After a prolonged period of failure to update, I am trying to get back “au courant” with online activity. Nils Bunnefeld encouraged me to start Tweeting prior to participating in a field course in Africa, and I have now embedded the Twitter feed into the web page. Hopefully some of the material will now be more current than my recent record?
PhD studentship: how life history affects demographic and phenological consequences of environmental change
As part of the IAPETUS NERC doctoral training programme, I am happy to announce that along with colleagues Philip Stephens and Mario Vallejo-Marin, we are offering a competitive studentship in this year’s competition. Please go to the updated Opportunities page for more details and a link to the full studentship description.
Science cakes – catch-up post
I have been seriously negligent in keeping the website up to date, thanks in large part to a ridiculously busy semester of teaching. But Adam Hayward has shamed me into catching up on our science discussions by promptly writing up a summary of the last meeting (which I missed). So before I publish his summary, we have some catching up to do. The notes below are brief but hopefully give some of the flavour of the sessions this autumn, as well as some potentially useful links to the original science that we have discussed.
Let’s start with the first session of last semester. Expect me to fill in the subsequent sessions in the next few days as I sit with a warm drink and watch the snow fall from the in-laws home in Norway…
Oct 9, 2014
Attendees: Andy, Clare, Doctor Paine, Lilly, Luc, Matt, Robbie, Rosalind
Robbie began proceedings by introducing himself and presenting a stats problem related to a recent Nature paper by Wilson et al., 2014:
The authors evaluated the relative importance of human impacts as opposed to aspects of the chimp populations (such as the density of the population and the number of male chimps) when predicting the incidence of conspecific killings. They concluded that the best models did not include any predictor related to human impact, whereas the population parameters provided better models.
The authors then used model averaging to report the coefficients, and Robbie’s question concerned the suitability of model averaging for this kind of analysis. This stemmed in part from a comment he had received during a recent statistics workshop in which the instructor recommended averaging predictions rather than coefficients.
We discussed this for some time, and although there was broad agreement about the contours, there were still some differences of opinion at the end of our discussion. It seems to me that the question is partly about emphasis (are you more interested in hypothesis testing or in prediction?) and partly about the causes of an inability to distinguish between models (is there collinearity between predictors, and if so, what is the causal relationship between these predictors?). Andy pointed out that for most data sets, the difference in approach is probably trivial, and I agree. In fact, if the difference is not trivial, one might question why this is true and wish to think about the covariance structure of your dataset more carefully before using model averaging in the first place…
Andy provided the next contribution, stimulated by a paper that was given to him by his undergraduate tutees, by Amici et al., 2014.
The experiment in question tested for prosocial behaviour (the tendency to behave in a way that benefits another) in some monkeys and apes. For example, the subjects were given the choice of pulling a lever that gave food only to the lever puller, or to both the lever puller and his neighbor. The authors failed to find any evidence for prosociality (the subjects seemed indifferent to what was happening to neighbours so long as they were getting food themselves), having controlled for some potentially confounding variables that may have been the cause of inconsistent positive findings in earlier studies of the same phenomenon.
We spent some time wrestling with the implications of these negative results. Matt noted that testing these kinds of phenomena empirically is difficult, while Robbie wondered if the expectation of prosociality is prefaced on social structure which may or may not have been absent in the current study. Rosalind asked if a different version of the experiment involving related individuals would be more conclusive?
My own comment concerned whether the entire question is relevant – is there ever a free lunch in nature (i.e., is there ever a situation where what an animal does can help another at no cost at all to itself)?
Andy mentioned the court case involving the non-human personhood of an ape Does our provision of “human” rights to other apes depend on our assessment of their cognitive and altruistic tendencies? If our Science Cake session has just shown that chimps are murderous even without our intervention, and that apes and monkeys don’t give a damn about whether their neighbours get a biscuit, should that make us more or less inclined to attribute rights to other primates?
(Ed: I note that the court case has just recently been resolved as I write this, and the court did not consider the imbalance of evidence for chimpicide versus prosociality:
– does that excuse the long delay in transcribing Science Cake notes?)
We closed the first session of Science Cake with a discussion of some dance fly data that Rosalind presented involving associations between characteristics of male and female mating partners. This was mostly motivated by her lousy supervisor (me), who received her initial pitch about how to sell the work skeptically. We batted around some suggestions for a while, and while there was no firm resolution, I think Rosalind came away with some good ideas and left a promise that the data would resurface later for another discussion once she has a better sense of the sales pitch. Perhaps the winning bit of motivating was performed by Matt, and echoed by Tim (paraphrased here because my notes are imperfect and this was so long ago): “It’s not so much a question of what you do, but what you say, in other words, make sure that your analysis addresses a story that you can tell convincingly.”
To be continued….
This week Biology Letters published a short opinion piece by Sara Lewis and colleagues (including me) on the state of play for research on nuptial gifts in animals. Substances transferred from one mating partner to another are not limited to gametes, and this piece tries to clarify the conceptual significance of this behaviour and stimulate productive future research by carefully defining what a nuptial gift is and clarifying different categories of gifts on the basis of their source and transmission mode. The piece is described for a more popular audience here.
One of the best and worst parts of supervision is the mixed feeling of fledging a crop of graduates. This is definitely a happy occasion, and one worth marking. It represents the culmination of a lot of hard and industrious work. And while the collaborations will continue (we need to publish most of this work!), it’s still bittersweet to have so many great folks leave the lab. Best of luck to this year’s graduates! Please stay in touch regularly….
(NB: In spite of the authorship flag above, the section below is Claudia’s work, not mine! I am posting it on her behalf because she’s off somewhere sunny scuba diving, I think…. Luc)
I am synthesizing one last paper to conclude our “Journal Pub” session – I apologise it took so long! I read Karoline Fritzsche and Göran Arnqvist‘s paper “Homage to Bateman: sex roles predict sex differences in sexual selection”, published in Evolution in 2013.
In this paper the authors review the classic sex-role theory, which assumes sexual selection to be stronger in males in taxa with conventional sex roles (systems where the females are the choosy sex and contests for mates are mainly between males) and in females in systems with reversed sex roles. Little empirical work has been done to assess the relative strength of sexual selection on males and females, and no previous study has been able to provide measures in order to compare the strength of this process between sexes and between different taxa. For example, research is often limited to comparing sexual dimorphic traits between the two sexes of a species to evaluate the asymmetry of selection between males and females. The challenges of this type of study are reinforced by the debate regarding what type of measure should be used to quantify the strength of sexual selection (Fitze and Le Galliard 2011). To address these knowledge and evidence gaps, Fritzsche and Arnqvist aim to (i) clarify whether there is a single measure that is best for quantifying sexual selection, (ii) determine if it is best to base measures of sexual selection on phenotypic traits or variance across individuals, and finally (iii) assess how important is it to quantify the strength of sexual selection in both males and females.
The authors discuss these issues by presenting an experiment in which they observed the mating behaviour and reproductive success of both sexes of four seed beetle species. They used four related species of beetles: two of the Callosobruchus genus, females of which are the choosy sex, and two of the Megabruchidius genus, where females often compete against each other for mates. Fritzsche and Arnqvist then attempted to compare the relative strength of sexual selection between the two sexes, and test the validity of various measures such as variance-based measures – measures unrelated to differences between morphological traits, like Bateman gradients (which they also refer to as sexual selection gradients: these are estimates of the slope of a regression line of reproductive success on mating success, and therefore describe how much fitness each individual gains per successful mating) and the opportunity for sexual selection (variance in reproductive success). Other measures analysed by the authors were trait-based measures like the selection differential (covariance between a standardized trait and fitness), the mating differential (covariance between a standardized trait and mating success), and the residual selection differential (covariance between a standardized trait and the residual reproductive success calculated from the Bateman gradient).
The authors used body size as the trait against which to compute selection. They made this choice because body size tends to covary positively with mating success, and also because it is a comprehensive measure that reflects both phenotypic and genetic variation. Furthermore, the conspicuous sexual dimorphism in the seed beetles strongly suggests a history of sexual selection on this trait. In their mating trials, they presented five females to five males, four of which were sterile. Therefore, each mating assay yielded data on mating and reproductive success for all the females and only one male. The authors quantified mating success by observing how many times each individual was successful at mating, female reproductive success by counting how many offspring each of them produced, and male reproductive success by summing all the offspring produced by the five females in his assay group (which are necessarily the offspring of the lone fertile male).
The Bateman gradients were identified as the best measure to quantify the strength of sexual selection for comparisons between sexes and/or across species. This study’s results support the idea that variance-based measures of sexual selection are very accurate at representing the sexual dimorphism (behavioural and morphological) of these species of beetles. Indeed, Bateman gradients show both male and female mating adaptations, since changes in adaptations are depicted by changes in gradients, representing the properties of the specific mating system. Bateman gradients were identified as the most relevant because they were very good predictors of sexual dimorphism of both secondary sexual traits and mating behaviour across the different species. Bateman gradients were generally steeper in males than in females, and steeper in species with species where males are the choosy sex (Figure 1A). In Callosobruchus species sexual dimorphism was more pronounced, as were the male and female Bateman gradients. In contrast, the opportunity for sexual selection was larger in males of Callosobruchus species, in which females are choosy, but similar or stronger in females of species where the males are choosy (Figure 1B).
The authors’ results are consistent with the classic sex role theory, as the calculated Bateman gradients support that the strength of sexual selection is greater in males than in females. The strength of sexual selection varied with mating system. Sex role reversal is often associated with male provision of nutritious gifts to females, increasing female direct benefits with mating success and thereby increasing the female Bateman gradient in such systems. Indeed, male Megabruchidius provide nutritious ejaculates to females, which directly increase the number of eggs a female lays (Takakura 1999). In contrast, there is no conspicuous benefit to additional matings beyond the first one among female Callosobruchus (Arnqvist et al. 2005).
The authors argue that, in the absence of a trade-off between male nuptial gift provisioning and mating success, increased male nuptial gift-giving should increase the Bateman gradients not only of females but also of males. As expected, in this study the Bateman gradients of gift-giving males (Megabruchidius spp.) were the highest. Moreover, even though one might predict that in the Megabruchidius species, female Bateman gradients would be steeper than those of males (because of the sex-role-reversal), this does not necessarily need to be the case. Indeed, stronger sexual selection on females requires a male trade-off between mating success and pre-copulatory fertilization success, which is likely to occur under low resource levels due to the costliness of mating and of providing a nuptial gift. However, this trade-off could have been affected by the experimental design, because the provision of ad libitum food may have provided the male beetles with greater energy levels that they would have in nature. Also, the study males varied in resource acquisition, a fact that could have further weakened the potential trade-off, or perhaps made it harder to detect experimentally. Despite these issues, the higher sexual selection opportunity in female Megabruchidius (Figure 1B) conforms to the sex-role-reversed system.
The authors discuss the importance of post-copulatory sexual selection, which is mediated by female cryptic choice and/or sperm competition. This type of selection is an important component of the sexual selection acting on seed beetles. Residual selection is a measure of the “strength of selection on a trait due to factors other than mating success”, and is calculated as the covariance between a standardized trait and the residual reproductive success obtained from the Bateman gradient. Residual selection has been regarded as a measure of post-copulatory sexual selection and/or fecundity selection based on a trait (z)under sexual selection (in the case of this study, body size), including components from both natural (e.g. fecundity) and sexual selection (e.g. sperm competition). In males, this measure represents the covariance of z with both fecundity and success in sperm competition. Instead, female residual selection represents only the covariance of z and female fecundity.
Fritzsche and Arnqvist conclude their paper by highlighting how the data they gathered, and the measures of strength of sexual selection they calculated, were consistent with the sexual dimorphisms in behaviour and morphology in the studied species of seed beetles. Variance-based measures appeared to be more accurate at representing the sexual dimorphisms observed across the different beetle species. Bateman gradients in particular were the most informative measure of the strength of sexual selection, allowing comparisons between sexes and across species. However, the authors underline the importance of gathering data from both pre- and post-copulatory reproductive competition to provide deeper knowledge on how variation in the strength of sexual selection within and across species affects mating systems.
Reviewed paper: Fritzsche, K., Arnqvist, G., 2013. Homage to Bateman: sex roles predict sex differences in sexual selection. Evolution, 67(7): 1926–1936.
Arnqvist, G., Nilsson, T., Katvala, M., 2005. Mating rate and fitness in female bean weevils. Behavioural Ecology, 16: 123–127.
Fitze, P. S., Le Galliard, J. F., 2011. Inconsistency between different measures of sexual selection. American Naturalist, 178: 256–268.
Takakura, K., 1999. Active female courtship behavior and male nutritional contribution to female fecundity in Bruchidius dorsalis (Fahraeus) (Coleoptera : Bruchidae). Researches on Population Ecology, 41: 269–273.
Kokko, Klug & Jennions, 2012: Unifying cornerstones of sexual selection: operational sex ratio, Bateman gradient and the scope for competitive investment
This paper uses formal theory to further explore the circumstances (in terms of sex ratios or sex differences in time spent in or out of the mating pool) that favour investment in costly competitive traits. The authors consider why neither of two commonly used concepts for explaining variation in sexual selection (and previously discussed in Journal Pub), the operational sex ratio (OSR) and the Bateman gradient, are consistently good predictors of mating system. Kokko and colleagues suggest that both measures provide complementary information about a mating system, and that a more complete approach to explain variation in sexual selection would be to consider both measures, because the Bateman gradient describes the fitness gain per mating and the OSR measures the potential difficulty in obtaining mates.
Kokko et al.’s model adds investment costs to survival into existing mating system theory (“time-in, time out” of the mating pool framework; Clutton-Brock & Parker, 1992; Kokko & Jennions, 2008; Kokko & Monaghan, 2001; Kokko & Ots, 2006), to examine why the strength of sexual selection does not always covary with OSR, despite greater variance in male mating success as the OSR becomes more male-biased. They integrate measures of the OSR and Bateman’s principles with investment theory, then predict whether a costly trait that increases mating rate will evolve. Kokko et al. describe the relative importance of investment in mating rate as the scope for competitive investment (SCI): this metric conveniently assesses how much investment an individual ought to make in elevating mating success relative to other fitness components.
The paper draws three general conclusions:
“Conclusion 1. If individuals of a given sex have a very short dry time, then the scope for competitive investment becomes large – irrespective of the OSR.”(Kokko, Klug, & Jennions, 2012)
When time away from mating pool is brief (short dry time) for males, the scope for competitive investment is large and it will eventually cause the OSR to become male biased. However the OSR is not always male-biased when the SCI for males is high. When male dry time remains short, it’s still worth investing in competitive traits in situations where there are many females (males are not mate limited) as males get large benefits regardless (figure 1). If the only route to increase fitness for males is by increasing their mating rate (alternative routes such as investing in parental care are not available, so dry time remains short), then males will invest in increasing their mating rate regardless of the competitive environment in which they find themselves. For females (with dry times ranging from short to long) it is only worth investing in competitive traits (high SCI) when the OSR is female-biased (figure 1).
“Conclusion 2. When the dry time of one sex varies from short to long, we expect a positive relationship between the OSR and the SCI in this sex.”(Kokko et al., 2012)
When time away from the mating pool (dry time) is not restricted in males, and instead varies from short to long, for example if males invest increasingly more time to parental care, the scope for investing in the evolution of competitive traits is reduced as dry time increases, and intensified by mate limitation (male-biased OSR) (see figure 2).
At female-biased OSRs there is high scope for the evolution of female competitive traits, because an indirect effect of increasing male dry time is to decrease female mating rate (as males are removed from the pool). In this scenario OSR is a good predictor of mating system.
“Conclusion 3. If other life-history aspects vary, it is difficult to make simple predictions about investment in competitive traits based solely on the OSR.”(Kokko et al., 2012)
Kokko and colleagues illustrate how when comparing study systems that differ in one or more life history trait or population parameter, OSR is not always good predictor of mating system (figure 3). They consider the scope for evolution of male competitive traits under three mate encounter rate scenarios when sex ratio at maturation varies among species.
As the OSR becomes more male-biased the benefit of mating increases along with the scope for competitive investment for males (figure 3). When the SCI, Bateman differential and OSR covary positively it explains why in systems where ‘all else is equal’ OSR can be used as good predictor of investment in competitive traits (if we consider each curve in figure 3 independently). However, the predictive power of OSR disappears if the species or populations being compared follow different curves, as illustrated by the three mate encounter rate scenarios shown in figure 3. The authors illustrate this problem by comparing two species, A and B that differ in a single population parameter, density (shifting the mate encounter rate); species A has a low mate encounter rate, and species B has a high mate encounter rate. Even though the SCI, Bateman differential and OSR covary positively for each species, when we compare the scope for the evolution of competitive traits across species we find that species A can have a higher SCI at a female-biased OSR than species B at a male-biased OSR.
The fact that the OSR is only sometimes a good predictor of mating system can perhaps best be explained using a thought experiment. Kokko et al. ask whether it is worth investing in a sexual trait to increase mating rate despite an arbitrary cost (which they fix in their thought experiment at 30% of longevity). Figure 4 illustrates three scenarios where individuals differ in the time spent in the mating pool relative to ‘dry time’.
In the first scenario (case A) on average the individual spends quite a long while competing in the mating pool and a relatively short time out of the pool after each mating (dry time, presumably when an individual is preoccupied with other activities like refraction, feeding, laying eggs or parental care, and is therefore unable to mate). In this scenario it is worth investing in a trait that increases mating rate despite an associated cost of a 30% reduction in lifespan, as can be seen by the increase of 4 mating events in the lower pathway of the short lived individual. The second scenario (case B) has an individual spending even longer soliciting mates in the mating pool (representative of an OSR that is even further skewed towards the focal animal’s sex) and a similar dry time to scenario A. In this scenario it is also worth investing in a costly trait that increases mating rate: the trait increases lifetime reproductive success by two extra mating events. Case C shows a situation in which the time spent finding a mate is short relative to the time spent outside the mating pool after each mating. Here the meagre reductions in an already short time in the mating pool that would be conferred by diverting investment from longevity to mating rate are not worth the cost, and investment in sexual traits is therefore not favoured regardless of the OSR. This work clarifies some otherwise puzzlingly inconsistent empirical patterns in the literature, and provides clear directions for new empirical work, including my own PhD research on mating systems in dance flies.
Clutton-Brock, T., & Parker, G. (1992). Potential reproductive rates and the operation of sexual selection. Quarterly Review of Biology, 67(4), 437–456.
Kokko, H., Klug, H., & Jennions, M. D. (2012). Unifying cornerstones of sexual selection: operational sex ratio, Bateman gradient and the scope for competitive investment. Ecology Letters, 15(11), 1340–51.
Kokko, & Jennions. (2008). Parental investment, sexual selection and sex ratios. Journal of Evolutionary Biology, 21(4), 919–48.
Kokko, & Monaghan. (2001). Predicting the direction of sexual selection. Ecology Letters, 4(2), 159–165. Kokko, & Ots. (2006). When not to avoid inbreeding. Evolution; International Journal of Organic Evolution, 60(3), 467–75.
Lilly started this week’s proceedings by pointing out new work by Tom Price and colleagues, recently published in Proceedings B. Tom and his colleagues studied a latitudinal cline in rates of polyandry in North America that covaries with the prevalence of sex ratio (SR), a meiotic-driving X chromosome. A selfish genetic element on the driving X causes sperm that carry Y chromosomes to die during development, which has two consequences for fathers carrying SR:
- All offspring sired by SR males carry the driving X (which is great for the selfish genetic element);
- Fathers only produce half as many sperm (which is terrible for dads who have to compete for fertilizations within females who mate more than once). This is why SR is a “selfish” genetic element — it improves its own fitness at a cost to its bearer).
Given that SR carrying males produce fewer sperm, females who mate more than once incite sperm competition that favours males who do not carry SR. This is a winning outcome for males who do not carry SR, as they are more likely to win in sperm competition by producing twice as many sperm. It also is a boon for polyandrous females, who are more likely to produce sons, which are rare when SR is prevalent and therefore have relatively high fitness. Price and his colleagues show that the elevated polyandry observed in regions of high SR prevalence is heritable, and argue that polyandry may frequently evolve to help reduce the intragenomic conflict imposed by selfish genetic elements.
Andy Dobson introduced himself to Science Drinks by explaining his research interest in parasite-host dynamics and his recent and ongoing modelling work with Stu Auld on virulence evolution, which has been constrained by processing power of late. Tim P. suggested that implementing subroutines in platforms other than R might accelerate things, and used the word “vectorized” to describe this, which I somehow found amusing. The discussion soon degenerated into some speculation of who has the most computer power and who has the most data, to slow down even the biggest and baddest of PCs. Think Robot Wars for stats nerds (I retain the copyright for this idea but am open to negotiating TV rights).
Adam then brought in a few figures to illustrate his ideas for upcoming grant applications. These are naturally Top Secret! We wouldn’t want anyone to steal his ingenious plan to secure a big research council grant.
Tim then mentioned an inspiring astro-physical story that was recently published in Science on one of Saturn’s moons, Enceladus. Summarizing this kind of work is dangerous for an entomologist, but your bloggy servant will have a go anyway: Luciano Iess and colleagues used telemetric data and Doppler radar antennae during flybys of Enceladus by the Cassini spacecraft to map the gravity field of Enceladus. Their findings indicate a magnetic anomaly near the south pole of Enceladus that is consistent with a large subsurface ocean 30-40 km deep(!). Wow! Amazing how some advanced number crunching can illuminate us about the state of one of Saturn’s moons from so far away, using tracking and careful measurements of the time taken to bounce radar signals off objects….
Each of the undergrads in attendance then took a turn presenting the latest discoveries: Gregor showed us some of his most recent findings on the phenologies of dance flies; Claudia showed us some intriguing and contrasting effects of body size and mass on the accumulation of resources by crickets, and Toby showed us data indicating that the relationship between compound eye facet size (which prevails in unspecialized eyes) and interommatidial angle may be disrupted among flies with derived “bright zones”. Watch this space for more in the coming weeks.
Andy then asked a provocative question: how does brood parasitism in birds (such as is seen in cuckoos) evolve in the face of imprinting, which is the phenomenon that leads many birds to identify with whatever rears them? We engaged in quite a lot of speculation without finding convincing answers. I did since find a handy webpage containing an expert answer from Naomi Langmore from ANU.
We wrapped up Science Drinks with a meaty paper by Mathieu Delcourt and colleagues in PNAS (not so hot off the press, but only recently read by me). Delcourt and his colleagues note that most populations tend to remain phenotypically stable over time in spite of strong directional selection (for example strong sexual selection for the increased expression of some sexual traits) and substantial genetic variance for the traits in question. Most of the time we assume that selection must be balancing on the character in spite of strong selection in one context (for example because overinvestment in a trait starves other important life history traits of resources). By measuring the genetic covariance between traits under sexual selection and total fitness, the authors here were able to use the multivariate Robertson-Price identity (also called the “secondary theorem of natural selection”, this equation is an alternative to the Breeder’s equation which Michael Morrissey and his colleagues note makes fewer assumptions) to demonstrate that in spite of substantial directional selection on male cuticular hydrocarbons (CHCs), there was little multivariate genetic covariation between these traits and fitness. Instead, their analysis of trait deviations revealed stabilizing selection on some aspects of genetic variance in CHCs. This work clarifies new methods for studying evolutionary responses (or the lack of them) in wild systems.
Attendees for the latest Science Drinks session at the William Wallace included:
Lilly started the session by introducing a paper on allometry in cervids by Lemaître and colleagues recently published in Biology Letters. Allometry is the study of scaling relationships, and is typically quantified by measuring the slope of the line fit between a log-transformed “body size” index and log-transformed trait values. But a review by MacLeod (2010) pointed out several problems with standard assumptions by scientists studying allometry, including that the best-fit line is straight. Lemaître and colleagues show that the allometric relationship between (log) body mass and (log) antler length in cervids is curved, with the increase in antlers leveling off once mass reaches about 110 kg. they argue that this curvilinearity may be explained by energetic constraints on investment in antlers among the largest species., and caution that researchers should be mindful of possible nonlinearities when assessing scaling relationships.
Adam shared a different comparative study, this one focusing on the lengths of telomeres in chimps and humans and recently published in the American Journal of Human Biology. Telomeres are a section of repetitive DNA that caps the end of each chromosome, and that tend to shorten progressively as an animal ages because with every cell replication, a tiny bit of the telomere at the very end of the chromosome cannot be copied. The enzyme telomerase can restore lost telomere, but because this kind of repair mechanism is costly, the length of telomeres may reveal both individual-level information about biological age and susceptibility to degenerative diseases, as well as species-level investment in somatic repair that has evolved in conjunction with maximum longevity (see Monaghan 2010 for a great review of telomere dynamics in a life history context). Tackney and colleagues (2014) predicted that because the maximum longevity of humans exceeds that of chimps, chimps might have shorter telomeres or faster rates of telomeric loss than humans. The computed the relative signals of telomeric and single copy gene signals (T/S ratios) in white blood cells, and found that while their estimates of telomeric loss for chimps (approx. 73 nucleotides per year lost) exceeded that of humans (approximately 40 nucleotides per year), these rates were not statistically distinguishable. Moreover, the chimp telomeres were actually longer, having a T/S ratio around twice that of humans. It’s unclear what the implications of this finding are, but it raises many intriguing questions about the complex relationship between longevity and investment in repair that concerns all students of life history.
Toby showed some new figures, hot off the printer, illustrating some of his Hons research findings about the relationships between eye facet size divergence (which involves adaptations for photosensitivity) and the angles between facets (which may limit acuity) in dance flies. Claudia showed off some new plots and tables of coefficients of her own, involving the study of courtship and mating as a function of diet and social environment in crickets. I don’t want to step on imminent punch-lines: both Toby and Claudia will submit their dissertations in a few short weeks, and we’ll have a chance to see summaries of their work then. Stay tuned!
I (Luc) wrapped up the session by calling attention to a paper just published as an “early-view” manuscript on the journal Evolution‘s homepage by Walker (2014). This paper deals with a problem that is well-known but still underappreciated: when dealing with observation data (unmanipulated experimentally), coefficients of regression analyses will be biased upwards if any of the predictors are correlated with other causal factors that have been omitted from the model through ignorance or negligence. Walker uses simulations to show that this upward bias can be substantial, and argues that the problem is especially acute if one seeks to interpret coefficients as evidence for causal relationships. He further argues that the problem is not necessarily avoided by having prior knowledge of the “right traits” to include in one’s model, and carefully measuring candidate rival traits that one can include as covariates. He writes,
“forty percent of the time an additional covariate is added, even using omniscient prior knowledge of effect size, the error in the estimate of the effect is worse than if the confounder had been left unmeasured”.
This is deeply unsettling research for someone like me who spends a lot of time trying to find patterns of covariance in observational data (much of it by necessity since some of our work is on organisms that do little in the lab except die). Walker does point to directed acyclical graphs (structural equation models) as promising some clarity concerning causal relationships in observational data, which is satisfying news given my recent obsession with them, but overall the MS is more cautionary than prescriptive:
“Unless a functional model generates very specific predictions of effect magnitude, observational data is better used for testing model assumptions and not the mere presence of an effect. For studies where experimental manipulation or functional modeling is not an option, we may have to be content with simply not knowing the magnitude of effects very precisely.”