May 13, 2010
Nature's Journal Club
A few months ago, I was invited to write a column for Nature's Journal Club. This series appears every week in their print edition and the text is also posted on the Journal Club's blog. The instructions were pretty simple: pick a paper with broad appeal, which has been published sometime in the last year or so and which has not appeared in Nature , and write about 260 words summarizing the results and describing why I like it. I was encouraged to be provocative, too, but I'll leave it to you to decide if I was that bold.
After mulling it over, I agreed to write a column on a paper by James O'Dwyer and Jessica Green, both at the University of Oregon in Eugene [2,3]. You can read the blog version of the column here or the print version here. Here's the setup:
Many species are concentrated in biodiversity hot spots such as tropical rainforests and coral reefs. But our estimates of how many species these and other ecosystems contain are very rough. Conservation efforts and ecological theories would be better served by a more accurate picture.
Our best guesses come from empirical species–area relationships, which count the number of species observed as a function of geographical area. These relationships show sharp increases at local and continental scales, but slow growth at intermediate scales. Despite decades of study, ecologists have no clear explanation of this pattern's origins or what causes deviations from it.
These species-area relationships (SARs) are ubiquitous in ecology largely because ecological survey practices have long focused on counting species within a specific study region. Most such data is collected from small survey areas and these data are then combined within a meta-study to get up to the regional or continental scales. Perhaps because of the ease of constructing SARs, much ink has been spilled over their structure. They're also our only reliable tool for estimating how many species live in places like the Amazon or the Great Barrier Reef, which are too large to survey completely.
What's clear from all this work is that there are some general patterns in SARs, and that if we want to use them in unconventional places, such as in estimating the number of microbial species in the world (or in smaller regions, like your gut), then we need a good theoretical explanation of where those patterns come from and what processes cause deviations from them at different length scales. That is, we need a good null model.
Creating one is largely what O'Dwyer and Green have done. There have, of course, been previous explanations of parts of the SAR pattern, with various amounts of biological realism. On the more unrealistic side, simple iid sampling from a sufficiently heavy-tailed distribution can generate SARs with power-law slopes in the right neighborhood. But, this kind of explanation ignores biological processes like speciation, extinction, dispersal in space, competition, etc., not to mention abiotic factors like geography and climate.
Building on previous work on neutral processes to explain biodiversity patterns, O'Dwyer and Green built a null model containing only the neutral processes of birth, death and dispersal. What makes this model different from, and better than, previous efforts is that it explicitly incorporates a notion of spatial structure by embedding species in space and allowing them to move around. This is helpful because it gets directly at the SAR. The problem, however, is that spatially explicit stochastic processes can be difficult to solve mathematically.
Fortunately, O'Dwyer and Green could use tools from quantum field theory, which is well suited to solving this kind of spatial stochastic model. Aside from the coolness of using quantum field theory in ecology and the fact that it predicts an SAR that agrees with decades of data, what I like about this result is that it illustrates two things close to my heart. First, it's a beautiful example of a null model. Because it includes only boring, neutral processes in generating its prediction for the SAR, when empirical data deviates from the model's prediction, those deviations can be interpreted as model mis-specification errors. In this case, that means interesting, non-neutral ecologically significant processes like competition, predation, habitat, climate, etc. In this way, it can generate new, specific hypotheses about what to test next.
The second is that this approach to model building moves the emphasis of the science away from small-scale (in time or space), context-dependent processes and towards more large-scale (in time and space) neutral dynamics and principles. This kind of perspective is currently more common in the physical sciences than in the biological ones , but I hope to see more of it in biology in the future , and it's one of the things I think physics has to offer ecology .
This aspect of O'Dwyer and Green's work fits nicely with my own on explaining why we see such huge morphological diversity in living and extinct species, and how whales got so much bigger than mice. In a way, the model I've been using is pretty similar to O'Dwyer and Green's: it omits all ecological processes, climate and geography, but includes neutral processes representing species birth, death (extinction), and dispersal (changes in body mass). The fact that both our neutral models do pretty well at correctly predicting the observed empirical data suggests that perhaps randomness, structured by a few relatively banal processes, might be a pretty good general explanation of how the biological world works at these very large scales. I suspect also that similar models, expanded to include some role for social institutions, will also work well to explain how societies work at large spatial and temporal scales. Finding out if this is true is something I hope to be around for.
 This criterion was a pleasant surprise. As much as I dislike Nature's outsized status and influence in science, I've been pleasantly surprised on several occasions by some of their policies. Someone there genuinely seems to care about the integrity of the scientific process.
 Full disclosure: Jessica recently joined the external faculty here at SFI and James will be starting as a postdoc at SFI in the Fall. That being said, I haven't really interacted much with either of them.
 O'Dwyer and Green, "Field theory for biogeography: a spatially explicit model for predicting patterns of biodiversity." Ecology Letters 13, 87-95 (2010).
 It's uncommon in the biological sciences, but not unknown. Mathematical evolutionary theory and population genetics are good examples of communities that frequently use null models in this way . I think the reason such an approach is more common in the physical sciences today is that we actually understand a great deal about the fundamental processes there, and what things can and should vary in different contexts, while we're still sorting those things out in biology. For sure, we're making progress, but it's slow going.
 It would be good for other fields, too, such as sociology and political science. The issue is, I think, that scientific progress toward general principles is always limited by the availability of data that reveal those principles. When scientists of any kind are restricted to having either rich data on a small number of examples (think of alchemy), or poor data on a large number of examples (think of polling data), it's hard to make real progress. In both cases, there are typically an embarrassment of reasonable explanations for the observed patterns and it's difficult to distinguish them with the crappy data we can get. This is partly why I'm excited about the increasing availability of "big data" on social behavior, largely coming out of digital systems like email, Facebook, Twitter, etc. These data are not a panacea for social science, since they have they have their own weird biases and pathologies, but they're rich data on huge samples of individuals, which is qualitatively different than what was available to social scientists in the past. Perhaps we can answer old questions using these new data, and perhaps we can even ask some new questions such as, Are the behavioral patterns at the population scale simply scaled up versions of the behavioral patterns at the individual scale?
 To summarize: what I think physics has to offer ecology, among other fields, is (i) a very impressive and useful set of mathematical tools and models, and (ii) a valuable shift in perspective, away from small-scale processes and toward large-scale processes and general principles. I'm not advocating that we replace ecologists with physicists, but rather that we encourage physicists to train and work with ecologists, and vice versa. Biology will always need scientists focused on understanding specific contexts, but it also needs scientists focused on synthesizing those context-specific results into more general theories, as I think O'Dwyer and Green have done. Generally, physicists often have a good intuition about which details will be important at the large-scale and they often have good mathematical tools for working out whether its true.
 The statistical models that underly most statistical hypothesis tests, which are ubiquitous in the biological and social sciences, are technically null models, too. But, in many cases, these are wholly inappropriate since their iid assumptions are grossly violated by the mechanistic processes actually at play. That being said, it can be hard to come up with a good null model because often we don't know which processes are the important ones to include. A topic for another day, I think.
May 11, 2010
A couple of weeks ago, Dan Dennett (yes, that one)  and I were chatting about cybersecurity, cyberwarfare, and the questions of what the world might look like in 10, 20 or 30 years if countries push forward with developing methods for attacking each other and defending themselves via the Internet. A stimulating discussion, for sure, but not what this blog post is about.
After the conversation, Dan introduced me to a great game he called Frigate Bird, which is played with Scrabble tiles. The game takes it's name from the species of bird, after their habit of stealing food from other birds, because players can (and almost must) "steal" words from each other as part of the game . In fact, it would be very impressive for someone to win without ever stealing a single word! The game is easy to learn, but hard to master. The key skill is being fast with anagrams as that's the way you steal a word from another player. (You can also "steal" from yourself, as a protective measure.) Below are the rules, written down by Dan himself. (And apparently written down for the first time ever, since the birth of the game several years ago.) If you try the game and like it, I'm sure Dan would like to know!
The Official Rules (written down for the first time, May 11, 2010)
The game is played with the tiles of a Scrabble board, but without the board itself. This makes it more portable than regular Scrabble—just bring the bag of tiles with you. You start by turning all the tiles face down (if you see the two blanks remove them now, or set them aside when they show up in the first game, since they are not part of the game). Any number can play. One player is designated as the "dealer." The dealer turns the tiles over, one at a time, making sure that all players get to see the new tile at the same time (speed is of the essence in this game). The new tile is immediately part of the pool of face-up tiles to which everyone has access at all times. The goal of the game is to make words from this pool as soon as you see them.
1. The usual Scrabble rules apply: no proper nouns, hyphenated words, contractions, .... (Having a dictionary handy to settle disputes is a good idea.)
2. Every word must be at least 4 letters (or 5 letters) long. (We have recently been playing the 5-letter minimum game, and it seems to be more interesting, since second-rate 4-letter words don’t get pulled out of the pool prematurely, diminishing the usable variety. You might want to warm up by playing the 4-letter version and then switch to the 5-letter version once players are familiar with the possibilities.)
3. To claim a word you must CALL IT OUT. (If you start reaching for the letters without saying anything and somebody else calls out the word, or a different word, they get the word.)
4. After a word is called out (and there is no challenge), the calling player assembles the word (facing the other players, upside-down for the player) in his area. This word belongs TEMPORARILY to the player who called it, but it (and every other word already assembled on the table) is ALWAYS vulnerable to theft by another player (or pre-emptive self-theft by the same player). A word may be thus stolen—or protected from being stolen—by calling out a new word that uses ALL the letters in the stolen word plus one or more letters from the pool. This is the eponymous "frigate bird" move. *[*The game was originally just called "lightning anagrams" but on a trip through the Galapagos with us, the science journalist Sherrie Lyons exclaimed "You . . . . FRIGATE BIRD!" when I stole one of her words. Frigate birds, which were wheeling overhead throughout our trip, typically wait for another bird to catch a fish and then dive-bomb it, stealing the fish from the original catcher. The epithet "FRIGate bird" trips so satisfyingly off the tongue at these moments of f-f-f-f-f-frustration and f-f-f-f-fury that we rebaptized the game on the spot.]
5. There are constraints on frigate-bird captures. You may not just add a prefix or suffix or plural to a word; the ‘stem’ of the word must be changed. Thus "march" may not go to "marched" but can go to "charmed". There are borderline cases that may be decided to suit the players. Definitely "chief" can go to "kerchief" and "pealing" to "appealing" since the meanings are so disparate; we have recently approved "afoot" to "barefoot" but others might be more purist. Alternations are OK. Thus "equip" can go to "piquet" which can THEN go (back) to "equipment."
6. Frigate-bird steals may be done at any time. The game ends when nobody can find any moves to make with the remaining tiles in the pool.
7. Scoring: in the 4-letter version, 4-letter words count 4, no matter what letters they use. All longer words count the Scrabble value of their tiles. Thus turning your own 4-point word ‘quip" to "equip," or "pique," adds 12 points to your score!) If somebody then steals pique" by going to "equipped" (legal, since not going from "equip") you lose your 16 point word and they get a 21-point word. A large lead can evaporate with a few end-game coups like this. In a recent game the back and forth sequence "grind," "grinned," "enduring," "laundering," "underlaying" was played.
8. The pace of "dealing" may be highly variable, with long pauses while everybody tries to find a use for a particularly juicy addition to the pool, but fairly rapid turnovers when the pool is particularly barren (e.g, when a third "u" arrives in the pool it is close to a certainty that no new combinations are possible, so turning over another letter speeds up the game). In general, if the dealer let’s his own survey of the prospects guide the speed, slowing down whenever a wealth of opportunities seem to be available, the other players will typically appreciate the extra time as much as the dealer does. Players may ask the dealer to slow down or speed up, and their requests should be honored.
9. We have officially granted that a legal frigate-bird might be accomplished by combining all the letters of TWO words on the table (using no additional letters from the pool) that this has yet to happen in our experience. An example would be if player A has "slots" and player B has "utopian," any player might call out "postulations" and win a place in Frigate Bird history. A player might put together two of his own words, for no additional score, but to keep some other player from doing this and taking the result.
10. Penalties: We have never felt the need to penalize people for calling out impermissible words, aside from disallowing the word and returning the tiles to their pre-call positions. Nor have we felt the need to punish challenges that were not upheld by a dictionary. Others might wish to experiment with more retributive policies, of course, which could introduce an element of strategic bluffing, but the game has enough rough-and-tumble action as it is, in our opinion.
11. A word on the phylogeny of the game: We, Dan and Susan Dennett, were introduced to the game in 2001, at the Villa Serbelloni in Bellagio, Italy, by the composer Nicholas Brooke and the English Professor Julie Barmazel. The game got its present name, as noted above, in 2005, at the World Summit of Evolution, in the Galapagos Islands.
 Dan Dennett is just finishing up a semester-long visit at SFI as the inaugural Miller Scholar. He's been a wonderfully active community member while he's been here, giving a public lecture on the origin of religion, inviting a speaker (his collaborator Matt Hurley) to discuss theories of humor, along with the usual fun and impromptu discussions over lunch and tea.
 Frigate birds are sometimes called "kleptoparasites" since they routinely steal food from other birds. In the spirit of this post, "kleptoparasite" would be a great frigate bird play perhaps by building onto "aspirate" (an anagram of parasite) or by combining "parakeet" and "pistol".
May 03, 2010
How to give a good (professional) talk
Partly because of my year-in-review blog posts (e.g., 2009) and partly because I actively keep track of this kind of stuff (to convince myself at the end of each year that I have actually gotten some things done over the past 12 months), I know that I've now given about 65 professional talks of various kinds over the past 7 years. I cringe to think about what my first few talks must have been like, and I cringe only a little less to think about my current talks. I'm sure that after another 650, I'll still be trying to figure out how to give better talks.
That being said, I do think I can recognize good advice when I see it (even if I have a hard time following it), and John E. McCarthy's advice  is pretty darn good. Some of it is specific to mathematics talks, but most of the points are entirely general. Here are the main bullet points (on each of which McCarthy further elaborates):
1. Don't be intimidated by the audience.
2. Don't try to impress the audience with your brilliance.
3. The first 20 minutes should be completely understandable to graduate students.
4. Carry everyone along.
5. Talk about examples.
8. Pay attention to the audience.
9. Don’t introduce too many ideas.
11. Find out in advance how long the colloquium is, and prepare accordingly.
13. You do not have to talk about your own work.
I also found Scott Berkun's book "Confessions of a Public Speaker" to be both entertaining and useful. Berkun is a professional public speaker who works the technology circuit, but a lot of his advice holds just as well for scientific talks. Here's a selection of his advice (some paraphrased, some quoted):
1. Take a strong position in your title.
2. Think carefully about your specific audience.
3. Make your specific points as concise as possible.
4. Know the likely counter arguments from an intelligent, expert audience.
5. You are the entertainment. Do your job.
6. A good talk builds up to a few simple take-home messages.
7. Know what happens next (in your talk).
8. The easiest way to be interesting is to be honest.
9. Do not start with your slides. Start by thinking about and understanding your audience.
10. Practicing your talk will make it much much better.
A couple of years ago, SFI hired a professional "talk" coach. One of her main suggestions, and one shared by Berkun, is that we should each video ourselves giving a talk and then watch it several times to see what exactly we do that we're not aware of. This is a cringe-worthy experience  but I can tell you it's highly useful. I think we as speakers are often completely unaware of our nervous tics or obnoxious speaker habits. Watching yourself on video is perhaps the only way to get unbiased feedback about them.
Tip to Cris.
 A little Googling suggests that McCarthy's advice originally appeared in Canadian Mathematical Society NOTES in 1999 and was then reprinted by the American Mathematical Society. And, it seems to have a long history of being passed around since then.
 "OMG. Do I really sound like that?" and "Woah. Do I really do that when I talk?"