Poster-time

In a couple of weeks I'll be attending the conference of the Population Association of America. It will be a bunch of demographers. Most of the conference is talks divided into topic-specific sessions. They don't have a session for biodemography, or really anything related to my research, but the poster-session is open to whatever topic, so I'm presenting a poster.

My title is, "Post-Reproductive Lifespan in Humans: Cultural Artifact, Widespread Primate Trait or Unique Adaptation?"

I've had fun making my poster, mostly because it is an excuse to play with Photoshop and Powerpoint instead of writing my thesis. For my poster I needed to find a compact easy way to display how the fertility and survival of a population changes with age, and simultaneously explain my methods. And I needed to be able to do that for several populations side by side in a small space. Now this is all tailored to make sense to the demographer, so it may not be that intuitive to anyone else, but I like what I've got. To clarify, in demographerese, lx means what portion of the individuals that survive to each age and mx means how fertile are individuals at that age.

By plotting mx and lx on the same graph I make it visually clear (to a demographer) that the population nears an endpoint to fertility (age M) long before it nears an endpoint of survival (age Z). I then go on to use some math and demographic methods to define good ways to measure post-fertile survival in ways that allow for straightforward comparisons between species. I call the two measurements G and S. But the fun part comes in when I use the graphical format established in Figure 1 to compare populations in Figure 2:


My hope is that having labeled and explained the parts of the graph in Fig. 1, the meaning of these graphs in Fig. 2 will be quickly obvious to the demography crowd. I'll leave you to interpret what these graphs say about post-fertile survival in humans and chimps in different environments. I can't give away everything.

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.

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.

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.

So many data, so little time.

I first met Jerram Brown in 1999, when I was a senior at Bennington College. My adviser, Betsy Sherman, had been his student many years earlier. I was about to graduate from college and he was about to retire from a very long and extremely successful career in exactly the part of biology I was interested in. For the previous 30 some years he had been studying the behavior, ecology, demography and so forth of the Mexican Jay. I asked him about the possibility of working for him, but I was too late, his retirement plans were gathering momentum. He officially retired in 2002.
When I graduated, I went to work for Glen Woolfenden, who had an almost as long-term study of a very closely related bird, the Florida Scrub-Jay. Glen and Jerram, for reasons I don't know, had some sort of tension between them, and I had no further contact with Dr. Brown's group.

Looking through the program of the Animal Behavior Society Meeting I just attended, I was very excited to see that Jerram Brown would be momentarily be coming out of retirement to accept a distinguished researcher award and deliver a plenary address on his work. That talk was the last morning of the conference, and it turned out far more interesting than even I had suspected.

His data set on the Mexican Jays is shockingly extensive. In addition to the multi-decade highly detailed demographic data, he has an enormous number of ancillary data sets, many of which he had never gotten around to publishing, because he had not found any theoretically important question they could be used to answer. But as he heaped data upon data, I came to realize something. These data he had gathered because he could, rather than because he had a particular question in mind, were potentially exactly the data one of my advisors, Ron Lee, needed to test some of his hypotheses on the importance of intergenerational transfers of resources (in this case food) to the evolution of longevity and sociality. Dr. Brown had records of >26,000 individual food transfers, including who was transferring, to whom, how old each one of them was and how they were related to each other. My heart started thumping. I had to get Jerram's data and Ron's theoretical framework and analytical prowess together. But most scientists jealously guard the data sets they spent their lives gathering. And Ron is already terribly busy with far too many projects, would he even be interested?

Then Dr. Brown said, as part of his planned talk, something I have never heard any scientist say before, even though we probably should all say it sooner or later, "I will gladly turn over my entire dataset to anyone who can make good use of the data." There was a loud gasp. It was me, but not only me, several people gasped. He may as well have said, "I will turn over all this gold ore I have spent my life mining to anyone who can smelt it."

Immediately after his talk, I went up to speak to him. I shook his hand, told him that I was a former student of his former student (in case it helped) and began to tell him about Ron and his work. Another fellow, a Dr. Ha, came up and said that he would like to apply for NSF funds to hire a post-doc to work with Dr. Brown to make sure the data set is preserved and made available. He said that if Ron were involved, this would increase the chances of getting the funding, as NSF would want to know the data would be put to good use.

With some trepidation, I emailed Ron and told him all this. He wrote back almost instantly saying it sounded like a great opportunity, and he would love to join this collaboration, but would want, "some more junior researcher with more years of research ahead of him/her" to be involved, and suggested that I was the "leading candidate."

So this all raises the very real possibility that I may be spending a couple of years immediately after my doctorate applying Jerram Brown's data to Ron's hypotheses (and perhaps a few hypotheses of my own). I haven't yet figured out if this is something I actually want to do, and would be able to accomplish, but the prospect is very exciting all the same.

Residual life

Most of us take for granted that we will live for a long time after we stop having kids (for those of us who will have kids). The standard picture for Americans these days is to stop at 40 or so, hope to live to twice that age. And this doesn't seem unrealistic. But when viewed evolutionarily, it seems a bit bizarre. Most organisms do not have a post-reproductive lifespan, human females very clearly do. I've previously written about the most common hypothesis to explain this fact, is the "Grandmother Hypothesis."

On Tuesday I was giving a talk on my research to Berkeley's Primate Research Group, including my experimental examination of the Grandmother Hypothesis. After the talk, I got a lot of good feedback, including an interesting question. How certain are we that females of other species of primates wouldn't live as long post-menopause as humans if they lived as cushy lives as we do?

The only answer I could give them is that I haven't seen any data suggesting otherwise. But then, on my way home, it occurred to me that I already have probably the world's best data set for answering exactly that question.

Primates in well run zoos tend to greatly outlive their wild cousins. Medical care, reliable food supplies, no predators and few pathogens. Not to say the life of a captive primate is perfect, or that there isn't significant variation in the quality of care, but for many species maximum longevity in captivity is much greater than in the wild. And it just so happens that I have life tables, including age specific reproductive rates and mortality rates, for 120 species of primates. These come from ISIS data, meaning data from relatively well run zoos, and I will need permission from ISIS to use them in this way, but I doubt they will have any major objections.

The idea of writing a paper based on data I already have is exciting to me. Usually I spend years between having an idea and having assembled the data to address it. I have almost all the data I need to address this question safely on several computers. I'll get the hang of this science thing yet.

Citizen science, and the limits thereof

One of my neighbors sent me a copy of a list that she was given by the people who sold her her house. They told her they saw all these species in El Cerrito Hillside Natural Area. At first was excited to see this list, and the first several species on there are quite reasonable, but a more careful look significantly lowered my level of confidence in these observations. This is a perfect example of why "citizen science" projects that rely on non-experts to gather natural history data require great care. Below I have pasted in the list, with some comments.

chestnut-backed chickadee
common bushtit
turkey vulture
cooper's hawk
american kestrel
red-tailed hawk
common crow
common raven
great-horned owl
ruby-crowned kinglet
woodthrush (Does not occur in CA, probably Hermit Thrush)
red-shafted flicker
winter wren
hairy woodpecker
downy woodpecker
robin
brown towee (Californa Towhee)
rufous-sided towee (Spotted Towhee)
oregon junco (Dark-Eyed Junco)
purple finch
house finch
pine siskin
wilson's warbler
tennessee warbler (does not occur in CA, Orange-Crowned Warbler)
american goldfinch
lesser goldfinch
lawrence's goldfinch
mockingbird
plain titmouse (Oak Titmouse)
scrub jay
steller's jay
mexican jay (Does not occur in CA, probably juv. scrub-jay)
cedar waxwing
western wood pewee
mourning dove
bead-tailed pigeon (Band-tailed Pigeon)
fox sparrow
savannah sparrow
harris's sparrow (Does not occur in CA, probably House Sparrow)
golden-crowned sparrow
starling
anna's hummingbird
allen's hummingbird
black phoebe
wrentit
marsh hawk (Same as Northern Harrier, listed below)
swainsons's thrush (possible)
mississippi kite (Does not occur in CA, probably Northern Harrier)
rufous hummingbird (nearly impossible to tell from Allen's except in hand)
canada goose
brown pelican (in park or flying in distance over bay?)
slate-colored junco (same as junco listed above)
warbling vireo
lazuli bunting (possible)
western tanager
bluegray gnatcatcher
empidonax flycatcher
bewick's wren
yellow-rumped warbler
townsend's warbler
hermit thrush
myrtle warbler (same as Yellow-rumped arbler)
osprey
beardless flycatcher (not found in CA)
red-breasted nuthatch
white warbler (not a real species, no idea)

Professional science is hard

Late at night, with a weary brain, I attempted to document some of the process of data analysis, typesetting, and the general level of complexity involved in the production of a moderately novel physics journal article. Using direct-screen-capture video software, I just flipped through the content that I have been staring at for weeks, giving a 4 minute silent film of my work. The production of this low-res video was a greatly refreshing 6 am escape from my writing, and stands as a slightly surreal and fuzzy look at the reality of being a physicist. Do not attempt to understand any technical content, that is not the point, this is just art therapy for the sleep-deprived.

If I had to choose a sound track to this, the first thing that comes to mind is the scene at the end of 2001 where Dave has to float into the airlock, and then slowly, manually deactivate the optic circuits of Hal.

Here it is:

From Data to a Paper

- Let me know any reactions that this may incite,

Stephen

A Deluge of Data

The International Species Information System is an organization charged with coordinating data gathering, storage and usage among zoos and aquaria around the world. They started out keeping track of things like which zoos had which animals, how individuals were related to each other and so on. Over time they have come to have enormous demographic and physiological databases and they hold individual data on millions of captive animals.

These data were originally collected for the use of ISIS member organizations in the management of their captive populations, but as the amount and quality of data has improved, these data sets have becomeof interest to a wide range of other researchers.

In my work, I am interested to know for as many species of primates as possible, whether the males live longer, the females live longer, or the longevity of the two species is approximately the same. I am specifically interested in the capacity for longevity of each sex of each species, rather than how long they live when exposed to disease, predators and famine. And so when I heard that ISIS had longevity data on hundreds of thousands of captive primates, I was very interested.

But of course, there are problems. The data were collected by, and are owned by, ISIS's member organizations. The fact that they were collected in a decentralized way means that a chimp can be recorded as having been born in 1692 when it was probably born in 1962. Some individuals have birth dates recorded, but no death dates, and so on.
Also, the zoos consider this type of data to be potentially sensitive. No zoo wants to be known as the zoo where Gorillas have the shortest life expectancy. So the data needed to have all possible identifying information removed before ISIS could get permission from the zoos to share the data with researchers. And only someone who is really familiar with zoo data could prune the bad data, process the data into sex*species specific life tables and get approval to let me use these.

Enter Laurie Bingaman Lackey of ISIS. She agreed to get me as much of the data as she could. But she is a busy woman, and this was for her basically a side project, undertaken to help me and get her name on a paper or two. And she had to go through each species individually, look for bad data, compile a life table from what remained. So it took a little over three years to get the data to me. Sex specific life tables for all 120 species of primates that ISIS has enough data on for my purposes. A Herculean effort. Now that I have the data, I have to remember what exactly I was going to do with them.