May 28, 2014
"At least once almost every 24 hours we undergo a remarkable transformation in which we cease common activities, become relatively unresponsive to external stimuli, and recall little other than fragments of dreams."

May 28, 2014

S De Glanville
Tentacle detail on the Portugese Man o’War (Physalia physalis) (via @Mudfotted.)

May 12, 2014

A solid way to accept someone’s feelings.

May 8, 2014
Sydney Brenner on the evolving concept of the gene

"The End of the Beginning", Science 24 March 2000:

Old geneticists knew what they were talking about when they used the term “gene”, but it seems to have become corrupted by modern genomics to mean any piece of expressed sequence, just as the term algorithm has become corrupted in much the…

April 28, 2014

Robert Burton, Anatomy of Melancholy

April 28, 2014
"And that which is more to be wondered at, [melancholy] skips in some families the father, and goes to the son, “or takes every other, and sometimes every third in a lineal descent, and doth not always produce the same, but some like, and a symbolizing disease.”"

— Robert Burton, The Anatomy of Melancholy (1621) via Scott Stossel, My Age of Anxiety

April 27, 2014

"Bill Hill, overlooking the Nisqually Glacier at Paradise on Mount Rainier, September, 1969" (by Joe Felsenstein)

April 10, 2014
Block diagonal R-structures in MCMCglmm

A new feature in MCMCglmm version 2.18 is block diagonal residual structures.

Block Diagonal R-structures are now allowed. For example imagine a bivariate model where pairs of observations are made on individuals of different sex. There may be a need to fit different 2x2 residual covariance matrices for the two sexes. rcov=~us(trait:sex):units would fit a 4x4 covariance matrix, but the between-sex residual covariances would be estimated despite not being identifiable (no individual can be both sexes). Now, models of the form rcov=~us(trait:at(sex, "M")):units+us(trait:at(sex, "F")):units can be fitted that allow the non-identified covariances to be effectively set to zero.

In other words, previously if you had 2 traits measured for females and males and you wanted to fit a sex specific residual with rcov=~us(trait:sex):units you would end up with a residual matrix like

for the variances (V) and covariances (cov) of females traits f1 and f2 and male traits m1 and m2. The bold components are estimable from the data while the red ones are not. However, because of the way the residual is set up MCMCglmm will try to estimate the red parameters as well.

With block diagonals, you are specifying a matrix like

where the gray cells are now ignored. This is accomplished by fitting two 2 × 2 matrices for the R structure then having separate terms in the rcov formula

March 31, 2014
Helping in cooperatively breeding long-tailed tits: a test of Hamilton’s rule

Ben J. Hatchwell, Philippa R. Gullett and Mark J. Adams

Phil. Trans. R. Soc. B 19 May 2014 vol. 369 no. 1642 20130565 doi:10.1098/rstb.2013.0565

When asked if he would lay down his life for his brother, the population geneticist J. B. S. Haldane is said to have quipped “No, but I would to save two brothers  or eight cousins.” Haldane’s response was an early invocation of what has been mathematically codified as “Hamilton’s Rule”:

$rB > C$

which says that an altruistic behavior by will evolve if the benefit to the recipient of the help ($B$) times the relationship between the helper and the recipient ($r$) is greater than the cost to the helper ($C$).

How well Hamilton’s Rule describes social evolution has been the subject of some controversy among evolutionary biologists but there have been very few empirical tests of it.

We set out to do just that with our data from our long-term study of long-tailed tits

Long-tailed tits are cooperative breeders, meaning that related individuals help each other to raise their offspring. Birds who fail to breed in a particular year join the nest of one of their relatives as a helper. This behavior is altruistic because helpers pay a cost: they have lower survival to the next year when they decide to help, this imperiling their future chances of having their own offspring. However, they also get a fitness benefit. The chicks that they help are more likely to survive. Because the helper is on average related to these chicks, the benefit to the helper’s ultimate fitness (in terms of the number of copies of their genes that make it into the next generation) outweighs the costs.

Abstract

Inclusive fitness theory provides the conceptual framework for our current understanding of social evolution, and empirical studies suggest that kin selection is a critical process in the evolution of animal sociality. A key prediction of inclusive fitness theory is that altruistic behaviour evolves when the costs incurred by an altruist (c) are outweighed by the benefit to the recipient (b), weighted by the relatedness of altruist to recipient (r), i.e. Hamilton’s rule rb > c. Despite its central importance in social evolution theory, there have been relatively few empirical tests of Hamilton’s rule, and hardly any among cooperatively breeding vertebrates, leading some authors to question its utility. Here, we use data from a long-term study of cooperatively breeding long-tailed tits Aegithalos caudatus to examine whether helping behaviour satisfies Hamilton’s condition for the evolution of altruism. We show that helpers are altruistic because they incur survival costs through the provision of alloparental care for offspring. However, they also accrue substantial benefits through increased survival of related breeders and offspring, and despite the low average relatedness of helpers to recipients, these benefits of helping outweigh the costs incurred. We conclude that Hamilton’s rule for the evolution of altruistic helping behaviour is satisfied in this species.

March 27, 2014

"Tits of the World" Poster Process.

(via notrare)

March 26, 2014
Shrout & Fleiss’s ICC in mixed model form

Shrout & Fleiss give guidelines for calculating intraclass correlation coefficients for estimating interrater reliability and agreement. The original paper shows how to calculate them using ANOVA but they can also be estimated in a mixed model framework. Noting these down so I remember them

1. "Repeatability"
$y = a + e$
$ICC(1, k) = \frac{\sigma_a^2}{\sigma_a^2 + \frac{\sigma_e^2}{k}}$
2. "Agreement"
$y = a + r + \epsilon$
$ICC(2, k) = \frac{\sigma_a^2}{\sigma_a^2 + \frac{\sigma_r^2 + \sigma_\epsilon^2}{k}}$
3. "Consistency"
$y = a + r + \epsilon$
$ICC(3, k) = \frac{\sigma_a^2}{\sigma_a^2 + \frac{\sigma_\epsilon^2}{k}}$

March 7, 2014

Good book.

(Source: bbww, via unfinished-photography)

February 24, 2014

via Fat Birds

February 17, 2014

February 13, 2014
"Every speciality changes its classification of illnesses every few years, as we learn more about illnesses, but only psychiatry gets abuse for doing so."