Reading List: Human Evolution books

These are scientific books about human evolution I always recommend. The target audiences run the gamut from academics to lay public, all are dumped together.

Sorted alphabetically by author name. Also check out my human evolution reading list for undergrads.

• Aiello L & Dean C. 1990. An Introduction to Human Evolutionary Anatomy.
• Allen NJ, Callan H, Dunbar R & James W (eds.). Early Human Kinship: From Sex to Social Reproduction.
• Arsuaga JL & Martínez I. 2005. The Chosen Species: The Long March of Human Evolution.
• Ash PJ & Robinson DJ. 2010. The Emergence of Humans: An Exploration of the Evolutionary Timeline.
• Beard C & Klinger M. 2006. The Hunt for the Dawn Monkey: Unearthing the Origins of Monkeys, Apes, and Humans.
• Boyd R & Silk JB. 2011. How Humans Evolved. 6^th ed.
• Cameron D & Groves C. 2004. Bones, Stones and Molecules: “Out of Africa” and Human Origins.
• Cavalli-Sforza LL. 2001. Genes, Peoples, and Languages.
• Cela-Conde CJ & Ayala FJ. 2007. Human Evolution: Traits from the Past.
• Chase P. 2006. The Emergence of Culture: The Evolution of a Uniquely Human Way of Life.
• Cochran G & Harpending H. 2010. The 10,000 Year Explosion: How Civilization Accelerated Human Evolution.
• Coolidge FL & Wynn T. 2009. The Rise of Homo sapiens: The Evolution of Modern Thinking.
• De Beaune SA, Coolidge FL & Wynn T (eds.). 2009. Cognitive Archaeology and Human Evolution.
• Falk D. 2012. The Fossil Chronicles: How Two Controversial Discoveries Changed Our View of Human Evolution.
• Fuentes A. 2012. Race, Monogamy, and Other Lies They Told You: Busting Myths about Human Nature.
• Gibson KR & Ingold T. 1995. Tools, Language and Cognition in Human Evolution.
• Grine FE, Fleagle JG & Leakey RE (eds.). 2009. The First Humans: Origin and Early Evolution of the Genus Homo.
• Hochberg Z. 2012. Evo-Devo of Child Growth: Treatise on Child Growth and Human Evolution.
• Ishida H, Tuttle R, Pickford M, Ogihara N & Nakatsukasa M (eds.). 2006. Human Origins and Environmental Backgrounds.
• Kappeler P & van Schaik CP (eds.). 2006. Cooperation in Primates and Humans: Mechanisms and Evolution.
• Kennett DJ & Winterhalder B (eds.). 2006. Behavioral Ecology and the Transition to Agriculture.
• Kingdon J. 2004. Lowly Origin: Where, When, and Why Our Ancestors First Stood Up.
• Klein RG. 2009. The Human Career: Human Biological and Cultural Origins. 3^rd ed..
• Larsen CS. 2002. Skeletons in Our Closet: Revealing Our Past through Bioarchaeology.
• Lewin R. 2004. Human Evolution: An Illustrated Introduction. 5^th ed..
• Lieberman DE, Smith RJ & Kelley J (eds.). 2005. Interpreting The Past: Essays On Human, Primate, And Mammal Evolution In Honor Of David Pilbeam.
• Marks J. 2003. What it Means to be 98% Chimpanzee: Apes, People, and their Genes.
• Minugh-Purvis N & McNamara KJ (eds.). 2001. Human Evolution through Developmental Change.
• Moran EF. 2007. Human Adaptability: An Introduction to Ecological Anthropology.
• National Academy Press. 2010. Understanding Climate’s Influence on Human Evolution. [Note: free PDF at link!]
• Richerson PJ & Boyd R. 2005. Not by Genes Alone: How Culture Transformed Human Evolution.
• Shea JJ & Lieberman DE (eds.). 2009. Transitions in Prehistory : Essays in Honor of Ofer Bar-Yosef.
• Stone L & Lurquin PF. 2005. A Genetic and Cultural Odyssey: The Life and Work of L. Luca Cavalli-Sforza.
• Stone L, Lurquin PF & Cavalli-Sforza L. 2006. Genes, Culture, and Human Evolution: A Synthesis.
• Stringer C & Andrews P. 2011. The Complete World of Human Evolution.
• Swisher III CC, Curtis GH & Lewin R. 2000. Java Man: How Two Geologists Changed Our Understanding of Human Evolution.
• Sykes B. 2001. The Seven Daughters of Eve: The Science That Reveals Our Genetic Ancestry.
• Tattersall I. 2000. Becoming Human: Evolution and Human Uniqueness.
• Ungar PS (ed.). 2006. Evolution of the Human Diet: The Known, the Unknown, and the Unknowable.
• Wessen KP. 2005. Simulating Human Origins and Evolution.
• Wood BA. 2005. Human Evolution: A Very Short Introduction.

• Wood BA, Martin LB & Andrews P (eds.). 2009. Major Topics in Primate and Human Evolution.

On Group Selection

A reader picked up on my parenthesised comment on EO Wilson’s kin selection paper in this post, which led to a substantial e-mail conversation on group selection, which I will distill here to show you that the noise against group selection doesn’t hold much water anymore.

Explaining the existence of altruism has always been one of the central problems of evolution. Altruism is defined as an action that is negative to the actor, and positive for a recipient. Why would an individual organism do something like that? Until the 1960s, group selection – the idea that natural selection can act to benefit groups – was viewed as an ideal candidate, since the positive effect for the group overpowers the negative effect on the individual, leading to that group outcompeting other groups where altruism does not exist. A “group” is nowadays best defined as a population of intercting organisms which affect each other’s fitnesses (Sober & Wilson, 1994).

The staunch opposition to and denial of group selection was popularised in the 1960s with George C. Williams‘s 1966 seminal book, Adaptation and Natural Selection. Among his evidence was that sex ratios in animals are fairly even, whereas group selection would predict biased sex ratios. The entire book consists of him calculating that “group-related adaptations do not, in fact, exist”, because its power would be too weak to influence the power of natural selection on the individual level, since individuals have a much faster turnover rate than groups, thus evolution happens more quickly in them.

Maynard Smith‘s work complemented and expanded on these conlusions (Maynard Smith, 1964, 1976), and he even came up with another alternative to group selection, evolutionary game theory, expounded in his 1982 book Evolution and the Theory of Games. Economists will be aware of game theory; this is the same thing. Behaviour is modelled by seeing the interactions of different individuals. If two altruists interact, the result is positive. If two selfishs interact, the result is negative. If an altruist and a selfish interact, the result is positive for the selfish, negative for the altruist. Therefore, altruism evolves only when altruistic interactions are much more common than selfish interactions. The role of other groups is irrelevant, selection takes place within the group only.

But since then, a lot of research and progress has been made, and group selection has been shown to be possible and even likely, and in my opinion, it is no longer valid to demonise group selection.

I mentioned in my natural selection lecture the classic demonstration of group selection in lab-reared beetles by Wade (1977), which proves that group selection can in fact be a dominant evolutionary force. A valid criticism would be to say that evolution in the lab is too artificial and not reflective of evolutionary forces in the wild.

But in the wild, group selection has also emerged as a favoured explanation for some scenarios. See Heinsohn & Packer (1995) for an example with lions and their territorial defence. The authors set the problem effectively: “If too few females accept the responsibilities of leadership, the territory will be lost. If enough females cooperate to defend the range, their territory is maintained, but their collective effort is vulnerable to abuse by their companions. Leaders do not gain additional benefits from leading, but they do provide an opportunity for laggards to gain a free ride”. The experiments involved changing the conditions slightly, and the diversity and complexity of reactions revealed that just invoking individual selection just wasn’t enough to explain everything; group selection fills the gap.

Even theory and modelling, what initially sounded the death knell for group selection, has recently been turned around to make it a plausible theory again. Models from the 1970s, such as Levin & Kilmer (1974), while adequate for their time, can’t compete with the much more powerful simulations and models computers are capable of today. When a larger multitude of factors is taken into account, group selection does come out as likely. As an example, check out Werfel & Bar-Yam (2004), whose model finds that a reduction in fertility might evolve to avoid resource overconsumption, echoing a classic group selectionist argument (see Borrello’s excellent book, Evolutionary Restraints: The Contentious History of Group Selection).

The opposition to group selection simply does not hold up anymore, since it’s now acknowledged that multilevel selection is the most valid way to look at natural selection, as has been realised since the 1970s. In the 1960s, WD Hamilton developed inclusive fitness theory, now better known as kin selection (Hamilton, 1964a, 1964b), which mathematised the advantage of parental care as an individualistic advantage borne out of the positive effect of making sure relatives have a high fitness, since they carry the same genes. This gave birth to the well-known Hamilton’s Rule: rb > c. Altruism can evolve only when genetic relatedness (r) and the benefit (b) are greater than the cost (c). This would explain why eusocial insect colonies, like ants, are all made up of sisters and daughters. This was initially seen as a futher blow to group selection.

However, in the 1970s, Hamilton combined his inclusive fitness theory with the Price equation and noticed that altruistic traits by themselves are disadvantageous, and only become advantageous when dominant in a population. In other words, an individual altruist is useless, but put many altruists together and the group emerges as a stronger collective than a group of selfish individuals. From such work emerged a new group selection; see Price (1970). Kin selection fits very snuggly within this new group selection. They are not contradictory, but complementary, not the least because the concepts of benefits and costs in both theories differ. Kin selection talks about absolute costs and absolute benefits, whereas group selection talks about relative ones (relative between groups).

This is what should get accepted, either implicitly or explicitly. Group selection is still viewed as a bit of a taboo in some circles, but the thinking behind it as part of a multilevel selection framework is solid and few would have a problem with it, since it has evidence to back it up from all sides. What one needs to do is merely acknowledge that absolutism is wrong: not every social organism evolves exclusively by group selection or by kin selection or by individual selection. Not all adaptations that benefit the group evolved by group selection, and group selection is by itself not sufficient for the evolution of group adaptations. There are multiple causes, and all levels are interlinked. It was failure to recognise the multidimensionality of the problem that resulted in the group selection controversy we all know and hate. In philosophical terms, explanatory pluralism is the way to go, recognising that there can be multiple explanations for the same set of facts depending on the focus of your research (this is also a point I stressed in my natural selection lecture).

References:

Borrello. 2012. Evolutionary Restraints: The Contentious History of Group Selection.

Hamilton WD. 1964a. The genetical evolution of social behaviour. I. Journal of Theoretical Biology 7, 1-16.

Hamilton WD. 1964b. The genetical evolution of social behaviour. II. Journal of Theoretical Biology 7, 17-52.

Heinsohn R & Packer C. 1995. Complex cooperative strategies in group-territorial African lions. Science 269, 1260-1262.

Maynard Smith J. 1964. Group Selection and Kin Selection. Nature 201, 1145-1147.

Maynard Smith J. 1976. Group Selection. The Quarterly Review of Biology 51, 277-283.

Maynard Smith J. 1982. Evolution and the Theory of Games.

Price G. 1970. Selection and Covariance. Nature 227, 520-521.

Sober E & Wilson DS. 1994. A Critical Review of Philosophical Work on the Units of Selection Problem. Philosophy of Science 61, 534-555.

Wade MJ. 1977. An Experimental Study of Group Selection. Evolution 31, 134-153.

Werfel J & Bar-Yam Y. 2004. The evolution of reproductive restraint through social communication. PNAS 101, 11019-11024.

Williams GC. 1966. Adaptation and Natural Selection.

How Do We Recognise Tool Use In Animals?

I got an e-mail asking about we recognise true tool use in animals. For me, two factors are the most important: flexible behaviour, and problem-solving. Both are linked: problem-solving requires flexible behaviour to be implemented.

To demonstrate this, I will use the woodpecker finch, Cactospiza pallida, one of Darwin’s finches from the Galápagos. They grab cactus spines and twigs in their beaks and use them to get their arthropod prey out of crevices. We know that this is really dynamic tool use that comes out of a cognitive thought pattern, rather than being a hardwired behaviour, because of two observations.

1. If needed, they will shorten twigs or smooth them out.
2. Tebbich et al. (2002) find that tool use only happens in arid areas, where prey mostly hides in tree holes, whereas tool use in humid areas where arthropods hang out in the open was negligible to non-existent.

In other words, the process involved in tool use in the woodpecker finch is problem-solving, not just a triggered instinct – this is why they modify their sticks, and why they even use the tool only in arid areas where it’s necessary. And both points automatically imply a flexible behaviour that allows the woodpecker finch to adapt cognitively to new environments and challenges.

Some people also place social learning as a criterion, based on numerous reports of the knowledge of tool use being passed on culturally by observation. However, I consider this an unnecessary limitation: there is no reason to discount solitary, non-social species. It may be that current research is just focused on typical examples (birds, primates), where social observation is commonplace, and that further research will uncover many examples of tool use from solitary species that are simply clever enough to solve problems on their own, without having observed the solution from tribe-mates before.

On a related note, I got an alert about this soon-to-be-released Cambridge University Press book, Tool Use in Animals: Cognition and Ecology, edited by Sanz, Call & Boesch. Given that these three are all leading primatologists, it comes as no surprise that 9/13 chapters deal with primate tool use. The title is thus a bit deceptive given that tool use is definitely known from other mammals, from birds, and from insects, but it seems like a good compilation of chapters nonetheless (and one can’t judge it without having read it first!).

References:

Tebbich S, Taborsky M, Fessl B & Dvorak M. 2002. The ecology of tool-use in the woodpecker finch (Cactospiza pallida). Ecology Letter 5, 656-664.

Maths and Biology Students: My take on EO Wilson’s essay

Last week, E. O. Wilson wrote this controversial Wall Street Journal essay that has set the biology blogosphere on fire, a fire that’s mostly headed towards Wilson. I don’t want to be left out of the fun and games, so I want to add my own opinion into the cauldron. Before doing so, I will stress that while I consider Wilson one of my personal idols as one of the last remaining old-skool field naturalists, I am setting aside this bias here.

In a nutshell, the essay’s premise is that to be a great biologist, you don’t necessarily need to be good at maths. My take on the essay is that it’s split right down the middle: half of it is good and I agree with, half of it I consider invalid or at least badly argued.

In Wilson’s general defence, I read it as a semi-autobiographical take on the issue. From this point of view, I will have to disagree with the many commentaries that say he was trying to advise students in a particular direction. No. Wilson merely took his own experiences and extrapolated from them in order to give a pep talk to those students who have trouble with maths.

His experience, as described in the essay, is that you don’t necessarily need to be even an adequate mathematician in order to be a biologist, because in biology, you get new hypotheses and ideas by creative thinking, writing down notes and observations from the field, and putting these all together.

I have a lot of empathy for this. In fact, I encourage my students to do exactly this: write down all your ideas, no matter how crazy. Mull them over, talk them through with colleagues. Come up with hypotheses, and test them. It’s how the scientific method works. To me, the students that impress me the most are the ones that ask odball question, that will come to me after class and share a wacky idea they thought of, or that will run impromptu experiments out in the field because they noticed something weird. It’s also how I work as a biologist: most of my knowledge and research ideas came about as the results of thought experiments I conducted by myself and had fact-checked by professors and colleagues.

But the flipside that Wilson seems to have ignored is that instead of writing your notes in English, you can write them in maths. He considers maths as just a toolkit, when in fact it’s a language. It’s just as creative to write down a mathematical equation as it is to write down an idea in English, and putting together a mathematical model is exactly analogous to combining a series of related ideas into a general theory.

The main difference, as I see it, is your own bias. As primarily a strictly empirical field biologist, I tend to jot down ideas in a strange variant of written English only I can understand (a security measure or lazy writing? You decide). But in some cases, maths was superior: when I was wrapping my head around the concepts of genetic drift, natural selection, or group selection, maths was my thinking language of choice, because it allowed me to focus on the relevant factors and their relative importances, rather than trying to concoct an intuitive story based on wild ideas in my head. I draw my personal line at evolutionary theory and some ecology, but I know people who can break down every part of an organism into equations, and others who can’t even stand the idea of putting together a model of energy transfer from Sun to top predator (something you learn in middle school biology).

Mathematical modelling isn’t for everyone, nor should it be for everyone. This is where I agree with Wilson. But there is a very important caveat: not all biologists should have to build models, but all should be equipped to understand models. This isn’t a contradiction: my genetic and developmental labwork skills are nothing short of pathetic, but that doesn’t prevent me from successfully working on microevolutionary problems that need insights from genetics and developmental biology. Similarly, even if you can’t build a fancy Lotka-Volterra competition model from scratch, that shouldn’t prevent you from reading one in an ecology paper or even downloading the R script and playing around with it.

This is the point I feel Wilson canvassed over. He seems content to leave well enough alone. Can’t understand these differential equations because you don’t know maths? That’s okay, here’s a paragraph explaining what they mean. Such a perspective is fine if you want to popularise the science, but as a working scientist, you must be able to get down and dirty not only with results and conclusions, but with the data-generating methods, which may be mathematical models. Just like you might criticise a phylogenetic paper for not having a large enough taxon sampling, you ought to be able to take a model and criticise it. If you can’t, then you risk intellectual dishonesty that may potentially be grave: if you can’t understand how the results came to be, then you’re in no position to judge the validity of those results, and by extension, you can’t in any honesty use that data for your own research purposes.

I can’t stress this point enough. You must realise that much of the critical basics of evolution and ecology, as two of the most important fields of biology, are based on mathematical models. Fisher, Haldane, Price, Maynard Smith. Household names in evolutionary theory; their foundational work is mostly centered around mathematical models they built. If you can’t understand them and aren’t willing to make the effort to do so, then quite frankly you can just give up on truly understanding evolutionary theory. (Sidenote: I’m torn on whether it’s okay to cite papers you don’t understand completely. Ideally not, but then again we do have a trust among us, reinforced by peer review, that the conclusions are tentatively reliable. But it’s iffy, I personally wouldn’t recommend it.)

All this time, I’ve been talking about mathematical models, because I see absolutely no excuse for any biologist to not be well-versed in statistical methods. You can be forgiven for not knowing the mathematical backgrounds for them (I confess ignorance for most), but considering the myriad books available specifically for biologists, and the ubiquitous need for statistics, they’re a required part of your basic toolset.

Wilson’s solution to not knowing maths is one I mostly agree with: collaborate and cooperate (hooray consilience!). As stressed above, I don’t agree in the case of statistics, but for mathematical models, do go ahead. Wilson has done this many, many times. He comes up with the ideas, goes to a trusted mathematician colleague, and they whip up a combo mathematical model and naturalistic explanation. This was how Wilson revolutionised biogeography and community ecology. His own ideas alone were great, but what really elevated them to biological mainstay status were the accompanying models by MacArthur. The firestorm he started over kin selection is very similar: he’s had these ideas in his head, and used the help of Nowak and Tarnita to develop the mathematical models to back them up. (About that: I think there was a lot of smoke in the negative reaction towards it, as I’m pretty certain kin selection has deficiencies, which was Wilson’s point).

Finally, I need to go back to my roots and stress that I, personally, will take data gathered from observation over mathematical models anytime, because while models offer incredible precision, that precision may well be illusory. Biology is an intrinsically messy discipline that doesn’t take too well to too much reductionism. Even when I have the skill to build a model for a phenomenon, I will always prefer getting empirical, experimental, field data from the real world.

That’s all I wanted to say about this. As a teacher, I’ve often observed the same thing that Wilson did: talented students turning away from biology because suddenly they come across maths. I had the very same problem, and my mathematical abilities are very specifically self-taught (I am completely helpless with any physics maths) and are merely an extension of my intuitive biological daydreams. For the gifted students who think they’ve hit a wall, here are my two tips.

• Befriend some mathematicians, or take some basics maths courses, or buy a “maths for dummies” book. Maths is basically a very logical language, and you just have to learn how to read it. Don’t be daunted by the apparent complexity of it (I personally find the writing of maths the most tedious aspect).
• Find mathematical models, break them down, reconstruct them. The letters there all stand for something. Follow the thought pattern through the equation, and you will be able to piece together a paragraph explaining the meaning of every single equation you come across. I did this as a student for all the models I came across, and also tried to reconstruct the classic models of evolutionary theory. This is how I learned maths.

And whatever you do, don’t give up. If all else fails (which I understand, as even after 4 years, I still couldn’t properly calculate how fast a stupid egg will drop from an airplane), you can always collaborate, or get into a field where you need not build any mathematical models.

Basically, where I agree with Wilson is when he says that creative thinking is the most important quality of a scientist. Where I disagree with him is when he says that expressing oneself mathematically is somehow not creative.

Evolutionary-Based Classifications vs. Similarity-Based Ones

I received an e-mail asking why building phylogenies is so hard, if it’s just looking for similarities between organisms. The reason is that building phylogenies is not looking for similarities between organisms.

A phylogenetic tree is a hypothesis of the relationships between the organisms represented in it. It’s a strict evolutionary diagram, since those organisms evolved in a certain pattern – they weren’t created ex nihilo, they have an evolutionary pedigree we try to uncover. The way to do that is to look for homologies, characters that only some of the organisms share. These are likely to be present in only those organisms because they evolved in the last common ancestor, meaning that these organisms descend from that last common ancestor and are thus more closely related to each other than to the rest. (Convergent evolution and reduction of characters throw big wrenches in the process; that’s why we use as much data as possible, to uncover a large amount of possible homologies.)

Homologies are not synonymous with similarities. Homologies are evolutionarily important units, while similarities are arbitrary.

You can classify organisms based on homologies to make a systematic classification (this is why the field is called systematics), or a phylogenetic classification.

A classification based on similarities does not necessarily have an evolutionary component to it. For example, you can classify organisms by locomotion type and end up with birds and bats clustered together, which makes little evolutionary sense. You can classify bacteria by pathogenicity, even though pathogenic bacteria evolve convergently in many classes. Such classifications do have their uses, but they’re not evolutionary, and cannot be shown on a phylogenetic tree.

However, they can be shown on a dendrogram, which can be made to look fairly identical to a phylogenetic tree, since it’s a very intuitive data visualisation. When constructing such similarity-based dendrograms, you can use the same tools and algorithms as for constructing a phylogenetic tree, but you must realise that the relationships you are supposedly uncovering are not evolutionarily-sound, since you’re making up categories as they suit you and whatever you’re categorising.

This is the critical distinction between a phylogenetic tree and a tree for clustering similar things together. The former is one based on evolution, the latter is based on custom categories.

However, what one can do is take the issue abstractly. For example, I am building a program that will allow the user to enter their favourite movie/genre/director/etc. and the program spits out a series of recommendations. This program is based on a massive database treated as a phylogenetic data matrix. Films from the same director are very likely to be clustered together. This is because they’re similar (directors tend to reuse themes and staff), but you can also look at them as having evolved from a common ancestor, with the director as a homology.

But that’s more of a philosophical and practically irrelevant distinction. In biology (and comparative linguistics!), homologies and similarities are not synonymous. Homologies can often be similarities, but similarity is not part of the definition of homology.

Much Ado About Randomness

I’ve seen some fuss being made about this new book, Bonner’s Randomness in Evolution. It’s fairly incomprehensible to me why it’s seen as controversial, although I can guess at why there is confusion, besides the back blurb stoking the fires by saying it “challenges a central tenet of evolutionary biology”, which it doesn’t. In fact, from what I read so far, I consider it an essential book for the layman interested in getting a comprehensive view of evolution.

The popular narrative of evolution, as taught in schools and retold by most science popularisers, emphasises the role of natural selection, a process that, while dependent on randomness and chance to work, effectively eliminates their influence by streamlining evolution along deterministic pathways.

As I emphasised in my lecture linked above, natural selection is not the default rule in evolution. It’s not the null hypothesis. It’s a working hypothesis that needs heaps of supporting evidence for every examined case. Treating evolution as a phenomenon primarily under the force of natural selection is a risky road that leads to adaptationism, the misguided and thoroughly debunked idea that seeks to explain every single character and trait as an adaptation to something.

This misconception of evolution being practically synonymous with natural selection is one that I try very hard to scrub off my students’ minds. Evolution is an entire framework, and natural selection is just one process within it. Its power can be great indeed, but it’s not always active, and it’s not always all-powerful.

But if natural selection isn’t the default force of evolution, what is? As I explained in this pretty old post, the majority of mutations (the raw data for evolution) are neutral, having no effect whatsoever (or, a negligible one at best). Therefore, the assumption of neutrality is the default one to make.

Mutations are random events, for our intents and purposes here (there are certain biases, but that’s getting into irrelevant technicalities). Most of them are neutral, and these neutral mutations will get passed on to the offspring, and may eventually get fixed in the population. This is what we call genetic drift, true “random evolution”. There’s no need to get into the complex mathematics of this (it’s probably the most sophisticated maths you get in all of evolutionary theory); suffice it to say that genetic drift is undoubtably the most important process when it comes to evolution at the molecular level, as is corroborated by evidence from wild populations. Most of the variation in DNA between species is attributable not to natural selection, but to simple genetic drift. In fact, genetic drift is powerful enough that, in some cases, harmful mutations can get fixed in a population through it.

Where the view stops being so clear is when we zoom out and start looking at macroevolutionary features – anatomies, morphologies, behaviours, traits that are commonly referred to as adaptations. Again, even for such features that directly affect reproduction and survival, natural selection must be demonstrated, not assumed. The reason is that, like at the molecular level, morphological characters are not all identical. A population of elephants do not all have the same trunk length. The variations, however slight, fluctuate and most likely have a neutral effect – just like most mutations. They will get passed on and maybe fixed at random, not necessarily being selected for.

The point I’m making is that in evolutionary biology, there is absolutely nothing controversial with saying that evolution has a significant random component. The book’s contents in this regard do not challenge anything, and the slight controversy about it is incomprehensible. In fact, I will be recommending this book for those infected by ultra-Darwinian thought. (“Ultra-Darwinians” are those who view natural selection as a single all-powerful force in evolution.)

There is one point where the book does raise controversy: the author advances the hypothesis that organism size affects the degree of neutrality. I will not comment on this since I’m still not through. I only wanted to address the silly kerfuffle made about “randomness”.

On a sidenote, I suspect one other factor at play is that many people approach evolution from an anti-creationist point of view, having learned evolution by memorising creationist debunkings, and are reacting to the word “random” because creationists will often say that all of evolution is random. Which it isn’t, because natural selection is still there, and it’s by definition a deterministic process.

Bryozoan Placentas

Celleporella hyalina. Source.

Pictured above is Celleporella hyalina, a species of cheilostome bryozoan. Bryozoans are aquatic, filter-feeding, colonial organisms – each opening up there is the house of an individual bryozoan, and they all live together in this colony that can sprawl over any substrate, often playing a significant role as foulers. Each house is technically called a zooid, and they’re made of calcium carbonate.

I bring them up because I got an e-mail asking whether any animals other than mammals have a placenta. In terms of the actual organ, no, the mammalian placenta is unique. But organs that serve the same function as the placenta do exist in some other animals, such as some bryozoans.

In bryozoans, fertilised eggs are kept in a special, fortified chamber, the ovicell, within the mother’s zooid. You can consider this like a womb. The ovicell has a door, the ooecial vesicle, that closes the ovicell off once implantation occurs – this protects the eggs from seawater.

Ovicell longitudinal section. Source.

In some bryozoans, the ooecial vesicle becomes enlarged as a special structure forms from its mother-facing wall: the embryophore. The light microscope picture above shows a longitudinal section through an ovicell. em is the embryo, eph is the embryophore; not shown to the right is the maternal zooid.

The embryophore keeps growing larger as the embryos grow, and its function is to pass on nutrients from the maternal zooid to the embryos in the ovicell. Therefore, we term it a placental analogue.