This means that Gregor will – like the best of us – get to spend the vast majority of his time glued to RStudio. Luckily for him, there is a wealth of information out there to help him get started. Even more luckily, it turns out that R users of random forest techniques also like to party.

Science Drinks – Oct 8, 2013

Attendees:

Dr Luc Bussière

Dr Timothy Paine

Dr Moha Abdelaziz Mohammed

Dr Evangelos Spyrakos

Elizabeth Herridge

Thomas Houslay

Jennifer McKeown

Toby Hector

Gregor Hogg

Sam Paterson

Claudia Santori

Hazel Smith

Tuesday saw the second biweekly “Science Drinks” of the semester. These events consist of staff and students from many scientific backgrounds in Biological and Environmental Science getting together to discuss cool science over beer (or a soft drink of your choice). The only requirement for attending is to bring along a science question, conundrum or interesting story or paper. I will give a brief synopsis below outlining some of the anecdotes, stories and discussions that took place in our session on Oct 8.

The evening started (after a trip to the bar) with Luc telling us all about the strange and wonderful swarming habits of the Mormon cricket (really a katydid!). These large insects form huge aggregations and ‘march’ through western North America eating everything in their way. This behaviour is apparently driven by the desire for food and salt leading them to try and catch the katydid in front (and stay ahead of the hungry katydid behind!). Luc went on to tell us how it is Mormon belief that when these swarms came to ‘plague’ them, God sent seagulls that ate until they vomited allowing them, as the story goes, to continue eating the crickets. It’s a nice story, although the vomit may have a little more to do with the katydids repugnant taste.

See here – for an insight into this natural phenomenon.

In the first Science Drinks of the semester, two weeks ago, Tim made the bold suggestion that humans have no muscles in their fingers. This was idea was promptly shot down by most of the group. However after some quick research and lots time squeezing and staring at our fingers we discovered that he was in fact correct. Apologies were given to Tim and the rest of us learned something quite interesting about our fingers.

Three researchers who were new to Science Drinks then gave brief descriptions of their interests and work.

Jen described how she uses mathematics to model protandry in natural systems. This lead to a lengthy discussion about the possibility that protandry is a sexually antagonistic trait.

Moha gave us an outline of his work on the Brassicaceae genus Erysimum. He works on mainland North America and several islands comparing how ecological and genetic mechanisms cause speciation and radiation. One of his main interests is the potential impact of plant-pollinator interactions on these processes. Interestingly he commented that just a single plant species might have up to 150 different pollinator species from 6 orders associated with it.

Evangelos gave an outline of his work looking at light penetration into lakes using satellite imaging. He then went on to explain to us biologists the applications of applied physics, such as playing pool.

The first paper of the night was presented to us by Gregor. The paper written by Healy et al., was titled: Metabolic rate and body size are linked with perception of temporal information. The article was intriguing to all and led to a lengthy discussion into the potential mechanisms for such increased visual perception speeds. Gregor also highlighted that in the literature, regions of insect eyes that have greater perception speeds (associated with mate assessment) are often called “love spots”. This hypothesis of ‘slow motion’ vision could have an interesting implication for my own work on acute visual zones in the eyes of male dance flies.

We then pondered the potential for sexual dimorphism in sensory perception systems due to different energetic requirements between males and females. The reasoning was that “females are egg machines” and so males may have higher levels of energy to expend on the metabolic costs needed for greater sensory perception speeds.

Reference:

Healy, K., McNally, L., Ruxton, G.D., Cooper, N. & Jackson, A.L. (2013). Metabolic rate and body size are linked with perception of temporal information. Animal Behaviour, 86, 685-696.

Link to paper: HERE

The second paper was presented by Tom and was titled ‘Repeatability of behaviour: a meta-analysis’. This paper made some interesting suggestions about the repeatability of behaviours and the recording of behaviours both in laboratory and field experiments. Key points included the authors’ findings that male behaviours were typically more repeatable than female behaviours, possibly because females are more variable in their mate choice (maybe due to some system of learning). The important difference between process and measurement error was also discussed.

Reference:

Bell, A.M., Hankison, S.J. & Laskowski, K.L. (2009). The repeatability of behaviour: a meta-analysis. Animal Behaviour, 77, 771-783.

Link to paper: HERE

We finally discussed the importance of fitting appropriate lines and error bars to graphs. Most importantly, when a variable has a definite top or bottom bound (such as data which can only be between zero and one), an error bar should not extend higher or lower than the point to which the data is limited.