Interview on the Career 100 podcast

I was interviewed recently for an episode of the Career 100 podcast, part of the College Funding Resource. The podcast is aimed at people trying to decide on a college course, and introduces them to various different careers. I was given the task of covering ‘biologist’, which – as you are probably aware – is a bit of a catch-all term that is difficult to negotiate in a half-hour interview! Hopefully I managed to make clear the breadth of different careers within the domain of biology, while making it clear I can only really advocate for the evolutionary sphere (and I feel like I’m only occupying a very tiny niche within that anyway!).

Anyway – it was a very fun interview to do, and if you want to hear me rambling at greater length than I get to do on Breaking Bio (although let’s face it, that is definitely for the best), you can find it here.

Plus I even got my own fancy little podcast cover art!

 

 

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My real science (Day 1)

In my last post, I gave a quick overview of my week curating the @realscientists twitter feed, but focused – unsurprisingly – on the delights of the ‘humpoff’ and its ensuring press coverage! As a new scientist takes the reins of that account each week, condemning the previous tweets to the depths of the internet, I thought I would collect my science-based tweets into a post here. Mainly to show how my week was basically an exercise in drawing diagonal lines. I’ll begin here with my tweets from the first day of my week in charge, lightly edited to add a mild bit of coherence:

Let’s start with a little background… obviously, no week on evolution would be complete without some Darwin quotes (taken from ‘Darwin and Genetics‘, an open-access paper by Brian Charlesworth & Deborah Charlesworth):

“The power of Selection, whether exercised by man or brought into play under nature through the struggle for existence and the consequent survival of the fittest, absolutely depends on the variability of organic beings. Without variability, nothing can be effected; slight individual differences, however, suffice for the work, and are probably the chief or sole means in the production of new species.” (DARWIN, 1868)

CHARLES Darwin was the first person to appreciate clearly that evolution depends on the existence of heritable variability within a species to generate the differences between ancestral and descendant populations. The development of Darwin’s thoughts on the nature and causes of evolution is clearly documented in his “transmutation” notebooks of 1836–1838 (BARRETT et al. 1987). Once he had decided that species originated by “descent with modification,” Darwin quickly realized the need to find a mechanism for accomplishing the changes involved. In formulating the idea of natural selection, he was greatly influenced by the experience of breeders in artificially selecting populations of domestic animals and plants. Chapter 1 of The Origin of Species (DARWIN 1859) is famously devoted to documenting the existence of variability in these populations and the effectiveness of artificial selection:

The key is man’s power of cumulative selection: nature gives successive variations; man adds them up in certain directions useful to himself” (DARWIN 1859, p. 30).

It was only a short step to applying this observation to selection in nature:

Can it, then, be thought improbable, seeing that variations useful to man have undoubtedly occurred, that other variations useful in some way to each being in the great and complex battle of life, should sometimes occur in the course of thousands of generations? … This preservation of favourable variations and the rejection of injurious variations, I call Natural Selection (DARWIN 1859, pp. 81–82).

In essence, Darwin identified three conditions that are necessary for evolution via natural selection. First off: a population must exhibit phenotypic variation. ‘Phenotype’ is a fancy word for the set of observable characteristics of an individual. An individual’s phenotype can be anything we measure: body size, shape, development time, lifespan… (And, yes, their behaviour too! But let’s leave that for a little bit later). Have a look at people around you: you (hopefully) all have the same number of fingers and toes, but what about something like, say, height? If you measured the heights of a population (and grouped number of ppl by height), your graph might look like this:

cont_dist

So: within a given population, individuals must be different from each other. What next?

Darwin realised that (ii) these variations must be associated with differences in survival and reproduction. (Hmm… as a very short man, I now regret using ‘height’ as the example trait in my previous tweet… ). Anyway! Individuals must be different from each other, and these differences create variation in how they survive and reproduce…

(iii) Variation in traits must be heritable: parents and offspring must resemble each other in these traits.

Famously, Darwin was unaware of Mendel’s work, meaning that he did not know what the mechanism of inheritance was. On Wed, I’ll talk a little abt quantitative genetics, which helps us understand the inheritance of traits that vary in a continuous fashion (NOTE: I don’t think I did). For now, let’s consider what contributes to phenotypic variation: obviously genes, but what else?

An individual’s environment can strongly affect its phenotype. Recently, the UK govt has come under fire for considering dropping free school meals – health professionals and academic studies claim that diet quality affects academic performance: environment changes the phenotype! Broadly speaking, that would represent a change in the population mean due to environmental effects: i.e., as diet quality (envt value) increases, so academic performance increases.

trait mean

But we also know that different genotypes vary in trait value:

trait mean2

…and genotypes can differ in how they respond to environmental change: known as genotype-by-environment interactions:

trait mean3

So: heritable phenotypic variation is needed for evolution by natural selection, and phenotypic variation can be affected by G, E, GxE! Excitingly, environmental effects can also change the extent of the variation available for selection to act on:

trait mean4

These lines represent what we call ‘reaction norms’: a reaction norm is a function that describes the change in a genotype’s phenotype over a range of environments. The ability of a genotype to express different phenotypes as environmental conditions change is known as ‘phenotypic plasticity’. Plasticity enables an organism to ‘fit’ its phenotype to the changing environment. Let’s look at a couple of examples…

The desert locust Schistocerca gregaria has a solitary form, in which individuals are well camouflaged and avoid others. However, in their gregarious form they are more active, brightly coloured and form vast migrating swarms.

locust1

This change is elicited by repeated physical contact, e.g. through touching or jostling, producing the plastic response. Rogers et al tickled locust legs repeatedly w/ paintbrushes, causing full behavioural ‘gregarisation’ within hours!

Screen Shot 2015-10-11 at 21.51.07Screen Shot 2015-10-11 at 21.51.22

You can watch a rather melodramatic video about this experiment:

youtube.com/watch?v=uURqcI

It’s since been found that the mechanism is serotonin-related – read more about this here.

locust2

This is cool, but I’m more interested in plasticity in ‘labile’ traits: those that are expressed repeatedly across an individual’s lifetime. For example: song! Plenty of animals sing to mark out territories or attract mates, and this is a trait that is expressed repeatedly. There has been a lot of interest in how animals can respond / have responded to the increase in background noise caused by human activity (@c_n_anderson then sent me two reviews on this topic: Patricelli & Blickley 2006Barber et al 2010). In one experiment, Verzijden et al recorded territorial male chiffchaffs singing along quiet riverbeds and near busy highways. They then raised the background noise at riverbed territories to highway levels, and recorded those birds again… and, finally, recorded the same birds again at normal riverbed background noise (having previously increased it). Here, you can see the plastic response of the riverside birds in minimum song frequency as background is manipulated:

Screen Shot 2015-10-11 at 22.03.12

So, why aren’t all animals all plastic all the time? There are costs and limits to plasticity; one such cost is ‘environmental mismatching’. What if there’s insufficient environmental information, or the wrong environmental information is used? The phenotype is then not the ‘right’ one for the environment. This is a particular problem for ‘developmental plasticity’, where individuals use information about their environment to ‘predict’ the optimal phenotype. There’s a nice paper by on ‘socially cued anticipatory plasticity’: Kasumovic & Brooks 2011. So, that might be bad when the trait is fixed at some point, but what abt individuals that can change the trait in a flexible fashion?

Firstly, let’s keep in mind that assessing the environment and changing trait values is likely to be costly (in time and resources – we’ll talk more later about the importance of resource costs). But also, plasticity itself – the ability to change – may be under selection! Not just the intercept, but the slope of the reaction norm! Plasticity can be adaptive (producing a phenotype in the same direction as the optimal value in the new environment), but can also be maladaptive. Only if the plasticity itself can be shown to have been moulded during evolutionary history to be more effective than a canalised (fixed) phenotype can we consider it to be adaptive. We’ll talk a little more about this tomorrow, but here’s a taster: Earlier, we considered a bird that changes its song frequency depending on background noise. What if only some individuals did that, while others maintained a steady frequency, no matter what the background noise. In some environments, plasticity is undoubtedly better. BUT… what if, in general, this actually gave no survival / reproduction advantage over time? In that case, while the ability to modulate frequency might appear to be adaptive, there’s no evidence that this is the case! MIND. BLOWN.

Oh, and also let’s remember selection acts on ‘extended phenotypes’. Fig from Bailey (2012):

ext_phen

(…but feel free to dust off your copy of excellent book as well!)

This resulted in a brief digression about studying plasticity in extended phenotypes using spider webs, and to social behaviour among spiders:

Michelle LaRue then tweeted me her recent paper, which posited that philopatry in king penguins was life history plasticity – perhaps an important way for animals to deal with climate change. This hints at some reasons as to *why* we are interested in plasticity: it lends to the idea of populations moving to novel environments, or coping with changes in environmental conditions.

And, with the sounding of a CLIMATE CHANGE KLAXON, my first day at @realscientists came to an end!

Outreach: starts with Real Science, ends in a #HumpOff

I am coming to the end of my week-long tenure at @RealScientists – a rotational twitter account that brings a different scientist to its readers every week. This week, I started with the best of intentions – tweeting about genotype-by-environment interactions, phenotypic plasticity, and studying ‘animal personality’…

…but things took a turn when I decided to follow Anne Hilborn‘s previous competitive hashtags and start a #HumpOff!

It soon took hold, with entries flooding in from across the world, and covering all manner of amazing humping-related facts! The ‘competition’ even made it into the media, with coverage from the Washington Post, New York Post, io9’s Gizmodo blog, Slate’s French site, what appears to be a Croatian tabloid, Serbian National Geographic, the French newspaper 20 Minutes, and the Independent! I was interviewed for the NY Post via email, but I had to come back from the pub to write my replies – none of which made it into the article. Let’s say it was because they were delayed, not that I’d had a really strong beer and started rambling a bit…! Anyway, I thought I should post my responses, in case anyone is interested in the purpose* behind the #HumpOff.

*may include post-hoc reasoning

How did the idea for HumpOff come about? Is it recurring or was this the first time?

I really enjoyed the ‘JunkOff’ hashtags that Anne Hilborn started recently, getting scientists (and non-scientists) to post photos of animal genitalia (perhaps I should revisit my use of ‘enjoyed’ there…!). My PhD was in the field of sexual selection and life history: I used crickets to investigate how males invest their energy in trying to attract females across different ages, and how this impacts investment at other ages, in lifespan, etc. The side effect of this is that I’ve spent a not-inconsiderable chunk of my adult life thinking and reading about animal sex! I know there is so much awesome and weird sex stuff out there in the animal kingdom – way beyond just genitalia(!) – and I wanted other people to know about it.

Is there a deeper goal? Like awareness or just for fun? 

It’s been a LOT of fun, and the main idea was just to make people aware of just how much crazy diversity there is in animal mating systems – not just between but also within species! There are weapons that evolved through male competition for mates, ornaments from female preferences, alternative strategies (big males that fight rivals for access to females, while smaller males ‘sneak’ matings behind their backs), sexual conflict – where males and females have conflicting interests, which can create ‘arms races’ in evolving adaptations (e.g. in water striders) – and even sex role reversal! For example, in some dance flies, which are studied by my PhD supervisor and his supervisor before him, the females can have ornaments that make them appear bigger in the mating swarms. These females don’t hunt, but instead rely on ‘nuptial gifts’ of prey, offered to them by males during courtship. Males prefer to mate with females that look big and bursting with eggs, so females with ornaments (that make them look bigger) will get more matings, hence more food – this is how we think the ornaments evolved:

Part of my job as an evolutionary biologist is to think about why there is so much diversity (both within and between species). What are the different pressures that select for adaptations? Why is there so much variation within populations, especially in sexually-selected traits? I want people to be aware of the amazing diversity in the animal world, even in the tiniest, weirdest creatures, and think about why these adaptations and systems have evolved.

Do you have a favorite tweet so far? 

So many favourites! Here are a few:

I broke the dreams of someone who posted a nice picture of mating damselflies that create a heart shape:

Male black widows use ‘mating plugs’ to stop subsequent males from inseminating the same female:

Female haglids eat the fleshy hindwings of males during mating – but that gives the males just enough time to attach their spiny genital sex traps, after which they can push the female off their wings but continue the mating!

I don’t even know what is happening here:

But I do love that Nate Morehouse provided a photo of the butterfly ‘vagina dentata’!

Will a winner be announced since its a “competition”? 

Haha, maybe! The anglerfish got a lot of traction, possibly because of the oatmeal comic, and the antechinus also has a lot of fans – which is fair enough, as it’s a tiny, cute mammal that sexes itself to death. However, I think I’ll use my own biases to pick an insect winner. Invertebrates have to get a win sometime, it may as well be for the #HumpOff!

Level up: professional photography status achieved!

IMG_8615

Thanks to the fine work of Cambridge’s Prof. Rebecca Kilner and her colleagues, in addition to her giving me access to her lab next year to photograph her beetles, today I have a photograph appearing in The Economist! The Kilner group have a new paper in the journal eLife that demonstrates how different levels of parental care have strong effects on offspring once they themselves have reached adulthood. I made the photograph above available via Creative Commons Attribution Licence so that it could be used in eLife, but The Economist wanted to use a different one, which they paid a licensing fee for (see below):

Screen Shot 2015-09-26 at 11.20.51

Check out their story here.

Coverage of the paper, along with my photos, is taking off – see IFLS, phys.org, the Naked Scientists (includes podcast interview with Becky), Cambridge University‘s general coverage (with links to Radio 4 interview with Becky)…

I had a lot of fun trying to photograph the behaviour of these beetles, so here are some more pics!

Understanding 3-way interactions between continuous and categorical variables, part ii: 2 cons, 1 cat

I posted recently (well… not that recently, now that I remember that time is linear) about how to visualise 3-way interactions between continuous and categorical variables (using 1 continuous and 2 categorical variables), which was a follow-up to my extraordinarily successful post on 3-way interactions between 3 continuous variables (by ‘extraordinarily successful’, I mean some people read it on purpose and not because they were misdirected by poorly-thought google search terms, which is what happens with the majority of my graphic insect-sex posts). I used ‘small multiples‘, and also predicting model fits when holding particular variables at distinct values.

ANYWAY…

I just had a comment on the recent post, and it got me thinking about combining these approaches:

Screen Shot 2015-06-02 at 17.32.18

There are a number of approaches we can use here, so I’ll run through a couple of examples. First, however, we need to make up some fake data! I don’t know anything about bone / muscle stuff (let’s not delve too far into what a PhD in biology really means), so I’ve taken the liberty of just making up some crap that I thought might vaguely make sense. You can see here that I’ve also pretended we have a weirdly complete and non-overlapping set of data, with one observation of bone for every combination of muscle (continuous predictor), age (continuous covariate), and group (categorical covariate). Note that the libraries you’ll need for this script include {dplyr}, {broom}, and {ggplot2}.

#### Create fake data ####
 
bone_dat <- data.frame(expand.grid(muscle = seq(50,99),
                                   age = seq(18, 65),
                                   groupA = c(0, 1)))
 
## Set up our coefficients to make the fake bone data
coef_int <- 250
coef_muscle <- 4.5
coef_age <- -1.3
coef_groupA <- -150
coef_muscle_age <- -0.07
coef_groupA_age <- -0.05
coef_groupA_muscle <- 0.3
coef_groupA_age_muscle <- 0.093
 
bone_dat <- bone_dat %>% 
  mutate(bone = coef_int +
  (muscle * coef_muscle) +
  (age * coef_age) +
  (groupA * coef_groupA) +
  (muscle * age * coef_muscle_age) +
  (groupA * age * coef_groupA_age) +
  (groupA * muscle * coef_groupA_muscle) +
  (groupA * muscle * age * coef_groupA_age_muscle))
 
ggplot(bone_dat,
       aes(x = bone)) +
  geom_histogram(color = 'black',
                 fill = 'white') +
  theme_classic() +
  facet_grid(. ~ groupA)
 
## Add some random noise
noise <- rnorm(nrow(bone_dat), 0, 20)
bone_dat$bone <- bone_dat$bone + noise
 
#### Analyse ####
 
mod_bone <- lm(bone ~ muscle * age * groupA,
               data = bone_dat)
 
plot(mod_bone)

summary(mod_bone)

While I’ve added some noise to the fake data, it should be no surprise that our analysis shows some extremely strong effects of interactions… (!)


Call:
lm(formula = bone ~ muscle * age * groupA, data = bone_dat)

Residuals:
Min 1Q Median 3Q Max
-71.824 -13.632 0.114 13.760 70.821

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.382e+02 6.730e+00 35.402 < 2e-16 ***
muscle 4.636e+00 8.868e-02 52.272 < 2e-16 ***
age -9.350e-01 1.538e-01 -6.079 1.31e-09 ***
groupA -1.417e+02 9.517e+00 -14.888 < 2e-16 ***
muscle:age -7.444e-02 2.027e-03 -36.722 < 2e-16 ***
muscle:groupA 2.213e-01 1.254e-01 1.765 0.0777 .
age:groupA -3.594e-01 2.175e-01 -1.652 0.0985 .
muscle:age:groupA 9.632e-02 2.867e-03 33.599 < 2e-16 ***

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 19.85 on 4792 degrees of freedom
Multiple R-squared: 0.9728, Adjusted R-squared: 0.9728
F-statistic: 2.451e+04 on 7 and 4792 DF, p-value: < 2.2e-16

(EDIT: Note that this post is only on how to visualise the results of your analysis; it is based on the assumption that you have done the initial data exploration and analysis steps yourself already, and are satisfied that you have the correct final model… I may write a post on this at a later date, but for now I’d recommend Zuur et al’s 2010 paper, ‘A protocol for data exploration to avoid common statistical problems‘. Or you should come on the stats course that Luc Bussière and I run).

Checking the residuals etc indicates (to nobody’s surprise) that everything is looking pretty glorious from our analysis. But how to actually interpret these interactions?

We shall definitely have to use small multiples, because otherwise we shall quickly become overwhelmed. One method is to use a ‘heatmap’ style approach; this lets us plot in the style of a 3D surface, where our predictors / covariates are on the axes, and different colour regions within parameter space represent higher or lower values. If this sounds like gibberish, it’s really quite simple to get when you see the plot:

heatmap

Here, higher values of bone are in lighter shades of blue, while lower values of bone are in darker shades. Moving horizontally, vertically or diagonally through combinations of muscle and age show you how bone changes; moreover, you can see how the relationships are different in different groups (i.e., the distinct facets).

To make this plot, I used one of my favourite new packages, ‘{broom}‘, in conjunction with the ever-glorious {ggplot2}. The code is amazingly simple, using broom’s ‘augment’ function to get predicted values from our linear regression model:

mod_bone %>% augment() %>% 
  ggplot(., aes(x = muscle,
                y = age,
                fill = .fitted)) +
  geom_tile() +
  facet_grid(. ~ groupA) +
  theme_classic()

But note that one aspect of broom is that augment just adds predicted values (and other cool stuff, like standard errors around the prediction) to your original data frame. That means that if you didn’t have such a complete data set, you would be missing predicted values because you didn’t have those original combinations of variables in your data frame. For example, if we sample 50% of the fake data, modelled it in the same way and plotted it, we would get this:

heatmap_samp

Not quite so pretty. There are ways around this (e.g. using ‘predict’ to fill all the gaps), but let’s move onto some different ways of visualising the data – not least because I still feel like it’s a little hard to get a handle on what’s really going on with these interactions.

A trick that we’ve seen before for looking at interactions between continuous variables is to look at only high/low values of one, across the whole range of another: in this case, we would show how bone changes with muscle in younger and older people separately. We could then use small multiples to view these relationships in distinct panels for each group (ethnic groups, in the example provided by the commenter above).

Here, I create a fake data set to use for predictions, where I have the full range of muscle (50:99), the full range of groups (0,1), and then age is at 1 standard deviation above or below the mean. The ‘expand.grid’ function simply creates every combination of these values for us! I use ‘predict’ to create predicted values from our linear model, and then add an additional variable to tell us whether the row is for a ‘young’ or ‘old’ person (this is really just for the sake of the legend):

#### Plot high/low values of age covariate ####
 
bone_pred <- data.frame(expand.grid(muscle = seq(50, 99),
                      age = c(mean(bone_dat$age) +
                                sd(bone_dat$age),
                              mean(bone_dat$age) -
                                sd(bone_dat$age)),
                      groupA = c(0, 1)))
 
bone_pred <- cbind(bone_pred,
                   predict(mod_bone,
                     newdata = bone_pred,
                     interval = "confidence"))
 
bone_pred <- bone_pred %>% 
  mutate(ageGroup = ifelse(age > mean(bone_dat$age), "Old", "Young"))
 
 
ggplot(bone_pred, 
       aes(x = muscle,
           y = fit)) +
  geom_line(aes(colour = ageGroup)) +
#   geom_point(data = bone_dat,
#              aes(x = muscle,
#                  y = bone)) +
  facet_grid(. ~ groupA) +
  theme_classic()

This gives us the following figure:

interaction_1

Here, we can quite clearly see how the relationship between muscle and bone depends on age, but that this dependency is different across groups. Cool! This is, of course, likely to be more extreme than you would find in your real data, but let’s not worry about subtlety here…

You’ll also note that I’ve commented out some lines in the specification of the plot. These show you how you would plot your raw data points onto this figure if you wanted to, but it doesn’t make a whole lot of sense here (as it would include all ages), and also our fake data set is so dense that it just obscures meaning. Good to have in your back pocket though!

Finally, what if we were more concerned with comparing the bone:muscle relationship of different groups against each other, and doing this at distinct ages? We could just switch things around, with each group a line on a single panel, with separate panels for ages. Just to make it interesting, let’s have three age groups this time: young (mean – 1SD), average (mean), old (mean + 1SD):

#### Groups on a single plot, with facets for different age values ####
 
avAge <- round(mean(bone_dat$age))
sdAge <- round(sd(bone_dat$age))
youngAge <- avAge - sdAge
oldAge <- avAge + sdAge
 
bone_pred2 <- data.frame(expand.grid(muscle = seq(50, 99),
                                      age = c(youngAge,
                                              avAge,
                                              oldAge),
                                      groupA = c(0, 1)))
 
bone_pred2 <- cbind(bone_pred2,
                   predict(mod_bone,
                           newdata = bone_pred2,
                           interval = "confidence"))
 
ggplot(bone_pred2, 
       aes(x = muscle,
           y = fit,
           colour = factor(groupA))) +
  geom_line() +
  facet_grid(. ~ age) +
  theme_classic()

Created by Pretty R at inside-R.org

The code above gives us:

interaction_2

Interestingly, I think this gives us the most insightful version yet. Bone increases with muscle, and does so at a higher rate for those in group A (i.e., group A == 1). The positive relationship between bone and muscle diminishes at higher ages, but this is only really evident in non-A individuals.

Taking a look at our table of coefficients again, this makes sense:


Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.382e+02 6.730e+00 35.402 < 2e-16 ***
muscle 4.636e+00 8.868e-02 52.272 < 2e-16 ***
age -9.350e-01 1.538e-01 -6.079 1.31e-09 ***
groupA -1.417e+02 9.517e+00 -14.888 < 2e-16 ***
muscle:age -7.444e-02 2.027e-03 -36.722 < 2e-16 ***
muscle:groupA 2.213e-01 1.254e-01 1.765 0.0777 .
age:groupA -3.594e-01 2.175e-01 -1.652 0.0985 .
muscle:age:groupA 9.632e-02 2.867e-03 33.599 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

 

There is a positive interaction between group A x muscle x bone, which – in group A individuals – overrides the negative muscle x age interaction. The main effect of muscle is to increase bone mass (positive slope), while the main effect of age is to decrease it (in this particular visualisation, you can see this because there is essentially an age-related intercept that decreases along the panels).

These are just a few of the potential solutions, but I hope they also serve to indicate how taking the time to explore options can really help you figure out what’s going on in your analysis. Of course, you shouldn’t really believe these patterns if you can’t see them in your data in the first place though!

Unfortunately, I can’t help our poor reader with her decision to use Stata, but these things happen…

Note: if you like this sort of thing, why not sign up for the ‘Advancing in statistical modelling using R‘ workshop that I teach with Luc Bussière? Not only will you learn lots of cool stuff about regression (from straightforward linear models up to GLMMs), you’ll also learn tricks for manipulating and tidying data, plotting, and visualising your model fits! Also, it’s held on the bonny banks of Loch Lomond. It is delightful.

—-

Want to know more about understanding and visualising interactions in multiple linear regression? Check out my previous posts:

Understanding three-way interactions between continuous variables

Using small multiples to visualise three-way interactions between 1 continuous and 2 categorical variables

New paper, new job, stuff like that

Things have been pretty slow on the updating side (yet again), but I have been busy with THINGS and also STUFF!

I have just had my first research paper (snappily titled ‘Sex differences in the effects of juvenile and adult diet on age-dependent reproductive effort‘) from my PhD published, in the Journal of Evolutionary Biology. Happily, the review process was really great – the editor (Thomas Flatt) was really helpful, and we had great, constructive reviews from Sue Bertram and Mike Kasumovic (who did sign their reviews, I’m not just outing them!).

 

Screen Shot 2015-05-18 at 17.32.04

 

In the paper, we manipulated resource acquisition at both the juvenile and adult stage in male and female crickets, and tracked allocation to age-dependent reproductive effort. Crickets are great for this kind of work, as reproductive effort is easy to quantify for both males and females (not the case in a great many organisms): fecundity (i.e., egg production) in females, and sexual signalling (time spent calling) in males. We investigated how resource acquisition affects allocation to reproductive effort over time, and also how this affects investment in longevity… Not only did we find some interesting results, but I also got to showcase the use of Zero-Altered Poisson (ZAP) models for male signalling! This is a really useful type of statistical analysis, as we can look at two factors within a single model:

  • What factors affect whether a male calls or not (binary ‘0/1’ response)?
  • Given that a male does call (i.e., a ‘1’ in the first part of the model), what factors affect how much he calls?

Screen Shot 2015-05-18 at 17.59.52

Screen Shot 2015-05-18 at 17.59.42

 

 

 

 

 

 

 

I’m currently working on a manuscript which will delve a little deeper into questions of male signalling, using ZAP models but also a pretty cool experimental design that I think has given us some really interesting results (let’s hope the reviewers agree!). I’ll also be talking about this work at the European Society of Evolutionary Biology (ESEB) conference in Lausanne this August (so hopefully I can get it submitted for publication soon!).

The other big news is that I have finally secured a new research position! I have joined Alastair Wilson’s group at the University of Exeter’s Penryn campus, and my postdoc will focus on the evolution of stress response. This will entail lots of behavioural work and measuring hormones, as well as some pretty intense stats and quantitative genetics! Also, I’m having to learn about vertebrates, as the study system will be guppies… but don’t worry, I’ll still be tweeting / going on about weird insect sex as much as possible. Everyone’s got to have a hobby.

Other stuff: I’ve had a couple of photos published in scientific journals, which happen to also be really cool papers so worth reading (see links below)! I am also working on a setup to get some good guppy photos ready for my own future talks / papers.

Burying beetle in Schrader et al’s work on using experimental evolution to study adaptations for family life (American Naturalist).

Wasp in Rojas et al’s primer on aposematism (Current Biology).

My 2014, in pictures (and also words)

2014 is drawing to a close; it’s been a weird year, and I didn’t realise until now quite how much I’ve neglected writing posts on here. I have been spending more time on photography, although you will have to indulge the first section of this post being filled with photos taken by others…

LEVEL UP: PhD achieved!

I have a good excuse for no blog posts during the first couple of months of 2014 at least, as my PhD thesis was due for submission at the end of February. My state of mind is probably evident in that the only photographs I took during this period were of my pet mantis eating some of my study species (the decorated cricket, Gryllodes sigillatus).

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Even more telling are the photos of me handing in my thesis.

Yes. That’s the face of a man who has barely slept for many days, getting blasted by a party popper. Thankfully I was looking a little better by April, when I defended my thesis (‘Causes of adaptive differences in age-dependent reproductive effort’) in my viva; Dr. Andre Gilburn (University of Stirling) and Dr. Alexei Maklakov (Uppsala University) were the examiners, and we had a really interesting and fun discussion! As several people have said, you should make the most of talking to the only people who will ever read your thesis…

Of course, I looked even better once I had donned my viva hat; this was devised and created by next-in-line at the Bussiere lab, Ros Murray. Lilly has a lot to live up to when it comes to making Ros’ hat!

I should take a quick moment here to thank not only Luc, a fantastic supervisor (and all-round awesome guy), but also Ros and Lilly for general labmate amazingness, the rest of the Bussière lab (particularly Claudia, Toby, Svenja and Rheanne), Matt Tinsley, Stu Auld, Pauline Monteith and Jim Weir. Also the crickets. Sorry you’re all dead! The crickets, I mean. All of the humans are still alive. I think. And if they’re not, I definitely had nothing to do with it.

 

Goodbye to Scotland

Kirsty also finished her postdoc at around the same time as my PhD ended, but we managed a few trips to see some wildlife before leaving our beloved tiny Stirling flat – including iconic golden eagles in Findhorn Valley:

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Which mainly involved doing this for ages:

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But it was awesome. Also awesome: OTHER STUFF IN SCOTLAND. The following photos were taken at the Argaty red kite station near Doune; Carron Glen nature reserve in Denny; Loch Garten; and Baron’s Haugh nature reserve.

 

Cambridge & macro

We moved to Cambridge in May, and I have been amazed at the increase in invertebrate diversity compared to central Scotland… also, we have a pretty great garden, and nice fields and nature reserves nearby to go wandering around! I have been using these opportunities to practise macrophotography, using my Canon 100mm macro, the Canon MP-e 65, and the Sigma 15mm lens for some wide-angle macro…

I have also been photographing burying beetles for Prof. Becky Kilner’s group at the University of Cambridge, and one of these photos was given a commendation in the Royal Entomological Society’s National Insect Week photography competition:

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Another macro photo was used as the cover photo for the new album by Fresh Eyes for the Dead Guy:

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Non-macro

I have also tried to branch out into some non-macro photography, particularly playing around with long exposures, flash, and some black-and-white work. The first photo here was taken on the banks of Loch Lomond in December, by the SCENE field station where I was teaching a workshop on statistics and R alongside Luc (another one to be held in April, and places are already running out!).

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Breaking Bio podcast

The podcast has had something of an up-and-down year, as we’ve all been crazily busy and it’s hard to pin everyone (plus guest) down for a timeslot weekly. However, we’ve still had some great episodes; here are some of my favourites:

The inimitable, execrable, unrepentant badass that is Katie Hinde. Did you know that studying the evolution of lactation was a thing? IT IS.

Marlene Zuk is a bit of a science hero. Strike that: she’s a LOT of a science hero. We talk rapid evolution and crickets, at least while I can stammer out some words (I was late and SCIENCESTRUCK).

We also got involved with #SAFE13, a really important movement that is well represented by the fantastic work of Kate Clancy, Katie Hinde, Robin Nelson, and Julienne Rutherford. Not quite as light-hearted as the others above, but certainly thought-provoking and well worth half an hour of your time.

 

To 2015!

Next year, I’ll be trying to write some fun posts on research that excites me, and hopefully illustrated with more photos! I am trying to concentrate on getting interesting shots, either due to animal behaviour or better composition. I would appreciate any comments on photos here or on my 500px / flickr pages; any comments or suggestions about blog post entries are always welcome too! Hopefully the BreakingBio podcast will start strongly again in 2015 too, as we have some great guests lined up to join us.

Also, I need a proper job. I’m trying to push through publications right now, so hopefully I’ll have some paper summaries of my own to come…