Saturday, January 24, 2015

Top Ten Best Scientific Figures

I recently saw a tweet by @carinadslr that linked to a blog post about the “Top Ten worst graphs”. This post provided an excellent opportunity for me to share a series of funny scientific figures I have been collecting. I sent them out in a series of 10 tweets and here compile them in one place.
I hope these brighten your day and bring you a chuckle or two. Please forgive the errors: #galapagosbandwidthsucks (as it should)

1. Pressures produced when penguins poo – Calculations on avian defeacation.
  

By Victor Benno Meyer-Rochow and Jozsef Gal. (2013. Polar Biology) LINK My favorite part of this figure is the use of a photograph of gravel juxtaposed with the cartoonish drawing of the penguin. And you have to love the alliteration in the title. I was pointed to this figure by a group of postdocs and students at the Hopkins Marine Laboratory who had been collecting “best figure 1” images. The research itself won an Ig Nobel Prize.

2. Spatial distribution of the montane unicorn.


By Stuart H. Hurlbert. (1990. Oikos) LINK This paper is a straight-faced use of “five populations of the recently discovered montane unicorn” to illustrate the statistical properties of various estimators of the spatial distribution of rare organisms. I found this figure in Steve Heard’s wonderful paper On whimsy, jokes, and beauty: can scientific writing be enjoyed?

3. Tree-hugging koalas demonstrate a novel thermoregulatory mechanism for arboreal mammals.


By Nathalie J. Briscoe, Kathrine A. Handasyde, Stephen R. Griffiths, Warren P. Porter, Andrew Krockenberger, and Michael R. Kearney. (2014. Biology Letters) LINKS As my tweets on this were coming out, @RiaRGhai sent me this one. The humor is somewhat diminished for me by the fact that I spent an evening in an Australian reserve looking for koalas and never saw one. That should be another category for the figure – a bare tree branch.

4. My baby doesn’t smell as bad as yours. The plasticity of disgust.


By Trevor I. Case, Betty M. Repacholi, and Richard. J. Stevenson. (2006. Evolution and Human Behavior) LINK In this case, there isn’t anything funny about the figure itself. The humor instead emerges when the reader mentally juxtaposes the serious presentation of data with a mental image of the field work involved. And you have to love the “someone else’s baby’s diaper” label.

5. Effects of different types of textiles on sexual activity. An experimental study.


By Ahmed Shafik. (1993. European Urology) LINK Back to absurd stand-alone figures, with another one coming from the “best figure 1” club at Hopkins Marine Lab. My favorite part here is the caption and the detailed representation of where the underpants are tied. Given the position of the ends of the string and how they seem to be lifting the underpants slightly, I can only assume that the model for this rat was drawn precisely at the moment its underpants were being tied.

6. Molecular phylogenetic analyses indicate extensive morphological convergence between the “yeti” and primates.


By Michael C. Milinkovitch, Aldagisa Caccone, and George Amato. (2004. Molecular Phylogenetics and Evolution) In contrast to the above serious papers, here is the first entire fake paper. Recognize the yeti drawing? Also, I read somewhere that this paper was published on April 1 and yet it has been cited in earnest by some people.

7. The photosynthetic cycle – CO2 dependent transients.


By A. T. Wilson and M. Calvin. (1955. American Chemical Society). LINK Serious paper, serious figure, but look closely at the inset provided by Steve Heard in his above-mentioned paper on whimsy, jokes, and beauty.

8. Fellatio by fruit bats prolongs copulation time.


By Min Tan, Gareth Jones, Guangjian Zhu, Jianping Ye, Tiyu Hong, Shanyi Zhou, Shuyi Zhang, and Libiao Zhang. (2009. PLoS ONE). LINK Figures don’t have to be static – they can be videos too. What really makes this figure work for me is the added soundtrack. Apparently other papers have now come out on cunnilingus in bats.

9. A possible role of social activity to explain differences in publication output among ecologist.


By Tomas Grim. (2008. Oikos) LINK A second appearance by Oikos. Do the editors there have a better sense of humor than elsewhere? In reality, several of the above graphs are just nods to funny papers, rather than funny figures on their own. This papers shows how Czech avian ecologists that drink more beer publish fewer papers and papers of lower impact. But what is cause and what is effect?

10. Beavers as molecular geneticists: a genetic basis to the foraging of an ecosystem engineer. By Joseph K. Bailey, Jennifer A. Schweitzer, Brian J. Rehill, Richard L. Lindroth, Gregory D. Martinsen, and Thomas G. Whitham. (2004. Ecology) LINK


The journal Ecology often encourages authors to add pictures of their organisms. So, nested with two pictures of cottonwood trees and their habitat (not shown here), is this picture of a beaver – from Legoland!


Here is another paper I originally tweeted that got bumped by the koala figure from my top ten list.
Ovulatory cycle effects on tip earnings by lap dancers: economic evidence for human estrus?


By Geoffrey Miller, Joshua M. Tybur, and Brent D. Jordan. (2007. Evolution and Human Behavior) LINK Like the disgust paper, the humor here doesn’t exist in the figure itself. Rather it is in the serious presentation of the figures juxtaposed with one’s speculations as to what the field work must have been like.

Friday, January 16, 2015

Felix Not Felicis: a tale of attempted photography


PERSERVANCE is the hard work that you do after you get tired of doing the hard work that you already did. (Quote on a tarp used to cover a moldering shelter found deep in the Trinidadian bush.)

Some days everything goes just perfectly. The stars and planets align. All the stoplights are green. All your shots go through the hoop. All of a sudden, you are lucky in everything. Perhaps someone slipped some Felix Felicis into your morning pumpkin juice. Of course, most other days are a mix of lucky and unlucky, good and bad. And, every once in a while everything goes spectacularly wrong – all at once.

Many of the best stories of wildlife photography first describe days and weeks where everything goes wrong – or just one critical thing goes wrong day after day after day. The bird of paradise you are watching never displays – or never displays in your direction. A branch is always between you and your subject no matter how you position yourself. The bird flies away just as you raise your camera to take the picture – again and again and again. But then one day, after weeks of perseverance, everything comes together and you finally get that shot. Those are the good stories – hardship, perseverance, and finally – success. By contrast, stories of easy success are boring and stories of hardship without reward are just depressing.

Now it is a strange thing, but things that are good to have and days that are good to spend are soon told about, and not much to listen to; while things that are uncomfortable, palpitating, and even gruesome, may make a good tale, and take a deal of telling anyway. (From the Hobbit).

I am motivated to reflect on these points based on my experiences in Panama this week. I was visiting in my role as Director of the Neotropical Environment (NEO) Graduate Option, a partnership between the Smithsonian Tropical Research Institute (STRI) and McGill University. While not teaching in the class or participating in meetings about the program or hanging out with NEO folks, my favorite activity is exploring for things to photograph –near and far, big and small, feathered and furred and scaled and chitined. After about ten visits over ten years, I have collected a modest but personally rewarding collection of natural history images

This year on the day I arrived, I immediately set out for the last few hours of light to photograph the capybaras I knew hung out in a small pond below the nearby Gamboa Rainforest Resort. They were indeed there, but they were also a bit flighty and I didn’t get any useful photos, except for some of two tiny babies that were reluctant to enter the water. After they finally ran into the bushes, I noticed that a line of leaf cutter ants (my favorite tropical insect) was snaking ACROSS THE SURFACE OF THE POND, weaving its way adroitly on top of the dense aquatic vegetation while the capybaras swam hidden below. I really wanted to get a good photo but the light was fading fast and a huge bush made approach to the line difficult. Tomorrow, I thought – and I will bring my GoPro on a long pole to get a video of them crossing the pond while avoiding the brush.

Awwwww. Baby Capybaras
The next day, after a trip to see the amazing underwater logging operation of CoastEcoTimber, I was back with enough time and light to try again. So, quickly packing up all my camera/video gear, I set off. Halfway there, I looked up at the canopy tower on the hill. Hmmm, I thought, maybe I should head up there first. I might get some good canopy bird photos in the late afternoon light and there will probably be good leaf cutter ants on the way there, as had been the case in the past. And maybe I can see more coatis, as I also had in the past.


By the time I neared to the top of the hill, which took some time, I found a decent track of ants. I took out the GoPro, assembled everything for optimal ant footage, and pushed the “on” button. Nothing. The battery was dead. No problem, I packed two extras – at least I intended to. Yet intent had not translated into action in my packing haste. No batteries. So I packed everything up again and set off for the tower – at least I could still get some good bird photos. Another five minutes of uphill hiking and I was there – but the tower was locked. Darn, it had never been locked before. After assessing the feasibility of climbing around the barrier, which would have been possible but rather difficult and certainly incriminating if someone arrived, I decided to set off for the capybaras and leaf cutters that I had seen the first day.

This time I took a different route back – a road that looked like it was going in the right direction but that I had never gone on before. As befit my luck, the road eventually ended without leading where I needed to go – but then I found a path. Everything went well on the path until I reach the bottom and realized I would have to slog through a field of thick grass that just screamed “hellish chiggers live here.” Sure enough, I am currently experiencing one of the itchiest chigger moments of my life. Moreover, by the time I got back it was too late to get the GoPro battery and slog back to the ants. Sigh. No good photos the entire evening when I had been so optimistic to start with. (I did snap a modestly interesting photo – just to have taken a photo of something – of a massive Nephila spider with its parasitic Argyrodes web-mate.)

This time at least Nephila (the big one) got its meal before Argyrodes (the little one) could steal it.
So there is my tale of just plain old bad luck (combined with poor planning) at multiple junctures contributing to an utter failure in my objective. Work without success. Hardship without reward. Perseverance without redemption. OK, so I am being a melodramatic here, and maybe “redemption” is just silly histrionics, and maybe I didn’t really persevere that much, and maybe I did get some nice photos of other critters on the days that followed. But I never went back to the capybaras and the leaf cutters that walk on water. Perhaps the next trip. Then I really will have a tale of perseverance and redemption to tell.

It's like in the great stories, Mr. Frodo. The ones that really mattered. Full of darkness and danger, they were. And sometimes you didn't want to know the end. Because how could the end be happy? How could the world go back to the way it was when so much bad had happened? But in the end, it's only a passing thing, this shadow. Even darkness must pass. A new day will come. And when the sun shines it will shine out the clearer. Those were the stories that stayed with you. That meant something, even if you were too small to understand why. (Sam in Peter Jackson’s Lord of the Rings.)


Goeffroy's Tamarin - Gamboa, Panama.
Geoffroy's Tamarin - Gamboa, Panama.
Ant defensive cordon - Pipeline Road, Panama.
Blue-crowned motmot - Gamboa Panama.
White-nosed coati - Pipeline Road, Panama.
White-nosed coati - Pipeline Road, Panama.




Friday, January 9, 2015

Do you pee more, or less, when you’re scared?

[ This post is by Christopher Dalton; I am just putting it up.  –B. ]

When I talk to people outside of ecology and evolutionary biology, I usually joke that my job as a graduate student is to “study fish pee”. This is intentionally self-deprecating, but it also sets up my next point: that fish pee is important and interesting. Fish pee (more formally: excretion, the release of dissolved chemicals as byproducts and excesses of metabolism) is important because it contains fertilizers (ammonium and phosphate) that can alter ecosystem function. Fish excretion is also interesting because it reflects animal physiology and can be used to assess how animal metabolism responds to varying environmental conditions. If I explain myself well, I may manage to convince my poor conversation partner that fish pee is worth studying.

While the importance of fish excretion to ecosystem function has been described by several authors (Zimmer et al. 2006; Small et al. 2011; Layman et al. 2013), much uncertainty remains as to why there is so much variation in the rates at which fish excrete. Lab studies have demonstrated that fish body temperature, body size, and food availability all cause some variation in excretion, but these factors explain just a fraction of the variation observed in empirical field studies. This unexplained variation may result from the stochastic noise caused by the stress of the experimental apparatus and difficulty of measuring nutrients, but it may also reflect variation that is induced by “cryptic determinism” due to knowable variables that have been unaccounted for in previous analyses.

Differentiating between measurement error and cryptic determinism is more than an academic exercise. If variation in excretion is due to noisy measurement methodologies, researchers will continue to struggle to predict excretion rates. In contrast, if environmental variables that we have not accounted for are causing this variation, then finding and measuring those variables will improve predictions of how much fertilizer fish will release as excretion. Describing and understanding environmental influences on excretion rates, moreover, would shed light on how evolution and phenotypic plasticity shape metabolic traits to enable survival in changing environments.

My advisor, Alex Flecker, and I undertook lab research in 2012 to assess one largely undescribed but potentially important driver of variation in fish excretion – predation risk. Our study sought to (1) understand how predation risk affects excretion by fish, and (2) explore whether these effects might reveal general adaptive responses of prey to the risk of being eaten.

Grasshoppers pee more with predators

Dror Hawlena and Os Schmitz, researchers at Yale University, motivated our work by finding that predation risk drove substantial variation in nutrient processing by a keystone invertebrate herbivore. In the old fields of Connecticut, Hawlena and Schmitz observed that the mere presence of a predatory spider increased the nitrogen (N) waste from grasshoppers. Increased grasshopper losses of N through excretion lowered the N content of their carcasses (Hawlena and Schmitz 2010a), which slowed soil respiration and decomposition in fields where predatory spiders lurked (Hawlena et al. 2012). Thus, by merely imposing risk on grasshoppers, spiders could elevate the N metabolism of their prey and alter ecosystem function. This interesting result raised the question of whether this response was general to all predators and prey, or something specific to this one empirical system.

Dror Hawlena and Os Schmitz combined their results with studies on laboratory model systems to suggest the answer is very general (Hawlena and Schmitz 2010b). Indeed, studies on laboratory animal models have repeatedly shown that exposure to predation risk increases expression of glucocorticoid steroids by prey. Elevated glucocorticoid expression results in more amino acid catabolism, which increases ammonia excretion and depletes tissue N reserves. Because the glucocorticoid response is thought to be conserved among all animals, it is possible that all animal prey excrete more nitrogen when predators lurk. Predators, then, may unlock nitrogen from their prey, driving variation in N excretion that researchers would have previously considered experimental noise.

We sought to explore this issue using another model for predator-prey interactions in nature, the Trinidadian guppy. Our study was designed to (1) explore the potential for a predator, the pike cichlid (Crenicichla sp.), to induce comparable metabolic plasticity in Trinidadian guppies, and (2) determine whether such predator-induced plasticity may be an adaptation that enhances guppy survival and reproduction in risky environments.


Our predatory fish, Crenicichla spp. This specimen was not used in this experiment, and was sent to us by an aquarium store in Portland, OR (“The Wet Spot”). It was sold to us as a “wild Crenicichla sveni”, from the Rio Orinoco in Colombia. Interestingly, its chemicals elicited the same response from guppies as did the chemicals emitted by Crenicichla sp. captured in Trinidad.

Measuring fish pee under duress

Our study was based on a design pioneered by Cameron Ghalambor, Corey Handelsman, and Emily Ruell (among others) at Colorado State University. Corey, Emily and Cameron modified complex zebrafish-rearing systems to breed and rear individual guppies from Trinidadian guppy populations. For experimentation, they varied the source of water flowing into each guppy tank, with some tanks receiving flow from a source with a single pike cichlid, and other tanks receiving flow from a source with no fish in it. Researchers in the Ghalambor lab found that the chemicals excreted by the pike cichlid consistently induced behavioral, metabolic, and life history responses in guppies (Torres-Dowdall et al. 2012; Handelsman et al. 2013).


Our zebrafish tanks, which we’ve modified to be guppy tanks as inspired by Ghalambor, Handelsman and Ruell at Colorado State.

We used a very similar design to expose 16 full-sibling groups of maturing female guppies to water either with or without the chemicals emitted by the guppies’ main diurnal predator, the pike cichlid. Over the course of seven weeks, we tracked how much N each guppy consumed in its food, how much N each retained in its tissues, and how much N each released as waste. We then used these measurements to assess how efficiently each guppy converted the N it consumed into the N in its tissue.


The “Excretionator 2000”, a device designed to enable collection of fish excretion samples with minimal invasiveness. Each basin contains water either with or without predator risk cues, pumped through each container continuously. 

Fish pee less around predators?

Contrary to our expectations and the results obtained in grasshoppers by Hawlena and Schmitz, guppies reared under predator cues excreted less N than guppies reared in predator-free control water. In fact, guppies reared with predator cues excreted nearly 40% less N than controls. Largely, this difference was due to cue-exposed guppies consuming less food in the presence of the predator cue (less consumed food = less N available to excrete). Independent of differences in their food consumption or size, though, cue-exposed guppies still excreted 10% less N than control guppies. Unlike grasshoppers, which accelerated processing and excretion of N under predation risk, guppies slowed N processing and excretion under risk.

Our measurements of growth efficiency suggest there may be an adaptive benefit to the lower N excretion of predator-exposed guppies: increased growth efficiency. Cue-exposed guppies retained N more efficiently than control guppies (20% more), despite consuming less food and growing more slowly overall. It is intriguing to speculate as to whether, in the presence of predator cues, guppy metabolism changed from one maximizing growth rate – rapidly producing new tissue at the cost of high consumption and high waste production – to one maximizing growth efficiency – slowly accreting new tissue but with low rates of food consumption and waste production.

Support for the adaptive benefit of lower N excretion comes from research on the physiology of food deprivation. Animals, ranging from fish to mice to birds, disproportionately reduce their N metabolism when faced with decreased food rations (McCue 2010). This shift in metabolic fuels spares amino acids and increases catabolism of lipids, enabling starving organisms to maintain muscle mass despite limited access to dietary amino acids. Predator-exposed guppies, in this case, may be responding more strongly to the physiological challenge of food deprivation than to the direct risk imposed by predators, which would be predicted to accelerate N catabolism, excretion, and tissue depletion. We suggest two general and competing predation-related influences impact the physiology of predator-exposed prey: (1) direct risk from predators accelerates N cycling, excretion, and tissue N losses, while (2) reduced feeding under predation risk, a common behavioral change caused by predators, slows nutrient cycling and excretion, increasing N retention in tissues.  (See table below; click on it to see it at larger size.)



Determining how much to pee when predators lurk

So how much should you excrete when faced with a predator? We suggest that depends on how well stocked your safe room is. If prey can shelter from predators in habitats that have abundant food resources, it may be adaptive for them to accelerate N cycling to maximize the energy available for predator encounters. The cost of their accelerated metabolism – increased loss of valuable amino acids – would be offset by the availability of amino acids in the abundant foods. If, however, prey do not have access to food in refuges, increasing N metabolism will only accelerate the negative fitness effects of starvation. For organisms facing food restriction in refuge environments, slowing N metabolism has a two-fold adaptive benefit: (1) it maintains valuable muscle protein under restricted feeding opportunity, and (2) it minimizes the amount of time spent feeding in vulnerable habitats.

In total, we found evidence that predators are central to consumer-mediated nutrient cycling, but we also found that the direction of the predator effect may depend on the environmental context of predator-prey interactions. In ecosystems where important nutrient recyclers shelter from predators in safe but food-restricted refuges, predation risk may reduce N excretion, slowing the supply of limiting nutrients to the base of the food web. Though our result runs counter to the notion of a single, general effect of predators on nutrient cycling by their prey, it also indicates that, by taking into account the natural history of predator-prey interactions, we may be able to more accurately predict how changes in predator communities will impact the function of ecosystems.

We are continuing our studies of guppies to explore how their metabolic responses to predation risk vary over time and with the variable evolutionary history of predators and prey.  By learning more about this interesting trait, we hope to help researchers in the quest to understand and predict how fish might function as fertilizers in streams across the globe.

References cited

Handelsman C a, Broder ED, Dalton CM, Ruell EW, Myrick C a, Reznick DN, Ghalambor CK (2013) Predator-Induced Phenotypic Plasticity in Metabolism and Rate of Growth: Rapid Adaptation to a Novel Environment. Integr Comp Biol:1–14. doi:10.1093/icb/ict057

Hawlena D, Schmitz OJ (2010a) Herbivore physiological response to predation risk and implications for ecosystem nutrient dynamics. Proc Natl Acad Sci U S A 107:15503–7. doi:10.1073/pnas.1009300107

Hawlena D, Schmitz OJ (2010b) Physiological stress as a fundamental mechanism linking predation to ecosystem functioning. Am Nat 176:537–56. doi:10.1086/656495

Hawlena D, Strickland MS, Bradford M a, Schmitz OJ (2012) Fear of predation slows plant-litter decomposition. Science 336:1434–8. doi:10.1126/science.1220097

Layman C a, Allgeier JE, Yeager L a, Stoner EW (2013) Thresholds of ecosystem response to nutrient enrichment from fish aggregations. Ecology 94:530–6. doi:10.1890/12-0705.1

McCue MD (2010) Starvation physiology: reviewing the different strategies animals use to survive a common challenge. Comp Biochem Physiol A Mol Integr Physiol 156:1–18. doi:10.1016/j.cbpa.2010.01.002

Small GE, Pringle CM, Pyron M, Duff JH (2011) Role of the fish Astyanax aeneus (Characidae) as a keystone nutrient recycler in low-nutrient neotropical streams. Ecology 92:386–97. doi:10.1890/10-0081.1

Torres-Dowdall J, Handelsman CA, Reznick DN, Ghalambor CK (2012) Local adaptation and the evolution of phenotypic plasticity in Trinidadian guppies (Poecilia reticulata). Evolution 66:3432–3443. doi:10.1111/j.1558-5646.2012.01694.x

Zimmer KD, Paul S, Herwig BR (2006) Nutrient excretion by fish in wetland ecosystems and its potential to support algal production. Limnol Oceanogr 51:197–207. PDF.

Carnival of Evolution #78 is up

Carnival of Evolution #78 is now up.  Given Bjørn’s chosen theme for this one, I think I will forego my usual custom of doing a Google Images search to find a relevant image.  :->

Our contribution to this edition of the Carnival is Sarah W. Fitzpatrick’s post on Retracing the legacy of guppy introductions past.  There’s lots of other good stuff in there, including an interesting post about why the claim that much of the human genome is functional implies that we should all have 7e45 children!

Enjoy!

Saturday, January 3, 2015

How to do statistics

This fall, I wrote a series of “How to” blog posts that proved somewhat popular, or at least well-read:


I hadn't initially planned a series like this, it just kind of emerged. However, I had long planned one particular “How to” post. Ironically, that post was the one I still hadn’t written. Now that it is 2015, the time seems ripe to get back to the original idea. (Thanks to Ben Haller, Gregor Rolshausen, Joost Raeymaekers, and Chuck Fox for critical comments that helped improve this post.)

How to do statistics.

I used to teach statistics. Really! I was a whiz at SPSS and Systat, and I could find my way around JMP. I was almost at the cutting edge, which then was SAS. No one complained seriously about the stats in the papers I submitted. Now, it seems that – with the same statistical skills as before, and maybe even a bit better – I have become a dinosaur. Increasingly, the feeling seems to be that you can’t be considered even moderately competent at statistics unless you can do a GLMM in R. In this sea-change from [insert your previous status package here] to R, I feel that several important points are getting lost – or at least under-emphasized. My goal in the present post is to revisit what statistics are supposed to be for and how you should do them. I do not mean the details of how to choose and run a particular model but rather how to view stats as a way of enhancing your science and refining your inference. I will outline these ideas through a series of assertions.

1. It’s all about the (appropriate) replication

An incredibly important route to improving your science is to maximize replication at the appropriate level of inference. Imagine you are interested in a particular effect, say the difference in an experiment between two treatments or the difference in some trait between populations in two environments. You need to here strive for maximum replication of the two treatments or the two environments. This might seem obvious but – as a reviewer/editor – I have seen many studies where people wish to make inferences about the effects of two environments, yet they have studied only one population in each environment. In such cases, they are entitled to draw conclusions about differences between the two studied populations but not between the two environments because – with only one population per environment – the investigator cannot gauge the difference between environments in relation to variation within environments. That is, it is quite possible that two populations within each environment would differ just as much as two populations sampled from the different environments. While the temptation is to get larger sample sizes for each measured population, what is much more important is to sample many populations. I have seen many papers rejected for lack of replication at the level for which inferences are desired.


2. The data are real – statistics are merely a way of placing a statement of confidence in an inference you draw from the data.

I have frequently seen students paralyzed by their inability to fit an appropriate error distribution in R. They spend weeks and weeks trying various options only to eventually give up and throw out the offending data. The opinion seems to be that, “if I can’t fully satisfy the requirements of a statistical test, then the data must be bad and I shouldn’t report them.” This is folly! The data are the real thing – the stats are just a tool to aid interpretation. What is infinitely better in cases where a perfect model cannot be fit is to present the data, analyze them the best possible way, and then own up to cases where the data do not fully satisfy the assumptions. The truth is that many statistical tests are extremely robust to small-to-modest violations of their assumptions as long as the P value (but see below) is not too close to the critical value.

Of course, I am not here advocating using a bad model when a better one exists. If a better model exists, by all means you should use it. However, this more practical point is already emphasized quite frequently nowadays to the point that it can become detrimental to a student’s progress, and I am here trying to push the pendulum back a bit. That is, finding the ideal model is valuable and helpful, but slavish dedication to this goal can sometimes detract from the quality of scientific education and insight. Of course, the most important thing is to have a good question and experimental design before you conduct the study, which will simultaneously improve the science and help avoid later statistical constraints.

3. It's not about the P value.

Although opinions are changing, many students are still fixated on obtaining a P value smaller than the critical level of 0.05. This goal is misguided – for three reasons. First, 0.05 is totally arbitrary. If you are focused on P values, what is much more useful is the actual P value – is it small or large? (Journals should always require actual P values in all cases.) Second, any particular set of data can be analyzed multiple ways and cycling through those options can lead to the temptation to choose the one that generates the smallest P value. Third, P values themselves (the probably that, if the null hypothesis is true and you reject it, you will be wrong in doing so) are a silly way to do science – sorry RA Fisher. Among the many reasons, the null hypothesis is – in traditional frequentist statistics – treated as a default rather than as an alternative model, and thus one often rejects the alternative hypothesis even when it has more support than the null hypothesis.

Instead of null hypotheses, it is much better to specify alternative hypotheses that are competed against each other with alternative statistical models to thereby judge the relative support for each hypothesis. Such comparisons can take the form of likelihood ratio tests, Bayesian credibility intervals, AIC comparisons, or the like. One might argue that a level of arbitrariness creeps in here (because a standard yes-no threshold is sometimes lacking) but the truth is that such approaches are much less arbitrary because they quantitatively compare the level of support for competing hypotheses. The author can then draw whatever conclusions he/she wants from the levels of support, while still allowing the reader to draw some other conclusion from the same model comparisons should they wish to do so.


4. Effect sizes are what matter.

P values are determined by an interaction between effect size (strength of an effect) and sample size. Thus, P values are NOT the strength of an effect. As a result, one cannot – without other information – say that a P value of 0.0001 represents a stronger effect than a P value of 0.05. It might simply be that the former analysis has a much larger sample size. Take simulation models as a particularly obvious example. In this case, one can have whatever sample size one wants given computing power and time. Thus, the exact same effect size (determined by the parameters of the simulation) can have totally different P values determined by the number of replicate simulations performed. If you have a tiny (but real) effect size, simply run more simulations and it will eventually become significant! The same logic applies to experiments and surveys. What matters are effect sizes based on how much variance in the data is explained, or based on the difference between group means weighted by the variance or the mean. Examples include R2, Cohen’s D, and Eta.squared.

Of course, one still wants to place a statement of confidence in assertions about a given effect size, which is where one adds P values or – better yet –model comparisons as discussed above. Note that, when true effect sizes are small, they tend to be overestimated when sample sizes are also small, which as generates the so-called funnel plot of meta-analyses. Thus, one still wants as large a sample size as possible and one would ideally correct the measured effect size for an estimate of the error – either using Bayesian approaches or through brute force. That is, a measured R2 can be adjusted by the R2 expected if no effect was present – with an example here.

Effect sizes (here estimates of the strength of selection) are higher when sample sizes are smaller. From Kingsolver et al. (2001 - American Naturalist).


5. Graph your data

In many meetings with students where I am to see the outcome of their experiment or sampling for the first time, I am presented with detailed statistical tables where the student emphasizes whether or not particular effects are significant in this or that model. I find myself incapable of interpreting these results without seeing the data in graphical format. In fact, I think a student should first graph the data in a manner that addresses the original question before running ANY formal statistical tests. This aids not only the assessment of assumptions for subsequent statistical tests (hugely influential outlier errors sometimes pop up when I ask a student to do this) but also reveals – at a first glance – the gestalt effect size assessment that rarely ever changes much as time goes on, notwithstanding any ups and downs that occur in the subsequent formal statistics. In this way, the student and supervisor can have a rough picture of what the experiment has revealed before having to worry about the statistical details. I would bet that 90% of the important work (if not the time investment) is done once you graph your data in a way that informs the original hypothesis/question.

All data sets have the same means, variances, correlations, and regression lines. Only graphing shows how different they really are: Anscombe's quartet from Wikipedia.

Some additional notes about statistical packages.

6. R is simply one of many useful platforms for drawing statistic inference.
Nowadays, students feel incompetent if they don’t analyze their data in R – hell, I even feel that way sometimes. However, R is simply a post-experiment tool – a hammer with which you help massage your data into optimal inference. SPSS, Systat, JMP, and SAS are also hammers – they too can massage your data. Perhaps R is a titanium hammer, better and more efficient at massaging the truth from data; but think of all the amazing inferences that were derived before R was popular. Does the failure of these countless previous studies to use R mean that we should not believe everything published before (and much published after) 2002? (Of course, re-analysis does change the conclusions of some previously-published and superficially-analyzed studies.) Does the fact that something else will eventually replace R mean that our current inferences with R then become incorrect? Nonsense. Valid and excellent inference can be obtained with any number of statistical packages.

Given that R is now the most common statistical program it does make sense for new (and old) researchers to start with (or switch to) R. However, the main advantage is not – in my opinion – dramatically improved inference but rather ease of communication with other scientists, such as through the sharing of code. Moreover, R has many other components not present in canned packages, such as data exploration tools, connection to database and file system structures on your computer, if-else statements, while loops and other programming tools, detailed plotting functions, connection to other programming languages such as C++ and Python just to name a few. It also contains user-motivated novel statistical tools for specific applications that are simply not available in other packages.
How to program a Christmas tree in R.
http://simplystatistics.org/2012/12/24/make-a-christmas-tree-in-r-with-random-ornamentspresents/
In reality, however, most scientists seek much simpler assistance from statistical analysis, for which other packages can do the trick. Moreover, efforts to master R can take so much time and dedication that students sometimes neglect what is really important in science: good and novel ideas, good experimental design, diligent execution with high replication and large sample sizes, effective visual presentation of information, and common sense deduction. I would much rather have a student who mastered these skills and analyzed their data in SPSS than I would have a student who was an R whiz but neglected the key skills of scientific investigation. Of course, what I really want is student who can do both, but the former is vastly more important. (Of course, most students who do learn R certainly don’t regret it afterward.)

7. R has its own foibles.

Any statistical program has bugs or flaws, and R is no different. Many issues with existing packages have been pointed out well after those packages were used in published studies. The simple fact is that R is modified by many people and can (like other statistical packages) suffer from the inadvertent introduction of errors that it takes time for others to discover and the originators to correct. Moreover, R has its own set of defaults that can be confusing or misleading. For instance, the standard default in R is Type I sums of squares (SS), whereas the default in many other stats packages in Type III SS. These different SS options have their own sets of positives and negatives and supporters and detractors. However, one must understand the differences between them. Of critical importance, Type I SS fits the first term of the model first before fitting other terms, whereas Type III SS fits all of the terms simultaneously. As a result – and as my students found out – you can get very different results if you run the same analysis in R and some other package, as well as if you change the order of entry of the terms in the model in R. (For my money Type III SS is usually more appropriate and my students now usually specify this option in R.)

It is important to make clear that I am not suggesting that students forsake the use of R for some other package. In most cases, they should probably use R. What I am instead saying is that learning R is not the most important (although it could be the most useful) thing you do in your education. Do not think that R = science and that, if you don’t learn R you are not a good scientist. Instead, think of R as a titanium hammer. If you need that hammer, then use it. If you don’t yet have any hammer, you might as well go titanium if you have the time. However, remember not to equate knowledge of R with intelligence or with a good study or with your own sense of self worth. Learn R for the right reasons and don’t let it become your raison d’etre – unless you wish to specialize in statistical analyses. Indeed, statistics and the development of R packages is certainly a branch of science in its own right - but my focus in the present post is empirical biologists who do not have a special interest in developing statistical methods.



Coda


There are some basic thoughts about statistics that are sometimes lost or forgotten in this brave new world of R-based statistics. The truth is, I am not a statistics expert by any stretch of the imagination, and so I have concentrated my comments on more basic, perhaps even philosophical, points. However, so much training is now provided in the mechanics of statistics, and R, that I think it is these more basic points that you are more in danger of forgetting or foregoing. Having said all this, it is perhaps time for #SPSSHero to also become #RHero, instead of relying on my lab members to do all the heavy lifting while I simply sit around and complain about it.

Monday, December 22, 2014

Conference: Speciation 2015! Register soon!

Hi all.  A quick announcement regarding an upcoming conference:

Who: You – or your grad students!
What: Speciation 2015
When: March 15–20, 2015
Where: Ventura, CA
Why: Modes of Diversification, Ecological Mechanisms, and Genomic Signatures
How: Register at http://www.grc.org/programs.aspx?id=16902

  There’s lots of additional information at the link above.  This is the world’s largest conference on speciation research, and they have put together a truly stellar list of invited speakers.  I wish I could go; so you should go, so that I can live vicariously through you.

  The conference is close to full, so get your registration in soon!

Friday, December 19, 2014

Best evolution books for Christmas

This is the time of year for lists: the best this, the most important that, and so on. Just such a list was circulating today on twitter about the best science books of the year, presumably also books you should be buying for Christmas presents. This discussion got me to thinking – what would be the best evolution books for Christmas? I don’t just mean published this year, I mean published in any year but still available. I also don’t mean technical books but rather popular books that you might give your friends or spouse or yourself. The kind of book you will read in bed at night rather than in your office in the morning. Books that are easy to read and a great advertisement for how cool evolution is and can be – either remotely or on the front lines of research. Meshing nicely with this idea was the realization that I have, over the years, benefitted greatly from Christmas books about evolution – mainly from my Mom but also from students, friends, colleagues and other family members. With all of this in mind, here is my subjective and incomplete listing of the best evolution books for Christmas (or any time). At the outset, I have to say that the first two books are by far the best of the bunch.

1. The Beak of the Finch by Jonathan Weiner.

This book, a Christmas present from my Mom in 1995, quite literally changed my life. It is a wonderfully written and engaging account of the importance of rapid evolution told mainly through the research on Darwin’s finches conducted by Peter and Rosemary Grant. Reading this book that fateful Christmas, I had an epiphany “you can actually watch evolution happen in real time” that almost instantly changed my career goals from wanting to study salmon to wanting to study evolution. To this day, my research focuses squarely on “rapid evolution” – I now even work on Darwin’s finches in Galapagos, as well as on other systems – most notably Trinidadian guppies – described in the book. This book is, for me, the best example of how important and dynamic evolution is and how exciting it can be to study. And it isn’t just me – the book won a Pulitzer Prize as well as many other accolades. If you read only one book about evolution, if you give only one book about evolution, this should be it.


2. Nature’s Nether Regions by Menno Schilthuizen

This recently-published book was also a gift from my Mom, although not for Christmas. She had read it and found it so much fun that she had to buy it for me – in hard cover no less (I always wait for paperback and, even then, for books to go on sale). The book describes, in extremely entertaining prose, the evolution of gentalia in animals, including humans. It is jam-packed with fascinating and extremely well researched examples of how evolution is shaped by penis-driven internal courtship, as well as other titillating (but not superficially) topics. It is the sort of book that you can’t help but read out quotes to your spouse or students or at parties. The sort of book where you can’t help but laugh or exclaim out loud, or think “Wow, I had no idea the clitoris was that big” or that “As familiar as the old in and out is, there is nothing about the biology of reproduction that requires rhythmic penis movements.” (This last is an attempt to paraphrase from memory the infinitely more clever original sentence.) The book also proves that the best writers in English don’t have to be native English speakers.


3. The Voyage of the Beagle by Charles Darwin

Perhaps you weren’t expecting this one as it was published 175 years ago – yet it remains a compelling read. It has all of the innocence and excited of a pre-worried Darwin as he voyages around the world discovering new things. His excitement is contagious and much of the book reads as might a contemporary account of discovery, albeit set in an endearing (to the outside reader) context of the mid-1800s. I have given this book to a number of non-scientists and they have found it enjoyable. It is also worth saying that it is one of the best of the accounts of discovery from the golden age of natural history, among other great accounts I have read by Wallace and Bates and others. And, of course, one can’t help be chuffed by catching the early hints of Darwin’s emerging idea of evolution by natural selection.


4. The Song of the Dodo by David Quammen

Here is another book that, like the Beak of the Finch, has been around for quite a while (although not as long as the preceding book) and is critically acclaimed. It is a deft mash-up of island biogeography, conservation, invasion biology, and evolution. It describes in great detail and with personal stories and anecdotes from actual researchers on the ground how the geographic distribution of the world has shaped the distribution and evolution of animals and how this context has then shaped extinction risk through direct or indirect (e.g., invasive species) human influences. My only complaint is that the text is a bit bloated and, thus, sometimes tedious. I can’t help but think that cutting 20% of the book would have made it much more engaging.


5. Dark Banquet by Bill Schutt

Another Christmas present from my Mom, this relatively recent book describes the curious lives of blood feeders: ticks, chiggers, vampire bats, leeches, mosquitoes, and so on. This is another book that generates tons of anecdotes and comments that you just can’t help but share with others (George Washington’s death might well have been accelerated by doctors leeching too much blood). I even use it for teaching – my favorite way to discuss the kidney now is to talk about the difficult challenges and ingenious evolutionary solutions that vampire bats use to deal with their massive blood meals. On the negative side, it makes you itchy at night when reading about bed bugs. A great read though.


6. Dr. Tatiana’s Sex Advice to All Creation by Olivia Judson

This book is like Nature’s Nether Regions in that it takes on the titillating subject of sex and sexual selection. However, the main appeal is its clever gimmick. It is written as though the author is a sex advice columnist to various organisms who write in to describe their problems – sort of a Dr. Ruth or Dan Savage to the animal world. A fun read and it has been adapted into a TV show.


7. The Curse of the Labrador Duck by Glen Chilton

This book is a strange and whimsical choice but it is really fun to read. It describes the quest of the author – charged with writing a species account of the extinct Labrador Duck – to see, measure, and consider every Labrador Duck specimen in the world, something around 55 or so. He traipses around the world finding and measuring each specimen (often with considerable difficulty) and injects his own fun observations and perspectives on the places and people he visits. The numerous cute phrases are a bit much sometimes (and it isn’t really a book about evolution) but it is hard to find a book that feels more like spontaneous fun with natural history.  By coincidence, the duck on the cover is right outside my office door – but this has nothing to do with my affection for the book.


8. Various books about strange critters

I often read species-centric books, such as Moa, Platypus, Tasmanian Tiger, and A Fish Caught in Time. These books have a singular focus on a particular taxon that allows the author to create a much more coherent narrative than more general books about general phenomena, such as sexual selection or blood feeding (although the above books do a great job regardless). Among these books, I recall with most affection the Platypus book, by Ann Moyal, which is a fun account of how western science was long befuddled with the evolutionary position and significance of the playtypus. It describes the various well-known (Richard Owen) and obscure personalities who weighed in on the topic and how the discoveries changed our view of the evolution of vertebrates. A Fish Caught in Time, by Samantha Weinberg, is also outstanding.



Many other great books are out there but I don’t have time to describe them all – my apologies to many other great authors, with particular recognition of Carl Zimmer (Parasite Rex) and Rebecca Stott (Darwin and the Barnacle). I welcome further book suggestions by way of comments on this post. I would also like to apologize for any mistakes or bad grammar in the preceding. I only had the idea for this post an hour ago and it seemed appropriate to do NOW (enough time to buy those books) and I want to go play with my kids. So, until next time, clever spending and good reading. Happy holidays. 

Thursday, December 11, 2014

Retracing the legacy of guppy introductions past: local differentiation maintained despite high and rapid gene flow

[ This post is by Sarah W. Fitzpatrick; I am just putting it up.  –B. ]

When populations adapted to different environments come into contact through range expansions, invasions, or by human-assisted migration, the outcome is often unknown. How will immigrant individuals fare in the new environment and will they hybridize with native populations? If so, what impact does gene flow with non-native individuals have on the local populations? The question of whether gene flow between adaptively divergent populations promotes or constrains local adaptation is a long-standing puzzle in evolutionary biology and is increasingly relevant for designing effective conservation strategies.

In our recent study (Fitzpatrick et al., 2015) published in Ecology Letters, we turned to Trinidadian guppies, a model system for studying evolution in the wild, to ask questions about how gene flow affects fitness and local adaptation. The guppy system has proved powerful for understanding a diverse set of questions in ecology and evolution, but these rapidly evolving freshwater fish are perhaps most famous for the series of transplant experiments conducted in the streams of Trinidad. Caryl Haskins initiated the first introduction experiment in 1957, moving guppies from an environment where they experienced high mortality from predation to a site in a separate drainage where waterfall barriers limited the upstream colonization of most fish, including predators and native guppies. The introduction site thus represents a release from predation. John Endler and David Reznick and his colleagues have since repeated this transplant scenario in multiple independent drainages, and their studies of rapid adaptation and eco-evo feedbacks focusing on the introduced populations are iconic in evolutionary ecology.


In each of these introduction streams, native guppies existed downstream from introduction sites, and although the introduced populations were initially maladapted to their new environment, gene flow is expected because adaptive divergence has not led to reproductive isolation in guppies (Crispo et al., 2006). In fact, we expected much higher levels of downstream gene flow from introduction sites than what is observed in natural streams due to the fast life history of the introduced fish and female preference for novel males (Fig. 1; click to see at larger size).


Figure 1. Conceptual diagram illustrating the expected differences in amount of gene flow between natural streams and streams with introduced populations. In both hypothetical streams, predation level is colour-coded based on the species listed in the bottom key, and increases in the downstream direction. Black rectangles indicate waterfall barriers that limit upstream fish dispersal. The colour of fish indicates traits matched to a certain level of predation (e.g., the blue fish has traits that are adaptive in a low-predation environment). In the hypothetical natural stream, fish are perfectly matched to their level of predation and gene flow among populations is low, based on biological factors listed in the grey box. In the hypothetical introduction stream, guppies from high-predation (HP) environments were translocated upstream of naturally occurring low-predation (LP) populations. Gene flow is expected to increase relative to natural levels for the reasons listed in the grey box, and the effect of elevated gene flow on locally adapted traits remains unknown (indicated by grey fish and question marks).

Our team, from Colorado State University, asked what the effects of elevated levels of gene flow from an initially phenotypically divergent population would be on locally adapted phenotypes in downstream populations. Following the footsteps of Haskins, Endler, and Reznick, we sampled guppies from six historic introduction sites, from each of their source populations, and from multiple sites downstream from each introduction. We genotyped all individuals at ten microsatellite loci and quantified a suite of known fitness-related traits, such as male color, body shape, and some female life-history traits that tend to differ based on the level of predation experienced. We also included individuals from two native populations prior to the onset of gene flow, providing a powerful opportunity to compare pre- versus post-gene flow phenotypes and allele frequencies. At each site we classified the predator community as either low, medium, or high based on the complexity of the fish community observed.


Clockwise from back left: Jed Smith (undergrad researcher, CSU); Chris Funk (asst. prof, CSU); Sarah Fitzpatrick (PhD student, CSU); Lisa Angeloni (asst. prof, CSU).

We found that the genetic signature of introduced guppies swept throughout all downstream distances, indicating high levels of gene flow downstream from all introduction sites on a rapid timescale (Fig. 3a; click to see at larger size). However, despite genetic uniformity caused by introductions, guppies sampled from different predation communities along the streams maintained phenotypic traits that best allowed them to survive and reproduce, given the local predator community. In other words, genetic homogenization did not cause the loss of locally adapted phenotypes.


Figure 3. (a) Comparison of genetic differentiation (pairwise-FST) among all sites in natural streams vs. among all sites in streams after introductions took place. (b) Within-stream STRUCTURE plots and average pairwise-FST values for all six streams that experienced an upstream introduction. Each line in the plots corresponds to an individual with colours representing the proportion of an individual’s genotype assigned to a given genetic cluster. Old introductions show fine-scale genetic structure despite low genetic divergence (low FST). All sites from the three recent introductions conducted in the Guanapo drainage were included in the same analysis because they share the 5000 m and source sites. These recent introductions are more genetically homogeneous, with the exception of pre-introduction 0 m sites in Taylor and Caigual (shaded in blue) that are very distinct and genetically divergent (high FST) from the rest of the sites. Colored circles on the x axes indicate the predation level at each site: blue = low, green = mid, red = high, as defined in Fig. 1. All plots represent the (k) number of genetic clusters with the highest support (see Appendix S1).

We used the exchangeability analysis described in Hendry et al. 2013 to compare neutral genetic and phenotypic exchangeability among the common source site and two native low-predation sites that were sampled before and approximately 12 generations after gene flow. A major take-home message from our study can be gained from the results of this analysis. Namely, we found phenotypic divergence associated with the local predation regime despite neutral genetic homogeneity with the source of the introductions (Fig. 5; click to see at larger size).


Figure 5. Ordination plots and group classification based on discriminant analysis of principal components (DAPC) for neutral genetic loci (a) and phenotypic traits (b). Colours correspond to a priori groups based on population origin: native low-predation in purple, the same sites post-introduction in blue, and introduction source in red. Bar graphs below the dashed line show the mean (and 95% CIs) proportion of individuals from each population classified into each population. Each bar represents the classification of the population on the x axis, as labelled for one set of bars in (b). The bottom-left insets show eigenvalues of the four principal components in relative magnitude.

Phenotypes were measured from wild fish, so we are unable to separate the relative roles that phenotypic plasticity and adaptive evolution play in causing the observed phenotypic divergence, but we argue that both processes are likely involved. Ongoing work that includes common garden assays conducted before and several generations after the onset of gene flow, and wild pedigree reconstruction throughout the initial pulse and longer-term wave of gene flow, will add to our understanding of the mechanisms by which gene flow impacts adaptive evolution and population growth.

Although we expected to find high gene flow from the introduced populations, we were surprised by the near-extinction of native alleles, especially downstream from the set of recent introductions. However, differential rates of introgression across the genome may result in homogenizing effects of gene flow at neutral loci, while locally adapted native loci or genomic regions are maintained by selection.

Whether the loss of native genetic signatures at neutral loci represents a true detriment is an intriguing philosophical debate. The costs of gene flow between populations of the same species may be outweighed by the benefits in cases where selection for a local ecotype is strong, or where recipient populations are inbred. This study reveals how model systems that can be manipulated in the wild have the potential to inform smart management decisions for threatened populations.

Indeed, we have pioneers of model systems like Haskins, Endler, and Reznick to thank for building foundations that continue to generate creative questions and provide a deeper understanding of nature.

References

Crispo, E., Bentzen, P., Reznick, D.N., Kinnison, M.T., and Hendry, A.P. (2006). The relative influence of natural selection and geography on gene flow in guppies. Mol. Ecol. 15, 49–62.

Fitzpatrick, S.W., Gerberich, J.C., Kronenberger, J.A., Angeloni, L.M., and Funk, W.C. (2015). Locally adapted traits maintained in the face of high gene flow. Ecol. Lett.

Hendry, A.P., Kaeuffer, R., Crispo, E., Peichel, C.L., and Bolnick, D.I. (2013). Evolutionary inferences from the analysis of exchangeability. Evolution 67, 3429–3441.

Figures are cited based on the numbering in the manuscript.