No, I said “ten years”, not “tenure”…

It’s ten years (TEN! One-zero! 10!) since I finished my PhD. It has both flown by and also seems like a lifetime ago that I had the out-of-body experience that was my viva and the subsequent celebration. It’s now go to the stage where not only PhD students but post-docs are starting to ask me for advice and mentorship on their work and career path, and so I thought I’d share this ten-year journey and some thoughts about it along the way. This is very much NOT just my CV and publication record: it’s a record of successes, failures, and the inner monologue that accompanied it. I’m as surprised as anyone to find that I still work in academia, a career that I’m enjoying immensely, for its varied nature and interesting people. Ten years out from my PhD, I haven’t achieved stunning success, but I’m still in the game. As I used to half-joke among my fellow PhD students around writing-up time, I’m perfectly happy to settle for mediocrity in my career, provided that mediocrity is against a background of world-leading experts…

The dark days of February 2011, just before handing in, and when frankly, things must have been getting to me.

First, a brief career timeline.

2011: The end of a fantastic PhD experience with Loeske Kruuk, and a week later, off to Sheffield to begin a post-doc on human life-history evolution with Virpi Lummaa.

2012: Struggles with a new environment, a new (lack of) social network and a new study system.

2013: Final PhD paper published. Unsuccessful application for a post-doc at Exeter. Turn down a post-doc at Griffith.

2014: Unsuccessful interview for a post-doc in Liverpool. Leave Sheffield and begin one-year post-doc in Edinburgh with Dan Nussey. Main aim: get a UKRI Fellowship.

2015: Unsuccessful interviews for NERC and Royal Society/Wellcome Trust Sir Henry Dale Fellowships. Bollocks. Successful interview for University of Stirling research fellowship. Move to Stirling.

2016: Unsuccessful interview for Lectureship in Biosciences at Exeter.

2017: Unsuccessful interview for a NERC Fellowship. Reserve list, for fuck’s sakes. Unsuccessful interview for Lectureship in Behavioural Ecology at Sussex. Unsuccessful interview for Lectureship in Evolutionary Medicine at Cambridge. Mental health crisis.

2018: Move to the Moredun Research Institute on a Moredun Foundation research Fellowship. Move house. The boy is born.

2019: Finding my niche: (relative) expertise in statistics; complete ignorance about livestock science and farming. Another mental health crisis.

2020: Discovery of SSRIs revolutionizes my life, despite it being The Year Of No Publications. Unsuccessful application for BBSRC Standard Grant. Put in another one later in the year on a totally different topic, because why not?

2021: Unsuccessful interview for Assistant Professor in Veterinary Parasitology at Calgary. First me-led papers in livestock parasitology published. Terrible reviews on that second BBSRC grant. The girl is born. Moredun contract is renewed and starting to feel like home…

A lot changes in ten years.

Now, this isn’t your textbook, smooth, PhD à PostDoc à (Fellowship) à Job route that many folk will follow, but I have still managed to maintain a sense of progress, at least in my own head. How? Three things are important I think, in no particular order:

  1. The desire for an academic career, which includes the desire to keep going despite rejections;
  2. Some ability that enables you to carve out a niche;
  3. Luck

Now, I was lucky. I did my PhD on a big ol’ long term-project that lots of people have heard of and generally associate with high-quality work and folk who are reasonably good at analysing data. As such, I was exactly the sort of person that was wanted for my first post-doc and I was lucky that it came up when it did. Another joy of my PhD was that it was quite varied: I worked on elements of life-history evolution like ageing, but it also had a parasitological and even mildly immunological element. As a result of this, I was lucky enough to have the choice of another post at the end of my PhD: a year working in the US on more elements of host-parasite relationships. Why did I choose the post-doc I did? It was a combination of the security of a longer post, the excitement of working on a new system, and (let’s be honest) apprehension about moving abroad. Do I regret the choice I made? No. Do I wonder how my career and life had panned out if I’d chosen the other post? Yes.

Lambing 2008 on St Kilda. I made three great friends, and one rudimentary beard.

So, the post-doc was good for me, because I had the ability to do what was required (analyse data and write papers) and I got some good publications out of it. On the other hand, perhaps it was me that was good for the post-doc, because I didn’t really learn much that was new, in terms of approaches to doing science: as in my PhD, data was being collected by someone else, and I was “simply” analysing it (and admittedly coming up with hypotheses as well).

I therefore kept my old links with the Soay sheep project going and continued thinking about that system with some terrific mentors, Dan Nussey and Andrea Graham. I was also incredibly lucky in that my post-doc PI Virpi Lummaa was content with me to continue this work even though this wasn’t what she was paying me to do. Given that I know of people being told by their (otherwise very reasonable and lovely) post-doc PIs that they have to work on their old PhD work or any new external work at evenings and weekends, I know that I was incredibly lucky in working for Virpi. I’ve therefore been extremely lucky in having not only fantastic mentors, but fantastic PIs as well, and this enabled me to keep multiple avenues of research open, which I think is a very good idea indeed.

Oh yes, added bonus of my post-doc in Sheffield: also getting to do some parasitology on these guys in Myanmar

At the end of my post-doc, I had another choice, between returning to Edinburgh or moving to Brisbane. The return to Edinburgh was another of those lucky moments: Dan Nussey was a good friend, a brilliant mentor and an incredibly successful scientist, so he had enough money to pay for a year to write a couple of papers with him and apply for fellowships. This was an easier choice than at the end of my PhD: I felt I was ready to write a fellowship application and my new girlfriend lived in Scotland. In the end, I wrote a fellowship on reproduction-immunity trade-offs in Soay sheep and one on paternal ageing in humans; they were both unsuccessful, but I’m still with the girlfriend and we have two kids, so it worked out OK…

I did, however, manage to get a University-funded fellowship at Stirling. Once again, this was where my choice of post-doc and keeping my Soay sheep research going paid off: four years out from my PhD, I had something like 15 papers, including publications in Science, PNAS and PLoS Biology, so I was a pretty strong candidate. You can see that I also had other plans that didn’t pay off, including interviews for a couple of other post-docs that I didn’t get, so going for multiple posts is 100% a good idea. You probably already know this though!

I enjoyed my time at Stirling, but I was becoming increasingly less sure about where my niche and my future career path lay. The publications had dried up, mainly because I spent most of my time writing applications for other jobs and UKRI fellowships. I had several near misses, including a close shave with a NERC fellowship when I was next cab off the reserve list. I was in a very bad place mentally and sought counselling for the first time. My Sunday League football career had ended, but had been replaced by mountaineering and hill running, which provided huge mental solace. Spending the weekend scrambling up another Munro rather than being kicked and called names (let’s say) turned out to be more fun.

Mountains are nice. Burly opposition centre-backs are not.

One of the best things that I did at Stirling was supervising undergraduate students. I’d had a Master’s student at Sheffield, and this worked out very well in that we ended up expanding their analyses and turning it into a paper (with their permission and involvement!). At Stirling, aware that I wasn’t exactly a paper machine, I designed a project that I thought could be turned into a paper. The student turned out to be utterly brilliant, and after producing a smashing project, we continued to run with it during their Master’s and then their PhD, and produced a really nice paper out of it. This really emphasized the importance to me of valuing undergraduate students and treating them as colleagues in research: this mind set has worked really well for me in the years since and I’ve had some smashing students as a result. Offering stats-based projects might have helped, admittedly…

At this point, another huge slice of luck. I’d met Tom McNeilly from the Moredun Research Institute in something like 2014. A ruminant immunologist, he was starting to work with Dan on immunological questions in the Soay sheep and wanted someone to take a look at a dataset he’d collected on immune function during pregnancy and lactation in domestic sheep. This was pretty much my fellowship application, so I helped analyse the data. (7 years later, the paper has been written up, but still hasn’t been submitted!) At the end of 2017, Tom approached me to say that Moredun were starting a research fellowship scheme, and that I should apply: my combination of some knowledge of veterinary parasitology plus ability to perform analyses beyond an ANOVA would probably go down very well. I was already writing a proposal to stay at Stirling, based more on human life-history evolution, and in the end I was offered both.

The decision to move to Moredun was a fairly easy one, mainly because it felt like I had managed to find a niche. At Stirling, I was just another evolutionary ecologist, and one with a set of skills and experience possessed by a shedload (more like a double garage-load) of other people in the field. At Moredun, I had a set of skills possessed by zero other people in my department and not all that many other people in the field as a whole. I’m still an absolute novice when it comes to livestock disease management and agricultural policy and practice, but it’s fun learning. I’m now publishing papers and writing grants on livestock parasitology, ad contributing to writing Moredun’s latest Scottish Government research programme. So, while I was lucky to have the contacts that provided me with the opportunity, I was also ready to fill a much-needed niche at the Institute. Along with this, I’m still doing a wee bit on the Soays, so I get to combine some more applied research with some ecology.

A visual metaphor for academia: getting buried but actually enjoying it

I don’t really know how to end this. I guess my hope is that by sharing my career path and some of the inner monologue that accompanied it, someone somewhere will feel reassured or take something useful away. Perhaps best to finish with something for folk who are just out of their PhD and are maybe worried an academic career isn’t for them, because of how they feel in certain situations. The following are still true of me, ten years post-PhD, and so they are in no way a barrier to a continuing academic career and by no means an indication you don’t “belong” (whatever that means).

  1. I find it physically impossible to introduce myself to people I’ve never met before at conferences.
  2. I can only just about manage to introduce myself to people I’ve met before at conferences.
  3. If I know I am about to ask a question in a seminar or at a conference, my heart beats faster, and all the moisture seems to disappear from my mouth and reappear on my palms.
  4. I have never written a function in R.
  5. I didn’t spend my childhood pond-dipping, bird-watching, or moth-catching. I played football, cricket and the MegaDrive.
  6. It still takes me about a day to properly review a manuscript, because I’m paranoid I may have either missed a glaring error, or misinterpreted something perfectly sensible as a glaring error.
  7. I really struggle to keep up with the literature, because I’m concerned that I should be working on my own stuff, even though I know reading papers is incredibly important.
  8. PhD students, Master’s students, and undergraduates have intelligence and ability far beyond mine.
  9. I feel guilty that writing or reading blog posts or Tweets is a waste of time, even though I’m equally sure they can be extremely useful ways of making new contacts or hearing about the latest research or academic issues.
  10. I don’t really like coffee. Tea all the way.

Effects of liver fibrosis on performance of beef cattle varies among breeds and producers

AVTRW meeting poster presentation, Moredun Research Institute, Sept 16th-17th 2019



This project emerged from a collaboration between Scotbeef Ltd and researchers at Moredun and SRUC. Consumers always want more information about where their food came from, and concerns of meat consumers typically focus on the health and welfare of the animals and any drugs the animal has been exposed to.

Scotbeef is one of Scotland’s largest red meat producers and currently collects extensive data on the animals slaughtered at their abattoirs. The data include, but are not limited to, data on the origins of the animal, the holding the animal was kept at, carcass parameters and performance, and carcass pathologies. There is currently no data on the drug or vaccine treatment: all of this is recorded by farmers but not passed onto Scotbeef and hence the consumer.

The ultimate aim of the project is to develop a mobile phone app that will enable farmers to record treatments and upload these records to Scotbeef’s data systems. This will enable each end product to be traced back to the farm and any drug and vaccine treatments given to the animal revealed.

The app is currently at the beta stage. In the meantime, our focus at Moredun has been the existing abattoir data and what it can tell us about treatments and disease. Our focus has been on pathologies of the liver, specifically fibrosis, which can be a result of active or past liver fluke (Fasciola hepatica) infection. Fluke are responsible for considerable health and economic costs in the sheep and cattle industries, but control is difficult. Neither sheep nor cattle appear to have effective acquired immunity; vaccine development is yet to succeed; drug resistance is growing; effective management requires also dealing with the intermediate mud snail host.

Since cattle do not appear to show effective resistance to fluke, they may be evoking some sort of tolerance mechanism- the ability to maintain health, productivity or fitness in the face of increasing stress, infection, or pathology. Promoting tolerance of fluke in cattle, through breeding or management, may be one strategy for disease control which has yet to be sufficiently explored.

Here, we aimed to (1) investigate associations between carcass pathology and performance parameters and (2) estimate among-breed and –producer variation in tolerance to liver pathology using data collected from one of Scotland’s largest abattoirs.



Data from 92,119 cattle slaughtered at Scotbeef Ltd in 2018 were analysed with linear mixed-effects models. The key response variables were age at slaughter (how long it takes an animal to be ready for slaughter) and daily dead weight gain. Key explanatory variables were presence/absence of liver fibrosis, abscesses and pneumonia, as well as fibrosis score (0-3). All models accounted for effects of breed and producer ID.

Since this is not a published study, and since if you’ve got this far, you’ve probably read the poster and chatted to me about the methods, I’m not putting any more details here. For more information on the analyses, contact me at




(17) age and pathology

Figure 1: Predictions from linear mixed effects models of carcass pathology and age at slaughter; points show model estimates and 95%CI. Plots in red show statistically significant associations.


(17) dwt and pathology

Figure 2: Predictions from linear mixed effects models of carcass pathology and daily dead weight gain; points show model estimates and 95%CI. Significant associations are shown in red.


(17) breed tolerance age

Figure 3: Results of a random regression model of the association between age at slaughter and fibrosis score showing variation between breeds; here the variation is non-significant (LRT = 2.95, DF = 2, P = 0.229) . The left-hand panel shows estimates from the model where the association is allowed to vary between breeds; each line represents a breed. The central panel shows a histogram of intercepts in the different breeds; i.e. it’s a histogram of age at slaughter where fibrosis score = 0 on the left-hand plot. The vertical red line shows the average age at slaughter. The right-hand panel shows the non-significant variation in slopes, represented as the difference in age at slaughter between an animal of a given breed with fibrosis score 3 and an animal of that breed with a fibrosis score of 0. For example, the vertical red line shows that animals from the average breed with a fibrosis score of 3 take 21 days longer to reach slaughter than animals from the average breed with a fibrosis score of 0.


(17) prod tolerance age

Figure 4: Results of a random regression model of the association between age at slaughter and fibrosis score showing variation between producers. Interpretation is as for Figure 3; in this case, there is significant variation (LRT = 65.76, DF = 2, P < 0.001) between producer slopes. 


(17) breed tolerance dwt

Figure 5: Results of a random regression model of the association between daily dead weight gain and fibrosis score showing variation between breeds. Interpretation is as for Figure 3; here, there is significant variation (LRT = 7.04, DF = 2, P = 0.030) between breed slopes.


(17) prod tolerance dwt

Figure 6: Results of a random regression model of the association between daily dead weight gain and fibrosis score showing variation between producers. Interpretation is as for Figure 3; there is significant variation (LRT = 66.42, DF = 2, P < 0.001) between producer slopes in this case.



What the results show

 Liver pathology, specifically fibrosis, is associated with increases in slaughter age and reductions in daily dead weight gain in beef cattle, accounting for variation between breeds and producers, and for the effect of treatment for fluke.

Further, increasing fibrosis score is association with production parameters to a different extent in different breeds and producers. In other words, animals of some breeds and from some producers seem to tolerance high levels of fibrosis: performance is not adversely affected in animals with high fibrosis scores compared to those with low or absent fibrosis. Meanwhile, animals from some breeds and producers do not seem very tolerant: animals with high fibrosis scores perform very poorly compared to those with low scores.

How we might interpret this

 Assuming that liver damage tells us something about fluke infection, the results suggest that fluke have a significant impact upon production parameters. These effects, however, are not universal, because breeds and producers differ in the extent to which liver fibrosis was associated with performance. If we can assume breed differences are largely genetic, and producer differences are largely environmental, the results suggest that tolerance of fibrosis, and potentially fluke infection, can be influenced by both genes and environment. This conclusion offers a glimpse at new methods for attenuating the impact of fluke on beef cattle performance and increasing the efficiency of food production.

 Some limitations

Liver fibrosis may be the result of factors other than fluke infection, although fluke infection is likely to be a major contributor. Further, the data do not tell us anything about whether infections are active or historical; such analysis required collection of fresh livers and enumeration of fluke. Finally on this note, higher fibrosis scores do not necessarily indicate a higher fluke burden; although some studies have shown a correlation, it may not be strong enough to draw firm conclusions.


Environmental effects on evolutionary change


For the first time since last July I HAVE A NEW PAPER TO WRITE ABOUT! It shouldn’t be that exciting because (1) it isn’t made of chocolate and (2) it’s on a fairly niche issue in evolutionary ecology, but after a year largely spent writing for (and failing to get) grant money, it’s nice to have something new to blog about…

Our work addresses the issue of how environmental conditions influence evolutionary change. For evolution of a trait (say, beak size) to occur, two conditions are usually required: that the trait is under natural selection (i.e. associated with fitness) and heritable (i.e. is influenced by genetic effects which are passed from parents to offspring).

However, when we measure selection, heritability and evolution in natural populations, we often observe strong selection (e.g. individuals with bigger antlers have more offspring), strong heritability (e.g. a substantial amount of the variation in antler size is due to additive genetic effects), but NO EVOLUTION. In other words, although the conditions for evolution of antler size are met, we see no change in antler size across time. This is the ‘paradox of stasis’.

(16) Deer

(L) Male red deer with larger antlers have greater reproductive success and antler size is heritable. Despite this leading to an expected increase in ‘breeding values’ for antler size across time, (R) breeding values have remained unchanged. Figure from Kruuk et al 2002.

One explanation for this paradox is that organisms live in environments which are constantly changing. From year-to-year, for example, there may be changes in climate, competition, predation, food availability and so on. As very neatly described (but by no means initially suggested) by this paper, if genetic variation and selection vary across environmental conditions in a certain way, this can constrain evolution.

For example, if in warm years, selection is strong but genetic variance is weak, and in cold years selection is weak but genetic variation is strong, in no environment will there be the combination of strong selection and strong heritability for evolution to occur:

(16) WoodBrodie

An illustration of how a negative correlation between the strength of selection and genetic variation across environments can constrain evolution. In both of the example scenarios, evolutionary change is small because of a lack of either genetic variation or selection.

In natural populations, we have LOADS of evidence to suggest that selection is stronger under worse conditions. This means that if genetic variation is weaker under worse conditions, we can offer an explanation for why evolution is constrained. However, it’s a different story for genetic variation. We reviewed the literature and found 91 estimates of how genetic variation changed with different measures of environmental conditions. 52% found no change; 18% found changes which were not statistically tested; 29% found changes and 18% found weaker genetic variation under worse conditions. Of these, lots used statistical techniques which have been superseded, and indeed overall findings of ‘no change’ have been getting more common in recent times, even with larger datasets and more accurate statistical tools. Overall then, evidence for changes in genetic variation with environmental conditions is pretty rare.

Barely any of these studies also measured selection, so couldn’t determine whether evolution should be constrained or sped up by changes in genetic variation. For two exceptions, here’s a paper on birthweight in Soay sheep and laying date in great tits; you’ll notice that they’re both in the excellent PLoS Biology, showing that this question is maybe a big deal after all!

In our paper, we wanted to show how selection on and genetic variation in six different traits measured in wild Soay sheep changed with environmental conditions. The Soay sheep live on the island of Hirta in the St Kilda archipelago and have been intensively studied since 1985. There’s more about the sheep over on the study systems page, but if you can’t be bothered with that, here’s a nice view of the study area and some lambs:

(16) St Kilda

The Village Bay study area on Hirta, by Alex Sparks; new lambs pictured in spring, by Kara Dicks.

We took six different measurements from sheep captured 1988-2011: body weight, leg length, horn length, horn growth, testicular circumference and parasite count. Some individuals were captured many times over the years; many were captured just once. As a measure of environmental conditions, we used population density. This is essentially a measure of competition: in years when there are more sheep, conditions are worse because there’s lots of competition for food and individuals lose weight, get infections, and are more likely to die.

As expected, we found that all traits were under selection: individuals which were bigger and have fewer parasites were more likely to survive and reproduce in a given year. We also found that selection on most of the traits was stronger when population density was higher. For example, at low population density, fitness increased relatively slowly with increasing leg length; at high density, fitness increased more rapidly with increasing leg length:

(16) SxE

Estimated annual fitness (show as numbered contours) at different combinations of leg length and population density. The actual data are represented by red points for females and blue points for males. Selection is stronger at high density (e.g. 600) since as you move from left to right (i.e. as density increases) you pass through more contours (fitness increases from 0.1 to 0.9) than at low density (e.g. at 300 fitness only increases from 0.5 to 1).

Next, we analysed how genetic variation in each of the traits changed with population density. We used an analysis called an ‘animal model’ to estimate the genetic variance for each trait: this technique uses information on relatedness between individuals to estimate how much of the variation in a trait is due to similarity between relatives. Most of the traits were heritable: genetic effects explained 3-21% of the variation in the traits.

(16) heritability

Estimated heritability for body weight, leg length, parasite faecal egg count, horn length, horn growth, testicular circumference and annual fitness. The heritability is the proportion of variation explained by additive genetic effects. Traits in blue are statistically distinguished from zero.

We then looked at how the genetic effects changed with population density, using random regression models (nicely explained in the supporting information of this article). Aaaaaand…we basically found that genetic effects didn’t change with population density: essentially, genetic variation was the same across all environments. The large table of statistical estimates needed to illustrate this is just too exciting to handle and we didn’t draw a graph, so here’s a handy illustration:


On learning of the results of the study, Village Bay’s spokesheep gave the reaction of the community as a whole.

What do all of these (possibly slightly deflating) results tell us?

Overall, while we found that selection is stronger at higher population density, we also found that genetic variation didn’t change with density. This result means that there is no relationship between the strength of selection and genetic variation across environments: in this case, changes in selection and genetic variation with density doesn’t help us to explain the paradox of stasis.

The most useful result of this paper is the stark contrast it offers with the results of decades of lab and field experiments which show that genetic variation always change with environmental conditions. If you grow plants which differ genetically (‘genotypes’) in different environmental conditions in a greenhouse, a genotype which does well under one set of conditions will often do badly under another set. Similarly, if you take plants of the same species from different locations and swap them (in a ‘reciprocal transplant’), the plants will do worse in an alien environmental than in their home environment. In other words, they find that genetic effects change across environments:

(16) CKH

The studies of Clausen, Keck and Hiesey in the 1940s provided some of the first evidence for changes in genetic effects across environments. They transported ecotypes of the same plant species across three sites separated by hundreds of miles and thousands of metres in altitude and observed variation in the performance of the different ecotypes and their crosses. From Clausen et al 1941.

We didn’t. This may be because we measured changes across environments in which the sheep have evolved: even though high density is generally bad, they’ve been adapting to this environment for several thousand years. High density is not that bad, compared to moving a plant hundreds of miles from its home, or into a totally different greenhouse at the drop of a hat: such big differences in environments may explain why experimental studies are more likely to find effects. We also studied individuals within a single natural population, with natural levels of genetic variation, rather than inbred lines in the lab, or from different locations, with more contrasting genetics. Again, this makes it more likely that experimental studies will detect effects, while studies of natural populations will find it harder.

None of this means that changes in genetic effects with environmental conditions aren’t an important force in evolution. It just means that measuring them in natural populations is bloody hard.

How density shapes your destiny


This is a report I wrote for the Phylo-Eco-Geo-Evo Journal Club which is run by Lynsey Bunnefeld, who has some how managed to make a group of conservationists, ecologists, geographers and evolutionary biologists come together for a fortnightly(ish) meeting!  The paper I chose comes from the incredible long-term study of Bighorn sheep, which has been produced some of the seminal work in population ecology and evolution over the last couple of decades:

Pigeon, G., Festa-Bianchet, M. & Pelletier, F. (2017) Long-term fitness consequences of early environment in a long-lived ungulate. Proc. R. Soc. B. 284.

The conditions which an individual organism experiences during early development have a profound effect on their success in life. For example, poor maternal nutrition may lead to underdeveloped or small offspring, which are likely to have reduced fitness in later life. A host of studies in the lab and field have shown that the quality of environmental conditions (nutrition, population density, climate, predation, infection) during early life are strongly associated with body mass, survival, ageing rates, reproductive success, disease resistance and lifespan.

There are two (non-mutually exclusive) explanations for why early-life conditions influence later performance. First, a non-adaptive explanation: if conditions are good, development is good and the individual is well set-up for a successful life; if conditions are bad, development is sub-optimal in some way and the individual struggles. This is known as the ‘silver spoon’ hypothesis (although The Who would call it the ‘plastic spoon’ hypothesis), and under this scenario, a bad start in life always leads to poor performance in adulthood. Second, an adaptive explanation: the individual senses its environment during development, assumes that it reflects the environment it will encounter later on, and develops in such a way as to maximise its performance under those conditions. Under this scenario, if conditions match during early and later life, no matter how bad those conditions are, individuals will have higher fitness. This is the ‘predictive adaptive response’ hypothesis, and it has been a popular (but controversial) explanation for the origins of metabolic disease in humans.

Mono Digital

‘I was born with a plastic spoon in my mooooouuuth/The North side of my town faced East and the East was facing Sooooouuuuth….’

Few studies have tested for predictive adaptive responses in long-lived wild animal populations, because it’s difficult: such a test requires measurement of environmental conditions in early life, plus measures of both environmental conditions and performance in later life. Gabriel Pigeon and colleagues, from the University of Sherbrooke in Canada, used more than 40 years of data on a bighorn sheep population to test for predictive adaptive responses in a recent paper. They concentrated on female sheep, and asked whether (1) probability of weaning a lamb and (2) probability of survival in a given year were dependent on early-life environmental conditions, current conditions, and an interaction between the two. They tested 12 different environment variables, including population density and a large number of climatic variables (although only density was important, with the hypothesis being that higher density = more competition = poorer nutrition).

(15) bighorns

A bighorn ewe and her lamb. Those aren’t especially big horns, but the males have REALLY big horns, oh yes they do.

They ran a large number of models for both response variables, including linear and non-linear effects of early-life variables and crucially, interactions between early-life and current conditions. They also attempted to separate out within- and between-cohort and –individual effects, which was rather cool, using a really nice approach developed a few years ago. In short, it was pretty thorough.

Population density at birth explained 32% of variation in weaning success: females born in high density years were less able to wean lambs. There was also an interaction with current population density: in high-density years, females were less likely to wean lambs, but this was only true of females who experienced high density around birth themselves. In other words, experiencing poor conditions in early life made individuals less able to deal with poor conditions in early life (Figure a below). However, population density at birth was very weakly (and non-significantly) associated with survival (Figure b).

(15) Pigeon Fig 2

There were some interesting (if rather mind-bending) results concerning how the current population density influenced weaning success, illustrated below (in the SI, where I went digging, so you/other PEGE members don’t have to!). In (a), each line represents the change in weaning success with increasing current density in a given cohort, and the redder the line, the higher the early density was in that cohort. There are between-cohort effects, because the cohorts are responding differently to current density; however, there is no average within-cohort effect, because the average cohort would show a line with a slope of approximately zero. In (b) each line represents an individual. There are between-individual effects, because individuals who experienced higher current density had lower fitness, but there are no within-individual effects, because all individuals responded in a similar manner to increases in density. This suggests the absence of individual plasticity, and that density affects all members of a cohort in a similar way.

(15) Pigeon S1

The main conclusion of the paper is that analyses did not support a predictive adaptive response. This is perhaps not surprising, given similar conclusions in a recent(ish) meta-analysis of experimental studies in plants and (short-lived) animals and even some not-especially-convincing (OK, it’s mine) stuff on humans using data on climate and famines. Predictive adaptive responses are an incredibly intuitive and lovely hypothesis at first glance. I’ve found this when teaching undergraduates: given the question ‘how can low nutrition during gestation lead to diabetes in later life?’, one or two will always come up with the idea of predicting the future environment. Evidence in support of such responses are rare though. An interesting question to ask is ‘how predictive is predictive?’ Do the fitness benefits of developmental plasticity need to arise when you’re halfway through life? Reaching sexual maturity? Surviving to weaning/fledging? Surviving the pre-natal period? All have been suggested, but the best examples of predictive adaptive responses (for me) are very short-term responses: one in voles, and one in humans. Are they predictive or even adaptive? It’s up for debate.

How to (not quite) succeed at Fellowship interviews

(14) Interview panel

Don’t worry, your panel won’t be like this: unless your funding body is stuck in the Stone Age, there’ll be women on it too. And probably fewer cannibals.


At the start of last year I wrote a bloggy thing about Fellowships, with particular emphasis on which ones I (or you!) might apply for. I applied for two different schemes last October, with two utterly different projects. It was a lot of work, but my reasoning was to buy as many tickets to the Fellowship lottery as possible.

And it (sort of) worked: I got interviews for both of them (I’m not saying which just yet…). However, I recently found out that I didn’t get the first, and that I’m way down the reserve list for the other. Despite this, I got favourable comments overall: I just wasn’t quite good enough. So, I present:


A Guide To Not Doing Too Badly At Fellowship Interviews


This guide consists of advice that came out of the three mock interviews I had, with a large number of different people. I cannot recommend a mock enough. In fact, two mocks are better than one: before my NERC interview, I had one mock with people who could grill me about the science and one with people who had sat on panels before and knew what panels are after. These people provided some excellent advice, and the only reason they haven’t yet written a blog post is probably because they’re too busy doing the research they’ve been funded to do…



 Generally a fellowship interview will start with a short presentation by the candidate. The aim is to make yourself and the project sound great. This is kind of the easy bit, because you can practice it and say what you want. Obviously you want it to be as awesome as possible, but the impression I got is that this is unlikely to make-or-break your chances, unless you completely fuck it up by not being able to remember who you are or why on Earth you’re in Swindon.

The advice I got was to practice it to death, and then back to life again. I wrote a script, which I wouldn’t do for a normal conference talk. After a moderate amount of practice, it was absolutely shit: I was clearly trying to remember everything and it was incredibly wooden. After an insane amount of practice, it was good: because the words were seared on the inside of my skull, I didn’t need to remember them, which meant I could pretend to be relaxed by moving, breathing, changing the tone of my voice and looking the audience in the eye.


 You’ve done a talk before. The same rules apply. Make nice slides. Don’t use Comic Sans. Every single picture and figure has to be doing something: no putting up figures and not explaining what they are. LABEL YOUR FUCKING AXES. This is a rule for life.


(14) Label your axes


My body language came in for a bit of a hammering during all my mocks. I was told to make eye contact with the audience more, particularly when saying something important (if you say ‘I will be the best scientist ever…’ while looking at the floor, it lacks conviction). Don’t forget to look at everyone in the room though: everyone wants to feel loved and important.

As a shy sort, this kind of showmanship doesn’t come easily, and it seems incredible that a fantastic proposal/candidate would be rejected because they didn’t give it the full Liberace. The key thing I was reminded is that competition is exceptionally tight. You’re a great person, but there’ll be more great people interviewed than there are awards to be made. So every little helps. Also, I was told in my mock that my face lights up when I smile more. Which was weird.


(14) Liberace

That won’t be necessary.



 The impression I get is that people are looking for the same qualities in a research scientists as they are in a politician. They want great ideas, yes. But they also want you to show ‘leadership’, whatever that is. This turned out to be the main advice I was given throughout my interview prep: the panel want to see a future research leader.

Therefore, as well as outlining the coolness/novelty/impact of the project, I was encouraged to spend as much talk time as possible on why I was the right person for the job. In this case ‘the job’ wasn’t the fellowship, but taking a whole field of research forward in the future (more of this below). I therefore had an entire slide devoted essentially to ‘why me’, emphasizing the skills I’ve picked up, my track record and my ideas for the future, but I was also advised to lace my talk with additional references to how great I am (again, not an easy thing to do without feeling like an arse).

There are a zillion ways to structure a talk. If you want to see how I did mine, get in touch and I’ll happily send on my slides and script.



 Having finished your talk, wiped the sweat/blood from your brow and taken a sip of water to wash the taste of sick in your mouth, you’ll be invited to sit down and answer some questions.


 Style tips for answering questions seems a bit ridiculous, because you are basically just having a conversation with a bunch of people. However, I did get some tips during my mocks on how I can come across better, which probably need to be carried forward into everyday life.

Quite an important point I got was to make sure answers are punchy: quick answers mean more chance to explore more questions. I got into problems in my mock when I was answering technical questions, and not because I couldn’t, but because I started at the beginning. The tip I got was to assume some knowledge, get to the important bit, and then ask if I answered the question at the end of my answer: clarification can then follow. The same is true if you find yourself rambling– try and cut to the chase.

On the flip side, when I got an easy question in my mock, I was apparently just batting it aside with a bit too much efficiency. This was especially the case with some of the potentially difficult questions which I’d pre-empted (e.g. ‘This method is better because- next’). It was suggested I make the questioner feel clever with phrases like ‘that’s a good question/interesting point’, ‘I’ve thought a great deal about this’, although doing this in response to every question will get a bit tiresome.


(14) Easy question

‘Obviously I’ll control for that with a random effect of subject ID. Next.’


One final style tip I got was to come across more personably. It was suggested I smile more (because my face lights up, remember), make eye contact more (especially when answering a toughie) and have more open body language (no hunching or crossed arms). The thought that this might sway a decision depresses me beyond belief (stupid extroverts), but it’s probably worth bearing in mind, mainly because by pretending to more relaxed and confident, I felt it.


The list of questions you can ask is basically infinite. If you want to know what I was asked, get in touch.

A great many of the questions will be about the project itself, which you should be able to answer because you’ll be aware of the pros and cons of the project. However, one of the best reasons to have a mock is to be prepared for the scenario where someone asks a question where it’s clear they haven’t understood something fundamental. There’s nothing to be done here apart from staying calm. Similarly, it’s good to practice for the scenario when someone hasn’t read the proposal and asks why you haven’t done something when you DEFINITELY HAVE DONE IT. Take a minute to breathe and gently correct them.


(14) It's on page 5

‘IT’S ON PAGE FIVE, YOU DI- I mean, um, sorry…’


People on my mock panel(s) were keen to get me to build the case for, in particular, two things: the host institution and my future. Of these, I think the latter is WAY more important, unless you’re basing yourself somewhere you’ve been for the last five years (expect questions about independence).

Building the case about yourself, I was told, is all about leadership. Doing exciting work which leads the field BUT ALSO getting lots of money so other people can do exciting work in your group AND ALSO mentoring people AND heading large collaborative grants. Being able to say exactly what grants you’re going to apply for in the next five years, how many people will be in your group, and what they’ll be working on will be key to showing that you have lots of potential to be funded for years to come. Another key part of this is deciding what makes you different from your collaborators and your competitors (or potential collaborators, in my book!): why can you do this project and no-one else can?



At the end of a very rapid 20-30 minutes, there’ll be an opportunity to ask a question yourself, which seems only fair. Forget about having to ask a question to look clever: that may well apply in a faculty or post-doc interview, but it doesn’t here. I’d make sure you got across everything you wanted to, perhaps by re-iterating all the strong parts of you application, or returning to a question you didn’t nail the first time.

Now- time to run for the train, pausing only to grab the largest tea/beer/cake you can lay your hands on. You’ve bloody well earned it.



Most of the above is excellent advice from many people who have names which can be sorted by the order of the first letter of their last name, including Stuart Auld, Lynsey Bunnefeld, Luc Bussière, Maggie Cusack, Elena Gheorghiu, Al Jump, Phyllis Lee, Fiona Millar, Matt Tinsley and Andrew Tyler. They are in no way responsible for the swearing or GIFs.

Now tell me that wasn’t fun?



Having some actual 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.

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…



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.

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


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

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.

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.