If you’re more bored than I am (and I’m not bored at all — I’m neck deep in work), try out these evolution games on the web. Any good? Useful for the kiddies? I don’t know. Somebody else review them for me.
Most of you don’t understand evolution. I mean this in the most charitable way; there’s a common conceptual model of how evolution occurs that I find everywhere, and that I particularly find common among bright young students who are just getting enthusiastic about biology. Let me give you the Standard Story, the one that I get all the time from supporters of biology.
Evolution proceeds by mutation and selection. A novel mutation occurs in a gene that gives the individual inheriting it an advantage, and that person passes it on to their children who also gets the advantage and do better than their peers, and leave more offspring. Given time, the advantageous mutation spreads through the population so the entire species has it.
One example is the human brain. An ape man millions of years ago acquired a mutation that made his or her brain slightly larger, and since those individuals were slightly smarter than other ape men, it spread through the population. Then later, other mutations occured and were selected for and so human brains gradually got larger and larger.
You either know what’s wrong here or you’re feeling a little uneasy—I gave you enough hints that you know I’m going to complain about that story, but if your knowledge is at the Evolutionary Biology 101 level, you may not be sure what it is.
Just to make you even more queasy, the misunderstanding here is one that creationists have, too. If you’ve ever encountered the cryptic phrase “RM+NS” (“random mutation + natural selection”) used as a pejorative on a creationist site, you’ve found someone with this affliction. They’ve got it completely wrong.
Here’s the problem, and also a brief introduction to Evolutionary Biology 201.
First, it’s not exactly wrong — it’s more like taking one good explanation of certain kinds of evolution and making it a sweeping claim that that is how all evolution works. By reducing it to this one scheme, though, it makes evolution far too plodding and linear, and reduces it all to a sort of personal narrative. It isn’t any of those things. What’s left out in the 101 story, and in creationist tales, is that: evolution is about populations, so many changes go on in parallel; selectable traits are usually the product of networks of genes, so there are rarely single alleles that can be categorized as the effector of change; and genes and gene networks are plastic or responsive to the environment. All of these complications make the actual story more complicated and interesting, and also, perhaps to your surprise, make evolutionary change faster and more powerful.
Think populations
Mutations are the root of biological variation, of course, but we often have a naive view of their consequences. Most mutations are neutral. Even advantageous mutations are subject to laws of chance in their propagation, and a positive selection coefficient does not mean there will be an inexorable march to fixation, where every individual has the allele. This is also true of deleterious mutations: chance often dominates, and unless it is a strongly negative allele, like an embryonic lethal mutation, there’s also a chance it can spread through the population.
Stop thinking of mutations as unitary events that either get swiftly culled, because they’re deleterious, or get swiftly hauled into prominence by the uplifting crane of natural selection. Mutations are usually negligible changes that get tossed into the stewpot of the gene pool, where they simmer mostly unnoticed and invisible to selection. Look at human faces, for instance: they’re all different, and unless you’re looking at the extremes of beauty or ugliness, the variations simply don’t make much difference. Yet all those different faces really are the result of subtly different combinations of mutant forms of genes.
“Combinations” is the magic word. A single mutation rarely has a significant effect on a feature, but the combination of multiple mutations may have a detectable or even novel effect that can be seen by natural selection. And that’s what’s going on all the time: the population is a huge reservoir of genetic variation, and what we do when we reproduce is sort and mix and generate new combinations that are then tested in the environment.
Compare it to a game of poker. A two of hearts in itself seems to be a pathetic little card, but if it’s part of a flush or a straight or three of a kind, it can produce a winning hand. In the game, it’s not the card itself that has power, it’s its utility in a pattern or combination of other cards. A large population like ours is a great shuffler that is producing millions of new hands every day.
We know that this recombination is essential to the rapid acquisition of new phenotypes. Here are some results from a classic experiment by Waddington. Waddington noted that fruit flies expressed the odd trait of developing four wings (the bithorax phenotype) instead of two if they were exposed to ether early in development. This is not a mutation! This is called a phenocopy, where an environmental factor induces an effect similar to a genetic mutation.
What Waddington did next was to select for individuals that expressed the bithorax phenotype most robustly, or that were better at resisting the ether, and found that he could get a progressive strengthening of the response.
This occurred over 10s of generations — far, far too fast for this to be a consequence of the generation of new mutations. What Waddington was doing was selecting for more potent combinations of alleles already extant in the gene pool.
This was confirmed in a cool way with a simple experiment: the results in the graph above were obtained from wild-caught populations. Using highly inbred laboratory strains that have greatly reduced genetic variation abolishes the outcome.
Jonathan Bard sees this as a powerful potential factor in evolution.
Waddington’s results have excited considerable controversy over the years, for example as to whether they reflect threshold effects or hidden variation. In my view, these arguments are irrelevant to the key point: within a population of organisms, there is enough intrinsic variability that, given strong selection pressures, minor but existing variants in a trait that are not normally noticeable can rapidly become the majority phenotype without new mutations. The implications for evolution are obvious: normally silent mutations in a population can lead to adaptation if selection pressures are high enough. This view provides a sensible explanation of the relatively rapid origins of the different beak morphologies of Darwin’s various finches and of species flocks.
Think networks
One question you might have at this point is that the model above suggests that mutations are constantly being thrown into the population’s gene pool and are steadily accumulating — it means that there must be a remarkable amount of genetic variation between individuals (and there is! It’s been measured), yet we generally don’t see most people as weird and obvious mutants. That variation is largely invisible, or represents mere minor variations that we don’t regard as at all remarkable. How can that be?
One important reason is that most traits are not the product of single genes, but of combinations of genes working together in complex ways. The unit producing the phenotype is most often a network of genes and gene products, such at this lovely example of the network supporting expression and regulation of the epidermal growth factor (EGF) pathway.
That is awesomely complex, and yes, if you’re a creationist you’re probably wrongly thinking there is no way that can evolve. The curious thing is, though, that the more elaborate the network, the more pieces tangled into the pathway, the smaller the effect of any individual component (in general, of course). What we find over and over again is that many mutations to any one component may have a completely indetectable effect on the output. The system is buffered to produce a reliable yield.
This is the way networks often work. Consider the internet, for example: a complex network with many components and many different routes to get a single from Point A to Point B. What happens if you take out a single node, or even a set of nodes? The system routes automatically around any damage, without any intelligent agency required to consciously reroute messages.
But further, consider the nature of most mutations in a biological network. Simple knockouts of a whole component are possible, but often what will happen are smaller effects. These gene products are typically enzymes; what happens is a shift in kinetics that will more subtly modify expression. The challenge is to measure and compute these effects.
Graph analysis is showing how networks can be partitioned and analysed, while work on the kinetics of networks has shown first that it is possible to simplify the mathematics of the differential equation models and, second, that the detailed output of a network is relatively insensitive to changes in most of the reaction parameters. What this latter work means is that most gene mutations will have relatively minor effects on the networks in which their proteins are involved, and some will have none, perhaps because they are part of secondary pathways and so redundant under normal circumstances. Indirect evidence for this comes from the surprising observation that many gene knockouts in mice result in an apparently normal phenotype. Within an evolutionary context, it would thus be expected that, across a population of organisms, most
mutations in a network would effectively be silent, in that they would give no selective advantage under normal conditions. It is one of the tasks of systems biologists to understand how and where mutations can lead to sufficient variation in networks properties for selection to have something on which to act.
Combine this with population effects. The population can accumulate many of these sneaky variants that have no significant effect on most individuals, but under conditions of strong selection, combinations of these variants, that together can have detectable effects, can be exposed to selection.
Think flexible genes
Another factor in this process (one that Bard does not touch on) is that the individual genes themselves are not invariant units. Mutations can affect how genes contribute to the network, but in addition, the same allele can have different consequences in different genetic backgrounds — it is affected by the other genes in the network — and also has different consquences in different external environments.
Everything is fluid. Biology isn’t about fixed and rigidly invariant processes — it’s about squishy, dynamic, and interactive stuff making do.
Now do you see what’s wrong with the simplistic caricature of evolution at the top of this article? It’s superficial; it ignores the richness of real biology; it limits and constrains the potential of evolution unrealistically. The concept of evolution as a change in allele frequencies over time is one small part of the whole of evolutionary processes. You’ve got to include network theory and gene and environmental interactions to really understand the phenomena. And the cool thing is that all of these perspectives make evolution an even more powerful force.
Bard J (2010) A systems biology view of evolutionary genetics. Bioessays 32: 559-563.
I’m one of those dreadful animal-centric zoologically inclined biologists. Plants? What are those? Fungi? They’re related to metazoans somehow. Lichens? Not even on the radar. The first step in fixing a problem, though, is recognizing that you have one. So I confess to you, O Readers, that my name is PZ, and I am a metazoaphile. But I can get better.
My path to opening up to wider horizons is to focus on what I find most interesting about animals, and that is that they are networks of cells driven by networks of genes that generate patterned responses of expression by cell signaling, or communication. See? I’m already a little weird. Show me a baby bunny, and I don’t just see a cute little furry pal with an adorable twitchy nose, I see an organized and coherent array of differentiated tissues that arose by a temporal sequence of cell-cell interactions, and I just wanna open him up and play with his widdle epithelial sheets and dismantle his pwetty ducts and struts and fibers and fluids, oochy coo. And ultimately, I want to take apart each cell and ask why it has its particular assortment of genes switched off and on, and how its state affects its neighbors and the whole of the organism.
Which means, lately, that I’ve acquired a growing interest in bacteria. If I were 30 years younger, I could probably be seduced into a career in microbiology.
There are a couple of reasons why an animal-centric biologist would be interested in bacteria. One is the principle of it; the mechanisms that animal cells use to build complex arrangements of tissues were all first pioneered in single-celled organisms. We have elaborated and added details to gene- and cell-level phenomena, but it’s a collection of significant quantitative differences, with nothing known that is essentially new in metazoan cells. All the cool stuff was worked out by evolution in the 3-4billion years before the Cambrian, a potential that simply blossomed in the past half-billion years into big conglomerations of cells. Understanding how the building blocks of multicellularity work individually ought to be a prerequisite to understanding how the assemblages work.
But there’s another reason, too, a difference in perspective. It is our conceit to regard ourselves as individuals of Homo sapiens, a body of cells clonally derived from a single human cell. It’s not true. It turns out that each one of us is actually a whole population of species, linked by our evolutionary history and lumbering through the world as a team. Genus Homo is also genera Escherichi and Bacteroidetes and Firmicutes and many others.
Physiology
Let’s begin with the most widely known factor: we’re mostly bacterial in cell numbers, with about ten times as many bacterial cells as human cells. Most of these are nestled deep in our guts, where they are indispensible. In mammals, they help break down complex polysaccharides which we can then absorb through the wall of the digestive tract — these are compounds that would be simply lost without bacterial assistance. Even more dramatically, termite guts contain colonies of bacteria that produce enzymes to break down cellulose. Another insect, aphids, live in plant saps which have negligible protein components, and they rely on gut bacteria that can synthesize nine essential amino acids. One cool feature is that the bacteria can’t complete the synthesis of leucine; the last step is carried out by aphid enzymes. The synthetic pathway is split acros two different species!
Another weird twist is that gut bacteria can affect morphology (or vice versa; physiology influences which gut bacteria thrive). Mice with a genetic predisposition to obesity were found to have a different distribution of gut bacteria; fat mice are full of Firmicutes, while lean mice are loaded with Bacteroidetes. Something in the genetics of the obese mice seems to favor the proliferation of that one species. Cause and effect is not so easily separated, though, since doing a fecal transplant and inoculating the guts of germ free mice with the bacteria from obese mice vs. lean mice has a surprising effect: the mice given obese mouse fecal enemas subsequently increased their body fat by 60%. The bacteria promoted more fat storage in the host animal.
So what, you may be thinking, it’s mice. However, it turns out that obese humans tend to have reduced amounts of Bacteroidetes species in their guts than lean people, and weight loss is accompanied by an increase in Bacteroidetes. Fecal transplants are not recommended as a weight loss technique…at least not yet.
They have worked for some other problems. Crohn’s disease and ulcerative colitis are diseases that involve intestinal inflammation, and they’re also associated with imbalances in the species distribution of gut bacteria. Some promising treatments have involved collecting feces from healthy individuals, and using a nasogastric tube to inoculate the guts of Crohn’s patients with the stuff. Ick, I know, but it seems to have worked surprisingly well in a small number of patients.
Development
Bacteria are present in the gut from a very early age, and populate the digestive epithelia. There must be interactions going on, and it appears that the bacteria are actually regulating the growth of the gut lining.
Germ-free zebrafish lines have no gut bacteria, and they also have problems. The intestinal lining arrests its development and fails to fully differentiate; the lining also grows much more slowly. They also have difficulty absorbing some nutrients. Add bacteria, though, and growth and differentiation resume. This is a case where the developmental program and the bacterial influences are interdependent, and it makes sense — they’ve co-evolved.
It’s not just fish, either — these are conserved interactions across the vertebrates. Mice exhibit the same dependence on gut flora for development of the intestinal lining.
The very best example of a developmental dependence on bacteria, though, is in squid. The bobtail squid has a light-emitting organ that relies on colonization by a luminescent bacterium, Vibrio fischeri. The animal gleans the bacteria from the water with a special ciliated epithelium and secreted mucus that seems to be just the right flavor for Vibrio, and the bacteria migrate deep into the light-emitting organ. Once colonized, the squid dismantles the harvesting cilia and downregulates the secretion of mucus. If no bacteria of the right species are present, it maintains the cilia. If the bacteria in the organ die, resumes mucus production.
Evolution
If something affects development and physiology, it affects evolution, so evolutionary importance is simply rather unavoidable. However, there’s also one somewhat surprising observation (to me, at least — microbiologists probably expect it): different species of related organisms can have different microbial populations, even when raised in identical conditions. Different Hydra species in the lab under controlled conditions have recognizably different populations of bacteria living on their epithelia, and Hydra of the same species collected in the wild have similar distributions of species. The properties of each Hydra species uniquely favor different distributions of bacteria, and the bacteria are also preferentially colonizing particular species of Hydra.
Hydra are wonderful experimental animals in that one can ablate stem cells for a particular tissue type, and still get an animal that develops and lives; do the same thing to a vertebrate, for instance knocking out the mesodermal lineage in the embryo, and you get an aborted blob. In Hydra, you get a tissue that survives and is colonized by bacteria…but the kinds of bacteria populating it is different from the populations in the intact animal. The animal and the bacteria are swapping molecular signals that specify favored relationships. Again, these are coevolved populations that recognize molecular properties of the host and symbiont.
This is all getting very complicated. I’m used to thinking in terms of networks of genes: there are regulatory interactions between genes in a single cell that establish cell-type specific patterns of gene activity; all express a common core of genes, but different cell types, such as a neuron vs. a cell of the digestive epithelia, will also have their own unique special-purpose genes switched on. I’m also comfortable thinking of networks of cells: cells are in constant negotiations with their neighbors, mainting a common pattern of expression within a tissue, and defining interacting edges with other tissues. Cells are continually sending out messages about their state into the system and responding to local and global signals. All this is part of the normal process of thinking developmentally.
Now, though, there’s another layer: we have to think in terms of networks of species that cooperate in the development and physiology of individual multi-cellular organisms. Purity is compromised. My precious animalia — they’re inconceivable without bringing bacteria into the picture.
Fraune S, Bosch TCG (2010) Why bacteria matter in animal development and evolution. Bioessays 32:571-580.
Larry Arnhart wrote a strange article in which he tried to claim Darwin and evolution for libertarianism, or as they prefer to call it nowadays, “Classical liberalism”. I was invited to give a reply, along with a few other people, but I can give the gist of my reaction here: no one gets to claim a biological justification for their political philosophy. Evolution does not endorse libertarianism, socialism, communism, or capitalism, and even if it did nudge one way or the other, that does not mean that we shouldn’t oppose the brutal short-term expediency of natural processes.
Who remembers Robert Ardrey? I must shamefully confess that I was a fan back in the 1970s, when the ‘killer ape’ hypothesis was in the air. This was the idea that one of the things that made humans different and drove the evolution of the human brain was aggression and competition, specifically that big brains evolved as a weapon in a multi-million year intra-specific arms race. Arms races are cool concepts that, when first introduced to natural selection, seem like powerful mechanisms to drive the evolution of elaborate features.
I outgrew Ardrey, have no fear. As I learned more biology, I came to mistrust those umbrella hypotheses that appeared to explain everything with one simple premise — biology is complicated, and rarely fits into those simple categories. I also began to get suspicious of poorly credentialed popularizers who too often seemed most adept at twisting research to fit whatever hobby horse they were riding at the time (see also Elaine Morgan).
But mostly, it didn’t fit what we see in the real world. Think about your interactions with other human beings: probably, unless you’re in the military, the most ferociously antagonistic conflicts you encounter involve commenting on Pharyngula. The internet has a reputation for being contentious, but get real: it’s slinging words back and forth, feelings might get hurt, but the consequences to your survival and mating prospects are very, very low. You could even argue that most of the jostling isn’t about destroying our enemies, but about increasing in-group solidarity.
It’s even more true outside the abstract world of the internet. Most of our interactions with other people are regulated by deep-down protocols that we’re socialized into — if someone cuts into a queue ahead of you, we don’t pull out a stone axe and take care of the problem, we either roll our eyes and acquiesce or we complain verbally and get other people to shame the interloper. It’s relatively harmless. We go to work, and maybe you share an office with annoying jerks (of course you do, we all do), but we don’t go on a rampage and fight the boss for dominance, so we can purge the tribe of the ones we detest, who borrow our stapler and don’t give it back — no, we grumble and accommodate and cope somehow, and maybe try to work our way into a better position with social networking.
It’s what we are. We are social animals. In the history of our species, I think the most important signature of our evolutionary history is the construction of cohesive social units, groups larger than the individual, that allow us to survive better together than alone. We aren’t warring animals, we’re cooperative animals.
War is a byproduct. We’ve evolved and enculturated mechanisms that allow groups of increasingly larger size to persevere — a paleolithic tribe of 30 people is one thing, a nation of hundreds of millions or billions is another — and war occurs when these groups bump into one another. War is not the central activity of the human species, though, but a fringe event, a side-effect of processes that reinforce group cohesion and secondarily create friction when diverse social units encounter one another and demand responses that aren’t so strongly reinforced within our groups.
Anyway, that long ramble is an introduction to an interesting article on the history of war in primates. It’s just not that common in our closest relatives, the chimpanzees, and recent accounts of coordinated gang attacks may be unusual responses to high environmental stresses. This is not a good time to be a chimpanzee.
There are known examples of death by lethal tools in the human archaeological record, and this isn’t an argument that everyone since the dawn of time has been living in peaceful coexistence, but the documentable evidence of war is very thin. People are lovers, not killers.
Archaeologist Jonathan Haas of the Field Museum in Chicago concurs: “There is a very tiny handful of incidences of conflict and possible warfare before 10,000 years ago. And those are very much the exception.” In an interview with me he attributed the emergence of warfare in prehistory to growing population density, diminished food sources and the separation of people into culturally distinct groups. “It is only after the cultural foundations have been laid for distinguishing ‘us’ from ‘them,'” Haas says, “that raiding, killing and burning appear as a complex response to the external stress of environmental problems.”
On the other hand, Haas adds, “groups that are at war in one era or generation may be at peace in the next.” War’s recent emergence, and its sporadic pattern, contradict the assertion of Wrangham and others that war springs from innate male tendencies, he argues. “If war is deeply rooted in our biology, then it’s going to be there all the time. And it’s just not,” he says. War is certainly not as innate as language, a trait possessed by all known human societies at all times.
That’s the good news: warfare is a pathological condition for our species, brought on by external stress. We are not biologically committed to fighting one another.
The bad news is that external stresses are growing, with increasing demands for oil and water and food, with looming climate change likely to disrupt environmental stability, and with overpopulation increasing the friction within and between groups. Brace yourselves.
I might suggest, though, that the greatest human successes have arisen from developing tools to strengthen social unity and encourage competition — killing your neighbors is a sign of failure.
I use a very pretty radial tree of life diagram fairly often — the last time was in my talk on Friday — and every time I do, people ask where I got it. Here it is: it’s from the David Hillis lab, with this description:
This file can be printed as a wall poster. Printing at least 54″ wide is recommended.
(If you would prefer a simplified version with common names, please see below.)
Blueprint shops and other places with large format printers can print this file for you.
You are welcome to use it for non-commercial educational purposes.
Please cite the source as David M. Hillis, Derrick Zwickl, and Robin Gutell, University of Texas.
About this Tree: This tree is from an analysis of small subunit rRNA sequences sampled
from about 3,000 species from throughout the Tree of Life. The species were chosen based
on their availability, but we attempted to include most of the major groups, sampled
very roughly in proportion to the number of known species in each group (although many
groups remain over- or under-represented). The number of species
represented is approximately the square-root of the number of species thought to exist on Earth
(i.e., three thousand out of an estimated nine million species), or about 0.18% of the 1.7 million
species that have been formally described and named. This tree has been used
in many museum displays and other educational exhibits, and its use for educational purposes
is welcomed.
There’s also a simplified version:
Both of those are available as scalable pdfs, so you can zoom in and out to get just the right view, which is very handy.
Jonathan Sarfati, a particularly silly creationist, is quite thrilled — he’s crowing about how he has caught Richard Dawkins in a fundamental error. The eye did not evolve, says Sarfati, because it is perfectly designed for its function, and Dawkins’ suggestion that there might be something imperfect about it is wrong, wrong, wrong. He quotes Dawkins on the eye.
But I haven’t mentioned the most glaring example of imperfection in the optics. The retina is back to front.
Imagine a latter-day Helmholtz presented by an engineer with a digital camera, with its screen of tiny photocells, set up to capture images projected directly on to the surface of the screen. That makes good sense, and obviously each photocell has a wire connecting it to a computing device of some kind where images are collated. Makes sense again. Helmholtz wouldn’t send it back.
But now, suppose I tell you that the eye’s ‘photocells’ are pointing backwards, away from the scene being looked at. The ‘wires’ connecting the photocells to the brain run over all the surface of the retina, so the light rays have to pass through a carpet of massed wires before they hit the photocells. That doesn’t make sense…
What Dawkins wrote is quite correct, and nowhere in his refutation does Sarfati show that he is wrong. Instead, Sarfati bumbles about to argue against an argument that no biologist makes, that the eye is a poor instrument for detecting patterns of light. The argument is never that eyes do their job poorly; it’s that they do their job well, by a peculiar pattern of kludgy patches to increase functionality that bear all the hallmarks of a long accumulation of refinements. Sarfati is actually supporting the evolutionary story by summarizing a long collection of compromises and odd fixes to improve the functionality of the eye.
There’s a fundamental question here: why does the vertebrate eye have its receptors facing backwards in the first place? It is not the best arrangement optically; Sarfati is stretching the facts to claim that God designed it that way because it was superior. It ain’t. The reason lies in the way our eye is formed, as an outpocketing of the cortex of the brain. It retains the layered structure of the cortex, even; it’s the way it is because of how it was assembled, not because its origins are rooted in optical optimality. You might argue that it’s based on a developmental optimum, that this was the easiest, simplest way to turn a light-sensitive patch into a cup-shaped retina.
Evolution has subsequently shaped this patch of tissue for better acuity and sensitivity in certain lineages. That, as I said, is a product of compromises, not pre-planned design. Sarfati brings up a series of odd tweaks that make my case for me.
The vertebrate photoreceptors are nourished and protected by an opaque layer called the retinal pigmented epithelium (RPE). Obviously, you couldn’t put the RPE in front of the visual receptors, so the retina had to be reversed to allow it to work. This is a beautiful example of compromise: physiology is improved at the expense of optical clarity. This is exactly what the biologists have been saying! Vertebrates have made a trade-off of better nutrient supplies to the retina for a slight loss of optical clarity.
Sarfati makes the completely nonsensical claim that the presence of blood vessels, cells, etc. in the light path do not compromise vision at all because resolution is limited by diffraction at the pupil, so “improvements of the retina would make no difference to the eye’s performance”. This is clearly not true. The fovea of the vertebrate eye represents an optimization of a small spot on the retina for better optical properties vs. poorer circulation: blood vessels are excluded from the fovea, which also has a greater density of photoreceptors. Obviously, improvements to the retina do make a difference.
It’s also not a condition that is universal in all vertebrates. Most birds, for instance, do not have a vascularized retina — there is no snaky pattern of blood vessels wending their way across the photoreceptors. Birds do have greater acuity than we do, as well. What birds have instead is a strange structure inside their eye called the pecten oculi, which looks kind of like an old steam radiator dangling into the vitreous humor, which seems to be a metabolic specialization to secrete oxygen and nutrients into the vitreous to supply by diffusion the retina.
Sarfati also plays rhetorical games. This is a subtly dishonest argument:
In fact, cephalopods don’t see as well as humans, e.g. no colour vision, and the octopus eye structure is totally different and much simpler. It’s more like ‘a compound eye with a single lens’.
First, there’s a stereotype he’s playing to: he’s trying to set up a hierarchy of superior vision, and he wants our god-designed eyes at the top, so he tells us that most cephalopods have poorer vision than we do. He doesn’t bother to mention that humans don’t have particularly good vision ourselves; birds have better eyes. So, is God avian?
That business about the cephalopod being like a compound eye is BS; if it’s got a single lens, it isn’t a compound eye, now is it? It’s also again pandering to a bias that our eyes must be better than mere compound eyes, since bugs and other lowly vermin have those. Cephalopods have rhabdomeric eyes, meaning that their photoreceptors have a particular structure and use a particular set of biomolecules in signal transduction, but that does not in any way imply that they are inferior. In fact, they have some superior properties: the cephalopod retina is tightly organized and patterned, with individual rhabdomeres ganged together into units called rhabdomes that work together to process light. Their ordered structure means that cephalopods can detect the polarity of light, something we can’t do at all. This is a different kind of complexity, not a lesser one. They can’t see color, which is true, but we can’t sense the plane of polarity of light in our environment.
I must also note that the functions of acting as a light guide (more below) and using pigment to shield photoreceptors are also present in the cephalopod eye…only by shifting pigments in supporting cells that surround the rhabdome, rather than in a solid RPE. Same functions, different solutions, the cephalopod has merely stumbled across a solution that does not simultaneously impede the passage of light.
Color vision, by the way, is a red herring here. Color is another compromise that has nothing to do with the optical properties of the arrangement of the retina, but is instead a consequence of biochemical properties of the photoreceptors and deeper processing in the brain. If anything, color vision reduces resolution (because individual photoreceptors are tuned to different wavelengths) and always reduces sensitivity (you don’t use color receptors at night, you may have noticed, relying instead on rods that are far less specific about wavelength). But if he insists, many teleosts have a greater diversity of photopigments and can see colors we can’t even imagine…so humans are once again also-rans in the color vision department.
Sarfati is much taken with the discovery that some of the glial cells of the eye, the Müller cells, act as light guides to help pipe light through the tangle of retinal processing cells direct to the photoreceptors. This is a wonderful innovation, and it is entirely true that in principle this could improve the sensitivity of the photoreceptors. But again, this would not perturb any biologist at all — this is what we expect from evolution, the addition of new features to overcome shortcomings of original organization. If we had a camera that clumsily had the non-optical parts interposed between the lens and the light sensor, we might be impressed with the blind, clumsy intricacy of a solution that involved using an array of fiber optics to shunt light around the opaque junk, but it wouldn’t suggest that the original design was particularly good. It would indicate short-term, problem-by-problem debugging rather than clean long-term planning.
Sarfati cannot comprehend why the blind spot would be a sign of poor design, either. He repeats himself: why, it’s because the eye needs a blood supply. Yes, it does, and the solution implemented in our eyes is one that compromises resolution. I will again point out that the cephalopod retina also needs a blood supply, and they have a much more elegant solution; the avian eye also needs a blood supply, but is not invested with blood vessels. He gets very circular here. The argument is not that the vertebrate eye lacks a solution to this problem, but that there are many different ways to solve the problem of organizing the retina with its multiple demands, and that the vertebrate eye was clearly not made by assembling the very best solutions.
Sarfati really needs to crawl out of his little sealed box of creationist dogma and discover what scientists actually say about these matters, and not impose his bizarre creationist interpretations on the words of people like Dawkins and Miller. What any comparative biologist can see by looking at eyes across multiple taxa is that they all work well enough for their particular functions, but they all also have clear signs of assembly by a historical process, like evolution and quite unlike creation, and that there is also evidence of shortcomings that have acquired workarounds, some of which are wonderfully and surprisingly useful. What we don’t see are signs that the best solutions from each clade have been extracted and placed together in one creature at the pinnacle of creation. And in particular — and this has to be particularly grating to the Genesis-worshipping creationists of Sarfati’s ilk, since he studiously avoids the issue — Homo sapiens is not standing alone at that pinnacle of visual excellence. We’re kinda straggling partway down the peak, trying to compensate for some relics of our ancestry, like the fact that we’re descended from nocturnal mammals that let the refinement of their vision slide for a hundred million years or thereabouts, while the birds kept on optimizing for daylight acuity and sensitivity.
Their project in bacterial evolution in the lab has reached the milestone of 50,000 generations.
Andy Schlafly has still got nothin’.
Only ours are methodologically valid. It’s a common creationist tactic to fling around big numbers to ‘disprove’ evolution: for instance, I’ve had this mysterious Borel’s Law (that anything with odds worse than 1 in 1050 can never happen) thrown in my face many times, followed by the declaration that the odds of the simplest organism forming by chance are 1 in 10340,000,000. It’s complete nonsense, of course — their calculations all ignore the reality of the actual events, assuming that everything must form spontaneously and all at once, which is exactly the opposite of how probability plays a role in evolution. It’s annoying and inane, and the creationists never seem to learn…perhaps because the rubes they pander to are easily dazzled by even bogus mathematics, so they keep doing it.
We’re going to have to start firing back. Doug Theobald, a long-time contributor to Talk.Origins and the Panda’s Thumb, has written a very nice paper testing the likelihood that all life on earth is not related by common descent, and he comes up with some numbers of many digits to support evolutionary theory. Nick Matzke has a summary, and the story has been written up for National Geographic.
Basically, the idea is this: take a small set of known, conserved proteins that are shared in all organisms, not restricting ourselves to one kingdom or one phylum, but grabbing them all. In this paper, that data set consists of 23 proteins from 12 taxa in the Big Three domains: Bacteria, Archaea, and Eukarya. Then set up many different models to explain the relationships of these species. For instance, you could organize them into the classic single tree, where all are related, or you could model them as three independent origins, for each of Bacteria, Archaea, or Eukarya, or you could postulate other combinations, such as that Bacteria arose independently of Archaea and Eukarya, which share a common ancestor. Finally, you tell your computer to do a lot of statistics on the models, asking how likely it is that two independent groups would each arrive at similar sequences, rating each of the models for parsimony and accuracy against the evidence.
And the winner is…common ancestry, with one branching tree! This is what we expected, of course, and what Theobald has done is to test our assumptions, always a good thing to do.
More complicated permutations of these models were also tried. What if there were a significant amount of horizontal gene transfer? Would that make multiple origins of modern life more likely? He was testing models like the ones below, where the dotted lines represent genes that leap across taxa to confuse the issue.
The answer here is that they don’t. These models can also be evaluated by statistical methods, and the best fit is again the one on the right, with a single ancestral root. People might recall the infamous “Darwin was wrong” cover from New Scientist—well, these results say that New Scientist was wrong, the existence of extensive horizontal gene transfer does not negate the fact of common descent.
So what’s the big number? There are lots of them in the paper, since it evolves many comparisons, but Theobald distills it down to just the odds that bacteria have an independent origin from Archaea and eukaryotes:
But, based on the new analysis, the odds of that are “just astronomically enormous,” he said. “The number’s so big, it’s kind of silly to say it”–1 in 10 to the 2,680th power, or 10 followed by 2,680 zeros.
One in 102680? Hey, aren’t those odds a little worse than Borel’s criterion of one in 1050?
Stay tuned to the Panda’s Thumb. Apparently, once he finishes up the trifling business of wrapping up a semester’s teaching, Theobald will be putting up a synopsis of his own and answering questions online.
Theobald D (2010) A formal test of the theory of universal common ancestry. Nature 465(13):219-222.
You don’t have to tell me, I know I’m late to the party: the news about the draft Neandertal genome sequence was announced last week, and here I am getting around to it just now. In my defense, I did hastily rewrite one of my presentation to include a long section on the new genome information, so at least I was talking about it to a few people. Besides, there is coverage from a genuine expert on Neandertals, John Hawks, and of course Carl Zimmer wrote an excellent summary. All I’m going to do now is fuss over a few things on the edge that interested me.
This was an impressive technical feat. The DNA was extracted from a few bone fragments, and it was grossly degraded: the average size of a piece of DNA was less than 200 base pairs, much of that was chemically degraded, and 95-99% of the DNA extracted was from bacteria, not Neandertal. An immense amount of work was required to filter noise from the signal, to reconstruct and reassemble, and to avoid contamination from modern human DNA. These poor Neandertals had died, had rotted thoroughly, and the bacteria had worked their way into almost every crevice of the bone to chew up the remains. All that was left were a few dead cells in isolated lacunae of the bone; their DNA had been chopped up by their own enzymes, and death and chemistry had come to slowly break them down further.
Don’t hold your breath waiting for the draft genome of Homo erectus. Time is unkind.
We have to appreciate the age of these people, too. The oldest Neandertal fossils are approximately 400,000 years old, and the species went extinct about 30,000 years ago. That’s a good run; as measured by species longevity, Homo sapiens neandertalensis is more successful than Homo sapiens sapiens. We’re going to have to hang in there for another 200,000 years to top them.
The samples taken were from bones found in a cave in Vindija, Croatia. Full sequences were derived from these three individuals, and in addition, some partial sequences were taken from other specimens, including the original type specimen found in the Neander Valley in 1856.
The three bones used for sequencing were directly dated to 38.1, 44.5, and 44.5 thousand years ago, which puts them on the near end of the Neandertal timeline, and after the likely time of contact between modern humans and Neandertals, which probably occurred about 80,000 years ago, in the Middle East.
Just for reference: these samples are 6-7 times older than the entire earth, as dated by young earth creationists. The span of time just between the youngest and oldest bones used is more than six thousand years old, again, about the same length of time as the YEC universe. Imagine that: we see these bone fragments now as part of a jumble of debris from one site, but they represent differences as great as those between a modern American and an ancient Sumerian. I repeat once again: the religious imagination is paltry and petty compared to the awesome reality.
A significant revelation from this work is the discovery of the signature of interbreeding between modern humans and Neandertals. When those humans first wandered out of the homeland of Africa into the Middle East, they encountered Neandertals already occupying the land…people they would eventually displace, but at least early on there was some sexual activity going on between the two groups, and a small number of human-Neandertal hybrids would have been incorporated into the expanding human population—at least, in that subset that was leaving Africa. Modern European, Asian, and South Pacific populations now contain 1-4% Neandertal DNA. This is really cool; I’m proud to think that I had as a many-times-great grandparent a muscular, beetle-browed big game hunter who trod Ice Age Europe, bringing down mighty mammoths with his spears.
However, it is a small contribution from the Neandertals to our lineage, and it’s not likely that these particular Neandertal genes made a particularly dramatic effect on our ancestors. They didn’t exactly sweep rapidly and decisively through the population; it’s most likely that they are neutral hitch-hikers that surfed the wave of human expansion. Any early matings between an expanding human subpopulation and a receding Neandertal population would have left a few traces in our gene pool that would have been passively hauled up into higher numbers by time and the mere growth of human populations. In a complementary fashion, any human genes injected into the Neandertal pool would have been placed into the bleeding edge of a receding population, and would not have persevered. No uniquely human genes were found in the Neandertals examined, but we can’t judge the preferred direction of the sexual exchanges in these encounters, though, because any hybrids in Neandertal tribes were facing early doom, while hybrids in human tribes were in for a long ride.
Here’s the interesting part of these gene exchanges, though. We can now estimate the ancestral gene sequence, that is, the sequences of genes in the last common ancestor of humans and Neandertals, and we can ask if there are any ‘primitive’ genes that have been completely replaced in modern human populations by a different variant, but Neandertal still retained the ancestral pattern (see the red star in the diagram below). These genes could be a hint to what innovations made us uniquely human and different from Neandertals.
There’s good news and bad news. The bad news is that there aren’t very many of them: a grand total of 78 genes were identified that have a novel form and that have been fixed in the modern human population. That’s not very many, so if you’re an exceptionalist looking for justification of your superiority to our ancestors, you haven’t got much to go on. The good news, though, is that there are only 78 genes! This is a manageable number, and represent some useful hints to genes that would be worth studying in more detail.
One other qualification, though: these are 78 genes that have changes in their coding sequence. There are also several hundred other non-coding, presumably regulatory, sequences that are unique to humans and are fixed throughout our population. To the evo-devo mind, these might actually be the more interesting changes, eventually…but right now, there are some tantalizing prospects in the coding genes to look at.
Some of the genes with novel sequences in humans are DYRK1A, a gene that is present in three copies in Down syndrome individuals and is suspected of playing a role in their mental deficits; NRG3, a gene associated with schizophrenia, and CADPS2 and AUTS2, two genes associated with autism. These are exciting prospects for further study because they have alleles unique and universal to humans and not Neandertals, and also affect the functioning of the brain. However, let’s not get confused about what that means for Neandertals. These are genes that, when broken or modified in modern humans, have consequences on the brain. Neandertals had these same genes, but different forms or alleles of them, which are also different from the mutant forms that cause problems in modern humans. Neandertals did not necessarily have autism, schizophrenia, or the minds of people with Down syndrome! The diseases are just indications that these genes are involved in the nervous system, and the differences in the Neandertal forms almost certainly caused much more subtle effects.
Another gene that has some provocative potential is RUNX2. That’s short for Runt-related transcription factor 2, which should make all the developmental biologists sit up and pay attention. It’s a transcription factor, so it’s a regulator of many other genes, and it’s related to Runt, a well known gene in flies that is important in segmentation. In humans, RUNX2 is a regulator of bone growth, and is a master control switch for patterning bone. In modern humans, defects in this gene lead to a syndrome called cleidocranial dysplasia, in which bones of the skull fuse late, leading to anomalies in the shape of the head, and also causes characteristic defects in the shape of the collar bones and shoulder articulations. These, again, are places where Neandertal and modern humans differ significantly in morphology (and again, Neandertals did not have cleidocranial dysplasia — they had forms of the RUNX2 gene that would have contributed to the specific arrangements of their healthy, normal anatomy).
These are tantalizing hints to how human/Neandertal differences could have arisen—by small changes in a few genes that would have had a fairly extensive scope of effect. Don’t view the many subtle differences between the two as each a consequence of a specific genetic change; a variation in a gene like RUNX2 can lead to coordinated, integrated changes to multiple aspects of the phenotype, in this case, affecting the shape of the skull, the chest, and the shoulders.
This is a marvelous insight into our history, and represents some powerful knowledge we can bring to bear on our understanding of human evolution. The only frustrating thing is that this amazing work has been done in a species on which we can’t, for ethical reasons, do the obvious experiments of creating artificial revertants of sets of genes to the ancestral state — we don’t get to resurrect a Neandertal. With the tools that Pääbo and colleagues have developed, though, perhaps we can start considering some paleogenomics projects to get not just the genomic sequences of modern forms, but of their ancestors as well. I’d like to see the genomic differences between elephants and mastodons, and tigers and sabre-toothed cats…and maybe someday we can think about rebuilding a few extinct species.
Green RE, Krause J, Briggs AW, Maricic T, Stenzel U, Kircher M, Patterson N, Li H, Zhai W, Fritz MH, Hansen NF, Durand EY, Malaspinas AS, Jensen JD, Marques-Bonet T, Alkan C, Prüfer K, Meyer M, Burbano HA, Good JM, Schultz R, Aximu-Petri A, Butthof A, Höber B, Höffner B, Siegemund M, Weihmann A, Nusbaum C, Lander ES, Russ C, Novod N, Affourtit J, Egholm M, Verna C, Rudan P, Brajkovic D, Kucan Z, Gusic I, Doronichev VB, Golovanova LV, Lalueza-Fox C, de la Rasilla M, Fortea J, Rosas A, Schmitz RW, Johnson PL, Eichler EE, Falush D, Birney E, Mullikin JC, Slatkin M, Nielsen R, Kelso J, Lachmann M, Reich D, Pääbo S. (2010) A draft sequence of the Neandertal genome. Science 328(5979):710-22.
