The differential biology reader

It is wonderful to see ecologists asking structural questions about personality using measures of many behaviors but I am a bit puzzled by the statistical analysis in this paper by Wilson et al on house crickets:

We tested whether laboratory-reared male and female European house crickets, Acheta domesticus, exhibited behavioral syndromes by quantifying individual differences in activity, exploration, mate attraction, aggressiveness, and antipredator behavior. To our knowledge, our study is the first to consider such a breadth of behavioral traits in one organism using the syndrome framework.

My first question is: what software were they using that didn't throw up an error when doing a PCA on only two variables (in general you need at least 3 variables, otherwise you are just getting the correlation between the two variables).

Second, why not instead do a PCA on all the raw variables? This would be another way to look for behavioral suites across contexts. There may be latent variables here that span the presupposed behavioral context.

Wilson et al. Behavioral correlations across activity, mating, exploration, aggression, and antipredator contexts in the European house cricket, Acheta domesticus. Behav Ecol Sociobiol (2009) doi:10.1007/s00265-009-0888-1

photo cc-by alles-schlumpf

Loehlin:

There are at least 18 ways in which personality-oriented behavior geneticists are concerned with environments. They include three forms of gene-environment correlation, eight varieties of shared environment, three of unshared environment, three forms of gene-environment interaction, and the environment of evolution.

Loehlin details the ways that environmental variance might be partitioned into shared and unique pre- and post-natal environments and how parents and children can be environments for each other. He also lists the "Environment of human evolution" which isn't something that will fall out of a variance component model but should be considered from comparisons across species: our personality structure is nothing new or unique.

Loehlin, JC (2009) Environment and the behavior genetics of personality: let me counts the ways. Pers Indiv Dif doi:10.1016/j.paid.2009.10.035

photo cc-by G u i d o

A report from the folks at Hunch (Mac vs PC People: Personality Traits & Aesthetic/Media Choices) tells us what we already knew: Mac users like to be different while PC users prefer to stick with what they're comfortable with. While the questions are nominally about aesthetics, media and consumer choices, and "personality," for the most part they've actually made a questionnaire that captures several facets of the personality dimension Openness to experience.

A lot of the questions hit on the facet of Artistic interests ("Which type of art do you prefer?", mostly through the choice of Modernism, and I would hazard that most folks who chose the Impressionist painting were picking 'flowers' rather than 'Monet'; all of the items about aesthetic or design preferences) although there are a few questions related to Extraversion ("How often do you throw parties?") and Agreeableness (attitude to authority). Other questions probably hit on both high Openness and low Conscientiousness at the same time (task preference).

NB: the sample size for some of these items is huge! (N > 50,000) Of course, as I tell my students, a little more methodological details would be nice:

Summary findings in this report are noted when there is a statistically significant difference in the answers of the two subsets being compared.

The first step in making sense of your data is just that : make sense of it. What is this a record of? What do the variables mean? Stefano Mazzocchi argues that a lot of the movement to open data is focusing only on the arcana of delivery, and in the process it is hard to determine what individual data points refer to:

the fact that without a high relational density, having a dataset in RDF doesn’t give any practical advantage over having it in its original format.

Yet, from a marketing/political point of view, the simple act of “triplifying” a dataset and make it available on the web as linked dataseems to make it appear all more powerful, all more useful and it’s being used a lot as a way to promote the idea that the web of data is finally getting traction.

By grinding all those rectangular datasets into triples, they’ve actually managed to make it *less* useful than in its original form. In the original form at least I had a little context of what this data was for and from, which is lost here. A surprising achievement, but I bet you won’t read about it at semantic web conferences any time soon.

I don't find that this is a particularly novel problem. Even in flat files you come across data where information is coded (such as '0' and '1' for sex, or something more obscure like species or condition.) If you've taken a look at older government provided data sets, you need to do a lot of flipping back and forth between the data and the document explaining the variable codings. Perhaps the amount of data these folks are dealing with just magnifies it out of all proportion.

Nakamichi et al report two cases of Japanese macaque grandmothers caring for their offspring:

In the first case, a 24-year-old grandmother provided essential care for the survival of her 2-month-old granddaughter for at least 6 days during which the mother had temporarily disappeared from the group for reasons unknown. In the second case, a 14-month-old granddaughter began sucking from her 23-year-old grandmother within 6 weeks after her mother gave birth to a younger sibling. For at least 6 months, the grandmother exhibited various patterns of maternal behavior toward her granddaughter. The behavioral data obtained in this study indicated that old yet healthy females without dependent offspring could contribute to the survival of their young grandchildren.

This study may roll into that bare valley of science: the anecdote. I don't have a problem with small sample sizes, personally, and short communications like these inform other researchers of what you saw and asks them (implicitly) to keep their eyes open.

Nakamichi, Onishi, and Yamada. 2009. Old grandmothers provide essential care to their young granddaughters in a free-ranging troop group of Japanese monkeys (Macaca fuscata). Primates (online first) doi:10.1007/s10329-009-0177-7

It is an individual difference so obvious to us that we never think about it. People look different from each other. I'm not talking about fluctuating asymmetry or melanin content or facial attractiveness, but rather your overall physiognomy. Perhaps there is some advantage to being identifiable to other individuals:

Identifying broad-scale evolutionary processes that maintain phenotypic polymorphisms has been a major goal of modern evolutionary biology…Traits used for individual recognition are strikingly variable and have evolved independently in numerous lineages…Theoretical models suggest that individuals may benefit by advertising their identities with distinctive, recognizable phenotypes. Here, we test the benefits of advertising one's identity with a distinctive phenotype. We manipulated the appearance of Polistes fuscatus paper wasp groups so that three individuals had the same appearance and one individual had a unique, easily recognizable appearance. We found that individuals with distinctive appearances received less aggression than individuals with nondistinctive appearances. Therefore, individuals benefit by advertising their identity with a unique phenotype…Given that recognition is important for many social interactions, selection for distinctive identity signals may be an underappreciated and widespread mechanism underlying the evolution of phenotypic polymorphisms in social taxa.

Sheehan and Tibbetts. Evolution of identity signals: frequency-dependent benefits of distinctive phenotypes used for individual recognition. Evolution (online in advance of print) doi:10.1111/j.1558-5646.2009.00833.x

photo cc-by designwallah

OpenMx is a lovable tool.

OpenMx is community software for working up structural equation models from within R. Because of this, your data don t need to be dropped gently into a CSV baby crib before being molested.

photo cc-by hi-phi

The Blind, 2009

A professor, recently retired from my department, noted last week that the fantastic footage of nature documentaries has made watching animals a boring procedure for students. Animals just don't do all that much most of the time (that includes you!)

The Institute of Critical Zoologists takes a different perspective, asserting through their work that our observations are a burden to animals and not just to ourselves. The photographic part of the project, which I discovered in "portfolio," expresses an impossibility of removing observer effects from our ethological data collection.

More endearing—and troubling—still is the garb of scientific credibility the Institute adopts through its description of its mission and its projects (including a compelling faux-history of how the Institute was founded). Do our real efforts in journals, conferences, grant applications, and monographs come across as so staid?

Except my university's electronic subscription to J Theor Biol only goes back to 1995! (what's that you say about trudging over to the library?)

Johnson et al look beyond heritability:

The heritability of human behavioral traits is now well established, due in large measure to classical twin studies. We see little need for further studies of the heritability of individual traits in behavioral science, but the twin study is far from having outlived its usefulness…Twin samples continue to provide new opportunities to identify causal effects with appropriate genetic and shared environmental controls.

To understand why heritability estimates are no longer important, it is necessary to understand that they are completely dependent on the specifics of the samples and environmental conditions from which they are take…This means that little can be gleaned from any particular heritability estimate and there is little need for further twin studies investigating the presence and magnitude of genetic influences on behavior.

Johnson et al ask that we unpack the C of the ACE model rather than just focusing on the additive genetic variance. Components of variance only get us so far in building up causal stories of psychological differences. They argue that twin studies are still useful for teasing apart environmental causation from individuals' inherent dispositions to choose different environments.

None of this should be controversial, although it needs to be said. However, how much of this is a symptom of seeing the world through only one model? I think this is what Greg Carey is getting at with the Bouchard Prize:

Perhaps the major reason for abandoning C and E is that they have operated as taken-for-granted, set-piece structures that have prevented us behavioral geneticists from thinking deeply about the environment.

In other models for estimating heritability, such as the animal model, there is nothing sneaky about specifying other environmental effects exactly, such as those from identifiable early rearing conditions. The problem, as always, is having enough data to get good estimates of these effects. Of course, evolutionary biologists and animal breeders will always be interested in exact values of h².

Johnson et al. Beyond heritability: twins studies in behavioral research. Current Directions in Psychological Science (2009) vol. 18 (4) pp. 217-220

Carey. The environment in behavioral genetics research.

photo cc-by onefromrome