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41lYx8Va7WL._SY344_BO1,204,203,200_ People routinely mistake the action of adaptive evolutionary process as occurring on the level of the species. Not only is this a misunderstanding that crops up in the general public, but I’ve talked to biologists who make the same mistake. The reality is that the mainstream tradition in modern evolutionary biology is very skeptical of “for the good of the species” arguments. For me one simple reason is that I don’t think species are necessarily a clear and distinct taxonomic class. But the major factor is the reality that altruism of this sort is vulnerable to being superseded by an invading selfish free-riding strategy. As a matter of pervasive phenomena much of the “struggle for survival” that an organism experiences won’t be due to exigencies of environment or the threat from other lineages, but rather within one’s own species. Though this can be conceptualized in terms of violence, more often one can chalk it up to competition for finite resources in a Malthusian world at carrying capacity.

417SDKP-XhL._SX323_BO1,204,203,200_ The logical conclusion leads to the sort of individual-level focus that is at the heart of The Selfish Gene, though Richard Dawkins’ book is to a large extent an exposition of a Neo-Darwinian tradition which goes back to R. A. Fisher, and matured with W. D. Hamilton and George Williams. But over the past few decades there has been a small group of biologists who have rebelled from the focus on individuals and genes, and made the case for selection operating at multiple levels or biological organization, from the intra-genomic all the way to “super-organisms” such as ant colonies. Rather than old-style species/group selection, the new theorists refer to “multi-level selection.” The primary force behind this movement has been David Sloan Wilson. I like David personally and he’s a great scientist (I did a BloggingHeads with him 6 years ago). But he has a tendency in my opinion of declaring unilateral victory when most people would argue that there’s still a lot to hash out, and the war continues. His new book, Does Altruism Exist?, is in my Kindle “to-read” stack, but from what I’ve seen of the reviews he does do this again! (no worries, the rest of the book looks interesting anyway)

41z97bDZvUL._SY344_BO1,204,203,200_ My own views have evolved…over the years I have realized I am not entirely satisfied with models of human cultural variation that are individual level or entirely non-adaptive. I have long been broadly sympathetic to the project of Peter Richerson and Richard Boyd of using the frameworks developed in evolutionary biology to understand cultural processes. Additionally, I’m a big fan of Joe Henrich’s research, to the point of pre-ordering his book The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter six months ahead of time. There are good reasons why above-the-individual level selection would be able to operate in humans. The reduction in a verbal sense is that human cultural phenomena are such that between group variation can dwarf within group variation. The canonical example of this is language, where differences between groups are very large, and rather smaller within groups. This is simply a function of how culture, and language in particular, spreads in a population: it can be asymmetric in terms of vertical transmission. This is in contrast to genes, where you have equal contributions from both parents. One can imagine a population expanding into another where it absorbs individuals from hostile groups, changes its own genetic makeup, but by and large maintains its cultural integrity. To give a concrete example, the Xhosa people of South Africa are approximately ~25 percent Khoisan in genetic ancestry. But their culture is not “25% Khoisan.” There are influences, such as click sounds in their language, but those are accents on the basic Bantu cultural substrate which is preserved, and ties them with populations in Central and Eastern Africa.

51aEM-jiATL._SX323_BO1,204,203,200_ It is a rather different matter with biological processes because of the enforced symmetry in transmission. Maintaining between group variance requires ingenious processes, which some find implausible. But ultimately it’s an empirical matter on a species-by-species basis. I would commend readers to look through first half of Wilson’s Unto Others to get a sense of how inter-demic selection processes might be ubiquitous. My position on the role that biological above-the-level-of-individual selection plays in evolution is to be skeptical in the generality but open-minded in the specifics. After all, bdelloid rotifers show that there are cases where complex asexual species can persist, even if the general rule about asexual lineages is that they are prone to extinction.

For whatever reason arguments about multi-level selection get rather heated among evolutionary biologists. It’s often closely related to the debate about kin selection (see this post from Jerry Coyne, and follow the links). I suppose David Sloan Wilson would suggest that it’s an illustration of inter-group competition, as individuals conform to particular positions due to their identity as members of a coalition.

cover With the preliminaries out of the way, I’d like to recommend a series of papers in the Journal of Evolutionary Biology which are open access to readers if they want to dig further (especially for those with a formal bent). First, The genetical theory of multilevel selection. The title should give some readers a clue as to the tradition which this theorists works in! Charles Goodnight makes a spirited response in Multilevel selection theory and evidence: a critique of Gardner, 2015 (his point about some researchers who come out of quantitative genetics has always been obvious to me when I read their papers; it’s a different tradition). Finally, the original author responds: More on the genetical theory of multilevel selection.

I’m not an evolutionary theorist, so I’m not going to take sides (though to be honest I always find Goodnight to be a little too vociferous for my taste, but perhaps that’s just how it comes across in print). Rather, I’m chewing through some of the ideas, and find that these papers are excellent starting points to explore the literature. It’s also nice that they’re open access, as there are people who are not in academia who might have some things to say about these topics, or, who might pursue research as a career after stumbling upon these sorts of papers.

Note: If the copious references to the Price Equation confuse, try this paper.

• Category: Science • Tags: Evolutionary Biology 
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9780198504405 When it comes to a field like genomics there’s really no point in reading a textbook beyond the elementary level because it’s moving so quickly that things get out of date within the year. But that’s not always the case. I quite like Dover Books for their math section. Math is true, even if it was written in 1920 (the prose is often a bit stilted, but that’s not what you’re focusing on in any case). Evolutionary biology is somewhere between genomics and math. There is much that gets out of date rapidly, as science proceeds, but there is a broader scaffold which remains true no matter the passing decades. For intelligent lay persons who are interested in evolution my own suggestion is to just read The Origin of Species, and then R. A. Fisher’s The Genetical Theory of Natural Selection. After that popular works by Richard Dawkins make a lot more sense.

Only text on evolutionary biology I've ever read (as opposed to evolutionary genetics)

Only text on evolutionary biology I’ve ever read (as opposed to evolutionary genetics)

But why stop there? On Twitter there is a hashtag, #EvoBioClassics, which will be of interest to anyone who wants to understand evolutionary biology. These are papers which are highly cited and referred to, but often not read as much as they should be. One of the great virtues of them being classics is that you can usually find ungated versions of the paper somewhere.

Here are my suggestions:

Fisher, R A. 1918. “The Correlation Between Relatives on the Supposition of Mendelian Inheritance”. It reconciled genetics and evolutionary biology.

Price, George 1970. “Selection and Covariance”. It’s a paper that W. D. Hamilton would have wanted to write. It condenses a lot of concepts into a very elegant form.

Trivers, R. L. 1971. “The Evolution of Reciprocal Altruism”. This is a term that’s used a lot. So it benefits one to go back to the source.

In any case, check out the hashtag. Also, this page has a great list.

• Category: Science • Tags: Evolutionary Biology 
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The original robots

We are haunted by Hamilton. William D. Hamilton specifically, an evolutionary biologist who died before his time in 2000. We are haunted because debates about his ideas are still roiling the intellectual world over a decade after his passing. Last summer there was an enormous controversy over a paper which purported to refute the relevance of standard kin selection theory. You can find out more about the debate in this Boston Globe article, Where does good come from? If you peruse the blogosphere you’ll get a more one-sided treatment. So fair warning (I probably agree more with the loud side which dominates the blogosphere for what it’s worth on the science).

What was Hamilton’s big idea? In short he proposed to tackle the problem of altruism in social organisms. The biographical back story here is very rich. You can hear that story from the “horse’s mouth” in the autobiographical sketches which Hamilton wrote up for his series of books of collected papers, Narrow Roads of Gene Land: Evolution of Social Behaviour and Narrow Roads of Gene Land: Evolution of Sex. For the purposes of the issue at hand the first volume is obviously more important, but the second volume has an enormous amount of personally illuminating material because of Hamilton’s untimely passing in 2000 before it could be edited. In Ullica Segerstrale’s Defenders of the Truth and Oren Harman’s The Price of Altruism Hamilton looms large as a major secondary character in the narrative. The Altruism Equation, A Reason for Everything, and The Darwin Wars, all give him extensive treatment, both his scientific ideas and relevant biographical context. Hamilton’s scientific influence on Richard Dawkins was enormous. There are nearly fifty references to him in both The Selfish Gene and The Extended Phenotype. In writing his obituary Dawkins began: “W. D. Hamilton is a good candidate for the title of most distinguished Darwinian since Darwin.” In terms of the details of his science, Hamilton proposed that genetic relatedness between individuals can explain altruism within groups. In this way Hamilton reduced a phenomenon which had often been explained as a group-level one (e.g., “for the good of the species”) to an individual-level one (e.g., “for the good of the individual/gene”). According to Hamilton when he was a young scientist in the early 1960s most people did not perceive this problem to be a problem at all, and he had difficulty finding support for this line of research, and was in fact warned off it by his superiors. The end culmination of those early years of lonely introspection were two dense, abstruse, and difficult papers (in part due to their peculiar notation), The genetical evolution of social behaviour – I and The genetical evolution of social behaviour – II. But the basic heuristic at the heart of these papers was condensed earlier in a short essay in The American Naturalist as Hamilton’s Rule:

rB > C or rB – C > 0

Where in the context of an altruistic act across two individuals:

r = genetic relatedness between them
C = cost to the individual performing the act
B = benefit to another individual who is the recipient of the act

What the above equation is stating is that when the benefit multiplied by the genetic relatedness exceeds the cost, that is, it is greater than zero, the behavior will spread through natural selection. In contrast, when the cost is greater than the benefit multiplied by the genetic relatedness the behavior will be disfavored.

Here’s a “toy” illustration. Imagine a gene, G, in two variants, G1 and G2. The “ancestral” “wild type” is G1, while G2 is an allele which induces altruistic behavior toward nearby con-specifics. Assume that nearby con-specifics are likely to be genetically close, perhaps siblings. The altruistic behavior induces a cost at no benefit to the individual which is engaging in altruism, but it results in a benefit at no cost to the individual which is a recipient of the act. But here’s the key: nearby kin are much more likely to have the altruistim conferring allele, so G2 is likely increasing its own fitness because recipients of the altruistic behavior may also carry G2. Obviously the details matter here in evaluating exactly whether altruism spreads. What is the magnitude of the cost? What is the magnitude of the benefit? What is the extent of relatedness measured against a basal expectation?

There are also some presuppositions within the original theoretical framework of altruism evolving because of gains to inclusive fitness. Hamilton assumed weak selection, additivity of costs and benefits of fitness components, as well as a specific way to frame genetic relatedness. It has long been a question how robust the Hamiltonian framework is to deviations from its assumptions, deviations which are likely to occur empirically. But empirically measuring fitness in the wild as well as genetic relatedness was either impossible or difficult.

So how to get around this? A new paper was published in PLoS Biology does just that with literal a toy model of robots controlled by a simple neural network. The robots seem to mimic foraging behavior, which impacts their fitness in relation to how they replicate their digital genome to the next generation. Remember, Hamilton’s model was very theoretical, so even if there are literal artificialities here it is still an interesting empirical example which moves us closer to concrete reality. A Quantitative Test of Hamilton’s Rule for the Evolution of Altruism:

One of the enduring puzzles in biology and the social sciences is the origin and persistence of altruism, whereby a behavior benefiting another individual incurs a direct cost for the individual performing the altruistic action. This apparent paradox was resolved by Hamilton’s theory, known as kin selection, which states that individuals can transmit copies of their own genes not only directly through their own reproduction but also indirectly by favoring the reproduction of kin, such as siblings or cousins. While many studies have provided qualitative support for kin selection theory, quantitative tests have not yet been possible due to the difficulty of quantifying the costs and benefits of helping acts. In this study, we conduct simulations with the help of a simulated system of foraging robots to manipulate the costs and benefits of altruism and determine the conditions under which altruism evolves. By conducting experimental evolution over hundreds of generations of selection in populations with different costs and benefits of altruistic behavior, we show that kin selection theory always accurately predicts the minimum relatedness necessary for altruism to evolve. This high accuracy is remarkable given the presence of pleiotropic and epistatic effects, as well as mutations with strong effects on behavior and fitness. In addition to providing a quantitative test of kin selection theory in a system with a complex mapping between genotype and phenotype, this study reveals that a fundamental principle of natural selection also applies to synthetic organisms when these have heritable properties.

The science at the heart of this paper isn’t too knotty. There wasn’t a complicated novel derivation or forbidding statistical model. Rather, the authors ingeniously managed to test experimentally the simple insights of the Hamiltonian framework. The results were surprisingly unsurprising.

I reedited the panels below, but they show the consistency of the basic results. Keep in mind that the replicates varied r, B, and C.

Not to be too cute, but the robots followed Hamilton’s Rule in a robotic fashion! When the cost was too great altruism was disfavored and when the benefit was great enough altruism was favored. In a situation of balance there was the sort of stochastic fluctuation about the initial condition you’d expect. One thing to remember is that the authors simulated mutation and recombination, so selection wasn’t the only evolutionary parameter at work. The panel to the left shows how the level of altruism varied as a function of relatedness, with the ratio between cost and benefit held constant. The top left panel shows a situation where c/b = 0.01. That means that the benefit to the recipient is 100 times greater than the cost to the altruist. Even at low levels of relatedness the altruism spreads. As the ratio between cost and benefit converges upon 1, where the cost now equals the benefit, the relatedness threshold for a given level of mean altruism across these groups increases. This is all as you’d expect, boringly thrilling to see a simple model validated.

The authors also tested the impact of switching to a mutation of large effect (so not weak selection) as well as epistatic gene-gene interactions (so introducing nonlinearities which deviate from the assumption of additivity). Basically they seem to have found that both of these impacted behavior and fitness, but not the ultimate outcome. I’ll quote, because honestly I’d have liked to see more of this in the paper, but that’s probably for the future:

To determine whether mutations in our neural network had pleiotropic and epistatic effects and whether there were departures from weak mutations effects , we conducted additional experiments at the last generation in two treatments with intermediate r and c/b values (treatment 1: r = 0.25, c/b = 0.75; treatment 2: r = 0.75, c/b = 0.25). First, for each treatment, we subjected 4,000 individuals (one in each group) to a single mutation of moderate effect…In the first experiment, performance was significantly affected by a much higher proportion of the mutations than the level of altruism…1.36% of the mutations affecting the level of altruism also translated into a significant change in performance, indicating widespread pleiotropic effects. Similar results were obtained in the second experiment with 4.91% of the mutations affecting the level of altruism also significantly affecting performance. Second, we tested for epistatic effects by comparing the effect of a single mutation in 4,000 individuals with two allelic variants at another locus…genetic background significantly influenced the effect of the mutation in 2,371 (59.3%) of the cases in the first treatment and 2,336 (58.4%) of the cases in the second treatment. These results demonstrate that epistatic interactions are also widespread. Finally, our experiments showed frequent departures from weak effects on behavior and fitness. Performance changed by more than 25% for 1,616 (40.4%) of the mutations in the first treatment and 1,776 (44.4%) of the mutations in the second treatment, and the level of altruism changed by more than 25% for 552 (13.8%) and 1,808 (45.2%) of the mutations in the first and second treatment, respectively.

But in the discussion, they note:

Despite the fact that the assumptions mentioned above were not fulfilled, Hamilton’s original 1964 rule always accurately predicted the conditions under which altruism evolved in our system. Whatever the c/b value used, altruism always evolved in populations where r was greater than c/b. This finding is important given that the assumption of weak selection, additivity of costs and benefits of fitness components and absence of pleiotropic and epistatic gene interactions are also likely to be violated in real organisms that also have a complex mapping between genomes and phenotypes.

The likely power of this sort of inclusive fitness does not in my book invalidate other forces which shape social behavior such as reciprocal altruism. Nature is one, how we carve it at its joints is our business. Hopefully we’ll see a lot more tests of social behavior using robots in the future. And, with the rise of cheap typing of individual identity with genotyping there is the possibility for better assessments of relatedness and fitness across generations in nature itself.

Citation: Waibel M, Floreano D, & Keller L (2011). A Quantitative Test of Hamilton’s Rule for the Evolution of Altruism PLoS Biology : 10.1371/journal.pbio.1000615

(Republished from Discover/GNXP by permission of author or representative)
🔊 Listen RSS One of the most persistent debates about the process of evolution is whether it exhibits directionality or inevitability. This is not limited to a biological context; Marxist thinkers long promoted a model of long-term social determinism whereby human groups progressed through a sequence of modes of production. Such an assumption is not limited to Marxists. William H. McNeill observes the trend toward greater complexity and robusticity of civilization in The Human Web, while Ray Huang documents the same on a smaller scale in China: A Macrohistory. A superficial familiarity with the dynastic cycles which recurred over the history of Imperial China immediately yields the observation that the interregnums between distinct Mandates of Heaven became progressively less chaotic and lengthy. But set against this larger trend are the small cycles of rise and fall and rise. Consider the complexity and economies of scale of the late Roman Empire, whose crash in material terms is copiously documented in The Fall of Rome: And the End of Civilization. It is arguable that it took nearly eight centuries for European civilization to match the vigor and sophistication of the Roman Empire after its collapse as a unitary entity in the 5th century (though some claim that Europeans did not match Roman civilization until the early modern period, after the Renaissance).

It is natural and unsurprising that the same sort of disputes which have plagued the scholarship of human history are also endemic to a historical science like evolutionary biology. Stephen Jay Gould famously asserted that evolutionary outcomes are highly contingent. Richard Dawkins disagrees. Here is a passage from The Ancestor’s Tale:

…I have long wondered whether the hectoring orthodoxy of contingency might have gone too far. My review of Gould’s Full House (reprinted in A Devil’s Chaplain) defended the popular notion of progress in evolution: not progress towards humanity – Darwin forend! – but progress in directions that are at least predictable enough to justify the word. As I shall argue in a moment, the cumulative build-up of compelx adaptations like eyes strongly suggest a version of progress – especially when coupled in imagination with of the wonderful products of convergent evolution.

Credit: Luke Jostins
Credit: Luke Jostins

One of those wonderful products is the large and complex brains of animals. Large brains are found in a disparate range of taxa. Among the vertebrates both mammals and birds have relatively large brains. Among the invertebrates the octopus, squid and cuttlefish are rather brainy. The figure to the right is from Luke Jostins, and illustrates the loess curve of best fit with a scatter plot of brain size by time for a large number of fossils. The data set is constrained to hominins, humans and their ancestors. As you can see there is a general trend toward increase cranial capacities across all the human populations. Neandertals famously were large-brained, but they exhibited the same secular increase in cranial capacity as African Homo. On the scale of Pleistocene Homo and their brains the idea of the supreme importance of contingency seems ludicrous. Some common factor was driving the encephalization of humans and their near relations over the past two million years. This strikes me as very strange, as the brain is metabolically expensive, and there are plenty of species with barely a brain which are highly successful. H. floresiensis may be a human instance of this truism.

But what about the larger macroevolutionary pattern? Is there a trend toward larger brain sizes in general, of which primates, and humans in particular, are just the most extreme manifestation? Some natural historians have argued that there is such a trend. But, there is a question as to whether increased brain size is simply a function of allometry, the pattern where different body parts and organs tend to correlate together in size, but also shift in ratio with scale. The nature of physics means that very large organisms have to be more robust because their mass increases far faster than their surface area. By taking the aggregate relationship between body size and brain size, and examining the species which deviate above or below the trend line, one can generate an encephalization quotient. Humans, for example, have a brain which is inordinately large for our body size.

And yet there are immediate problems looking at relationships between body and brain size, and inferring expectations. Different species and taxa are not interchangeable in very fundamental ways, and so a summary statistic or trend may obscure many fine-grained details. A new paper in PNAS focuses specifically on various mammalian taxa, corrects for phylogenetics, and also relates encephalization quotient by taxa to the proportion of social animals within each taxon. Encephalization is not a universal macroevolutionary phenomenon in mammals but is associated with sociality:

Evolutionary encephalization, or increasing brain size relative to body size, is assumed to be a general phenomenon in mammals. However, despite extensive evidence for variation in both absolute and relative brain size in extant species, there have been no explicit tests of patterns of brain size change over evolutionary time. Instead, allometric relationships between brain size and body size have been used as a proxy for evolutionary change, despite the validity of this approach being widely questioned. Here we relate brain size to appearance time for 511 fossil and extant mammalian species to test for temporal changes in relative brain size over time. We show that there is wide variation across groups in encephalization slopes across groups and that encephalization is not universal in mammals. We also find that temporal changes in brain size are not associated with allometric relationships between brain and body size. Furthermore, encephalization trends are associated with sociality in extant species. These findings test a major underlying assumption about the pattern and process of mammalian brain evolution and highlight the role sociality may play in driving the evolution of large brains.

A key point is that the authors introduce time as an independent variable, so they are assessing encephalization over the history of the taxon. This is clearly relevant for humans, but may be so for other mammalian lineages. The table and figures below show the encephalization slope generated by using time and body size as the predictors and brain size as the dependent variable. A positive slope means that brain size is increasing over time.

[nggallery id=21]

Two major points:

- Note that the slope is sensitive to the level of taxon one is examining. A closer focus tends to show more variance between taxa. So, for example, humans distort the value for primates in general. Bracketing out anthropoids paints a more extreme picture of encephalization, a higher slope. In contrast, the lemurs and their relatives exhibit less encephalization over time.

- The correlation between proportion of species which exhibit sociality and encephalization of the taxon is strong. From the text:

Encephalization slopes were correlated with both the proportion of species with stable groups (order R = 0.92, P = 0.005, n = 6; suborder R = 0.767, P = 0.008, n = 9; Fig. 2 A and B) and the proportion in either facultative or stable social groups (order R = 0.804, P = 0.027, n = 6; suborder R = 0.63, P = 0.04, n = 9).

The last figure makes it is clear that the correlations are high, so the specific values should not be surprising. Don’t believe these specific figures too much, how one arranges the data set or categorizes may have a large effect on the p-value. But the overall relationship seems robust.

A highly encephalized “alien”

What to think of all of this? If you don’t know, one of the authors of the paper, Robin Dunbar, has been arguing for the prime importance of social structure in driving brain evolution among humans for nearly twenty years. The relationship is laid out in his book Grooming, Gossip, and the Evolution of Language. Robin Dunbar is also the originator of the eponymous Dunbar’s number, which argues that real human social groups bound together by interpersonal familiarity have an upper limit of 150-200. He argues that this number arises because of the computational limits of our “wetware,” our neocortex. Those limits presumably being a function of biophysical constraints.

One interesting fact though is that the median cranial capacity of our species seems to have peaked around one hundred thousand years ago. The average human today has a smaller brain than the average human alive during the Last Glacial Maximum! (see this old post from Panda’s Thumb, it’s evident in the charts) This may be simply due to smaller body sizes in general after the Ice Age. Or, it may be due to the possibility that social changes with the rise of agriculture required less brain power.

Ultimately if Dunbar and his colleagues are correct, if social structure is the most powerful variate in explaining differences in brain size when controlling for phylogenetics and body size, then in some ways it is surprising to me. After all, it does not seem that ants have particularly large brains, despite being extremely social and highly successful. Clearly the hymenoptera and other social insects operate on different principles from mammals. Instead of
developing “hive minds,” it seems as if in mammals greater social structure entails greater cognitive structure.

Citation: Susanne Shultz, & Robin Dunbar (2010). Encephalization is not a universal macroevolutionary phenomenon in mammals but is associated with sociality PNAS : 10.1073/pnas.1005246107

(Republished from Discover/GNXP by permission of author or representative)
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Razib Khan
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