The Pith:Climatic and biological evolutionary pressures on an ecosystem complement at different scales. Neither is “dominant,” as that framing is not even wrong.

Yesterday I alluded to the Court Jester hypothesis of evolutionary change, which is often contrasted with the Red Queen hypothesis. The main embarrassment for me as a person who fancies himself a fan of evolutionary process is that I hadn’t ever heard of the Court Jester Hypothesis before yesterday. Therefore I went back to the paper which outlined many of the basic ideas of the model in 2001, Distinguishing the effects of the Red queen and Court Jester on Miocene mammal evolution in the northern Rocky Mountains. To be fair, the hypothesis itself is a tightening of a range of ideas which were long in the air. I did know, for example, about the Turnover-pulse hypothesis. These are all a set of models which emphasize the abiotic selective pressures on life forms, as opposed to the biotic ones. An abiotic pressure would be something like the Younger Dryas cold snap. A biotic pressure might be an exotic invasive species spreading through the landscape.

In my own mind selection is selection, so I didn’t distinguish them too stridently. In fact, most people seem to have abiotic pressures in mind when they conceive of natural selection, so I generally prefer to emphasize the competition and cooperation between and within species. Additionally, it seems that biotic models are more formally tractable and elegantly constructed (I know much of climate change is cyclical, but I assume that “catastrophe” exhibits a poisson distribution?). I generally lack the “thick” knowledge to really make sense of a lot of detailed natural historical treatments, so I probably avoided them because I didn’t think I’d get much out of them. In hindsight, this seems foolish and shortsighted. Rather like economists focusing on equilibrium states because of their ease of modeling when periodic exogenous shocks are a major variable within our real lives.

Now let’s hit the abstract:

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Call to Participate in a New Study on Social Networking and Personal Genomics:

Do you share your information with others? How has your personal genetic information influenced your lifestyle and the way you approach your health and medical decisions? Can genetic information create new communities and connections?

…

The Social Networking and Personal Genomics Study at the Center for Biomedical Ethics invites participants between the ages of 18 and 75 to spend approximately 2 hours with us in a focus group setting. Participants must have purchased direct-to-consumer personal genetic information from 23andMe, Inc., shared their information with others, and be willing to discuss their perspectives and experiences. Focus group members will receive a $50 gift card for their participation and childcare will be available on an as-needed basis at no cost. For additional information or to enroll, please contact Simone Vernez, Project Manager, by email at svernez@stanford.edu or by telephone at (650) 723- 9364. For more information on the study itself, including specific research aims and funding please visit http://bioethics.stanford.edu/research/SocialNetworkingandPersonalGenomics.html. For general information about participant rights, contact 1-866-680-2906.

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Its demographics:

As of the census…of 2000, there were 13,138 people, 2,229 households, and 2,137 families residing in the village. The population density was 11,962.2 people per square mile (4,611.5/km2). There were 2,233 housing units at an average density of 2,033.2 per square mile (783.8/km2). The racial makeup of the village was 99.02% White, 0.21% African American, 0.02% Asian, 0.12% from other races, and 0.63% from two or more races. Hispanic or Latino of any race were 0.93% of the population.

…

The 2000 census also reports that only 6.2% of village residents spoke English at home, one of the lowest such percentages in the United States…46% spoke English “not well”….

There were 2,229 households out of which 79.5% had children under the age of 18 living with them, 93.2% were married couples living together, 1.6% had a female householder with no husband present, and 4.1% were non-families. 2.8% of all households were made up of individuals and 2.1% had someone living alone who was 65 years of age or older. The average household size was 5.74 and the average family size was 5.84. In the village the population was spread out with 57.5% under the age of 18, 17.2% from 18 to 24, 16.5% from 25 to 44, 7.2% from 45 to 64, and 1.6% who were 65 years of age or older. The median age was 15 years. For every 100 females there were 116.3 males. For every 100 females age 18 and over, there were 118.0 males.

The median income for a household in the village was $15,138, and the median income for a family was $15,372. Males had a median income of $25,043 versus $16,364 for females. The per capita income for the village was $4,355. About 61.7% of families and 62.2% of the population were below the poverty line, including 63.9% of those under age 18 and 50.5% of those age 65 or over.

According 2008 census figures, the village has the highest poverty rate in the nation, and the largest percentage of residents who receive food stamps. More than five-eighths of…residents live below the federal poverty line and more than 40 percent receive food stamps, according to the American Community Survey, a U.S. Census Bureau study of every place in the country with 20,000 residents or more….

And now a New York Times article about the community, A Village With the Numbers, Not the Image, of the Poorest Place:

“It is, in a sense, a statistical anomaly,” Professor Helmreich said. “They are clearly not wealthy, and they do have a lot of children. They spend whatever discretionary income they have on clothing, food and baby carriages. They don’t belong to country clubs or go to movies or go on trips to Aruba.

This is the human past, and perhaps the human future. Gains in productivity and wealth are translated into raw human numbers. While affluent self-actualizing members of society wring hands over whether to bring children into this “horrible world,” there are communities and cultures which have no such hesitation. They breed and they flourish. No fear, just hope of the promise to come. They will be the inheritors. They are rich in the possibilities of future.

Hispanic – Definitions in the United States:

The 1970 Census was the first time that a “Hispanic” identifier was used and data collected with the question. The definition of “Hispanic” has been modified in each successive census. The 2000 Census asked if the person was “Spanish/Hispanic/Latino”.

The U.S. Office of Management and Budget currently defines “Hispanic or Latino” as “a person of Mexican, Puerto Rican, Cuban, South or Central American, or other Spanish culture or origin, regardless of race.”

Because Hispanics can be any race, you need to look at their own self-identification. The breakdowns as per the American census are that somewhat over 50% of American Hispanics/Latinos identify as white, most of the rest as “some other race,” with a small minority as black, Native American, etc.

This came to mind when I saw this paper in BMC Genetics, Comparing self-reported ethnicity to genetic background measures in the context of the Multi-Ethnic Study of Atherosclerosis (MESA). The issue is that when you’re doing association studies between genes and diseases you want to control for population structure. For example, if disease X is found in Chinese Americans to a higher degree than the general population, then all the alleles distinctive to Chinese Americans would correlate with disease X in an aggregated pool. Self-reports are pretty good, but on the margin there is now some juice to squeeze out of the data sets by using ancestrally informative markers to “clean up” the outliers within the populations.

Here are the results:

Four clusters are identified using 96 ancestry informative markers. Three of these clusters are well delineated, but 30% of the self-reported Hispanic-Americans are misclassified. We also found that MESA SRE provides type I error rates that are consistent with the nominal levels. More extensive simulations revealed that this finding is likely due to the multi-ethnic nature of the MESA. Finally, we describe situations where SRE may perform as well as a GBMA in controlling the effect of population stratification and admixture in association tests.

Below is a principal component analysis plot which illustrates the largest dimensions of genetic variation in their data set for the individuals from four different populations, African Americans, European Americans, Hispanic Americans, and Chinese Americans. I thought of the above census results when I saw the distributions on the plot:

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First, if it is clear that you haven’t read the post itself and leave a comment I won’t just not publish it, but I’ll ban you. Second, if you complain about this in the comments, I’ll ban you too. Now that you feel appropriately welcome, I want to explore some of the issues beneath Chris Mooney’s post, Vaccine Denial and the Left:

So I want to further explain my assertion that vaccine denial “largely occupies” the political left. It arises, basically, from my long familiarity with this issue, having read numerous books about it, etc.

First, it is certainly true that environmentalists and Hollywood celebrities have been the loudest proponents of anti-vaccine views. To me, that is evidence, although not necessarily definitive. So is the fact that we see dangerously large clusters of the unvaccinated in places like Ashland, Oregon, and Boulder, Colorado, which are very leftwing cities.

What’s tricky is, there’s not a standard left-right political ideology underlying this. Rather, it seems more associated with a Whole Foods and au natural lifestyle that, while certainly more prominent on the bicoastal left, isn’t the same as being outraged by inequality or abuses of the free market.

This is a tricky issue. There is a stereotype that liberals who reject religion tend to gravitate toward New Age/environmentalist spirituality. “The mind abhors a vacuum” model. I used to accept this, but if you poke around the General Social Survey the reality is more complicated. For example, you can look up attitudes toward genetically modified food and astrology. The results don’t fall neatly into a Left-Right dichotomy. Part of the issue is that there has been aggregation of distinct groups into on catchall category. Consider me. I identify as a conservative, which would indicate a far higher odds of me being a Creationist, but I’m clearly not.

There aren’t any questions about vaccination in the General Social Survey, but there are several about trust and faith in science, or lack thereof. First I pruned all of the questions which were before 1998. So the results below are for the 2000s by and large. After that I had a set of variables to play with, to serve as replicates in terms of observing trends. Below are three tables with my results.

Table #1 is just a set of results which shows how political ideology, party identification, and educational attainment, correlate with attitudes toward science. So in that table the columns add up to 100%. So below 4% of liberals strongly agree while the assertion that “we trust too much in science,” and 21% strongly disagree.

The second table is limited to self-identified liberals. I wanted to query how attitudes toward science vary by demographic among liberals. In this case the rows add up to 100% on the margin (rotated from the first table). So in terms of those who strongly agree that we trust too much in science, 29% are male and 71% female, among self-identified liberals. Remember that in some classes there won’t be a 50/50 breakdown, so look for the variation in relative trends.

Finally, for the third table I have a regression. I now divided the sample into liberal and conservative groups, and ran a set of variables to predict opinions on the questions which I’ve covered so far. The first row has the R-squared, the magnitude of which illustrates how much the listed variables predict variation on the question. Subsequent rows have beta values for the variables, which indicate the direction and magnitude of the effect from that given variable. The questions are all easily numerical, or recoded as numerical (e.g., atheist, agnostic…to total belief in God is 1, 2…6). To get an intuition as to what’s going on, just look at each variable and its value. Those which are bold are statistically significant at p = 0.05. For example, among liberals confidence in belief in god seems to decrease trust in science. Socioeconomic status seems to increase it.

Please note that I’ve omitted some categories for variables where the sample size is too small, so some rows/columns may be less than 100% (e.g., Jews in “religion”). Additionally I’ve removed some response classes where N < 25, as the noise can confuse the trend line.

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Some strategies (H/T Michelle). Unfortunately the efficacy of many of these tactics varies by discipline, and it is often really hard to get a hold of something without academic access when it comes to the biological sciences. There’s arXiv and SSRN for physical and social scientists, but in the biosciences you have clusters of journals such as at PLoS and BioMed which are relatively exceptional archipelagos of easy access. If you follow one of the Eisen brothers you’ll also be made very conscious of the unfortunate fact that many a time papers which are supposed to be open access at mainstream publishing houses such as Nature are mistakenly gated because of the content management’s default settings. This technical sloppiness is rather galling because these publishing houses are often very vigilant about scanning the web (I assume via crawlers) looking for people who have put up “their”* content for free (yes, I know this from personal experience).

I have many smart and motivated readers who happen not to have academic access. It’s definitely a consideration when I want to blog a paper whether it is open or gated. Obviously I blog gated papers, but it does change the nature of the task when many of your readers aren’t going to be able to see the primary source themselves.

* I hope I’m not getting anyone in trouble when I pass on the reality that many of the original content producers often are excited when they see unauthorized distribution, because they’re thrilled that people care enough about their research to do this.

I own a book of Motoo Kimura’s collected papers, and of course I have a copy of John Gillespie’s Population Genetics: A Concise Guide. But I’d forgotten the acrimony between the two men. Gillespie has been retired for half a decade now, while Kimura died in in 1994. I randomly stumbled onto an old newspaper story from 1992 covering the feud between these two eminent population geneticsts, Scientists in Open War over “Neutral Theory” of Genetics. It was in the Sacramento Bee, which is based near Gillespie’s university. The background is that both were principals in the “neutralist–selectionist” debate of the 1970s and 1980s. Kimura was one of the main theoretical architects of the neutral theory of molecular evolution, which eventually spread its influence to the point where old-line adaptationists such as Richard Dawkins had to offer up a counter-argument.

Some choice bits:

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Foraminifera, Wikimedia Commons

The Pith: The tree if life is nourished by agon, but pruned by the gods. More literally, both interactions between living organisms and the changes in the environment impact the pulsing of speciation and extinction.

No one can be a true “Renaissance Man” today. One has to pick & choose the set of focuses to which one must turn one’s labor to. Life is finite and subject to trade offs. My interest in evolutionary science as a child was triggered by a fascination with paleontology. In particular the megafauna of the Mesozoic and the Cenozoic, dinosaurs and other assorted reptilian lineages as well as the hosts of extinct and exotic mammals which are no more. Obviously I don’t put much time into those older interests at this point, and I’m as much of a civilian when I read Laelaps as you are. More generally when it comes to evolution I focus on the scale of microevolution rather than macroevolution. Evolutionary genetics and the like, rather than paleontology. This is in part because I lean toward a scale independence in evolutionary process, so that the critical issue for me has been to understand the fundamental lowest level dynamics at work. I’m a reductionist.

I am not quite as confident about the ability to extrapolate so easily from evolutionary genetic phenomena upwards in scale as I was in the years past. But let’s set that aside for a moment, and take a stroll through macroevolution. When I speak of natural selection I often emphasize that much of this occurs through competition within a species. I do so because I believe that the ubiquity of this process is often not properly weighted by the public, where there is a focus on competition between species or the influence of exogenous environmental selective pressures. The intra- and inter- species competition dynamic can be bracketed into the unit of selection debate, as opposed to the exogenous shocks of climate and geology. The former are biotic and the latter are abiotic variables which shape the diversity and topology of the tree of life.

A new paper in Science attempts to quantify the effect of these two classes of variables on the evolutionary arc of a particular marine organism over the Cenozoic, roughly the last 65 million years since the extinction of the dinosaurs. Interplay Between Changing Climate and Species’ Ecology Drives Macroevolutionary Dynamics:

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I said a few weeks ago that I would release a ~1 million markers out to the public. I haven’t gotten around to it because of legalities. Well, I finally went to the Creative Commons website and filled out their form. So all the details below….

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The Pith: Over the past 10,000 years a small coterie of farming populations expanded rapidly and replaced hunter-gatherer groups which were once dominant across the landscape. So, the vast majority of the ancestry of modern Europeans can be traced back to farming cultures of the eastern Mediterranean which swept over the west of Eurasia between 10 and 5 thousand years before the before.

Dienekes Pontikos points me to a new paper in PNAS which uses a coalescent model of 400+ mitochondrial DNA lineages to infer the pattern of expansions of populations over the past ~40,000 years. Remember that mtDNA is passed just through the maternal lineage. That means it is not subject to the confounding dynamic of recombination, allowing for easier modeling as a phylogenetic tree. Unlike the autosomal genome there’s no reticulation. Additionally, mtDNA tends to be highly mutable, and many regions have been presumed to be selectively neutral. So they are the perfect molecular clock. There straightforward drawback is that the history of one’s foremothers may not be a good representative of the history of one’s total lineage. Additionally the haploid nature of mtDNA means that genetic drift is far more powerful in buffeting gene frequencies and introduced stochastic fluctuations, which eventually obscure past mutational signals through myriad mutations. Finally, there are serious concerns as to the neutrality of mtDNA…though the authors claim to address that in the methods. I should also add that it also happens to be the case that there is less controversy and more surety as to the calibration of mutational rates of mtDNA than the Y chromosomal lineages of males. Their good for determining temporal patterns of demographic change, and not just tree structures.

Here’s the abstract, Rapid, global demographic expansions after the origins of agriculture:

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