john hawks weblog

paleoanthropology, genetics and evolution

life history

  • Membership has its privileges

    Thu, 2011-01-13 00:41 -- John Hawks

    A new paper in PNAS by Erik Trinkaus covers the mortality patterns of old versus young adults in Neandertals, early modern humans in the Levant and early Upper Paleolithic people of Europe [1]. The paper has gotten a lot of attention from the press, including the NY Times: "Life Span of Early Man Same as Neanderthals’". Reporters worldwide (so far, 30 articles in Google News) were relying on a press release issued from Trinkaus' university.

    I read this paper and got a sinking feeling. The results and methods looked to me very similar to those used by Rachel Caspari and Sang-Hee Lee in a series of papers from 2004 onward [2] [3] [4] [5]. Their initial paper attracted comments by Kristen Hawkes and James O'Connell [6], and Tom Minichillo [7]. This has been an active and highly cited research pathway with papers in PNAS, the American Journal of Physical Anthropology and the Journal of Human Evolution. I was extremely surprised that Trinkaus hadn't cited any of this work.

    Anyone can compare these papers and draw their own conclusions. In my opinion, the results reported in the press are not new.

    Caspari and Lee compared the proportions of old and young adults in Neandertals, early Upper Paleolithic and (in their 2006 paper) early modern humans. They ignored juvenile remains and focused only on the subject of adult mortality, only in those two age classes. They assigned the dental remains to categories based on wear criteria and tested the significance of sample differences. They showed that early modern humans and Neandertals have similar ratios of old to young skeletal remains. They tested whether burial versus non-burial archaeological contexts might influence the observed ratio of older to younger individuals. They demonstrated limits on the phylogenetic interpretation of the results and suggested behavioral and cultural factors that could account for them. I think their work was clever and simple, but more important it has been highly cited and presented at national meetings. These are not obscure sources.

    I'm not the citation police -- I probably make more errors than anybody. But in my opinion Trinkaus' new paper uses substantially the same methods and finds the same results as Caspari and Lee without giving them credit. I find only one difference in method -- Trinkaus cut off his age categories at 20 and 40 years instead of 15 and 30. And one difference in result -- Trinkaus found fewer older adults in the early Upper Paleolithic compared to Caspari and Lee. If either difference is important, there's no citation to let us know why. It's like Caspari and Lee's papers have slipped down the memory hole.

    NAS members can submit papers to PNAS directly, relying on reviews from peers that they select themselves. The editorial policy of the journal makes it very difficult to reply to these papers, and certainly no reply could gain the attention that this paper has already received.

    Lucky for me, I just happen to have a blog for such occasions.

    I wrote to Trinkaus to invite him to provide his comments for me to publish them along with my post. I knew he would probably have a different interpretation than me of the issues. He very kindly took the time to compose replies to my questions. I publish them here unedited along with my follow-up questions.

    His comment on my initial request:

    Thanks for this. I intentionally did not refer to those papers since they, at least in the original PNAS paper, completely ignored taphonomical and behavioral issues. My original 1995 paper, which I seem to remember they did not cite (I am in Spain on a slow internet connection), heavily emphasized those issues, and this paper does as well. The issue is not just longevity - it is its combination with all of the various biases in the samples, biases which are just as important as the presumed demographic ones.

    Best, Erik

    My first followup:

    Thanks, Erik, I really appreciate your reply. I hope Spain is treating you better than Wisconsin is treating me this week!

    Naturally I disagree but I am very glad to be able to include your comment.

    I did check the citations of all the papers, thinking that you might have missed them for that reason. They did cite you. The 2004 PNAS paper discusses taphonomy extensively, including the quantitative comparison of burials versus non-burials. The exchange with Hawkes and O'Connell also discusses taphonomic issues. The behavioral issues are the subject of the 2006 paper, which was titled "Is human longevity the consequence of cultural change or modern biology?"

    --John

    Trinkaus' reply:

    Two comments.

    I do not make it a point to quote everybody with whom I might disagree.

    Your blog on the Zhirendong mandible could have benefitted from a reading of Weidenreich, Dobson and Trinkaus and/or Schwartz and Tattersall on what constitutes a modern human chin. Saint Cesaire and the Vindija mandibles do not have it, any more than ER 730 does, despite incipient trigones on them.

    I wrote back:

    Hi, Erik --

    Thank you again for your responses. They have helped me to understand your position.

    If PNAS has the same review standards as my blog, maybe I should just concede. It would take fifty undergraduates to find all my errors.

    But I think we completely agree that a scholarly mandibular description should cite the sources you mention, both classic and recent.

    What I don't understand is why you disagree in the present case. If you had ignored Rachel and Sang-Hee's papers because you thought they had nothing of value, why did you use such similar methods and come to such similar conclusions? Did you think those methods and conclusions were so obvious that they don't need citation? If so, then why did you issue a press release?

    --John

    Trinkaus replied:

    I originally found the Caspari and Lee PNAS paper unconvincing, and put it out of my mind. None of my peer-reviewers noted its absence, and it was not cited in recent papers relating to the topic (i.e., Smith et al. 2010). It did not occur to me to cite it, and nor did it occur to other people directly and actively involved in Neandertal/modern human life history. Hence it was not there.

    I did not previously comment on the your blog on the Zhirendong mandible, since I almost never respond to such commentaries. I only did it since you raised issues relating to your blog. That paper appears to have set a record for misquotes (Dennell could not possibly have read it for his Nature commentary).

    Best, Erik

    I finished the exchange:

    Hi, Erik --

    Well, thank you again for taking the time to comment. I do appreciate it, particularly since you're out of the country.

    --John

    I think our exchange was much more productive than a formal comment could be. Trinkaus wrote that his omission had been intentional, and I take him at his word that he "put it out of his mind". I am glad that I was able to bring his attention to the problem, though I surely wish that a better review had been done in the first place. I'm sure he still disagrees but I hope he will take the opportunity to engage with the current literature. Maybe someone can suggest some more studies to replicate.

    I've pretty consistently criticized scientists who issue hyped-up press releases. They draw my attention. It is a frequent feature of press releases that they claim unmerited novelty and ignore prior work. This feature rarely creeps into the actual published paper in such an obvious way.

    I'm also disturbed by the power imbalance this case demonstrates. Sitting here watching MythBusters with my daughter who wants to be a scientist someday, I hope we can start to do better.

    References

    1. Trinkaus E. 2011. Late Pleistocene adult mortality patterns and modern human establishment. Proceedings of the National Academy of Sciences [Internet] 108:1267–1271. Available from: http://dx.doi.org/10.1073/pnas.1018700108
    2. Caspari R, and Lee S-H. 2004. Older Age Becomes Common Late in Human Evolution. Proceedings of the National Academy of Sciences, U. S. A. 101:10895–10900.
    3. Caspari R, and Lee S-H. 2005. Are {OY} Ratios Invariant? A Reply to {Hawkes} and {O'Connell} (2005). Journal of Human Evolution [Internet] 49:654–659. Available from: http://dx.doi.org/10.1016/j.jhevol.2005.08.005
    4. Caspari R, and Lee S-H. 2005. Taxonomy and Longevity: A Reply to {Minichillo} (2005). Journal of Human Evolution [Internet] 49:646–649. Available from: http://dx.doi.org/10.1016/j.jhevol.2005.07.003
    5. Caspari R, and Lee S-H. 2006. Is Human Longevity a Consequence of Cultural Change or Modern Biology?. American Journal of Physical Anthropology [Internet] 129:512–517. Available from: http://dx.doi.org/10.1002/ajpa.20360
    6. Hawkes K, and O'Connell J. 2005. How Old Is Human Longevity?. Journal of Human Evolution [Internet] 49:650–653. Available from: http://dx.doi.org/10.1016/j.jhevol.2005.04.012
    7. Minichillo T. 2005. Paleodemography, Grandmothering, and Modern Human Evolution: A Comment on {Caspari} and {Lee} (2004). Journal of Human Evolution [Internet] 49:643–645. Available from: http://dx.doi.org/10.1016/j.jhevol.2005.04.011
    Tags: 
    ethics
    metascience
    Upper Paleolithic
    aging
    life history
    Neandertals
    Synopsis: 
    I pick a bone with an author whom I think hasn't acknowledged important prior work.

  • Questioning the "evolution of an underclass"

    Thu, 2010-07-29 08:30 -- John Hawks

    A little life history theory can be a dangerous thing. Case in point: "Die young, live fast: The evolution of an underclass." The article discusses correlations among longevity, health, income, and age at first birth within industrialized societies and cross-culturally worldwide.

    Evolutionary theory predicts that if you are a mammal growing up in a harsh, unpredictable environment where you are susceptible to disease and might die young, then you should follow a "fast" reproductive strategy - grow up quickly, and have offspring early and close together so you can ensure leaving some viable progeny before you become ill or die. For a range of animal species there is evidence that this does happen. Now research suggests that humans are no exception.

    The cross-mammal generalization is true, and the article discusses correlations within human populations that run the same way. Women who live in communities with shorter life expectancies and shorter expected healthy lives also tend to have their children younger and with shorter birth intervals.

    It should be obvious but I'll point it out anyway: The use of the word "evolution" is misleading. The comparisons among human groups discussed in the article are not cases of biological evolution; they are mostly social effects of industrialization. I wouldn't rule out long-run changes in gene frequencies coming from such systematic fitness differences, but calling it the "evolution of an underclass" is alarmism.

    The article goes on to quote public health researchers and sociologists who seem to confuse correlation with causation. They assume that making communities live longer will cause young women to wait longer to reproduce.

    That could happen, sure. The article suggests a plausible causal hypothesis: Young women decide that they'd better reproduce in a hurry so that they'll have healthy older relatives around to help them. Or they decide they've got nothing else to wait for, so they'd better have kids.

    But if you dig a little further into life history theory, there are alternatives. The cross-species comparison reflects the outcomes after a long process of adaptation, and appears simple mainly when you compare species across several orders of magnitude of longevity. Looking at any set of closely related species, the story is more complex.

    The dynamics by which a population may pass from one value to another can also be described mathematically, and interestingly that math may lead to different ideas about how age at first birth might respond to differences in longevity. Hamilton (following Fisher) noted that in a growing population, a reproductive bonus goes to younger mothers; in a shrinking population it's the opposite. A rational person might conclude that longer healthy lifespans would cause the population to grow (as indeed happened during the early twentieth century) and therefore decide that younger births will increase fitness. That is, if fitness outcomes were relevant.

    I don't think people generally care about fitness outcomes, and so I don't think there's much reason to expect the correlations in today's population to remain as strong as health improves. It seems pernicious to argue that we should invest in health because it will make women put off reproducing. Why not just recognize that health is an intrinsic good?

    Tags: 
    social
    development
    life history
    health

  • Bioethics pair

    Sat, 2010-02-13 19:23 -- John Hawks

    A pair of articles in my browser tabs refer to bioethics.

    Ronald Bailey, in Reason, writes about the "ethics" of life extension research:

    "How dare you do this research? The earth is already being raped by too many people, there is so much garbage, so much pollution."

    Ten years ago, an anti-aging researcher described this hostile reaction to her work in the pages of The New York Times. Not much has changed since then.

    I had exactly the same reaction from my undergraduate students last time I taught my anthropological genetics course. Sure, they said, people might like to live longer. But wouldn't that be a bad thing? The world is too crowded as it is.

    I'm thinking that death was far beyond the horizon of their bright young minds.

    Much of Bailey's article describes the way ethicists try to put a numerical value on happiness, multiplied by a number of years. It's not different in principle from an economist estimating the financial damage of an early and unexpected death. But somehow it seems laughable to me -- as if an individual's happiness were the only important variable. What about the value of grandparents to their descendants, or the value of living history to the whole population?

    This happened to hit my desktop at the same time as Sally Satel's article, titled "The limits of bioethics".

    She describes the history and scope of bioethics. Satel points out that the name "bioethics" was coined by Sargent Shriver, amid a burst of interest in the problems of biological and medical decisions in the late 1960's. She mentions the establishment of think tanks and expansion of the bioethicists' brief during the 1970's and 1980's, and touches on the controversies over "conservative" bioethics during the last decade.

    What is the proper role of ethicists in decision-making? Here's Satel's conclusion:

    At their best, bioethicists are scholars who study the intellectual and social history of value controversies in medicine and biotechnology. They can teach us about the technical and cultural antecedents of modern debates and show us how to engage in disciplined moral inquiry. They are skilled at drawing conceptual maps of the dilemma at hand while enumerating various ways to resolve it. In these ways, bioethicists have much to offer. But beyond this, their value is mainly cosmetic or bureaucratic. When called upon by politicians, their main task is to neutralize explosive issues or to provide ethical cover for decisions that have already been made. When physicians summon them, it is mostly to mediate disputes between patients, staff, and family members regarding end-of-life decisions. The media tap bioethicists for high-minded sound bites. In hospitals and in governmental agencies, they man the regulatory ramparts.

    Maybe some bioethicists would disagree, but I think most see themselves as scholars instead of apparatchiks.

    Tags: 
    history of medicine
    ethics
    biotech
    life history

  • Human lifespans have not been constant for the last 2000 years

    Tue, 2009-08-25 01:07 -- John Hawks

    Few things are worse than a skeptic sloppy about checking his facts. For example, the "Bad Science" feature of LiveScience claims that we're not getting any older these days:

    Human Lifespans Nearly Constant for 2,000 Years

    Discussions about life expectancy often involve how it has improved over time. According to the National Center for Health Statistics, life expectancy for men in 1907 was 45.6 years; by 1957 it rose to 66.4; in 2007 it reached 75.5. Unlike the most recent increase in life expectancy (which was attributable largely to a decline in half of the leading causes of death including heart disease, homicide, and influenza), the increase in life expectancy between 1907 and 2007 was largely due to a decreasing infant mortality rate, which was 9.99 percent in 1907; 2.63 percent in 1957; and 0.68 percent in 2007.

    But the inclusion of infant mortality rates in calculating life expectancy creates the mistaken impression that earlier generations died at a young age; Americans were not dying en masse at the age of 46 in 1907. The fact is that the maximum human lifespan — a concept often confused with "life expectancy" — has remained more or less the same for thousands of years. The idea that our ancestors routinely died young (say, at age 40) has no basis in scientific fact.

    That's a fair criticism -- "life expectancy at birth" is indeed a misleading statistic if your goal is to compare the health of adults. But then the column starts criticizing perfectly true statements in other reports, and in the end goes completely off the rails:

    When Socrates died at the age of 70 around 399 B.C., he did not die of old age but instead by execution. It is ironic that ancient Greeks lived into their 70s and older, while more than 2,000 years later modern Americans aren't living much longer.

    Syllogistically speaking, Socrates didn't die of natural causes, therefore the Greeks had lifespans the same as ours. Or something.

    Well, it's just not true. You can see for yourself easily with a little reading. For example, a free article (PDF) by John Bongaarts and Griffith Feeney reviews the concepts and provides convenient summary figures of mortality rates by age in the U.S. for 1950 and 1995. Age-specific mortality rates have declined across the adult lifespan. A smaller fraction of adults die at 20, at 30, at 40, at 50, and so on across the lifespan. As a result, we live longer on average. Reductions in juvenile and infant mortality also contribute to increased life expectancy at birth, but the same trend is evident if we consider life expectancy at 15, 20, 30, or even 80. We live longer now than in the past.

    What about 2000 years ago? In addition to its Socrates reference, the "Bad Science" column cites:

    * An article on Egyptian pyramid builders in the November 2001 issue of "National Geographic" noted, "Despite the availability of medical care the workers' lives were short. On average a man lived 40 to 45 years, a woman 30 to 35."

    The column later describes the statement as "completely wrong".

    The age structure of ancient populations is a matter of great interest within anthropology and archaeology. Some think we can draw many conclusions from skeletal samples; others are more cautious in their application of models to the past. But there's no doubt that Romans, Egyptians, and Greeks were dropping dead at age 30, 40, 50 and 60 -- at much higher age-specific mortality rates than today. Estimating the overall age profile is difficult and requires models. But testing the "Bad Science" assertion is much easier -- if human lifespan had really not changed in 2000 years, then 35-year-olds shouldn't have left their skeletons very often in the Roman catacombs. Unfortunately (for them), we find those 35-year-old bodies. A rough estimate (gleaned from tomb inscriptions that give ages) is that half of Romans who lived to age 15 -- and therefore escaped juvenile mortality -- were dead before age 45.

    That leaves us with one remaining issue -- the maximum lifespan. This statistic really hasn't changed very much in the last 50 years -- the oldest-living humans in 1960 were between 110 and 115; that's how old the record-holders are today. Only a handful of people have, to our knowledge, ever lived longer.

    So in this respect, it may seem reasonable to say that the human lifespan has been fairly constant. But I would challenge even that assertion. For one thing, the maximum lifespan just isn't very relevant to the population. Only a tiny fraction of people today survive to age 100. That maximum lifespan may tell us something about human biological systems, but what really matters to demography are age-specific mortality rates across adulthood -- the full range of times when most people die.

    More important, we don't have a clue what the maximum lifespan may have been 200, 500, or 2000 years ago. Such a tiny fraction of people make it above age 100 today that we could hardly expect to find any of them at all from skeletal samples. Nor can we expect accurate ages from historical records -- Methuseleh, anyone? It seems reasonable to say that the maximum lifespan, at some point in human history, was increased by sedentism, nursing care, stable food availability, and other cultural innovations. With higher infant, juvenile, and adult mortality, even those with perfect genes would be a lot less likely to get the chance to live to extreme ages. But in skeletal terms, at least, the hypothesis may not be testable.

    In every way we can measure, human lifespans are longer today than in the immediate past, and longer today than they were 2000 years ago. Infant and juvenile mortality do make a difference -- especially if we use "life expectancy at birth" as the statistic -- but age-specific mortality rates in adults really have reduced substantially.

    That's a good thing!

    UPDATE (2009/08/25): Dienekes points to a study of "men of renown" in classical Greece, which found a median length of life of 70 years. He notes that living to advanced ages of 80 or even 90 was not unheard of in antiquity.

    No disagreement here -- some people did live that long. The point is that the population had higher mortality than today (although classical Greece might well place favorably compared to some present high-mortality populations).

    Some reactions:

    1. "Men of renown" generally have to get to a certain age (say, 30) before they're worth renowning. Early adult mortality isn't figured in.

    2. Dienekes refers to Psalm 90:10 and other sources referring to the length of life. Saying "the length of life is 70" is basically saying that you know old 70-year-olds, not that any given individual had a high chance of living to 70. Even so, today it's formulaic to say the length of life is 100.

    3. We still don't have a clue as to the maximum lifespan in classical times -- attestations of the ages of extreme individuals may be correct, but we have no way now of establishing their reliability. Ramesses the Great lived into his 90th year, and it's by no means impossible that some in the classical Mediterranean lived to be over 100 (Dienekes mentions some attested ages in that range, Isocrates at 98 seems especially credible). I don't think the null hypothesis of identical maximum is testable given all the complications, but we may point out that there are presently people older than 110 years living in many countries.

    Tags: 
    debunking
    life history
    health

  • Bigger brains, more cancer?

    Wed, 2009-06-10 14:08 -- John Hawks

    Rachael Rettner reports on a hypothesis that human cancer risk may be a side-effect of brain evolution. The hypothesis emerges from studies of gene expression, which show that regulated cell death (apoptosis) is downregulated in some human tissues:

    The researchers are tying these two hypotheses together. They think that reduced apoptosis may have helped people acquire their large brains. But it may also have made us more prone to cancer.

    "It's kind of hard to explain why we could have evolved to have a less efficient apoptotic system," says [Georgia Tech researcher John] McDonald. "So the hypothesis we came up with was that maybe selection to increase brain size was what put the selective pressure on the system to reduce apoptosis." And even though less apoptosis may have meant more cancer, there would not have been selective evolutionary pressure against it since most cancers don't appear until after reproductive age, McDonald adds.

    The article leads with the assertion that humans have more cancer than chimpanzees. Undoubtedly that's true, but I think it's a tough comparison to make. Chimpanzees in the wild only live into the bottom range of ages where humans start to have a high cancer risk. Neither captive nor wild chimpanzees have been observed in sufficient numbers to have accurate estimates of the rates of rare diseases, and captive chimpns Some cancer risk alleles, like BRCA1, hit earlier in life, but they are special cases -- rare, recent, and possibly selected for pleiotropic effects on other traits.

    No question, some species must have adaptations to reduce the incidence of cancer per cell: whales, for example, live as long or longer than humans and have vastly larger cell numbers. If they had the same cancer risk per cell, every whale would be half tumor. But humans didn't just add brain tissue, we added mass and reduced the proportion of other tissue types including muscle and gut. I'm guessing this is a more complicated issue than the simple hypothesis would suggest. Still, there's nothing impossible about it....

    Tags: 
    life history
    aging
    development
    disease
    brain

  • Worm longevity and the germline

    Tue, 2009-06-09 08:13 -- John Hawks

    Nicholas Wade writes about experiments that link germline gene regulation to life extension in C. elegans:

    A little piece of the germline’s immortality, it now seems, can be acquired by the ordinary cells of the body, and used to give the organism extra longevity.

    Tags: 
    life history
    aging
    genetics

  • A Snowdrift game version of hunting

    Fri, 2009-06-05 23:39 -- John Hawks

    I want to run through some examples of how we can apply game theory to consider hunting decisions in human groups. First, I describe a simple Snowdrift model applied to hunting. This is part 2 of a series, part 1 introduces the topic of the Snowdrift game.

    A reader sent along a story after reading the first post:

    In reading your snowdrift blog post, I was reminded of an experiment that does not require game theory to understand. You may have heard of it. Two pigs are in a pen. One is dominant. To get food one of them presses a bar, but the food is dispensed at the other side of the pen. If the subdominant pig presses the bar, it gets no reward, as the dominant pig hogs the food, eating it all. The result is that the dominant pig presses the bar while the subdominant pig waits at the food trough. Then the dominant pig rushes over to the trough to push the subdominant pig aside. Both pigs get fed, but the dominant pig does all the work

    It's a great example of asymmetrical rewards. I'll get to those in the next few posts on this topic, because the asymmetries are very important to understanding dynamics in hunter-gatherers. But first, we have to describe the simple symmetrical case, including the algebra defining the evolutionarily stable equilibrium between the two simple strategies.

    Suppose we have two hunters, who will share whatever game either of them kills. A man may choose on a given day to hunt. By hunting, he suffers a cost c and brings back a large fixed benefit b for each man. The two men may both choose to hunt on the same day, resulting in the same benefit b but a lowered cost c∕2 for each man. The two men decide whether to hunt simultaneously and without conferring — that is, there is no information transfer between them that would affect their decisions.

    Here is the payoff matrix of the game for player 1 (choices on left) given the strategy selected by player 2 (on top):

    hunt no hunt
    hunt b - c∕2 b - c
    no hunt b 0

    Given the existence of the two strategies, “hunt” and “no hunt,” the ESS is the ratio at which the two strategies have equal expected returns. If individuals select a strategy and do not vary, the ESS represents the frequency of these variants in the population. If in contrast, individuals can choose to adopt either strategy, then the ESS also will be the optimal proportion of the two strategies in one individual’s repertoire. The two strategies will yield equal payoffs when the ESS satisfies the following equation, where p represents the proportion of “hunt” and 1 - p the proportion of “no hunt”:

    p(b- c∕2)+ (1- p)(b - c) = pb
    		(1)

    …which simplifies to p = 2(b - c)∕(2b - c). That expression is positive where b > c, and approaches unity where c is very small relative to b. If in contrast b ≤ c then the scenario is the Prisoner’s Dilemma, where the only ESS is a pure “no hunt” strategy.

    Let’s also look at a slightly different case. As above, each man’s return from hunting will be b regardless of whether one man or both choose to hunt. But in the payoff matrix below, the cost of hunting is also the same whether one man or both choose to hunt. So there is no reduction in the cost of hunting if both men do it.

    hunt no hunt
    hunt b - c b - c
    no hunt b 0

    Now, in this case, the ESS satisfies the equation:

    p(b- c)+ (1- p)(b - c) = pb
    		(2)

    Again, p is the frequency of the “hunt” strategy. This simplifies to p = (b-c)∕b, which again yields the Prisoner’s Dilemma when b < c.

    OK, that’s the simple Snowdrift game model, described in the language of hunting instead of winter car accidents. It is quite simplistic in many ways. We might expect real hunters to have successes and costs that vary as stochastic functions of the environment. A real hunter must decide whether to hunt based not only on the odds his companion will hunt, but also upon some appraisal of the companion’s likelihood of success. Men in hunting societies are not paired up by the buddy system, but instead make their decisions about hunting in the context of a larger group’s activities.

    Maybe most confusing, there are two possible kinds of currency in which benefits and costs may be expressed. A benefit from hunting may be most naturally measured in calories. If we average hunting returns across many episodes, then our result would be mean calories per day, or per hour of effort. Likewise, it might seem natural to discuss costs in terms of calories, as we might consider the cost of locomotion or cost of transport associated with foraging.

    But the only currency that matters to evolution is fitness. We cannot assume that maximizing caloric returns will maximize fitness. Transport and locomotor costs may be minor compared to the mortality risk from predation when foraging far from camp. The caloric benefits from hunting matter more to a starving child than to a satiated adult.

    So the measures of costs and benefits that define the ESS should be expressed in terms of fitness. That’s a problem, because fitness outcomes are a lot harder to measure than caloric returns. To figure out caloric expenditure and returns, you can measure oxygen consumption, work out distances, and weigh meat. To measure fitness, you have to record lifetime reproduction. To assess the relationship between caloric returns and fitness, you need a lifetime of caloric returns.

    So far, hunter-gatherer demographic data and hunting returns are both known from a small number of transverse studies. Longitudinal data on hunter-gatherer demography are limited, and mostly known by retrospective methods — that is, informants share their knowledge about the history of their groups. The fitness effects of a single individual’s hunting effort over time are not known.

    If fitness outcomes are hard for the scientist to measure, they are equally hard for a social actor to predict. Even intelligent actors like humans know little about the effects of their actions upon their future reproduction. Men sometimes do poorly with information directly relevant to fitness, like “Is the child mine?” That’s not to say that men may not follow highly sophisticated strategies to allocate hunting effort. But we should develop explanations that do not assume that a man knows the fitness benefits and costs of his choices.

    Next: Life history and asymmetrical strategies

    Tags: 
    life history
    game theory
    hunting
    cooperation
    hunter-gatherers

  • Snowdrift games, cooperation, and "tragedy of the commune"

    Tue, 2009-06-02 23:27 -- John Hawks

    It’s the second day of June, which means it’s a good time to consider snowdrifts. OK, maybe not – but at least we’re far enough from winter now that the thought of snowdrifts out the window isn’t enough to give me a chill.

    The Snowdrift Game is a theoretical model of cooperation within the context of game theory. I gave a short introduction to game theory a couple of years ago, focusing on the games of Chicken and the Prisoner’s Dilemma. There are really only two formal varieties of two-player games involving cooperation or defection in the absence of information transfer. When defection is always the optimal strategy, it’s the Prisoner’s Dilemma. When a mixed strategy of cooperation and defection is optimal, it’s Chicken.

    But there are other names for this game. I’m not sure why, exactly—I suppose it’s because teenage boys in dragsters don’t appeal to everybody. One familiar name is the Hawk-Dove game. An individual can adopt two strategies: either attack and fight for a resource, or share equally and retreat when attacked. In the game, fighting carries a high cost (like wrecking your car into somebody) so a mixed strategy is optimal. When hawks are common, it’s better to be a dove and avoid fighting. When doves are common, it’s better to be a hawk because you always win.

    A third name for this game is Snowdrift. Imagine you’re riding in a car that becomes stuck in a snowdrift. You and a fellow passenger share the same interest: you both want the snowdrift to be removed. But who’s going to get out and shovel? It might seem fair just to get out and shovel the snow together—in other words, to cooperate. But what if the other passenger just sits there and refuses to help? If the cost of shoveling is low compared to the benefit of getting out of the drift, it will be in your interest to shovel by yourself. Sure, the other passenger is a freeloader who shares the benefit undeservedly, but so what? If the cost of shoveling was too high for you to bear, you’d have refused to do it, letting both of you freeze there. That would be the Prisoner’s Dilemma. But if the cost of shoveling is low compared to the costs of doing nothing, then a mixed strategy will be optimal. As long as freeloaders aren’t too common, that strategy will pay off. So a population engaged in the Snowdrift game will come to a mixed proportion of shovelers and freeloaders.

    Doebeli et al. (2004) considered the Snowdrift game as a model for the evolution of cooperation. A mixed strategy of cooperation and defection can emerge under a Snowdrift game system of payoffs, which makes it very different from the Prisoner’s Dilemma. Remember that in the Prisoner’s Dilemma, defection always generates a higher payoff than cooperation, regardless of the opponent’s strategy. So stable cooperation can only evolve under a Prisoner’s Dilemma system of payoffs if some kind of information transfer is possible. One example is the Iterated Prisoner’s Dilemma, in which two players encounter each other repeatedly. In this circumstance, one player can punish defection, leading to conditional strategies — the most famous of which is “tit for tat” — that yield a positive payoff for cooperation. It is worth pointing out that the cumulative payoffs under “tit for tat” or other conditional strategies come to approximate the payoffs of the Snowdrift game. The transfer of information changes one payoff structure into another.

    Here, we have unveiled a different paradox of cooperation, which could be termed the ”tragedy of the commune”: In a cooperative system, in which every individual contributes to a common good and benefits from its own investment, selection does not always generate the evolution of uniform and intermediate investment levels but may instead lead to an asymmetric stable state, in which some individuals make high levels of cooperative investment and others invest little or nothing.

    In practice, it is often difficult to determine the payoffs in social interactions and hence to distinguish prisoner’s dilemma and snowdrift interactions [a phage system marks a rare exception, but interestingly, selection turns the prisoner’s dilemma into a snowdrift game (24)]. Nevertheless, the mere existence of high- and low-investing individuals has often been taken as prima facie evidence that the interaction is governed by a prisoner’s dilemma, with some additional mechanism, such as reciprocity, responsible for the co-existence of altruists and nonaltruists. The tragedy of the commune, however, provides a quite different and, in many ways, simpler explanation for the coexistence of high- and low-investing individuals, which potentially applies to a wide range of cooperative and communal enterprises in biological systems (Doebeli et al. 2004:861–862).

    How is this relevant to paleoanthropology? The last paragraph of the paper suggests one way:

    In behavioral ecology, classical examples of cooperation include collective hunting and territory defense in lions (28), predator inspection in sticklebacks (29), and alarm calls in meerkats (30). In theoretical discussions of these examples, the existence of cooperators providing a common good and defectors exploiting it has been assumed a priori. The tragedy of the commune, however, suggests an evolutionary mechanism for the emergence of distinct behavioral patterns with differing degrees of provisions to the common good. This mechanism may also apply to cultural evolution in human societies, in which large differences in cooperative contributions to communal enterprises could give rise to conflicts on the basis of accepted notions of fairness (Doebeli et al. 2004:862).

    Food sharing in human hunter-gatherers includes many asymmetries. For example, hunters differ greatly in their hunting returns and expenditure of effort. Yet good hunters tolerate the presence of poor hunters and share food with them. As with hunting but extended to both men and women, people invest greatly varying degrees of effort into gathering plant foods, with resulting variation in caloric returns. Some of the variation in investment and success is age-related, some is likely directly environmentally induced, and some may reflect frequency-dependent strategies.

    Over the next few days, I’ll be considering human hunting from the perspective of the Snowdrift game. I’ll start with some very simple deterministic models and then try to make them a bit more relevant by considering the effects of stochastic payoffs and asymmetries among players.

    Next: Defining the Snowdrift game for hunting

    References:

       Doebeli M, Hauert C, Killingback T. 2004. The evolutionary origin of cooperators and defectors. Science 306:859–862. doi:10.1126/science.1101456.

    Tags: 
    cooperation
    hunter-gatherers
    game theory
    life history

  • Neandertal longevity and pulp cavities

    Thu, 2009-02-19 19:18 -- John Hawks

    Rachel Caspari has been doing some amazing work with micro-CT scans of Neandertal teeth. The work got profiled this week by Elizabeth Culotta in the Science "Findings" blog:

    Caspari analyzed a trove of 120,000-year-old Neandertal fossils from the site of Krapina in Croatia. Excavated more than 100 years ago, the assemblage contains bones of 75 to 83 individuals, which apparently accumulated within 10,000 to 20,000 years. Caspari estimated their age at death from the teeth; in young people, teeth are generally pristine, while the enamel is worn away in older people. And over time, a tooth's pulp cavity shrinks as additional dentine is deposited into it. In a new method, Caspari used nondestructive micro CT scans to measure pulp cavities.

    After aging each specimen, she found that the Krapina Neandertals died before the age of 30. "There were very few old Neandertals, if any," she said. For every 10 young adults found, only four older Neandertals were found.

    This method is more complicated to carry out than Caspari and Sang-Hee Lee's earlier work using dental wear categories. So it's very heartening that it leads to the same result. That's significant because it further documents the substantial difference in adult mortality rates: Neandertals had a much greater annual chance of death than early Upper Paleolithic Europeans.

    I've been giving talks lately that touch on the possible causes of this big difference in life history, and the likely demographic and evolutionary consequences of it. Caspari and Lee's earlier work points to Upper Paleolithic cultural innovations as the probable cause of their lower mortality rates, and that may have been one of the most important drivers of recent human evolution.

    Tags: 
    Neandertals
    life history

  • Live fat die young, bearwise

    Wed, 2008-10-01 09:35 -- John Hawks

    This story describes research on the longevity and maturation of wild bears who have invaded urban habitat in Nevada:

    It turns out that urban black bears are much heavier and more likely to die violent deaths than their wilder peers, the study found. Oh, and if female, they're more likely to get pregnant at a younger age.

    The research is describing cities and suburbs as "population sinks" for bears. The idea is that the urban habitat is drawing in bears (to eat garbage) who would otherwise live in marginal wild areas. Since those marginal wild areas are themselves probably population sinks -- and the urban bears are reproducing younger -- it's not obvious that this is a net loss to the bears. But the behavioral and physiological consequences of the human diet -- fatter bears who reproduce younger -- and the interaction with the high death rate (from being hit by cars) is fascinating.

    Tags: 
    life history
    population sinks
    wildlife
    non-primate
    conservation

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Neandertals

For years, I've worked on their bones. Now I'm working on their genes. Read more about the science studying these ancient people.

Denisova

From a finger bone of an ancient human came the record of a completely unexpected population. My lab is working on the science of the Denisova genome.

Acceleration

The advent of agriculture caused natural selection to speed up greatly in humans. We're uncovering some of the ways that populations have rapidly changed during the last 10,000 years.

Malapa

Just outside Johannesburg, the Malapa site is producing some of the most exciting finds in human evolution. This site is the headquarters of the Malapa Soft Tissue Project.