Now tell me that wasn’t fun?

I was recently approached by our fantastic Research and Enterprise Office here at Stirling to write a blog post about what inspired me to become a biologist, my experiences as an early career researcher and what I’m trying to do in my current role as an Impact Fellow. And here is it!

This was actually quite a difficult task for me, probably because in between the challenges of being an early-career researcher (largely trying to do interesting science, writing funding applications, reading rejection emails and worrying where the next job’s going to come from), I don’t really appreciate how much I actually like what I do- or why I started doing it in the first place.

So this blog post was actually a really good opportunity to ask myself (1) why did I decide to try and do biology for a living? and (2) do I still enjoy it? The answers, which can be found in the blog post in much more detail are (1) finding stuff out is fun and (2) emphatically yes…but it’s maybe not as much fun as actual fun.

Having some actual fun. 



Fitter. Happier. Fewer early-life infections?

A person lucky enough to survive to the age of 20 in 1840s London could expect to live for a further 40 years, to the age of 60. In 2011, a 20-year-old Londoner would expect to live for a further 60 years, to the age of 80. That’s a 50% increase in remaining lifespan, in a little over 150 years. How has this happened?

In 19th century Europe, around 40% of children died of infectious diseases like smallpox, whooping cough and measles before they reached adulthood. However, it’s important to recognize that both of our 20-year olds survived to age 20, so the difference in their life expectancies has nothing to do with the fact that children in modern London are unlikely to catch dangerous infections. OR DOES IT?


(13) Scream_wikimedia commons
From Wikimedia Commons


Looking at patterns of mortality across time throws up some interesting results. For example, a study of 19th and 20th century populations in Britain and Sweden showed that the death rates of individuals were more closely related to their year of birth than to the year in which their mortality was assessed.


(13) Kermack table
Mortality rates per thousand individuals in England and Wales, 1845-1925. The diagonals show data for the same cohort of individuals followed across their lives, and it’s apparent that the mortality rate remains similar as the cohort ages. The best predictor of mortality is birth year, not calendar year. For example, the cohort born aged 10 in 1855 is the same group who are aged 60 in 1905 and their mortality rates are identical. Meanwhile, the cohort aged 10 in 1905 has a much lower mortality rate.


The authors concluded that:

The figures behave as if the expectation of life was determined by the conditions which existed during the child’s earlier years…the health of the child is determined by the environmental conditions existing during the years 0-15, and the health of the [adult] is determined preponderantly by the physical constitution which the child has built up.

 So what might these ‘conditions’ be? An influential paper published in 2004 suggested that the link between early-life conditions and later-life mortality might be due to infections experienced in childhood. Infections elicit inflammatory immune responses, which may persist at a chronically high level. Chronic inflammation is linked to risk of heart disease, stroke and cancer in later life. The ‘cohort morbidity phenotype’ hypothesis suggests that since childhood infections have become increasingly rare, so have chronic inflammation, their associated pathologies and early death. Thus, we live longer.


(13) Cohort morbidity phenotype
A simplified version of the ‘cohort morbidity phenotype’ hypothesis. Infections cause inflammatory responses, which lead to atherosclerosis (thickening of artery walls) and thrombosis (clotting), which are linked to heart disease, stroke and mortality. The full version includes a couple of added nuances!


This is an exciting (and controversial) idea, so we tested it, in a new paper published in Proceedings of the National Academy of Sciences of the USA. The data we used came from, as I usually can’t help blurting out when meeting people and explaining what I do, ‘some dead Finnish people’. We had data on births, marriages and deaths from church records for over 7,000 individuals, born between 1751 and 1850, in seven different populations across Finland.


(13) Finns
A Finnish family, pictured in the late 19th century. Photo courtesy of Virpi Lummaa.


OK, I admit it: a number of previous studies have tested for links between early and later mortality. But…. these studies have looked at how many children born in a given year survived infancy (the ‘cohort mortality rate’), and then correlated that with the survival rate of these individuals in later life. Instead of using data on child deaths from all causes, we used data on child deaths from infections.

For each year of our study, and in each parish, we knew how many children were alive, and how many of those children died of an infectious disease. Our measure of disease exposure for a given birth year was the number of children who died of infectious diseases, divided by the number of children alive. We calculated this measure of disease exposure for each of the first five years of a child’s life. We then went on to use statistical models to determine the association between early disease exposure and:

  1. Mortality risk in adulthood
  2. Risk of mortality from cardiovascular disease, stroke and cancer
  3. Reproductive success

We predicted that our measure of early disease exposure should be linked with higher mortality risk, a greater risk of mortality from cardiovascular disease, stroke and cancers, and lower reproductive performance. And we found…


(13) Fanfare
From Disney’s Robin Hood


Nothing. There was no link between early disease exposure and adult (after age 15) mortality risk. We did find the expected differences between social classes, with wealthy farm owners and merchants surviving better than poor crofters and labourers, and between the sexes, with women surviving better than men. However, higher disease exposure was predicted to increase mortality risk by a piddling, and statistically insignificant, 2%.

We also found no association between early disease exposure and deaths specifically from heart disease, stroke and cancer. The (nowhere near statistically significant) trend was for a lower probability of death from these causes with increasing early disease exposure. Men were more likely to die from these causes, but there was no difference between the social classes.

Lastly, we found that reproductive success was not affected by early-life disease exposure. To take any survival effect out of the equation (not essential, so it turned out…), we only analysed people who survived to age 50, and who therefore had almost certainly reached the end of their reproductive lives. Early disease exposure was not linked to age at first birth, lifetime children born, child survival rate, or lifetime children surviving to adulthood. Excellent.


(13) Listening to the results
As the results were revealed, the excitement of the audience was almost palpable.


Normally at this point, there’d be loads of cool graphs and stuff…but we didn’t make any. Instead, take a look at the paper and the enormous supplement for details on the results! Before the headline conclusions….some caveats:

We have no idea who was exposed to disease. The only records of infections were where someone died and the cause was recorded in the church register as being of an infectious disease. This meant we had to assume that, in years when lots of children died of infections, our study individuals were (on average) more likely to get disease. But at worst, it’s possible that none of our study individuals ever got sick as kids.

What doesn’t kill you makes you stronger. It’s possible that individuals who were exposed to disease responded in two ways. They could have been weakened by disease and so died earlier, or they could have been the crème de la crème: robust enough to survive and be awesome at everything. A balance between damaged individuals and robust survivors would lead to…no net effect. So we may have actually found TWO cool effects…but without any evidence for them.

But…we did do some good things. We used a measure of disease exposure based on death from infectious disease, rather than deaths of all causes; we tested for effects on specific causes of death; we found the same patterns in seven parishes across Finland; we looked at effects on reproduction. We also did some cool (and relatively straightforward!) stats to remove temporal trends…but this isn’t the place for that.

Overall, we found absolutely bugger all evidence for a link between early-life disease exposure and mortality risk, cause of death, or reproductive success in later life. The results challenge the idea that extended lifespan in modern populations is due to reduced childhood disease exposure, but they certainly do not disprove it. It does seem however, that in common with a few other recent studies, the early-life environment may have weaker effects on events occurring in adulthood than do the conditions experienced during adult life.

If you’re interested in the paper, but can’t access it, please do get in touch.


Grant success auto

‘I have a really cool research idea, but I need some money to do it.’

‘Great, let us know what it’s about and we’ll give you some cash.’

‘Cool! We’ll use a new technique to cross-fundulate the hypoxyoffoffoffeller, aiming to test the hypothesis that paraselectorial emergence is a key property of metachromatophoratory heptacommunities’.

‘Er….on second thoughts, we’re not interested…’

Last week I went on a course on ‘Bidding for Research Funding’, organized by the University of Stirling’s amazing Research and Enterprise Office. Over two days John Wakeford and Robert Crawshaw, with a little help from a couple of folk from Stirling, told us all we need to know about writing successful applications for funding. With 18 months to run on my current research Fellowship, time is short and I need to secure funding if I’m to continue on my unlikely adventure in science. So what did we learn?


 (12) Never mind 

The course introduced us to some myths and facts about applying for funding. Some of these will be depressing, some positive. Some we all had a sneaking suspicion about anyway; some came as a complete surprise. So, some myths:

The best research will be funded. Plenty of Nobel prizes/Science papers were based on research rejected for funding at some stage. This made me want to ask ‘so my piece of crap has a chance then?’, but I thought better of it.

Funders just like big names. Hooray! The small/inexperienced researcher has a chance. If a proposal is great, no-one will care who wrote it.

ITV Archive
The little guy always has a chance if the proposal’s good! Photo from the ITV Archive.

It’s best to keep ideas to myself. The suspicion that we’re all trying to steal each other’s ideas is BALLS. If you discuss something with someone, and they then do something similar, EXCELLENT. Science works through accumulating evidence, so the more evidence to support/refute an idea, the better. By discussing it with colleagues, acquaintances, and folk met drunk at conferences, new ideas are formed and things can fall into place.

I’ll write the bid during my holiday. Don’t be a dick.

The Research Office will only delay it. These people are your guardian effing angels and you need their help (see below).

The funding panel will read my proposal. Well, they might, but only for three minutes, on a train while standing up and listening to a podcast.


Aside from the obvious, you know, science bit, what else does applying for funding require? Researching all the possible schemes you can apply for? Boring. Working out which ones you’re eligible for? Yuck. Calculating the financial details? No way. Luckily, there’s a lot of help available at most institutions. As soon as the workshop ended I had a meeting with Stirling’s Research and Enterprise Office. They gave me a list of things I might apply for (way more extensive than my list) and encouraged me to sign up for alerts from, which has information on all manner of funding schemes. It turns out there are lots of awesome people who will be able to help at every stage of the application process. And that’s before I’ve persuaded folk in my department to read my stuff, listen to my problems or give me mock interviews. I’m going to be making a lot of cake. This one’s super quick and easy. This one’s super fruity (though not strictly cake). This one’s an exact science.


 You’ve just flicked through thirty proposals and your eyes settle on the next one. OH GAHD. The title is really technical, the text is bunched up, and that figure is only understandable by someone with a PhD in Escherian geometry (yours was something to do with sea anemone ecology). Sigh. Probably best to give up on that one. But look at THIS one! This looks better. Why? Well, because…

It’s inviting to read. WHITE SPACE. There’s actual WHITE SPACE. And it’s not in Comic Sans. The reader isn’t confronted with a solid block of ink, every available nook and cranny (I don’t know what a cranny is either) filled with text. I feel better already.

I don’t know anything about metachromatophoratory heptacommunities, and yet the title is exciting! Who cares what the research is actually about? As long as the panel member wants to read it, the proposal has a chance…

(12) Freddie Starr
A memorable title can be a good idea. The content should be quite good too, though.

Reading this is actually quite pleasant. The. Sentences are. Short. The paragraphs are short. It’s punchy! Every word is useful, there’s no padding anywhere.

The heading and first line of each paragraph are memorable. The content of each paragraph is basically summarized in the first sentence. If more detail is wanted (reviewer), move on. If not (panel member), the main point has been made.

Well, that looks manageable. There are three objectives, over five years. That looks possible…no matter how many elements each objective has.

It’s on-target. It doesn’t meander off-topic. There’s no unnecessary showing-off about just how hard the stats are and how incredibly novel it is. Oh, and it’s actually relevant to the funding call.

My nan could understand it. Or my mate who’s a history teacher. Or my sister, who’s at high school.

These are some of the rules that were suggested to us when it came to actually writing bids for funding. It really seems that it boils down to making the thing as accessible as possible. If it’s incomprehensible to someone with scientific training, it’s probably incomprehensible to the people who’ll benefit from the research: the public, policymakers, or industry. Even worse, if it can’t be framed in words of one syllable, it gives the impression that whoever’s writing it doesn’t know the hell they’re talking about and are trying to hide behind protracted rhetoric resplendent with abstruse prolixity. Yes OF COURSE I needed a Thesaurus for that.


 Probably the most important thing I learned was how important the ‘Lay summary’ is. In previous applications, I’ve spent ages crafting every word of the ‘actual’ proposal: what I’ll do, and specifically why it’s important and cool. On the other hand, I’ve tossed off the Lay summary at the last minute and not shown it to anyone.

This is like creating an incredibly hi-tech gadget capable of doing wondrous things, and then covering it in beige plastic: if it look butt-ugly, no-one will give it a second glance. The proposal will be read by an expert reviewer who will chuck it out if there are fundamental flaws. Assuming the research is interesting and sound, the panel of people who make the final decision about who to fund will not be specialists. They’ll be given a hundred proposals to read, of which they might read the title and the Lay summary. Faced with conflicting reviewer comments on most proposals, the panel will choose something that sounds exciting. So the lay summary needs to be ACE.

Apparently, other things that folk on the panel possess are:

  1. A hatred of hyperbole
  2. A complete disregard for impact statements
  3. Three minutes to read your proposal
  4. A suspicion that bunched-up text means you’re crap at writing succinctly
  5. A lamentable but universal liking for ‘big names’ on grants written by newbies
  6. The scope of the call firmly in their sights. Don’t stray from it.


 Let’s assume you’re not funded. Since I have extensive experience of this, and none of actually being funded, I’m not really an authority on what you do if you get funded. I imagine you get REALLY drunk and then the rest of your life is full of sunshine, ice cream and Christmas.

(12) Frink drunk
Top scientist hears news of successful funding application.

If you’re not funded, after getting REALLY drunk (of course), get feedback. I assumed that this simply meant reading the reviewers’ comments, screaming ‘THIS IS BULLSHIT!!!!!’ and then moving on, but apparently this isn’t the best tactic.

(12) Frink drunk
Top scientist hears news of unsuccessful funding application.

Instead, get feedback from EVERYONE. Take the reviewers’ comments into account, yes, but also talk to your Research Office. They know what gets funded and what doesn’t, and so will be very able to make general comments on how to improve your proposal. Also, you can apparently contact the funder and discuss the prospects of a revised application, a strategy which had never occurred to me.

Then it might be time for reflection. We all live with rejection, and if you’ve applied for a grant, you’ve almost certainly been rejected before in your career: by a journal, for a job, by another funder. This means you probably took it on the chin. At the workshop, it was also suggested we might want to ‘review our career plans’ and ‘why we were doing it’. This may or may not be helpful….

Finally, try again. Another call, another idea, another funder, another year. Before that though, maybe go on holiday, have some chocolate, go for a run or generally recover.

Fieldwork 2016 #2: It’s Bus(y)ness time

Well, that went quickly. When I wrote my last post, I’d done a week of fieldwork and was just beginning to feel that I *might* not totally screw it up, and here we are: four weeks later, all done, all back and wondering what the hell just happened.

As a quick recap, I was on St Kilda, collecting faecal samples from female sheep in the weeks leading up to and followign the birth of their lambs. And why would I do such a perverted thing (a very reasonable question which EVERYONE asked)? I’m interested in how physiology and resistance to infection changes when individuals are making the huge effort involved in reproduction. This involved lots of wandering around in weather of varying foulness, identifying individual animals by their ear tags, very inconsiderately watching them pooing, then collecting the samples and getting them back to the freezer quickly. As the season wore on, I got to know many of the animals very well: what they looked like, where they’d be, and whether it would be easy to get a sample from them. There was the elusive BW153, who I saw in the first week and then never clapped eyes on again; BL226, who diligently produced twins in the middle of the season, raised them impeccably and obliging provided a sample every time I spotted her; and BL057, who could be recognized at a great distance and always provided a sample, though only after a sustained period of stalking. A phrase I thought I’d never use, but that also goes for ‘very inconsiderately watching them pooing’. As you may have gathered, this post is going to be low on scientific intrigue.

My dedication to the cause was tested when BW603, who’d been firmly refusing to provide a sample for days, left this right in front of me after breakfast. The little beauty. 

The sight of a bloke standing and staring at grazing sheep amused many of the tourists and employees working on the radar base on the island, but I managed to convince myself that I was doing work which required a great deal of skill by making a list of my methods for getting a sample from individuals I was targeting:

  1. Steadfastly watching them for ten minutes from two feet away
  2. Peering at them creepily through binoculars from behind a wall
  3. Walking past them eights times during the day
  4. Making them get up from their resting/cud-chewing
  5. Leaving them to get up from their resting/cud-chewing naturally
  6. Saying ‘SHEEP!’ at them
  7. Asking them nicely
  8. Launching personal attacks on their appearance and intellect

2 and 3 were unsuccessful, 1, 4, and 6 were very unsuccessful, and 5 and 7 generally elicited stern looks and contemptuous urination in my direction.

A sheep not doing a poo. Typical.

Anyway, the final score in all of this was 269 samples collected from 55 different females, with 34 sampled at least five times and many sampled six times.  This was as good as I’d being assuring people that it would be, and better than I’d secretly hoped for: it’s given me a nice spread of data from before and after the birth of the lamb, with many females sampled from a few weeks before to a few weeks after lamb birth. I’m not going to lie: it was a massive relief! As I went, I divided every sample into four: one subsection was used to count worm eggs in each sample while I was on the island; two more were stored at -20C for later analysis antibody and (funding-dependent) hormone assays; a fourth was stored at -80C for (funding-dependent) gut microbiota analysis. So…I have some data on changes in parasite burden during pregnancy and lactation, with more (hopefully) to come soon!

Desperately expensive kit for processing samples for worm egg counts: plastic jugs, tea strainers, beakers, scales. Also good for having afternoon tea.
Floating worm eggs in salt solution and trapping them for counting. Not visible: worm eggs.
Getting creative with my samples before storage (i.e. going insane after four weeks). This one’s going into the -80C freezer for future analysis of gut microbes.

Overall, I had a great trip. There were lots of great people out there doing other things; I went for a few lovely runs; there were vast quantities of biscuits. As fieldwork usually is, it was intensive: I was in the field or the ‘lab’ (or kitchen, as discussed last time) between 8 and 6 every day, with two half-days off for the whole month. As a colleague said, ‘you can be a dick to yourself when you’re your own boss…’. Normally I try to work a reasonable day, but I do like to have evenings and weekends off (and I bloody well hope you do too), so this was fairly intensive. I didn’t really think too much about this at the time, because I was essentially walking around a lovely, wild place for a month, which is exactly what I’d do if I had a month off. On the other hand, I didn’t really have time for any of the other work I had planned to do, like the paper I had to revise or the book chapter I had to prepare. Or, to put it another way, I decided that recharging by playing cards or reading a book after dinner was a better use of my time than two hours’ frustrated groaning over reviewer comments (OK, they were actually pretty helpful).

The lovely, wild place I was in for a month. It snowed. Did I mention it snowed? It also rained, hailed, blew a gale, and was sometimes even sunny. On the same day, naturally.


I did feel a certain amount of guilt about this at the time, but then I remembered this post about the ‘cult of busy’, which gives some excellent advice about how to use time more effectively. In the article, Natalie Cooper compares academics to the Four Yorkshiremen in the Monty Python sketchcompeting to explain just how terribly busy they are. In this world, everyone works 8am-6pm, breaks for dinner and is still answering emails at 11. Everyone wants to contact them at all times because they’re so desperately needed. Of course, this isn’t just true of academics. Nevertheless, the article has lots of good tips on how to be a bit more efficient and reduce one’s general workload and stress levels, which can only be a good thing.

The ranty bit of this is that I really wish that more folk, from PhDs to PIs, and in every place I’ve worked, would spend more time on activities which are seen as ‘not real work’ but which are great for themselves, their colleagues and their department in general. For example, some days I might spend the morning reviewing an exciting paper by a lab doing very similar and interesting (and usually better) things than me, chat to a friend about their data over tea, then go to a seminar about something totally unrelated to my interests, but which may have an engaging speaker whose style I can learn from or who has cool stats that could be useful one day. I mean, I haven’t done any ‘proper work’ and shit, that deadline is a day closer, but that person will have their review back (and I’ll have read a cool manuscript); my  friend will have a sounding board for their ideas (and I’ll have looked up an interesting new way of analysing zero-inflated data); the speaker will have had a decent audience (and I’ll have learnt how to use blank slides in a talk). All in all a pretty good day. To this end, I’m making an effort to be as efficient as possible in order to leave more time for these other (equally important) activities. I also genuinely think that many (probably even the majority of) people DO see things like this and see their reviewing, chatting and seminar-going as vital parts of being an academic. I just wish we’d admit it to ourselves and our colleagues more often!

Rant out. Was that a rant? Did it have a point, or was I too polite? Never mind. Let’s recover with a nice picture of a lamb:


Wasn’t that lovely?

To finish with, I should probably talk about some science. Trouble is, it’s been a little while since I’ve thought about anything other than fieldwork. I have a lot of labwork to do in the next few weeks in order to measure antibody levels in the samples I collected. This is going to build on this paper by Kathryn Watt from Edinburgh, who determined the relationships between antibodies measured in faecal and plasma samples collected at the same time. They found that parasite-specific antibody levels in plasma and faeces were significantly correlated, though not strongly. A very cool finding was that faecal, but not plasma, antibodies were associated with parasite egg counts. This suggests that measuring faecal antibodies could better reflect what’s actually happening at the site of infection (the gut) better that antibodies in the circulation. Kathryn is going to be teaching me how to do these assays, so I’m learning from the master!

The negative relationship between two faecal antibodies (IgA and IgG) specific to the parasitic worm Teladorsagia circumcincta and worm egg counts in samples collected in August 2013.

I’m also in the latter stages of a manuscript attempting to test the ‘cohort morbidity phenotype’ hypothesis, which is an interesting explanation for recent increases in lifespan: that reduced infections in early life have decreased levels of chronic inflammation and delayed the onset of diseases like heart disease, stroke and cancer. Hopefully more about that soon. Lastly, I’ve got a book chapter to plan. Oh no, and I have a quantitative genetic analysis to do.

Shit, I’m so busy…

Fieldwork 2016 #1: It’s a lovely day tomorrow

Through the miracle of SCIENCE (and the kindness of the National Trust for Scotland) I’m able to blog (sort of) LIVE from St Kilda where I’m staying for a month to do some fieldwork on the population of Soay sheep. My aim is a reasonably simple one: to collect as many poo samples as possible from as many adult females as possible, during the period in which they are (or are not) in the final stages of pregnancy and the onset of lactation. While I’m on the island I’ll be collecting data on intestinal worm infections by counting worm eggs in faecal samples, but I’ll also be dividing the samples up and freezing them for later analysis of immune markers (antibodies), gut microbiota, and (funds willing) hormones.

Village Bay on the island of Hirta, St Kilda, or my home for the next month. Yes, it IS always this sunny.

The main aims are to see whether (1) I can collect enough samples to make a larger-scale project worthwhile; (2) these samples can be analysed to provide meaningful data; (3) these data have the potential to tell us something new about what happens to defence against infection during reproduction. This is all quite exciting for me, because as I noted in my last post, I haven’t collected my own data since my undergrad degree, so this is the first time I’ve been in a lab for a while. And what a lab it is. 

The Featherstore, where the St Kildans used to store their grain (or something, anyway). It’s a lovely place to live and work, with the sound of waves lapping as I drift off to sleep (along with the hum of the -80 freezer). During the summer it’s used for blood processing work and has earned the sobriquet ‘The Bloodshed’. Obviously that won’t do at all, so I’ve decided to call it ‘The Pootique’.

It’s very exciting because I have access to lots of fancy toys, like two freezers, a microscope, cuvettes and a centrifuge, but my other lab essentials are mainly things you could buy in a supermarket: washing-up liquid, measuring jugs, sandwich bags, paper towels. There’s A LOT of washing up.

The interior of the Featherstore lab, complete with all mod cons (but no running water) and a fabulous sea view.

My day starts after breakfast when I head out just before 8 and start trying to collect samples. After a week here I’ve got samples from over 50 females and I’m now trying to re-sample them to get longitudinal data. This involves wandering around looking like a unthreatening tourist and spying on the sheep while they shit. If it’s an individual I need a sample from, I move in and collect it. Every half hour or so I go back to the featherstore to partition the samples and stick them in the fridge or freezer.

A selection of my field essentials. Clockwise from top left: Binoculars; cool bag; notebook and pen; sample bags and marker; radio. Not shown: waterproof jacket; winter hat; air of sheer bloody-mindedness.

So what have I learned so far? Essentially, my issues have fallen into two main groups, which fit neatly under two headings.

(1) Stuff I brought too much of: cool bags in the wrong size; sample bags; freezer blocks

(2) Stuff I brought too little of: cool bags in the right size; skill, good weather

Despite my inadequacies, and the tendency of the females I DON’T want samples from to be pooing while the ones I’m targeting to just munch grass and give me stern looks, I’ve managed to collect enough samples that I’m no longer feeling terrified that I won’t get any data. I’ve even done my first batch of worm egg counts. Maybe that fun should wait for next time.

I should also say that working on St Kilda does have its rewards, foremost among which are that it’s a STUNNING place. Having been here quite a few times before (but not for six years) and being keen to get going and actually collect some data, I haven’t really done much exploring until today. But since it’s Sunday (as I’m writing) I took half the afternoon off for a run around the bay. I didn’t want this to turn into holiday snaps, but they’re too pretty not to share. Be thankful, because if I blog again while I’m out, it’ll be about mashing, straining and pipetting liquid poo. Something for us all to look forward to.


Once more unto the field without friends, once more

I haven’t collected any data for myself since 2006. To be fair, this is isn’t because I’m a lazy so-and-so, parasitizing exceptional co-workers like a zombie ant fungus and driving them into collecting data for me. Not entirely, anyway.  Nevertheless, spending almost ten years as a biologist (how the hell did that happen?!) doing empirical research without having collected any data myself does seem a bit suspicious.

(10) Ophiocordyceps_unilateralis
Ophiocordyceps unilateralis, aka the ZOMBIE ANT FUNGUS, first described by Alfred Russell Wallace in 1859 (a vintage year for Victorian naturalists!). The infected any feels a compelling urge to find it’s way to the underside of a leaf, bite down on the leaf vein, and stay there until it dies a few days later. The fungus’ fruiting body then sprouts from the ant’s head, releasing its spores. Image from wikimedia commons.

Perhaps I should explain. I’ve only ever worked on long-term studies of wild animal populations, which are the results of the hard work of many people at many different institutions (see more on the study system page!). This has involved doing quite a bit of fieldwork, highlights of which have included helicopter rides, cuddling (well, OK, capturing) lambs, collecting elephant shit, not throwing up on boats (yet), floating lazily down the Irrawaddy, and eating a swan, but all of that has been for the collection of ‘core’ data for the various projects. As yet, I’ve not come up with a research idea and had enough research funds to be able to go to the field and actually collect the data. UNTIL NOW.

Next week, I’m heading to Hirta, the largest island in the remote St Kilda archipelago, home to a population of wild Soay sheep which have been studied for 31 years. I worked on this population during my PhD, which was mainly about how parasite infection had absolutely no effect on ageing whatsoever, and last went to the island in 2010. This year I’m going back for the lambing season, as a sort of exploratory trip before I write a bunch of research proposals later in the year. Lambs will be born and very cute they are too…but BORING. I’m really interested in what happens to how their mothers deal with parasite infections when they’re going through the extremely demanding process of carrying a lamb and then raising it to weaning.

How does carrying and rearing a lamb affect a female’s responses to infection? Work in domesticated sheep and on St Kilda shows that parasite counts can increase around the time of lamb birth, but we don’t really know to what extent individual females differ in their responses to infection at this time, nor why such differences occur.

To try and investigate this, I’m going to St Kilda for six weeks to do little else but collect sheep shit (an activity which I’m notorious for amongst my PhD cohort, my friends, my family, and, in an astonishingly short time, my new colleagues in Stirling). While other researchers are running around after lambs in the sunshine, I’ll be crouching, poised, binoculars raised and sandwich bag ready, waiting for specific female sheep to do a poo. By doing this again and again over six weeks, I’m hoping to collect enough longitudinal samples from enough females to count parasite eggs (a measure of infection burden) and to store samples in the freezer to analyse antibodies (a measure of the sheep immune response to worms), hormones and the diversity of their intestinal bacteria. To say what the specific questions I’m going ask are would be giving away a lot (including perhaps that I’m not quite sure what all the questions are yet…), but let’s just say that the demands of bearing and caring for offspring could be a demonstration of the predicted trade-off between reproduction and immune function, and how the trade-off is mediated by reproductive physiology, nutrition and patterns of selection.

(10) Ele shit
After years of training, I’m able to instantly identify these faecal samples as having come from elephants rather than sheep.

I’m sitting here quite nervously, having posted off my cutting-edge field equipment (collection bags, cool box, freezer packs, collection bags, duct tape, forceps, collection bags and red wine), hoping that I haven’t forgotten anything and that the 3kg of salt I’ll be able to fit into my rucksack won’t upset the staff at the Loganair check-in desk. If I have a good trip, I’ll have enough samples to be able to see whether these assays work well enough to justify a full-scale grant-writing assault in the coming year. If I have an excellent trip, I’ll have enough samples to write a paper. If I have an out-of-this world trip, it’ll be sunny.

Village Bay, Hirta, St Kilda, April 2008 on a good day.
Village Bay, Hirta, St Kilda, April 2008 on a bad day.

Science Drinks #2: Information Transfer, Daddy Cool, Sex Hormones, Bright Blue Flies

Welcome back to Science Drinks! This is a fortnightly meeting of folk in Biological and Environmental Sciences (BES) at the University of Stirling which takes place (currently) at the Meadowpark pub. Science Drinks was established by Luc Bussière as a way of getting people together to chat about their weird data, new papers, or science news stories in a more relaxed environment than a lab meeting or journal club.

In attendance this week were Luc, Lilly Herridge, Frances Fraser-Reid, Nadine Royle, Frederick Hunter, myself and SOMEONE NOT FROM BES WHAAAAAT?!, Jessica Enright, a Lecturer in the Maths department at Stirling who has research interests in contagion networks, models of infectious disease, games on graphs, and graph theory. Luc’s new twitter account is already reaping rewards: Jess heard about Science Drinks through one of Luc’s tweets, so there’s just a chance he’ll persevere and tweet again!

WARNING: we seem to have got though a lot more than we did last week, so I hope this is the point of the day where you’ve committed to a cup of tea and are not planning to do any *actual* work…

Luc immediately put Frances, Nadine and Freddie on the spot and made them give ‘elevator pitches’ of their undergraduate research projects. This is a very useful skill to a researcher at any level, and is actually very difficult to do: the idea is to give a 30-second summary of who you are, what you do, and why it’s interesting/important in the time it takes for your trip in an elevator with someone you’ve just met. Great tips can be found here! We tried to think of the British equivalent (The Queueing Patiently Pitch? The Standing Awkwardly Close On A Crowded Train Pitch?), but we couldn’t.

We then got onto a couple of other ways of presenting information. Luc told us all about Edward Tufte’s thoughts on why presenting information using “slideware” (i.e. Powerpoint) sucks. Tufte thinks that too much information is lost, and that presentations should just involve giving the audience a table of information and then fielding questions. I can’t help feeling that most of the ‘story’ behind our science would sadly be lost this way.

Without Powerpoint, this little chap would be replaced by reams of tables and parameter estimates. IS THAT WHAT YOU WANT?!

Lilly then brought up Tom Houslay’s recent blog post on applying the ‘story circle’ to academic writing. Tom suggests that if we want people to read our research, surely it’s better to make it a pleasurable experience, rather than a dreary slog through experimental procedure and results written in the passive voice. He introduces the ‘story circle’ of one of his favourite writers, Dan Harmon, which essentially describes a journey, beginning with a need to go somewhere and find something, followed by a journey of discovery and ending with CHANGING THE WORLD. Since this is essentially how we do science, Tom applies this to how we could write papers in a more dynamic and interesting way.

Story circle
Dan Harmon’s story circle, taken from the LA screenwriter

To some science then! Lilly spoke about her grapple with using allele frequencies to estimate the mating rates of dance flies, of which there are hundreds of species, some of which have ornamented females. The flies gather in large mating swarms and males collect nuptial food gifts, which females need since they can’t catch prey themselves. Females attract males and their gifts by looking fecund, and so they have very cool features including pennate spines and inflatable abdomens to make themselves look swollen with eggs. Lilly has dissected the spermatheca (sperm storage organs) from hundreds of females and is using microsatellite markers to determine how many males a female has mated with. Currently there are two methods for estimating mate number from genotyped stored sperm: first, using allele counts to give a conservative minimum estimate of mate number; and second, using population allele frequencies to estimate the most probable number of mates. Both methods rely on the one (the most) variable locus to obtain an estimate, but Lilly has been considering ways of using information from multiple loci to give her more precise estimates of mate number.  Jess suggested a  simulation approach, computing all possible combinations of allele frequencies, based on the frequencies of the alleles in the population, for a realistic range of mate number (e.g. 1-30 males), to give the likeliest number of males for a given combination of alleles.

A view of a long-tailed dance fly (Rhamphomyia longicauda) female down the dissecting microscope. Photo by Lilly Herridge, used with permission.

Freddie then spoke about a really nice paper about which he is writing a review of as part of his third-year undergraduate course. The paper studies how monarch butterflies protect their offspring from a protozoan parasite. The parasite lives on the abdomen of adults but is then transferred onto the surface of eggs and the milkweed plants onto which the butterflies lay their eggs. When the larvae hatch, they consume their own egg cases….and become infected with the sneaky parasite!  Larvae can (yay!) resist the parasite by consuming chemicals (cardenolides) present in medicinal milkweed species. The study tested whether parents could transfer protection to their offspring, by rearing butterflies on medicinal and non-medicinal milkweed species. They showed that offspring were more resistant to infection when their fathers were reared on medicinal milkweeds, and females produced eggs with higher levels of cardenolides if they mated with males from medicinal milkweed. Hence, dad seems to be able to transfer protection to his offspring, just by eating stuff and then transferring it, via sperm, to the female he mates with. We all tried hard not to think about a human analogy here because UGH. Anyway, the mechanism has yet to be determined, though it is possible that compounds could be transported with sperm, and then absorbed by the female. If you’re the sort of perverse individual who is somehow excited by that sickening thought, you should go find out more on the butterfly parasite work at Jaap de Roode’s lab. I definitely have.

A monarch butterfly (Danaus plexippus) feeding on swamp milkweed (Asclepias incarnata), a species low in protective cardenolides. No idea which way round it is. Must be the lack of a face. Photo from wikimedia commons.

We then (OH WOW FINALLY!) got onto the meta-analysis which I was going to talk about last week, on the effects of sex hormones on immune function. This is a question at the heart of the hotly-debated ‘immunocompetence handicap hypothesis’ (ICHH). ICHH suggests that testosterone is linked to the development of the showy ornaments which males use to attract mates, but also suppresses immune responses. This means that only the very best males can afford such showy ornaments AND suppress their parasite loads. Hence, females should pick the males with the largest ornaments. For the ICHH to be true, testosterone should have a negative effect on immune function, but a meta-analysis of ten years ago showed that it did not. Also, since females tend to mount more effective immune responses than males, female sex hormones, such as oestrogen, should be linked to more effective immune responses. The new meta-analysis reviewed almost 500 effect sizes from 130 studies to work out the effects of testosterone and oestrogen on immune function in experiments, and correlations between hormones and immune function in correlational studies. They showed that manipulating testosterone has a negative effect on immune function, and that manipulating oestrogen seems to have a positive effect, though this depended on the immune measure used. Some bad news was that if you just go out and measure natural levels of hormones and immune function in a wild animal, there is unlikely to be a strong correlation. We did wonder about where stress hormones come into this, because they are also elevated during reproduction and have documented effects on immunity, which may be positive (if acute) or negative (if chronic). Perhaps a meta-analysis of the effects of glucocorticoids is in order?

‘What do you mean, ‘no’?’ Is the peacock’s testosterone-fueled tail feather display a signal of his ability to cope with the effects of parasites? Caption from Punch, photo from wikimedia commons.

Finally (I hear you sigh…), Luc showed some data from an experiment he did with Lilly and Svenja Kroeger, his former undergraduate project student and now marmot-chasing PhD student in Aberdeen. They wanted to know how often males and females of different species visited dance fly mating swarms. Every day, they went out into the field, caught some flies and then dunked them in paint (or something). They then returned to the mating swarm the next day with another colour, and noted how many of the previous colour were present, and so on. He brought along a couple of graphs which showed….something exciting, but you’ll just have to wait for the paper, you highly persistent so-and-so. And now it really is time you got back to work, don’t you think?

A dance fly (Hilara maura), either very cold, or recently dunked in dye. Photo by Lilly Herridge, used with permission.