PDF - Climbing Mount Improbable. While an enzyme molecule or an eye might seem supremely improbable in their complexity, they are not accidental, nor need . Climbing Mount Improbable by Richard Dawkins. Penguin Books Ltd,. Harmondsworth, Middlesex, England. Reviewed by Jonathan Sarfati. This is the latest in a. 5 days ago Climbing Mount Improbable - [Free] Climbing Mount Improbable [PDF] [EPUB] George Herbert. Leigh Mallory (18 June – 8 or 9 June.
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Library of Congress Cataloging-in-Publicaiion Data. Dawkins, Richard, —. Climbing mount improbable / Richard Dawkins; original drawings by Lalla Ward. most prominent Darwinian of our age. Download Climbing Mount Improbable pdf. Read Online Climbing Mount Improbable pdf. About Books [READ PDF] Climbing Mount Improbable by Richard Dawkins NEW RELEASES: none Creator: Richard Dawkins Best Sellers.
A new book by Richard Dawkins has become a publishing event. And good luck to him. Dawkins has done more than anyone, with the possible exception of Stephen J. Gould, to promote his field. But this popular pinnacle has its burdens, and given the fact that we are talking science, and not fiction responsibilities.
The parables — riveting biological narratives, enthralling as the Arabian Nights tales — continue to ring the changes.
Yet the central message, that DNA transcends the significance of the organism, remains the same. In other words, it is the information contained in genes that is of supreme consequence in the story of life on this planet.
At the heart of the book, however, is a gloss to this oft rehearsed thesis that amounts to a new emphasis rather than an original theme. How does chance operate in the Darwinian algorithm?
But, according to Dawkins, the problem of chance is also baffling to scientists and mathematicians. A farfetched speculation it might be thought and, in any case, so what? The hard, strap-like leaves of some reeds have quite sharp edges. This sharpness is probably an incidental by-product of other properties of the leaf. You can cut yourself on a reed, enough to annoy but not enough for the sharpness to provoke suspicion of design.
No doubt some leaves are sharper than others and you could search the lakeshore for the sharpest reed you could find. Now here is where we part company with stones.
Don't just cut with your reed knife, breed from it.
Or breed from the same plant from which you plucked it. Allow the sharpest plants to cross-pollinate, kill the blunt plants: it doesn't matter how you do it, just see to it that the sharpest plants do most of the breeding. Not once, but generation after generation. As the generations pass you'll notice that there are still bluntish reeds and sharpish reeds around, but the average reed will become steadily sharper.
After generations you'd probably have bred something that would give you a decent close shave. If you bred for rigidity at the same time as breeding for keenness of edge, you could eventually cut your throat with a broken reed.
In a sense you have done no more than find the quality you seek: no carving, whittling, moulding or whetstone grinding, just finding the best of what is already there. Sharp leaves have been found, blunt leaves have been discarded.
It is like the story of finding sharp stones but with one significant addition: the process is cumulative. Stones don't breed whereas leaves, or rather the plants that make leaves, do. Having found the best blade of a generation you don't simply use it until it wears out. You ratchet your gain by breeding from it, transmitting its virtue to the future where it can be built upon. This process is cumulative and never-ending. You are still only finding and finding, but because genetics enables cumulative gain the best specimen you can find in a late generation is better than the best you can find in an earlier generation.
This, as we shall see in Chapter 3, is what Climbing Mount Improbable means. The steadily sharpening reed was an invention to make a point. There are, of course, real examples of the same principle at work. All the plants in Figure 1. It is a rather nondescript plant which doesn't look much like a cabbage.
Humans have taken this wild plant and, over a short period of centuries, shaped it into these really very different kinds of food plants. It is a similar story with dogs Figure 1. Although hybrids between dogs and jackals and between dogs and coyotes do occur, it is now accepted by most authorities that all breeds of domestic dogs are descended from a wolf ancestor top left who lived maybe a few thousand years ago.
It is as though we humans had taken wolf flesh and shaped it like a clay pot. But we didn't, of course, literally knead and press wolf flesh into the shape of, say, a whippet or a dachshund. We did it by cumulative finding, or, as it is more conventionally put, selective breeding or artificial selection.
Whippet-breeders found individuals that looked a little bit more whippet-like than the average. They bred from them, and then found the most whippet-like individuals of the next generation, and so on. Of course it wouldn't have been so relentlessly simple as that, and the breeders wouldn't have had the concept of a modern whippet in their heads as a distant target. Perhaps they just liked the look of the physical characteristics that we would now recognize as whippet-like, or perhaps those visible qualities came along as a by-product of breeding for something else, like proficiency in hunting rabbits.
But whippets and dachshunds, Great Danes and bulldogs, were made by a process that resembled finding more than it resembled clay-modelling. Yet it still is not the same as pure finding, because it is cumulative over generations. That is why I call it cumulative finding.
Accidental objects are simply found. Designed objects aren't found at all, they are shaped, moulded, kneaded, assembled, put together, carved: in one way or another the individual object is pushed into shape. Designoid objects are cumulatively found, either by humans as in the case of domestic dogs and cabbages, or by nature in the case of, say, sharks. The fact of heredity sees to it that the accidental improvements found in each generation are accumulated over many generations.
At the end of many generations of cumulative finding, a designoid object is produced which may make us gasp with admiration at the perfection of its apparent design. But it is not real design, because it has been arrived at by a completely different process.
Dog generation times are a little shorter than ours but, even so, it takes more than a human lifetime to propel dog evolution to any notable extent. Humans have bred chihuahuas in about a ten-thousandth of the time it took nature to breed wolves from their chihuahua-sized though not chihuahua-shaped , insectivorous ancestors who lived back when the dinosaurs died out.
Even so, artificial selection of real, living creatures — at least creatures bigger than bacteria — is too slow to make an impressive demonstration for impatient and short-lived humans. You can speed the process no end with a computer.
Computers, whatever their faults, are blindingly fast, and they can simulate anything that can be precisely defined, which includes reproductive processes like those of animals and plants. If you simulate heredity, that most basic condition for life, and provide for occasional random mutation, it is truly startling what carl evolve before your eyes in a few hundred generations of selective breeding.
I pioneered this approach in my book The Blind Watchmaker, using a computer program of the same name. With this program you can breed, by artificial selection, creatures called computer biomorphs. Computer biomorphs are all bred from a common ancestor that looks like this ; in very much the same sense as all the breeds of dogs were bred from a wolf. This needs some explanation. They are basically built as a branching tree, or a segmental series of such trees joined up to one another.
Genes in real trees, like genes in us and genes in bacteria, are coded messages written in the language of DNA. The DNA is copied from generation to generation with great, though not perfect, fidelity. Biomorph genes are not made of DNA but this difference is trivial for our purposes.
When a biomorph has a child, the child inherits all the genes of its parent it only has the one parent, for there is no sex , but with some possibility of random mutation. A mutation is a slight random increase or decrease in the numerical value of a gene. So a child might be like its parent but with a slightly steeper angle to one of its branches because the numerical value of its Gene 6 has increased from 20 to When in biomorph breeding mode, the computer draws a biomorph in the centre of the screen, surrounded by a litter of randomly mutated offspring.
Because their genes have changed only slightly, the offspring always bear a family resemblance to the parent, and to each other, but they often display slight differences that the human eye can detect. As generations go by, the selector can guide evolution in very much the same way as humans guided the evolution of domestic dogs, but much faster.
One of the things that surprised me when I first wrote the program was how quickly you could evolve away from the original tree shape.
Each one of the biomorphs in Figure 1. Because the creatures breed in a computer, you can whistle through many generations of evolution in a matter of minutes.
A few minutes of playing with this program on a modern, fast computer gives you a hands-on, vivid feeling for how Darwinian selection works. The biomorphs in the safari park of Figure 1. They are pretty close cousins, at that. All have the same number of genes sixteen. They differ only in the numerically coded values of those genes. You could go from any creature in the safari park to any other, or to any of trillions of other biomorphs, simply by selective breeding.
The most recent version of the program can breed biomorphs that vary also in colour. There are also new genes determining whether each branch of the tree is a line or a rectangle or an oval, whether the shape concerned is filled or empty, and how thickly the lines are painted. When using the colour program I find myself following evolutionary alleys not towards insects and scorpions but towards flowers and the sort of abstract patterns that might look good on wallpaper or bathroom tiles Figure 1.
My wife, Lalla Ward, has embroidered four of these biomorphs as chair covers, taking precisely one needlepoint stitch for each computer pixel. In this respect they are like cabbages or pedigree dogs. But artificial selection requires a human chooser and is not the main subject of this book. Following Darwin himself, I am using artificial selection as a model for a different process: natural selection. Finally, the time has come to speak of natural selection itself.
Natural selection is like artificial selection, but without the human chooser. The quotation marks are vital because nature doesn't consciously decide. This might seem too obvious to emphasize, but you'd be surprised by the number of people who think natural selection implies some kind of personal choice.
They couldn't be more wrong. It just is the case that some offspring are more likely to die while others have what it takes to survive and reproduce. Therefore, as the generations go by, the average, typical creature in the population becomes ever better at the arts of surviving and reproducing. Ever better, I should specify, when measured against some absolute standard. Not necessarily more effective in practice because survival is continually menaced by other creatures who are also evolving and perfecting their arts.
A species may get progressively better at the art of avoiding predators but, since predators are concurrently getting better at the art of catching prey, there may be no net gain. Artificial selection is relatively easy to achieve in the computer, and the biomorphs are a good example. It is my dream to simulate natural selection in the computer too. Unfortunately it is very difficult, for the following reason. I said that some offspring are more likely to die, and it might seem easy enough to simulate non-random death.
But, in order to be a good simulation of a natural death, the demise of the computer creature must result from some interesting imperfection, like having short legs which make it run more slowly than predators.
Computer biomorphs, for instance the insect-like forms in Figure 1. They don't have prey or food plants. There is no weather in their world and no disease. In theory we can simulate any of these hazards. But to model any one of them in isolation would be scarcely less artificial than artificial selection itself. We'd have to do something like arbitrarily decide that long, thin biomorphs can run away from predators better than short fat ones.
It is not difficult to tell the computer to measure the dimensions of biomorphs and choose the lankiest for breeding. But the resulting evolution would not be very interesting. We'd just see biomorphs becoming more and more spindly as the generations go by. It is no more than we could have achieved by artificially selecting the spindliest by eye. It does not have the emergent qualities of natural selection, which a good simulation might achieve.
Real-life natural selection is much subtler. It is also in one sense much more complicated though in another sense it is deeply simple.
One thing to say is that improvement along any one dimension, like leg length, is only improvement within limits. In real life there is such a thing, for a leg, as being too long. Long legs are more vulnerable to breaking and to getting tangled up in the undergrowth. With a little ingenuity, we could program analogues of both breakages and entanglements into the computer. We could build in some fracture physics: find a way of representing stress lines, tensile strengths, coefficients of elasticity — anything can be simulated if you know how it works.
The problem comes with all the things that we don't know about or haven't thought of, and that means almost everything. Not only is the optimal leg length influenced by innumerable effects that we haven't thought of. There is leg thickness, rigidity, brittleness, weight to carry around, number of leg joints, number of legs, taperingness of legs.
And we've only considered legs. All the other bits of the animal interact to influence the animal's probability of surviving. As long as we try to add up all the contributions to an animals survival theoretically, in a computer, the programmer is going to have to make arbitrary, human decisions. What we ideally should do is simulate a complete physics and a complete ecology, with simulated predators, simulated prey, simulated plants and simulated parasites.
All these model creatures must themselves be capable of evolving. The easiest way to avoid having to make artificial decisions might be to burst out of the computer altogether and build our artificial creatures as three-dimensional robots, chasing each other around a three-dimensional real world. But then it might end up cheaper to scrap the computer altogether and look at real animals in the real world, thereby coming back to our starting point!
This is less frivolous than it seems. I'll return to it in a later chapter. Meanwhile, there is a little more we can do in a computer, but not with biomorphs. One of the main things that makes biomorphs so unamenable to natural selection is that they are built of fluorescent pixels on a two-dimensional screen.
This two-dimensional world doesn't lend itself to the physics of real life in most respects. Quantities like sharpness of teeth in predators and strength of armour plating in prey; quantities like muscular strength to throw off a predator's attack or virulence of a poison do not emerge naturally in a world of two-dimensional pixels. Can we think of a real-life case of, say, predators and prey, which does lend itself, naturally and without contrived artificiality, to simulation on a two-dimensional screen?
Fortunately we can. I've already mentioned spider webs when talking about designoid traps. Spiders have three-dimensional bodies and they live in a complex world of normal physics like most animals. But there is one particular thing about the way some spiders hunt that is peculiarly suited to simulating in two dimensions. A typical orb web is, to all intents and purposes, a two-dimensional structure. The large black-and-white triangles in the background were added for purely decorative reasons.
The spider web is as good a candidate as I can think of for an interesting simulation of natural selection on a two-dimensional computer screen. First we pose the initial problem, then we think of possible solutions that might make sense.
Then we look at what the creatures actually do. That often leads us to notice a new problem facing animals of this kind, and the chain continues. I did this in the second chapter of The Blind Watchmaker, with respect to bats and their sophisticated echo-ranging techniques. Here I shall follow the same strategy in this chapter on spider webs. Notice that the progression of problem leading to problem is not to be thought of as marching through one animal's lifetime.
If it is a temporal progression at all the time scale is evolutionary, but it may sometimes be not a temporal but a logical progression. Our fundamental task is to find an efficient method of catching insects for food. One possibility is the flying swift solution. Take to the air like the prey themselves.
Fly extremely fast with the mouth open, aiming accurately with keen eyes. This method works for swifts and swallows, but it absorbs costly investment in equipment for high-speed flying and manoeuvring and a high-tech guidance system. The same is true of the bat solution, which is the nocturnal equivalent using sound echoes instead of light rays for guiding the missile. Mantises, chameleons and certain other lizards that have evolved independently and convergently to be like chameleons make a go of this solution by being highly camouflaged and by moving in an agonizingly slow and stealthy manner until the final, explosive strike with arms or tongue.
The reach of the chameleons tongue enables it to catch a fly anywhere within a radius comparable to its own body length. The reach of the mantiss grappling arms is proportionately of the same order of magnitude.
You might think that this design could be improved by lengthening the radius of capture even further. But tongues and arms that were much longer than the body's own length would be prohibitively costly to build and maintain: the extra flies they'd catch wouldn't pay for them. Why not build a net? Nets have to be made of some material and it won't be free. But unlike a chameleons tongue the net material doesn't have to move, so doesn't need bulky muscle tissue.
It can be gossamer-thin and can therefore, at low cost, be spun out to cover a much larger area. If you took the meaty protein that would otherwise have been used up in muscular arms or tongue, and reprocessed it as silk, it would go a very long way, much further than the reach of a chameleon's tongue.
There is no reason why the net should not occupy an area times that of the body, yet still be economically made out of secretions from small glands in the body. Silk is a widespread commodity among arthropods the major division of the animal kingdom to which both insects and spiders belong.
Stick caterpillars belay themselves to a tree with a single thread of the stuff. Weaver ants stitch leaves together using silk extruded by their larvae, held in their jaws as living shuttles Figure 2. Many caterpillars swaddle themselves in a cocoon of silk before growing into a winged adult.
Tent caterpillars smother their trees with gossamer. A single domestic silkworm spins nearly a mile of silk when it builds its cocoon. But although silkworms are the basis of our own silk industry, it is really spiders that are the virtuoso silk producers of the animal kingdom, and it is surprising that spider silk is not more used by humanity.
Weaver ants using larvae as living shuttles. Oecopbila smaragiina from Australia.
In his beautiful book Self-Made Man, the zoologist and artist Jonathan Kingdon speculates that spider silk may have inspired human children to invent one of our most vital pieces of technology, string. Birds, too, recognize the good qualities of spider silk as a material: species belonging to twenty-three independent families, which suggests that it has been discovered many times independently are known to incorporate spider silk into the fabric of their nests.
A typical orb-weaving spider, the garden cross spider Araneus diadematus produces six different kinds of silk from its rear-end nozzles, made in separate glands in its abdomen, and it switches between the different types for different purposes. Spiders used silk long before they evolved the ability to build orb webs. Even jumping spiders, who never build webs, leap into the air with a silk safety line attached, like mountaineers roped to their most recent secure foothold.
Silk thread, then, is anciently available in the spider tool-kit, and it is eminently suited to the weaving of an insect-catching net. On its own scale, the spider is like a swallow with a whale's gape. Or like a chameleon with a fifty-foot tongue. A spider web is superbly economical. Whereas a chameleons muscular tongue surely accounts for a substantial fraction of its total body weight, the weight of silk in a spiders web — all twenty metres of it in a big web — is less than a thousandth part of the weight of the spiders body.
Moreover, the spider recycles silk after use by eating it, so very little is wasted. But net technology raises problems of its own. A non-trivial problem for a spider in its web is to make sure that the prey, after hurtling into the web, sticks there. There are two dangers. The insect could easily tear the web and shoot straight through.
This problem could be solved by making the silk very elastic, but this aggravates the second of the two dangers: the insect now bounces straight back out of the web as if from a trampoline. The ideal silk, the fibre of a research chemist's dreams, would stretch a very long way to absorb the impact of a fast-flying insect; yet at the same time, to avoid the trampoline effect, would be gently buffered in recoil.
At least some kinds of spider silk have just these properties, thanks to the remarkably complicated structure of the silk itself, elucidated by Professor Fritz Vollrath and his colleagues at Oxford, and now at Aarhus, Denmark.
The silk shown enlarged in Figures 2. It is like a necklace whose beads contain reeled-in surplus thread.
The reeling in is done by a mechanism not fully understood, but the result is not in doubt. The web threads are capable of stretching out to ten times their resting length, and they also recoil slowly enough not to bounce the prey out of the web. Start on. Show related SlideShares at end. WordPress Shortcode. DorethaCorrea Follow. Published in: Full Name Comment goes here. Are you sure you want to Yes No.
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