Academic constipation

The NYTimes has a recent article about the current academic job market. In two words, it sucks. Professors who thought they could afford to retire are staying on, so positions aren't opening. Even when they do retire, hiring freezes are leaving their positions vacant until the economy improves, so post-doctoral researchers aren't likely to find a tenure-track position, and remain in underpaid temporary jobs. This means there are extremely few positions (as faculty or post-docs) for recent grad-students, and the usual routes out of academia, industry jobs, also aren't available. But while the routes out for grad-students are limited, recent college graduates who can't find real jobs are apply to graduate programs in record numbers.

While the situation is bleaker for the humanities, which have long been in decline economically, the scientific community in the US is keenly aware that however this turns out will have effects that will be felt for generations. The closest comparison I can make is the tremendous expansion of enrollment in colleges in the 1960s and '70s. As the students flooded in, colleges hired large numbers of professors right out of grad school. By the 1980s there were extremely few positions opening up, almost every professor had been hired in the previous two decades, and there were almost no retirements. A whole generation of academics had almost no chance of finding a professorship. I know several people of that era whose careers were permanently put onto alternate tracks because they simply couldn't find a professorship. Then, just in the last decade, that flood of professors hired to teach the baby-boomers have been retiring in droves, and many people were expecting a new wave of hires. More than one person has told me, "When I finished grad school, there were no jobs. When you finish, there will be openings everywhere." Now, given the economy into which I am graduating, I feel lucky to be in a sub-field that has some post-doctoral positions for a few years. Perhaps in three or four years universities will start filling all those vacant positions, and a new wave of young faculty, hopefully including me, will be an impediment to the career aspirations of our younger colleagues.

Evolutionary challenges to gene engeneering a better human

My friend Terry is a bioengineer, as well as a part time futurist. Much of what people in his field work on (as judged by what Terry talks about when he puts on his futurist hat and has a couple of glasses of wine) is thinking about how to modify the human genome to increase our lifespan and healthspan (I just made up the word healthspan, but I bet someone out there is already using it). Much of my work is to understand how and why we evolved to have the lifespan and healthspan we currently do. My understanding of my work is not promising to my understanding of this part of Terry's colleagues' work. In my view, bioengineering a much longer lived human will be extraordinarily difficult for several reasons.

First, we have a great many systems that seem to fail at about the same age, and there are good evolutionary reasons why this should be. Why bother building a femur that last longer than your heart, or your brain, or your pancreas? So to engineer a much longer-lived human, one has to be prepared to make a large number of changes to see a small effect. Terry counters that there will inevitably be some low-hanging fruit, and I concede this point. Simply by editing out some of the purely harmful mutations in the human gene-pool, we can probably extend average life span by a few months or maybe a couple of years. But because so many things fail at similar ages, no one or two or 100 changes could give us healthy 150 year olds.

Second, many individual genes have an enormous number of different effects in a wide range of systems, tissues and traits. When a gene has more than one effect, this is called pleiotropy, and we are chock full of pleiotropies, most of which we don't yet know about or understand. DNA is not like a blueprint, where you can just erase one wall, re-route a few wires, and draw in a new door. It is more like a vastly sprawling and disorganized system of interacting computer applications, add-ons, duplicates, and operating systems (only without any comprehensible order, annotation or easily understood compartmentalization). Something which functions as part of an unnecessary application may also be used in several disparate parts of the operating system. Modify a line of code and all sorts of unintended things can happen. Evolution has fine-tuned this system of interactions through millions of generations of trial and error, with emphasis on the error. Our best computer simulations are barely able to comprehend the folding of a single amino acid string into a protein, let alone a whole cell or organ or human, and animal models only go so far. So the process to modify evolution's optimization would not be fun, fast or clean. Our various bits are tuned to work together, and most potential single modifications can only move us away from that local optimum.

Terry counters that in many cases what evolution was tuning was utility in the form of health/strength/life vs. cost in the form of calories. A large part of the theory of life-history evolution is based on models where developing organisms have limited nutritional resources to invest in important tasks like growing, healing and reproducing. If one assumes unlimited calories are available, one can theoretically grow, reproduce and heal maximally all at the same time. And in Terry's view (which I can't help but see the wisdom in) anyone who can afford to play with the human genome can also afford plenty of potato chips. For the relevant population, calories are no longer limiting. In fact, we go out of our way to burn extra calories now. Spending calories lavishly to buy a few extra years of life or more garish secondary sexual traits is a win-win. The bioengineers of the future will have the advantage over evolution, because they won't have to worry about one of the main constraints evolution was dealing with, calorie restriction. So we may have to change a few things at once to make it all work well together, but we can do that. We can, in my imagining of Terry's thinking, reengineer the organism to its new environment.

It occurred to me last night that there is third, bigger and more insurmountable barrier to re-tuning. One that is not just a technological limitation: Breeding. Humans have been known to breed with each other, and in doing so they mix their genomes. You have half the genome of your biological father and half the genome of your biological mother. Imagine if your uber-mench father had a carefully altered suite of genes, and your mother was a good old-fashioned non-GMO woman. What do you get? You get half a carefully altered genome mixed with genes they were never designed to interact with. Chances are, you have all sorts of wacky health problems, and greatly reduced longevity. It would be like taking half the code of Mac OS 9 and half the code of OS X and expecting a stable operating system.

This means that every change and group of changes would have to be carefully designed to be back-compatible. The alternatives are gene altering the entire human population (which would never ever ever ever work (and I very rarely use that many "ever"s in a row)) or engineering the longevous new humans to be incapable of interbreeding with the old model. They'd have to start by separating off one population as a seperate species, Homo terrii, and only thereafter get serious about reengineering.

So suppose the engineers decide they want to make everything back compatible?
I'm not convinced this would work either. Most mutations are bad for you not only because they break a piece of the system, but because they make a new piece that doesn't work with what is already there. Requiring back compatibility means we have to have every piece work with not only the old set of genes and the new set of genes, but every possible combination of old and new. Evolution, largely free from constraints of time, funding and ethics, accomplishes this by letting those individuals who have bad combinations die out until there are very few harmful combinations possible. To extend the computer code analogy, this would be like trying to write OS XI in such a way that if one blended the code with OS X, it would still work. It is possible to do, but XI would end up looking an awful lot like X, too similar to be more than a service update.

This leaves only the option of creating a population incapable of breeding with normal humans and altering their genes extensively to try to overcome a large number of age-limiting factors at once. Again my understanding of evolution suggests a major difficulty. To do this successfully, one would need a large population all gene-altered simultaneously, to avoid inbreeding effects. One can't start a new population with just a few individuals and expect that species to have a decent chance of surviving well. Even if the species does make it through, there is likely to be an extended period of decreased lifespan and healthspan while the inbreeding kinks work themselves out and the population increases in size and genetic diversity.

Without doubting that bioengineers will continue to make things that seem impossible become projects of undergraduates, I consider it highly unlikely they will achieve any very significant advances in human longevity in the next few decades.

(NOTE: I sent this to Terry for comment or objection some time ago but he has been busy with 'job' and 'editing the book.' I take his failure to offer a substantive reply as evidence that in some basement deep under campus, his department is already failing to build an immortal human.)

Moving the last rotifer

For much of the last year my life and schedule have revolved around daily rotifer census. How often I go to campus, at what times, when I have time for anything else and the energy and time I have for anything else have all depended on lab work. When I could rely on my students to take care of it, I could do other things. Frequently, very frequently, my supply of dependable students was not up to the demands of taking data on and caring for several hundred animals each day. Even when my students are scheduled to do everything, it is rare for a day to go by without questions, problems or scheduling issues. If I am not in lab for a day or two both the quality of the data and the survival of the animals begins to decline.

So it feels like a big deal that my lab work will be done this week. Thursday. I've told my students that after that they are free to continue working on their side projects, but I'm not going to be in the lab. I'm not going to spend hours moving rotifers. I'm not going to be harassing them about keeping the lab organized and the rotifers' containers clean. I'm not going to be on campus six or seven days a week. I'm going to be at home, writing a thesis, and will come to campus on Wednesdays and Thursdays. And I'm taking my desktop (the lab's erstwhile main computer) home.

I like my students, and the rotifers are fascinating, and microscopes are fun. But I really like the idea of not needing to be in the lab every morning at 8. And the prospect of being able to have whole days to work on writing my thesis is positively thrilling.

Re-write

It finally occurred to me that the paper I have been writing on the evolution of post-fertile survival (a.k.a. post-reproductive lifespan) really needed to be two papers. I had too many interwoven points I was trying to make simultaneously, and the paper was getting too long and ungainly. So I needed to write two shorter papers, and as a bonus, I needed to have a draft of one to present at a lab meeting tomorrow. I sat myself down this morning at 8AM and wrote for 15 hours with only a few short brakes. Some of this was cutting and pasting, although the pasted bits often required significant revising. I now have a full rough draft of the text of one paper, except that it does not yet include the figures, the tables, the statistics, the references, the appendixes or the complimentary online material. Oh well, I should be able to fill in a few of the holes tomorrow afternoon. Now it is time to see if I can stand up and walk as far as the bed.

Persickity-Split

I've often vented about how terrible science journalism in this country is. The journalists never seem to understand the science they are writing about, and the more I know about the topic, the less they seem to know. I am finding now that as I gain greater expertise in particular topics, large portions of the scientific papers on those topics, written by scientists and published in peer-reviewed journals, strike me as incorporating significant misunderstandings. I many cases, I feel these misunderstandings are significant enough to call the value of the papers into question. By the time I retire, I will undoubtedly think even my own work is crap. I begin to understand why the practitioners in some fields seem to be primarily interested in trashing each other's work.

Progress!

The first draft of the abstract of a first chapter of my thesis! None of this will survive the editing process.

Human females have the unusual life-history trait of frequently surviving well past their reproductively fertile period. While a variety of adaptive hypotheses have been proposed to explain this trait, some authors argue that post-reproductive lifespan (PRL) is a phylogenetically widespread trait, requiring no special adaptive explanation for humans. Still others have argued that PRL is the result of cultural and physiological traits, not adaptive evolution. We suggest that the continued confusion on this front arises from two primary sources, the treatment of non-alternative hypotheses as mutually exclusive, and the use of PRL, an inconsistently calculated and theoretically ill-suited parameter. Given the drawbacks of PRL as a comparative measure, a variety of more useful and comparable measures of post-reproductive survival (PRS) can be calculated using data in the form of standard demographic life tables. Using life tables from 20 human populations, 78 non-human primate populations and two non-primate species, we present a set of measures of PRS which allow for direct comparability between populations and to evolutionary null hypotheses. We find strong support for the uniqueness of the scale of human PRS, for the widespread presence of PRS in primates and for the influence of culture in extending PRS.

Happy Birthday!

By the time Darwin was my age, I wasn't born for another 169 years!

Finallly!

We have submitted our paper on the meaning of the word behavior (or, since the journal is British, behaviour). It took much longer to get it to the point where we could all agree on it than I expected, and there are still a few stylistic points I am not fully satisfied with, but overall I think it is a good paper, and has a good chance of being accepted. I am sure none of the reviewers will entirely agree with out conclusions, but the point of the paper is that we don't agree, so I think that is okay.

My people are nerdier than your people

As I walked by two of my labmates today, one of them was introducing a visitor to the other.

LM1: "I initially falsely synonomized you with Robert!"
LM2: "He does phenotypically converge with Robert."

Translation into English:
LM1: "I mistook you for Robert at first!"
LM2: "He does look a lot like Robert."

Note that while the English version is shorter and easier to read, the original is easier for people in my line of work to say.

Carnival of Science!

The lead story on the BBC New's Science and Environment page is on the research of one of the post-doctoral researchers in my professor's lab. The idea is rather simple, the process was complex.
We know that climate changes over time, and that where one can find a particular type of habitat changes with the climate. Recently, using a wide range of data sources, scientists have been constructing both a detailed history of how climate has changed over time and what climate parameters limit the extent of particular habitats, such as South America's Atlantic Rainforest. My lab-mate, Ana Carnival (along with several collaborators) combined the climate history data with the climate requirement data to make maps of how the extent of the Atlantic forest has changed over the last 20,000 years. She found that there were a few relatively small areas that had been rainforest the whole time, even when climate shifts caused the rest of it to change to other habitat types, such as grassland. She identified these as 'rainforest refugia,' areas where rainforest species could survive through the millenia when the climate was inhospitable elsewhere. She then predicted that these refugia should be the centers from which genetic diversity spread to the rest of the forest once its borders once again grew. To test these predictions, she gathered genetic samples from three species of frogs which can survive only in the rainforest. Sure enough, the frog's DNA told the story she had predicted, confirming the refugia she had identified based on climate models. This is not only cool science, it has significant conservation implications. These refugia should house a large portion of the diversity found in the rainforest, because at some points in the last 20K years, all the rainforest species lived there. This suggests that if we are forced to make choices about which land to preserve (which we are) we might do well to preserve these refugia. And both the methods and the conclusions are potentially generalizable to other thretened habitats around the world.

One final thought: This is very cool work, and very much in line with what most people in my adviser's lab study, but it is so far from my own work that I barely understand the details. This may be why I don't notice any glaring errors in the BBC article, or maybe the UK press are not as bad at writing about science as the American press.

Public Science

The government, in its many forms, funds a large portion of academic science. This has given many people the idea that taxpayers should have access to the output of that science. But in many cases the output is a publication in a subscription journal, which non-subscribers don't have digital access to. Somebody up and said, "Hey, we paid for that research, we want access to it." So NIH has reached understandings with many of the corporations that publish scientific journals saying that if an author was funded by the NIH while working on any part of a paper, the journal has to make that article free to the public, even if the rest of the journal is subscription only.

This works out great for me. My fellowship is through National Institute on Aging, part of NIH. So any journal article I publish while I am on fellowship, or based on data I gathered while on fellowship, can't be hidden from the eyes of non-subscribers.

Referees

I'm submitting a paper to Animal Behaviour. Their instructions to authors require that I suggest four referees, people who they could send the paper to who are qualified to review it and decide if it goes in Animal Behaviour. They don't necessarily take my suggestions, but they require that I suggest.
I found myself rather stumped. I decided to write the paper because as far as I could tell, nobody had written anything similar. So who should I suggest they send it to?

I wrote to one of my professors for advice. One of his suggestions was that it was their job to figure out who was the best person to review it, and I should just make up four fictitious names and send them in. He even suggested a made up name to use: G. Hector Meckel.

This is the adviser who is notorious for scoffing at the etiquette and protocol of bureaucracies in general and the scientific societies in specific. Despite the humor value, I think I will submit real names of potentially interested people.

Oldest old

Among my demography colleagues there is considerable academic interest in the 'oldest old,' the people who make 90 year olds seem positively youthful. I once heard a series of talks on "Sardinian Super-Centenarians" (a truly lovely phrase to hear repeated in an Italian accent). So first you're old, then you are really old, then you are a centenarian, then you are a super-centenarian, then, once having turned 100 is ancient history, you get to be oldest old. And once you are oldest old, having outlived many millions of your cohort, lots of people start taking an interest in you. Locals take it as a mark of pride to have someone so incredibly longevous living among them. People wonder what kind of yogurt you eat, and how often. Geneticists want blood samples to find all the things that didn't kill you. Demographers need data on you to know how the rightmost extremes of their graphs of anything over age should look. Is mortality rate higher among 115 year olds than 116 year olds, and what does that tell us about the possibility of increasing human lifespan generally? Is maximum longevity still increasing with improved technology?
It is hard to find very many data points for these questions, and significant resources have been invested in scouring the world for very old people whose ages can be positively verified.

Tuti Yusupova of Uzbekistan is a good example. According to her recently "noticed" birth certificate, she is 128 years old, by far the oldest living person ever recorded. But was the birth certificate really made in 1880, or was it slipped into a folder in 1920 or 2008? Could it be a clerical error? Some priest or bureaucrat may have written the wrong year for some reason. These things are surely being investigated.

And is she the original Tuti? A colleague told me of a case in which a potential oldest woman turned out to have adopted her mother's name, persona and possessions when her mother died. In the process she added 30 years to her age. She was old, but not oldest old. Publicity surrounding her apparent record brought the truth to light. So my colleagues are understandably dubious about the new record holder. If she is that old (which I hope is the case just because a real record is nicer than a fake one) some of my colleagues will have to (slightly) modify their thinking about how long a human being can stay alive.

Editing I know not what.

My wife often asks me to edit her writings in linguistics. This is an interesting exercise, as my knowledge of linguistics is probably sufficient to get me a D+ on a Linguistics 1A final. And most of that limited knowledge is derived from the works I have helped to edit. This makes me very good at finding sentences that are not crystal clear, but very bad at knowing whether the lack of clarity arises from my lack of knowledge. The result is that Iris's writing ends up being much clearer to the non-linguist (or at least to me) than most technical writing is to non-experts.
Perhaps all academics should be encouraged to have their work edited by a practitioner of an unrelated field prior to publication.

Cephalic rotiferitis

I've been in the lab every day all day for the last week looking at rotifers. When I close my eyes, I not only see rotifers, I can count their eggs, see their teeth chewing and estimate their age.

Rep. Boehner, Ludite

This morning on NPR, I heard an interview with Rep. John Boehner, the House Minority Leader. When asked about his reservations with President Obama's stimulus plan, he responded by saying that some of the spending did not seem wise to him.

"Remember, the goal of the stimulus package is to preserve jobs and help create new jobs in America," Boehner said. "And I don't know how giving NASA $400 million to study global warming is going to meet the goals."

It occurs to me to wonder if perhaps Rep. Boehner has so little conception of how science works that he truely doesn't know that when money is spent to study a problem, that money goes into the economy. NASA does not simply trassubstantiate the money into knowledge about global warming. NASA employs thousands of Americans on problems such as these; NASA contractors employ many thousands more. NASA advances technologies that help create new jobs.

My guess is that Rep. Boehner knows all this. It seems likely that Rep. Boehner knows that engineers and scientists are being laid off along with workers in almost every other field. Rather, I suspect the congressman is simly trying to rally his political base by warning them that the government is spending money on a problem they have been trained to think is a liberal hoax, global warming.

Which shows a certain level of consistency. Rep. Boehner is as derisive of the conclusions of science as he is ignorant of the process by which we reach those conclusions.

Text is a battlefield

I have spent a week of evenings trying to impose order upon this section on the measurement of post-reproductive survival. The document is littered with dead and broken bits of cast away paragraphs, sentence fragments, disembodied equations, references to tables that don't yet exist and citations of papers I remember reading a long time ago but need to check what they actually say. Multiple passages providing identical information vie to eliminate each other. Clean up is going to be long and ugly. Some concepts that don't make it will be scavenged for other papers. Others, too badly broken, will simply be left for dead.

After all this textual carnage, I finally have a good idea of how to ram all my multi-dimensional conceptual links into a single linear string of text. I even have most of it there. If I pretend I have a year more to finish than I actually do, I feel like I am making great progress, and greatly enjoying it.

Scientist Poitical Humor

me: Obama has appointed two Berkeley professors.
Karen: Who are the Berkeley professors?
me: Christina Romer and Steve Chu
Karen: Do you know them?
me: No, but I know people who know them, which is the square root of as good.

Why most scientists are not poets

As I try to write my thesis, I am coming to a realization. Writing complex scientific chains of thought in a way that is both clear and interesting is hard. Much harder than the natural history writing I have much more experience with. One of the many reasons this is so is because scientific writing must rely almost entirely on sentence meaning rather than speaker meaning. My wife, a linguist, has helped me to appreciate the difference. Sentence meaning is the information actually contained in the words of the sentence, accessible without knowledge of the thought processes or social context of the speaker. If a young man says, "Oh, I hate spiders!" the sentence meaning is simply, "I hate spiders." However, if the young man has just been asked to help clean out a garage, walks into the garage, looks around, then turns to the person who asked for his help and says, "Oh, I hate spiders!" much more is conveyed. The young man is not just signaling his dislike of spiders, but also that he has detected spiders in the garage, and is therefore reluctant to help with the cleaning. He may even be conveying that while he doesn't want to help he is a friendly person and therefore is reluctant to refuse outright, but please take this hint. This additional information, the speaker meaning, is nowhere in the text of his words, but is obvious to most humans who have healthy social comprehension skills. Much of the artistry of writing is in the careful crafting of speaker meaning. Beautiful writing, poetry in particular, generally conveys far more through speaker meaning than sentence meaning. But scientific writing by tradition and necessity relies almost entirely on sentence meaning. If any piece of information or logic is a necessary part of a scientific argument, the author has little choice but to state it outright. Likewise, if something has not been stated, scientific authors generally cannot assume the reader knows or agrees with it. This of course goes only so far. Scientists, as humans using human languages, are incapable of avoiding speaker meaning entirely. But the injunction is clear: thou shalt not ask thine audience to read between the lines.

Writing a complex chain of mathematics laced evolutionary thought in a way that is readable and elegant is a real challenge. I begin to understand why so many scientists over the centuries have largely let elegance and readability fall by the wayside. I very much hope to avoid following that clearly but unattractively marked trail.

Students

One of the great things about having lots of students involved in my research over the last few semesters is that the ones who aren't that interested tend to drift away and the ones who are really interested and energetic keep coming back for more. It is like a distillation process where now, my last semester in grad school, I have this awesome group of highly motivated students and very few who are just along for the ride. It makes me happy.

Very rough section of a very rough draft.

Today between 11 and midnight I wrote a very rough section of a section of a rough draft of one of the several papers that will go into my thesis. Feels like progress. I don't expect to have much time for blogging over the next few months, but I will try to post bits like this that are potentially interesting, and that show what efforts keep me from having time for blogging.

The Measurement of Post-Reproductive Lifespan
Advancement in the study of PRLS has been hampered by differences over terminology, the use of a wide range of non-comparable measures and the failure to put measures of the scale of PRLS in the context of the time scales on which the organisms live.

Some authors have used the term "post-fertile" rather than "post-reproductive" arguing that anything that an organism does that increases her genetic representation in future generations is a form of reproduction, and that "post-reproductive" is therefore an inaccurate term to apply to post-fertile individuals who are still caring for their young (REFS). Indeed Hamilton (1966) argues that, "if the organism practises parental care 'birth' should be considered to occur... at the age at which the offspring becomes independent." While not disputing the biology behind this argument, we feel that the term "post-reproductive" is deeply enough ensconced in the literature on this topic that the use of alternative terminology to convey the same concept may tend to muddy communication. For this reason we use the term "post-reproductive" to refer to life after direct reproduction (fertility), excluding indirect reproduction (care of young and indirect fitness benefits).
Beyond semantic disagreements, so many methods have been used to calculate the scale of PRLS that efforts at comparisons across species and studies have been few and confusing. For example, XXXX and ZZZZ (REF) present a table of PRSL for 12 primate species, all given in units of years, but calculated in six different ways. Disagreements exist as to how to define the end of reproduction, how to determine the end of survival, and which individuals to include. The measures vary because the type of data used vary, and the interests of the authors vary, figuratively leading to comparisons of the shelf-life of apples to the refrigerator hardiness of oranges. The effect of sample size on these estimates is generally not addressed.
Even when these drawbacks are not found, authors generally fail to correct for the overall longevity of the species in question. One should expect a species that lives 100 years to, on the average, experience more years of PRLS than a species that lives 20 years. Without a denominator related to the time scale of the organism's life history, the numerator of PRLS is fairly uninformative.

In this study, we use a type of data that allow for broad comparability: age specific mortality and fertility figures as calculated in standard demographic methodology. Because the form of the data is highly standardized, the same measures can be calculated across taxa, for males and females, and in a wide range of environments. The use of data sources as information rich as are age specific mortality and fertility tables allows for the use of multiple measures which illuminate different aspects of PRLS, but which need not be falsely compared to each other, because we can calculate every measure for each population for which these data are fully available. Furthermore, the use of age-specific demographic tables allow us to put our measures of PRLS in the context of the reproductive and actuarial longevity of the organisms, allowing for meaningful comparisons between populations with very different lifespans.

New York's Geese are exploding in another way

The Christmas Bird Count is one of the great success stories of Citizen Science. Every year in late December, tens of thousands of volunteers across North America brave snow and sleet and Christmas Shopping traffic to go out and see how many birds they can see. Everyone writes down how many individual birds they see of each species, and where they were looking, and all these data are collected into a central database. The Audubon Society's scientists know how many people were looking, and where, and they have large numbers of observers, so they can calculate pretty reliable winter range maps, and calculate rates of population change in each area.

Looking at their data for Canada Geese, a pretty remarkable number emerges. The number of Canada Geese in New York State in December has, for quite a while, been increasing by 22% per year! At that rate of increase, population would double about every three and a half years (1.22^3.5=2), and the population doubled at this rate starting about 1955 and leveled off about 1990, so it had time to double about 10 times. 2^10=1024. So for every goose in New York in late December before the 1950s, there is now a kilogoose in the same area at the same season. To put it another way, most CBC observers in NY in the first half of the 20th century saw not one geese. The average NY state observer these days sees 50 or 60 geese.

Here is a graph from the CBC historical query page.


There is a lot of noise in the data, but the trend is very clear.

So why are there so many more geese in NY in the winter? Partly, there are just more geese everywhere. Humans have been good to Canada Geese. Lawns, golf courses and grain stubble are all feasts for geese. We've killed off a lot of the natural predators, and we don't hunt them ourselves as much as we used to. And New York winters aren't nearly as cold and snow-buried as they used to be, especially around the city, which means fewer of the geese bother migrating any further south than New York.

In considering the causes of the bird-plane collision that caused yesterday's much publicized crash, we should keep in mind that a few decades ago, there would have been no geese to hit in January above the Bronx.

Plane-Strike, not "Bird-Strike"

Yesterday a plane that had taken off from New York City was forced to make an emergency water landing shortly after take off. The reason given? "Bird-strike" on both engines minutes after takeoff. It seems quite likely that the cause was truly a collision between a flock of birds and the plane. But does it really make sense to call it "bird-strike?" Canada Geese, the type of bird mostly likely to have been involved, fly about 40 mph. The takeoff speed of an Airbus 320 is 170mph, its cruising speed above 500mph, and the plane was presumably somewhere between these two speeds when the collision happened. So the plane was probably going five to ten times as fast as the birds.

So saying that the birds "slammed into the plane" as some news sources have done is akin to saying, "the pedestrians slammed into the speeding tanker truck." The birds were relatively stationary and the plane plowed through them. All the humans survived. The birds got the worst of it. Not to imply that the people on the plane were at fault. Perhaps the pilot's instruments don't even register anything as small and dispersed as a flock of geese. But really, does it make sense to blame the geese?

Can't live with'em can't live without 'em.

Biologists and Ecologists have (mostly) learned to give at least some forethought to the consequences of introducing non-native species. The problems invasive species cause are often most sever on islands, where the native species often have limited evolutionary experience dealing with the relative of the invaders. For example, many island endemic bird species were flightless, as there were no land based predators to need to fly away from, so why bother building all that expensive escape equipment when there is nobody to escape from. Other island nesting birds, like those on Macquarie Island can fly (except the penguins) but don't seem to have the right tricks in their behavioral repertoires to escape from introduced predators.

A recent attempt to help these birds is making ecologists aware that one has to be very careful not only about introducing invasive species, but also about removing them. A commentary in Nature describes the story:

Cats were introduced to the Macquarie Island in 1818; sealers introduced rabbits 60 years later.

The rabbits tore through the island's vegetation. In 1968, the rabbit flea was introduced. Once that had established the lethal myxomatosis virus — which the flea spreads — was introduced in 1978.

Rabbit numbers crashed, but then the cats, which had previously eaten rabbits, switched their attentions to the island's birds.

But once the cats were gone, the few hardy rabbits that had survived both the cats and the myxomatosis emerged and began doing what rabbits do best — breeding and eating.

So the island has cats, rabbits, rabbit fleas, rabbit viruses as well as rats and mice, all introduced. The cats were bad for the birds because they ate them. The rabbits were introduced and were bad for the birds because they destroyed the vegetation, but good in that they distracted the cats. And the cats were at least partly good for the birds, because they ate the rabbits. Some kind of unstable plateau was reached. And then disease was introduced to reduce the rabbit population, leaving a whole bunch of hungry cats, which were bad for the birds. But then the cats were killed off, which was bad for the birds and everything else (except the rabbits, rats and mice) because for the first time there were rabbits without cats on the island. The rabbits, unchecked, ate through most of the island's plants. And this, of course, is a vast over simplification.

The Australian authorities who run Macquarie Island are now trying to figure out how to exterminate the rabbits, rats and mice without harming other native populations, such as the seals and sea lions that breed on the island. They estimate it will take tens of millions of dollars, and that may be optimistic. Few efforts to irradicate rodents from any land mass of decent size have succeeded. Macquarie Island, at 128 km², offers a lot of hiding places big enough for a rat. Miss one pregnant rat and in just few years you are back to square one. And who knows what effect the rats would have without rabbits around harboring disease and eating potential cover? Rats eat bird eggs, and have been known to finish off large bird colonies in just a few years.

The lesson learned is that just because introducing species is generally bad, removing those introduced species from an ecosystem that has started to adjust to their input isn't always good. At least it has to be done very carefuly.

If this science thing doesn't work out.....

As I've mentioned before, my cousin David is the National Sales Director at Ozone Socks. Ozone has a yearly sock design competition. I submitted three designs themed around (what else?) science.
I just got the call from David. He had to recuse himself from much of the judging, in order to avoid bias. He didn't even tell them I was his cousin, he claims. But my oceanic drawing, "30000 Feet" won second place. Iris will be so excited. We get 12 free pairs of socks.


Oh, and by the way. It is not at all certain they will actually make this sock. They have in effect purchased rights to my design for the price of 12 pairs of any socks they already make. If you want to see this sock made, and would purchase a pair, please contact@ozonesocks.com and tell them.

Just like old times, only with more world-savingness

My freshman year in college, my neighbor down the hall, the fellow in the picture above, was building a contraption that looked very much like this one, only much more home-made looking with various colors of extension cords, duct tape and PVC pipe. One end face the door of his room. The other pointed out his window, like a cannon out the gun port of a ship. I asked him if he was a pirate. We became friends anyway. He eventually explained that it was a sort of cannon, a "linear inductance accelerator." It used electrical current to induce electrical fields that accelerated little aluminum rings down the length of the gun and shot them out the window. They didn't shoot very fast or very far, but Stephen built the thing while he was in high school, mostly from stuff he scavenged from his parents' basement. When the campus security guards would do their once a semester safety checks of the dorm rooms, Stephen would put Christmas lights and stuffed animals all over it and tell them it was a sculpture project.

Stephen is one of those people who is instantly recognizable as brilliant. Walk into a room where Stephen is and try not to get knocked over by the slightest meanderings of his enormous brain. But in addition to intellectual brilliance, Stephen is incredibly tireless, multi-talented and helpful. He was known as the "campus super hero."

It was therefore with very little surprise then that I watched as Stephen got degrees in mathematics, physics, electrical engineering, plasma physics, then went to work for a company whose goal is to save the world by producing cheap clean electricity from hydrogen fusion. And the company Stephen went to work for is of course going to do it much sooner, cleaner and at 1/100th the cost of anything any government or university could think to try. The picture above is of Stephen working on a piston he designed for that fusion reactor, and is from an article in this month's Popular Science. The similarity to the linear inductance accelerator is mostly in my mind. I can tell the photo is posed, though, because Stephen is not grinning, which he normally would be when fine-tuning one of his machines.

Stephen, by the way is Stephen Howard, who has occasionally posted to this blog. These days he is too busy saving the world.

A tiger does not declare his tigritude, nor live in Africa.

Last night Iris, reading a book on French linguistics, came upon the word "tigritude." She asked me what it meant, and I Googled it. I came upon several references to a "Nigerian Proverb" stating that, "Un tigre ne proclame pas sa tigritude." (A tiger does not declare/proclaim/shout his tigritude.)

This stuck me as a useful saying, but something about it wasn't quite right. It took me until just now to remember. There are no tigers in Africa. Haven't been for at least a million years. Either that saying was handed down from before the time Homo sapiens existed, or it isn't a traditional saying from Nigeria.

I looked it up again, in more detail, and it is actually a quote, from the Nigerian author Wole Soyinka, whom Iris of course has read but I had never heard of.

It is funny how many people, including "proverb dictionaries" have labeled various versions of it as traditional Nigerian wisdom. Is "Happy families are all alike; every unhappy family is unhappy in its own way," an ancient Russian proverb?

Parameterization of the Damned

I have had a headache for the last two days trying to figure out a theoretical problem related to my work. My best attempt to explain the problem, and the closest to a solution I have thus come up with, can be found in the email below, written to one of my research assistants. This is the kind of symbolic thinking that makes my forehead tie itself in a knot. Tell me if what I wrote means anything to you, 'cause it doesn't say a whole lot to me.


Hey Nik-

I have another favor to ask. I've been wracking my brains trying to figure out a problem, that we have no null hypothesis for what G should be. I try to explain the problem and what I'd like to do about it below.

Post-Reproductive Lifespan as measured by G cannot be negative, in that an individual cannot invest in reproduction after her death. G cannot even meaningfully be zero unless every individual dies at age M, the age at which fertility drops of to 5% of its former maximum. If even one individual in the study population lives past age M, G is non-zero. This has left me struggling to figure out what a meaningful null hypothesis for G could be. The answer seems to be that there isn't one. G is a parameter designed for a quantitative, rather than qualitative distinction. Human G is very different from G of non-human primates, but it isn't meaningful to ask if G of non-human primates is different than zero, because we know without knowing anything about the populations that it will be.

The relevant question is: is senescence in fertility offset in age/time from actuarial senescence? If M, is the parameter we use to demarcate the end of fertility, we can use the exactly analogous measure, Z, to demark the end of meaningful survivorship. Z is defined as the last age for which p(x)≥0.05*max(p(x)). ( By the way, in case we don't have p(x) in the data you have, p(x)=1-q(x))
So then the question becomes, how much different is M from Z? Z-M is the post reproductive period, and (Z-M)/(Z-B) is the portion of the adult lifespan that is post reproductive. If we define S=(Z-M)/(Z-B), then S gives us a decent measure of how much reproductive senescence is offset from actuarial senescence. And one that I can at least imagine being zero, in that the rates of survival and fertility can drop simultaneously, even if each individual reproduces only before she dies.

Would you be so kind as to have Access calculate Z and S for the populations we have in the database, and then send me a spreadsheet with B, Z, S and M for each population? I'd like to get a sense of how these variables behave.

Thanks,
Dan

Evolution in the Great Pacific Garbage Patch

Put some sand grains in a round bowl of water, then twirl the water neatly, so that the water rotates cleanly around the inside of the bowl. You will observe most of the sand settle out of the water in the center of the bowl. Do the same thing with tiny bits of floating plastic, and you will get the same result. Now do the same thing to the Pacific Ocean with all the floating trash we throw into it, and you get the Great Pacific Garbage Patch. A large portion of the North Pacific naturally rotates in a current called the North Pacific Gyre. Take a boat to the center of rotation, and you will find enourous quantities of floating junk, especially plastic. Wave action and UV gradually break this plastic down into microscopic bits, which become part of the local ecosystem along with the water, plankton, fish and so forth. But nothing eats plastic, right?
This is where my current pondering comes in. Imagine that one of the hundreds of billions, maybe trillions, of plankton in the Great Pacific Garbage Patch had a mutation that allowed it to gain some slight advantage from plastic. It would surely find some tiny scrap of plastic, and would have a slight advantage over its competitors. If that helps it reproduce successfully, and its offspring carry that same mutation, then we have more plankton who gain a competative advantage from plastic. At the rates plankton reproduce, it is not long before the GPGP is full of plankton who can make some use of plastic. Now suppose another mutation in one of those plastic-loving plankton makes it even better at using plastic. Able to slowly digest some component of it or incorporate it into its protective covering. The larger plankton that eat those little plankton will also have to evolve to deal with plastic. Sooner or later, in the crazy fast generations of open ocean plankton, someone is going to start evolving down a path that leads to the ability to digest plastic. And when that happens, when the plastic in the North Pacific Gyre starts myseriously disapearing, don't expect the little plastic eaters to stay there. There are far to many ecosystems with far to much plastic available for plasticovores to be contained. Plastic is, after all, an organic substance with a high energy content. Sure it isn't biodegrable. Not yet.

Field Guide to the Birds of whereever I am right now.

Planning a recent trip to Baja California, I decided to buy a field guide to the birds of Baja, only to discover that there is no such book. Ornithologist colleagues suggested I just bring a guide to the birds of Mexico, but that seemed much less than ideal. Mexico has more than a thousand species of birds. Somewhere around 300 of those 1000 have ever been seen anywhere on the peninsula of Baja, and maybe a 150 of those have a moose's chance in Texas of showing up where we where when we were there. I looked through my very old edition of Peterson's Field Guide to the Birds of Mexico (so old they don't even have pictures of all the species because of the cost of printing illistrations), and decided that almost every bird we were at all likely to see was in the much more usable National Geographic Field Guide to the Birds of North America (which in this case means the US and Canada), so I just brought that.
Of the 66 bird species we saw, only one wasn't in the Nat Geo guide, or at least only one I successfully identified, the Grey Thrasher.

But this system of having to have a different guide for each place one goes is just so cumbersome, especially when one goes to a place for which a guide is not available. It's time for field guide 2.0. What I want is an electronic guide that detects where I am and what season it is, then displays a list of species that could possibly be there. This could even be a fairly simple application for an iPhone or PDA. I click on the name of the species I want to see, or the group I want to explore, and I get that page. If I know I am looking at a booby, but don't know which one, I click on the genus Sula. Based on the fact that I am in Baja in winter, I get pictures of a Blue-Footed Booby and a Brown Booby of the brewsteri subspecies.

If I am an ambitious birder and hope to find birds that aren't normally found where I am, I tell the program to be less picky in the list it gives me. If I am a novice who will only notice the species that there are at least a thousand of all aroud me, I can get a more selective list and have an easier time IDing the Yellow-footed Gulls.

This would require no new technology, only someone from one of the several companies who make bird guides to take the data they already have and slap them in a program. But no, instead they want to sell us stacks of bound paper. How 20th century.

(EDIT: Just after posting this it occurred to me that someone might already bo doing this. The closest I can find are "Handheld Birds" from National Geographic and iBird Explorer. They don't yet have any more birds than what appear in field guides to the US and Canada, and they don't seem to have the capability to let your wireless device filter by your location and season, but I hope that will come soon. It is clear at least that bird guides are going digital.)


Here, by the way, is the unorganized list of bird species Iris and I saw on our trip:

La Paz, La Ventana & Puerto San Carlos, Baja California Sur, Mexico Dec. 15-25th, 2008

Birds
1. White-winged Dove
2. California Quail
3. Magnificent Frigate Bird
4. Brown Pelican
5. Turkey Vulture
6. Crested Caracara
7. Merlin
8. American Kestrel
9. Western Gull
10. Gila Woodpecker
11. Cassin’s Kingbird
12. Cactus Wren
13. Northern Mockinbird
14. Gilded Flicker
15. Phainopepla
16. Western Scrub Jay
17. Spotted Sandpiper
18. Royal Tern
19. Ring-Billed Gull
20. Orange-Crowned Warbler
21. California Gnatcatcher
22. Common Ground Dove
23. Common Raven
24 Costa’s Hummingbird
25. House Finch
26. House Sparrow
27. Pyrrhuloxia
28. Hooded Oriole
29. Great Egret
30. Sanderling
31. Bonaparte’s Gull
32. Lesser Scaup
33. Double-Crested Coromorant
34. Green Heron
35. Little Blue Heron
36. Great Blue Heron
37. Snowy Egret
38. Great Egret
39. Cattle Egret
40. Tricolored Heron
41. White Ibis
42. Osprey
43. Red-Tailed Hawk
44. Heermann’s Gull
45. Western Sandpiper
46. Rock Pigeon
47. Mourning Dove
48. Anna’s Hummingbird
49. Blue-Footed Booby
50. Caspian Tern
51. Forster’s Tern
52. Eared Grebe
53. Yellow-Footed Gull
54. Ash-Throated Flycatcher
55. Grey Vireo
56. Verdin
57. Rock Wren
58. Semi-Palmated Plover
59. Long-Billed Curlew
60. Black-Throated Sparrow
61. Belted Kingfisher
62. Ladderback Woodpecker
63. Willet
64. American Oyster Catcher
65. Grey Thrasher
66. Brown Boobie

Co-authorship

Writing a paper with others, in the sense that we all have to agree to have our names on every bit of it, and publish it, is a difficult but rewarding process. The paper is surely improving because of it, and the disagreements have all been purely intellectual and cordial, but every step of the process has involved spirited discussion over a thousand details of fact, style and strategy. I have 'won' about as many of these discussions as I have 'lost' and I am satisfied with the outcome in pretty much every case. It will all be well worth it if we actually get the paper into print.

240 mile deep water?

Listening to NPR news this morning, I heard of a break in an underwater fiber-optic cable between Europe and the Middle East, just off the coast of Alexandria in "240 mile deep water."

It struck me as odd that this was such a minor news item. After all, the previous record for deepest water on earth was only seven miles (held by Challenger Deep, off the Marianas Islands.) NPR has just increased the deepest water on earth by 3328%, an astonishing accomplishment.

I am not sure what the message was supposed to be. Perhaps the water was 0.24 miles deep? The break was 240 miles from Alexandria? There are a great many plausible options.

To anyone to whom numbers communicate anything "240 mile deep water"just off shore in the relatively shallow Mediterranean should be instantly absurd. Unfortunately, basic competence in subjects such as science and math are not expected of those in the news business. If I were king...

A conjecture on the link between babies and lack of sex

I am told by those who have children that one of the many sacrifices couples make to raise a baby is opportunity for sexual intimacy. I don't have kids, so don't know from personal experience, but it certainly makes sense. Between exhaustion, vehement interruptions and company in the house, it can be hard for a couple to find the time, privacy and energy to maintain their pre-parental levels of activity. I have had friends say that it seems very much like the baby is plotting to destroy its parents' sex lives. It has just occurred to me that in a sense, this could be very true.

Evolutionarily, there are many ways in which the interests of the parent and the interests of the offspring are aligned. The fitness of both are improved if the baby grows, thrives and go on to produce its own offspring. They share many genes, and anything that is good for the one is at least a little bit good for the other. But some things that are good for the fitness of the parents are a net selective loss for the baby. Such as having another baby come along too soon. The parents of course are equally closely related to all their offspring, and therefore will tend to distribute care and resources between their children in a way that maximizes the number of future grandchildren. But the baby is twice as closely related to herself as she is to her full sibling; she is much better off monopolizing her parents' time and resources for longer than they might desire. The parents' fitness is maximized by having an interbirth interval just long enough to get a good return on their investment in this offspring, without unduly diminishing their opportunity to have more children in the future. The child's fitness is maximized by having the parents wait somewhat longer, until the diminishment of their future reproductive chances for each additional day waited is twice the per day increase in her own fitness gain. The technical term for this disalignment of interest, appropriately, is parent-offspring conflict.
At first glance, the advantage in the conflict over the length of the interbirth interval would seem to be distinctly on the side of the parents, rather than the sessile, pre-sentient, altricail lump of chub, digestive organs and breathing apparatus. But oh, those breathing bits can very easily be used to make sounds. Sounds that communicate desperate dire need to protect and nourish the baby. Sounds that cannot easily be ignored. What harm if one screams just a little bit louder, a little more frequently, screams and cries with slightly less provocation, and makes the parents increase their interbirth interval just a little while longer?

Before you label me a conspiracy theorist, let me be clear. I am not implying that the babies of the world are 'trying,' in any intentional way, to deprive their parents of sex. They don't have to try. It comes naturally to them.

Experts in a Lesser Known Phylum

Ask most people to name some phyla of animals and they will just look at you funny. Those who do know what you are talking about are likely to name Chordata, Arthropoda, Annelida, or maybe Mollusca. Most people will run out of Phyla long before getting to Rotifera. We humans tend not to pay a lot of attention to a Phylum whose members are mostly microscopic and don't cause any known disease. This is true not only among lay-folk, but among scientists as well. Web of Science, a catalogs of the scholaraly articles from about 8700 publications, lists fewer than 100 papers focusing on rotifers in the last year. Arthropoda, by comparison, gets more than 38,000 hits in the same period. So rotifers are not the best studied group in the world.
But those almost a hundred publications had to derive from somewhere. That somewhere is a scattering of experts across the globe. And it gets lonely being the only one in your city, state, country or continent with a strong interest in rotifers. (For example, I think I my lab is the only one in California which focuses on rotifers.) So what's a lonesome rotiferologist to do? Organize a conference, of course. Every two or three years there is a Rotifera conference somewhere in the world, and I have just found out that Rotifera XII is in Berlin, Germany next August. I very much plan on going, and hope to give a short talk on my work. They have about 60 slots open for presentations, which I think means almost everyone who studies rotifers will be there presenting. It should be interesting.

Le Deluge

I'm in a new place. For the first time in my career, I have gobs of data. Over the last couple of years, with the help of all my students, I have amassed a couple of enormous data sets. I've got this data-gathering thing down.

Faced with all these data demanding to be analyzed, written up and published, I have a new and different challenge. I need to decided which of the hundreds of different papers I could potentially write with all these data I actually will write. In some cases it is obvious that I need to write a particular paper. For other potential papers, it is fairly obvious that the opportunity cost would be higher than the benefit. This still leaves a vast middle ground.

I need to figure out how to think about how many, and which, papers to try to publish soon, which to present at conferences, get feedback, then publish, and which could be filed away in case I ever decide they are important.

Some of this last group I will use as motivational tools for my students, saying in effect, "I will put the time in to get the project you worked on published if you do a particularly good job moving the process along, and I will make you an author on the paper." Relatively few of my papers do I expect to be co-author on. Most I will need to include advisors, collaborators, students, or some combination thereof.

What is clear is that between now and next August (when I will move to Germany) I need to write about two papers a month, which is about two papers a month more than I am accustomed to writing.

40,000 Senegalis or My Building

The building I work in has a yearly energy bill of over $1,000,000 dollars a year. That translates to about 10 Million Kilowatt-Hours per year, the same as about 100 average American houses or 40,000 Senegalis. Granted, it is a big building, with a greenhouse on the roof, four elevators, large water dionization systems, lots of -80C freezers, class rooms, laboratories and offices. Still, that seems like a lot of electricity for one building. The building manager emailed everyone to ask if we had ideas for cutting that down some. I suggested getting a smaller autoclave. The building has three autoclaves, each big enough to park a Mini in. Hundreds of different people use these, each setting them to their own specifications, usually to autoclave one or two bottles or one tray of equipment. A machine with 1/100 of the internal volume would work for most of these jobs, and get it done faster. I emailed to suggest installing a smaller autoclave, and the building manager wrote back that, "buying and installing one would cost over $50,000, which is about $50,000 more than is available at the moment." I am sure he is right, still I can't help thinking that if we saved even 0.1% of the building's energy usage, the thing would pay for itself in five years. Different budget line though.

Journal of Biodemography

Yesterday on the BART I sat down to write a list of papers I hope to publish based on my rotifer work. Most of them I had a pretty good sense of what type of journal they should go into. But one, the article in which I present a detailed human-style demographic analysis of my rotifer population, I just didn't know. It is a paper that I specifically want to write to an audience of demographers, to say, "Hey Look! Other species are good for demographers to study other than humans!" But I was not at all sure a demography journal would accept a paper on a species other than humans. I emailed my demography professor, and he said he had never sen such a thing in a demography journal. And frankly, most biologists aren't that interested in this type of analysis, and I'm not sure what biology journal I would send it to. I decided that since there is a journal for everything, there must be a Journal of Biodemography. But nope. I looked it up. Nothing even vaguely like that exist as far as I can tell. So either I'll have to found the journal myself (unlikely) or I'll have to shoehorn it in somewhere.

I'm just too interdisciplinary for my own good.

Binning Algorithms for Metagenomic Sequencing

One of my assistants, SM, who is at least as smart as me and twice as hard working, wrote to ask my advice.

SM: Do you know of any good binning algorithms for metagenomic sequencing?
DL: Huh? What does, "binning algorithms for metagenomic sequencing," mean?

SM has not given me an answer. Either she assumes I am joking, and actually do know (which I don't) or she assumes it would take far too long to explain it to me (which I will pretend to resent.) So now I shall try to reckon out what "binning algorithms for metagenomic sequencing" means on my own.

Metagenomics, according to my sources (Wikipedia) "is the study of genetic material recovered directly from environmental samples." So, you take a pinch of garden dirt, extract all the DNA in it and then set out to study it in some way. You are metagenomisizing.

Sequencing, in the context of genetics, means figuring out the sequence of DNA bases (A's, T's, G's and C's) that make up part of the genome of an organism. So metagenomic sequencing presumably is taking the DNA from your pinch of dirt, then trying to figure out the sequence of DNA bases that made up all the genomes of all the organisms whose DNA are jumbled together in that dirt. A pinch of dirt, I am guessing, has DNA from hundreds of types of bacteria, a huge number of types of fungi, various protozoans and whatever else has dropped seeds, pollen, poo, tissue or hair in that vicinity in the recent past. And much of that DNA isn't going to be whole chromosomes, but whatever bits and pieces are still mostly intact after all that pooing and shedding and biodegrading. You'll have a real mishmash.

This, I suspect, is where the "binning algorithm" comes in. Binning is any process where you have a large number of elements and you want to separate them into a smaller number of categories. A binning algorithm would be a set of rules one uses to make those decisions on categorization. In the context of metagenomics, I'm guessing that each bin represents a species. You have a snippet of DNA and you need to assign it to an organism, so you don't just think that every bit of DNA is another organism, and you want to get a sense of how much representation you have of each species. So the set of rules you use to assign snippets of DNA extracted from your pinch of dirt to different species is your Binning Algorithms for Metagenomic Sequencing. I think.
My friend DS works on this kind of stuff. I'll write to him and ask.

UPDATE:
I wrote to SM and DS and asked:
Will one of you tell me what "binning algorithms for metagenomic sequencing" means?
I know what each word means, but I could come up with three or four very different guesses as to what the whole phrase means. What does each bin represent?

DS writes: [Bins represent] Taxa. In metagenomic sequencing, you get a soup of reads from all the strains of microbes present in your sample. "Binning" is the process of trying to guess which species each read comes from (or genus, or kingdom for that matter).

All methods in the literature so far are "supervised", meaning that you can only assign a read to a taxon bin if you know something about that taxon in advance (e.g., you have an isolate genome). However, environmental samples may contain previously unknown taxa: new bacterial divisions are still being discovered fairly rapidly, and at the strain level of course nearly everything is novel. A supervised binning process ought to throw up its hands at sequences from novel taxa, since they don't match any known bins. An "unsupervised" process would create new bins on the fly, in order to lump together reads that seem to be related to each other, independent of reference sequences. No published methods do that yet, though.

The accuracy of binning varies dramatically depending on the complexity of the community, the read length, the phylogenetic resolution you're asking for, and many other parameters.

Hope this helps,

-ds

Demographics of Science!

African Americans are generally underrepresented, both in the universities, and in the sciences. Berkeley is no exception in this case.

During my time in grad school I have interviewed well over 100 undergraduates who were applying to work with me, and taken on (as volunteers or paid workers) about 30 of them. Currently, I have 18 undergraduate collaborators. I've not given a great deal of thought to the demographics of this group, other than to notice that the great majority of my applicants (and therefore of my assistants) are female. A recent conversation (about Pres. Elect Obama) made me stop and think about the race and religion of this group. It is a very diverse group. I have had assistants who are Christian, Jewish, Hindui, Muslim and non-religious. Maybe other religions, I don't know. I have had assistants whose ancestors (or they themselves) came from East Asia, South Asia, the Middle East, Eastern Europe, Western Europe, Pacific Islands, Latin America and possibly other places I am not aware of. They have been male and female, heterosexual and homosexual. There are few places in the world where I could have ended up with a more diverse group, but I have no one of obvious African decent.

African Americans are not represented in my lab for a simple but sad reason. I have had not one African American applicant (that I am aware of), out of maybe 120. It is striking that African American representation in this group is lower than among our nation's elected officials. I am not sure why exactly this is, what combination of bias, cultural factors and public policies to blame, but I know this is one area where African Americans don't yet seem to have made sufficient inroads.

Reader JTE asks:

Q:What does

two individuals with the same genotypes, except for those genes determining sex (which is some species don't exist, where sex is environmentally determined),

mean?

A: I'm glad you asked.

It means that if I had one missing or dysfunctional gene on my Y chromosome (or was XX instead of XY), I would be phenotypically female, but the rest of my genome would be the same as it is now. A great many aspect of my physical, chemical, social and mental being (my phenotype) have been altered by the effects of this one gene, which acts as a sex switch. Switch on maleness, and a whole bunch of aspects of phenotype are altered. Don't switch it on, and you get a different phenotype.

In some species, there are no X and Y chromosomes, or anything equivalent, to act as a sex switch. Instead, whether an individual develops as a male or a female is determined by the environmental conditions which prevail at a certain point in development. In alligators for example, there is no genetic determination of sex. Instead, if the temperature around the egg is above a certain temperature at a certain point in development, the alligator becomes one sex (I think male, but I don't actually remember). If it is ?colder? than that temperature, you get a female alligator. Many of the aspects of the switch are the same, only the first step of the switch is very different.

So my colleague was pondering the fact that two individuals with similar, or even identical, genotypes can have importantly different phenotypes, based on the action of this switch. This means that whether this switch is on or off can greatly affect the actions of other genes, and therefore the effects those other genes have on the survival and reproductive success of the organism.

Intersexual Correlation

A colleague wrote to ask me what I thought about an idea he'd had. He was thinking about the fact that one could have two individuals with the same genotypes, except for those genes determining sex (which is some species don't exist, where sex is environmentally determined), and end up with significantly different phenotypes. In some traits (e.g. Hair color) these two individuals would be expected to have very similar traits, in others (e.g. genital morphology) they would be expected to be very different, and perhaps in some cases uncorrelated or negatively correlated. He wondered if this might affect the ability of individuals to choose mates who would produce highly successful offspring. For instance, a female sizing up a male would have a better sense of what that male's sons would look like than what his daughters would look like. A big very masculine male might tend to have oversized and somewhat unattractive daughters. My colleage wondered if this might confuse things enough to slow down the action of sexual selection, and allow a greater genetic diversity to remain in the population than would otherwise be the case. I found htis a very interesting question, and wrote the following reply:

There is a body of literature on the degree to which natural selection on the traits of one sex will affect the traits of the other sex. People often use the term "correlated evolution" to describe this sort of thing. When there is a correlation (positive or negative) in a trait between the female expressed genotype and the male expressed genotype, I've seen the phrase "intersexual correlation." I am not terribly familiar with this literature, I'm afraid.

This recent paper is the closest thing I know of to what you are talking about.

Whether any of this would lead to a greater genetic diversity in the population, I am not sure. The effects of natural selection may be somewhat weaker, as traits that are expressed in one sex but not the other are less often expressed, and therefore less often subject to selection (an epistatic interaction in effect). In the case of sexual selection, my guess would be that as long as degrees of intersexual correlation in particular traits evolve more slowly than do what cues individuals use to choose mates, choosers should evolve to focus on characteristics that are good indicators of fitness in both male and female offspring. I think this will generally be the case, as there is clearly very strong selection against those who use misleading cues in mate choice. I am not aware of any reason to think there would be rapid change in the degree of intersexual correlation in a wide range of traits all at once. As long as there is any consistently reliable signal available, the family lines that use it should tend to do better than the population average.

It raises an interesting set of questions, I am not sure how many of them there is any literature on.

Does this answer your question? If you want more expert answers we could ask Monty Slatkin, who I am sure has thought about this in some detail at some point.

Misquotes of Science!

Science is the least precise way of describing the world, except for all those others that have been tried.

Cannabalism, entropy, economics and consumerism.

Among the millions of species of organisms out there, you can find a species that specializes in eating almost anything. There are lion-poo specialists, feather-barb specialists, lichen specialist, you-name-it specialists. There are also lots of generalist species, and many of these generalists engage in cannibalism. But no species is a cannibalism specialist. I can say this with confidence, even though we don't know what most species eat in any detail. A species of dedicated cannibals would quickly run out of energy. Everything an organism does burns energy that cannot be retrieved. Thermodynamics and all that. If a population is to withstand the ravages of entropy for any time at all, there has to be a sizable inflow of concentrated nutrients and well-ordered energy. A population of cannibals has outflows but no inflows, and quickly changes food supply or goes extinct. Engaging in cannibalism can be beneficial for short periods under specific circumstance, but you can't eat all conspecifics all the time. That way lays rapid extinction. Similarly, an ecosystem cannot persist for any period of time without massive inward fluxes of organized energy. Sunlight, geothermal chemicals or organic detritus from one of these two are necessary inputs to every ecosystem we have ever come across. Without that, organized energy in the system quickly declines until life can no longer be supported.

This same logic applies in economics. Pyramid schemes and speculative bubbles ultimately must collapse, because there is no underlying production of valuable stuff to support the outflows of capital of those involved in the speculation. Extending the analogy only slightly further, we see why a "consumer services based economy" cannot long persist. We import the carpet-cleaning machine from China, but we cannot export the carpet-cleaning service. We import the yoga mats but can't export the yoga lessons. We bring in coffee beans but can't export the latte. The consumer services part of the US economy (the biggest part) sends money out but brings effectively no money in, feeding instead on money that is already in the system.

The fact that our consumer economy lasted as long as it did/has is a testament to why economics is a social science, rather than a natural one. Humans are inherently illogical, and economics needs an understanding of that as much as it needs equations to understand the ways in which we are logical. Economic theory worked out for any other species would perform terribly for humans, meaning economics is by necessity anthropocentric, and therefore a social science. This has allowed an economy with few inflows to persist by inventing imaginary inflows, known as international borrowing. Americans may not be able to give you anything back for your stuff, but if you lend us the money to buy it from you, we will promise that at some point in the future we will borrow more money from someone else to pay you back with interest. Stated this way, it is an obvious pyramid scheme. But we have preferred to think that because our economy is large, because it has been dynamic, we soon would no longer need to borrow. Instead, I hope, we have figured out that we have to consume at a level closer to the level at which we produce. Otherwise we are just eating our children's future earnings, which is a bit too close to cannibalism for my taste.

Tough Love

A few months back, one of my first and best lab assistants, LZ, was graduating. We were at a ceremony/lunch for her and the other students who had received an undergraduate research fellowship.
I said to her, "now that you are graduating, I want honest feedback on how I can improve as a mentor, and what things I should think about changing." She copped out, going into a long list of all the things I do right, then asking me what things I thought I needed to work on. She's a clever one, if overly tactful.

I said, "that's a total cop-out answer." She persisted in answering without answering, and in pushing me to answer my own question, so I did.

I told her that there are two main things I feel I really needed to figure out better. First was the balance between autonomy (allowing students to do what they want in their own projects, even if it might not work) and direction (giving students a project that is very likely to work, even if it is not exactly what they want to do). Second, I thought I was pretty good at picking good students, and at mentoring good students, but not so good at knowing what to do about the disinterested students who I mistakenly hired and couldn't really motivate. I tend to assume everyone on my team is competent, interested and motivated, and when any of these assumptions is violated, it takes me a while to convince myself that there is little doubt to give the benefit of, and a longer while to figure out what to do about it. In typical LZ fashion, she consented without actually stating agreement.

Recently, I have been trying to tackle the second problem, approaching students who I didn't feel were getting it done and letting them know where I thought they needed to improve. The results so far have been quite positive, and I am hopeful that despite LZ's concerted effort to be unhelpful, my conversation with her has helped me improve my mentoring.

So there.

On a related note:

University of California's endowment loses $1Billion in value.

"UC Berkeley spokesman Dan Mogulof said that if the financial markets continue their downward slide in coming years, there could be future reduction in endowment support for scholarships, research and funding to recruit and retain faculty, among other things."

Graduating into a Depression

The number of professorships in the country does not vary much from year to year. Usually the number of positions opening up approximately equals the number of professors dying, retiring or moving to other jobs. Occasionally a new campus opens or there is a major expansion of enrollment, and a bunch of new positions are created. Other times the economy sucks (to use the technical term) and as a cost-cutting measure positions are retired or left vacant for a few years. As an example, the budget problems California has been having ever since the dot com bust have caused UC Berkeley to greatly increase the average time between one professor leaving and another being hired.
Right now, professorships are hard to get and I fully expect them to get harder. Somebody or other, a housing economist I think, was on NPR today predicting that the housing market will hit bottom in another three years. I expect the academic job market to hit bottom around the same time, or possibly a year or two later.

Which brings me to me. I will be getting my PhD in a little under a year. I then expect to spend two or three years as a Post-Doctoral researcher in Germany. That should have me searching for an assistant professorship just about the time there are no professorships of any sort to be had.

President-Elect Obama, I think an investment in our nation's universities and research institutes would be a great way to stimulate the economy, improve education and develop the technologies we need to deal with environmental issues and health care. Sooner is better than later. I have this three-year plan.

Team of Science

We attempted to get me and my entire team of undergraduate rotifer wranglers into our tiny lab space all at once. Two people couldn't make it, but 12 of us plus a photographer jammed in. The room is 12m^2 but about half of space that is occupied with counters, furniture and large equipment. Hopefully at my next job I will have a larger lab space, a smaller team, or both.

Population Doubling

As I am preparing for my talk, I am doing some intense demographic analysis of my rotifer data set. One interesting factoid I have calculated is that the population doubling time, assuming I could keep an infinite number of rotifers and didn't have to get rid of any, is 28 hours.

A related calculation: If I started with one newly hatched rotifer and let the population grow (with my average age-specific reproductive rates and death rates), after one month I would have 159 million rotifers.

The average volume of a rotifer is about .001 cubic millimeters. A million of them pressed together makes one milliliter. A billion makes a liter. 10^27 would be a cubic kilometer. Earth's oceans have a total volume of 1.347*10^9 cu km, meaning I would need 1.347*10^36 rotifers to fill them completely with no space between rotifers. At the demographic rates they maintain in my lab, assuming I didn't cull any, this would take 138 days.

I only have time, container space and staff to keep track of 450 rotifers at a time, so I end up culling a significant portion of my population every day.

Rotifer Demography Talk Wednesday

I'm a biology grad student, but my funding and my fellowship are all through the Demography department. One service I return to the Demography department is to attend their weekly seminar and who ever the speaker is, suggest biological literature relevant to her topic of study. Some demographers take better to this than others. Most seem to appreciate the new perspective, even if they are not really interested in thinking about humans in a biological context. (For the record, I also go to biology talks and bring up demographic concerns.)

The next speaker I will have to deal with differently, because the speaker this coming week is me. I'll be presenting on demographic aspects of my rotifer research. Age specific mortality and reproduction. Effect of food supply on longevity. Infant mortality. I'll get into the biology a bit too, but mostly they'll want to hear about the demographics. If I was in my audience, I would suggest more of a focus on the biology.

Whose next?

Of the thirteen students working with me in on rotifer work, only one is white Christian heterosexual male. This never occurred to me before yesterday, when we were sitting around the lab, talking about the fact that should Obama win, he would be the first POTUS who was not a straight white Christian man.

I asked my students if they thought, now that we were getting a non-white president, we would have a female president, a non-Christian president or a homosexual president first. Some said we would have a woman soon, others said America would elect a Jewish president before a woman. Everyone agreed that there is still too much bias against homosexuals to have an openly gay president any time soon. I asked them if they thought Americans would ever elect a scientist as president. They all said no, and a couple of them said that was probably a good thing.

Three of my students a naturalized citizens, and therefore are barred by our constitution from running for president. But the other ten, in my opinion, should all have equal shots at the White House. The fact that they are all science students studying evolution at Berkeley means that this chance is zero is bearable, so long as it is an equal and unbiased zero.

Strengths

In science, as in most anything, it pays to know your strengths and weaknesses.

I am about as good as anyone I have known at understanding, remembering, integrating and evaluating biological concepts. I am terrible at calculus. I am very good at recruiting, evaluating and training assistants. I am hopelessly slow at learning programming. I have very steady hands for lab work and a hip that is bad enough to keep me from doing much field work. I am a gifted improviser and a mediocre follower of protocols. I am the king of scrounging and am pretty good at applying for funding, but I struggle with remembering to do the accounting or keeping track of receipts. I am a great teacher but a disinterested disciplinarian. I am unrivaled in my ability to start research projects, but need serious improvement in my ability to finish them. I have great fun with ideas but no fun with spelling. I collaborate well but self-motivate poorly. I am good at being blunt and bad at being not-blunt. I am a good scientist, but need improvement as an academic.

What I've been working on

Here is the last bit of my post-doc fellowship application:

Summary and Conclusion:
Evolutionary Biodemography has focused on explaining late-life mortality patterns and overall longevity. The evolutionary basis of early-life mortality has been studied more rarely and less systematically. My proposal boils down to four basic steps intended to firmly establish the field of early-life evolutionary biodemography:
1. Mathematically define and parameterize the age specific mortality patterns that characterize Human-like Early-life Mortality (HEM).
2. Compile, review and organize those evolutionary hypotheses potentially explaining HEM.
3. Use these hypotheses to predict life-history traits that may be necessary causative factors of HEM, and thereby predict which taxonomic groups are not subject to HEM.
4. Gather data to determine in what species or populations, if any, HEM does not occur, thereby testing my collection of evolutionary hypotheses.
Early life mortality has a tremendous effect on a wide range of populations, and our failure to date to understand its evolutionary basis is a major gap in our understanding of both evolution and demography. Working at MPIDR, I will begin to fill that gap.

Writing to the audience

One of the most basic pragmatic points of writing is tailoring the language one uses to one's intended audience. I would use different words in a text book for first graders than in a paper sent to a scientific journal, even if the exact same concept was being communicated. I have to estimate the assumptions, interests, background knowledge, tolerance for jargon and a host of other parameters about my readers in order to write in the most useful voice and tone.

This becomes a problem when I don't know who my audience is. I am applying for a DAAD research grant, and while I know I need to submit four copies of my proposal, I have no information on the four people who will be reviewing it. They may be four evolutionary biologists, in which case I would like to write a fairly detailed and technical proposal, using all the appropriate terminology, so as to show that I know my topic and have detailed plans. They may be four non-biologists, but still natural scientists. They may be social-scientists, or a mix of academics from all fields. They may be (although I doubt it) four German first-graders, in which case I would write a very different proposal. But as I don't know who they are, I have been trying to write a proposal which is appropriate to all of these groups, and finding it nearly impossible. How does one write an audience-neutral grant application?

All global warming is local

I got home from the lab late last night and turned on NPR. There was a voice I instantly recognized, my major professor, and the director of the MVZ, Craig Moritz. What, I wondered, was Craig doing in my radio at this late hour? Being interviewed by All Things Considered for this piece on the effects of climate change on the wildlife of Yosemite National Park.
Mean monthly minimum temperatures in Yosemite have risen by 6 degrees Fahrenheit in the hundred years since the MVZ's first director, Joseph Grinnell, surveyed the wildlife there. Apparently in response, many of the wildlife species in the park have moved their upper and lower limits thousands of feet higher than they were.

The project is described in great detail here, and a subset of the Yosemite data were just published in Science. I wasn't involved in this work, in case you were wondering.

Compresed Timeline

The Max Planck Society is a network of research institutes, mostly but not entirely in Germany. Many people consider it, to be the world's leading non-university research organization. The member institutes are more or less autonomous in terms of planning and executing research, as far as I understand, but all of them have the reputation for world-leading excellence.

A couple of years ago, at a conference on aging I had the pleasure of meeting the Executive Director of the Max Planck Institute for Demographic Research, Jim Vaupel. At the time, he and my professors, Ron Lee, discussed the possibility of me coming to MPIDR at some point. I was excited by the prospect. Here at Berkeley there is effectively no one outside of Ron's lab group who thinks much about the kinds of questions I do, while MPIDR has a whole Evolutionary Biodemography Lab, at which they think about and work on pretty much everything I do, plus a lot more.
But then I went off to PNG, and then I was injured, and pretty soon I figured the opportunity had passed. But then I got an email announcing that there was a fellowship available through the German Academic Exchange Service (better known by its German acronym, DAAD, for North American researchers to come work in Germany if they had the invitation of a German institution. The email conversation that followed was suprisingly short, spanning little more than 24 hours, and completely reorganized my timeline for finishing grad school. If I may paraphrase, it went something like this:

Me to Ron: Should I apply for a DAAD fellowship to work at MPIDR.
Ron to me: Do you want me to ask them?
Me: Yes, thank you.
Ron to Jim Vaupel: Dan is an excellent young biologist, should he apply for a DAAD fellowship to come work there?
Jim to Ron (to me): Yes, he should apply, but even if he doesn't get the fellowship he should come here as soon as is convenient, and we can support him.

Just like that, no application, no interview, I had a desirable post-doctoral position lined up at a time when the economy is tanking and most of my peers are wondering if there will be any positions for them at all. My deliberations consisted of describing the situation to my wife to make sure she didn't mind spending some time on the Baltic, and emailing Dr. Vaupel to make sure I understood him properly.

What this means for my grad-school timeline is that instead of 16 to 21 months, I have eight to ten months to finish. I was thinking I would finish December of 2009 or May of 2010. After the offer from MPIDR, I thought I would have to finish by August of 2009. Afer talking to my major proffessor today, it is clear I need to be pretty much done by May of 2009.

My department's commencment is May 23rd 2009, and I plan to walk then, if at all possible. I won't actually be finished at that point, but I will be finished enough to convince my faculty persons that I can file my disertation before the end of summer. My wife's graduation from UC Davis is mid-June 2009. That summer I will finish my dissertation, then we will pack up our lives, take the cat's to my sister's house, and fly to Germany.

That seems like a lot to accomplish in one year.

Yikes.

Student Researchers

I've added a Student Researcher section to my website, so all my students can have research sites. There will be more in the coming days.

I wiggle my eyebrows ~1000 times a day.

I spend about eight hours a day in front of a microscope. I generally have a student on either side of me. I look at the first rotifer in our population and report how many eggs and juveniles it has, whether it is alive, and anything else notable about it. "Two forty six dash bee five is alive has three eggs, two juveniles and extended foot syndrome. The largest juvenile has one egg."
The student on my left, at the computer, enters all of this into the spreadsheet and tells me what to do with the juveniles, based on our established culling rules. "Put the biggest juvi in two forty eight dash a one, cull the other two."
I pick up the mom rotifer in a specially bent glass pipette and wiggle my eyebrows such that my glasses slide off my forehead and onto my nose so that I can see the student to my right. She uses her pipette to point at the hole where the rotifer is going. I squeeze the bulb at the end of my pipette to eject the rotifer into that hole. She looks through a second microscope to make sure the rotifer is actually there. I push my glasses back up, look through my microscope, pick up the juvenile, wiggle my eyebrows again, move it to the well where it needs to go, then move on. All of this takes 15 seconds to one minute, depending on the complexity and which students are working with me. We repeat this process 450 more times each day. By the end of each day we have gathered more demographic data than many field studies of long-lived vertebrates do in several decades. By the end of a month we can see significant evolutionary changes based on the selective pressures we apply through our decisions about who to cull and how much to feed them. It is not glamorous, but it is effective.