Darwinism fails even with all the time in the world.

Debunking a popular myth: ”There’s plenty of time for evolution”

At this point, I imagine Matzke will want to cite a 2010 paper in Proceedings of the U.S. National Academy of Sciences (PNAS), titled “There’s plenty of time for evolution” by Herbert S. Wilf and Warren J. Ewens, a biologist and a mathematician at the University of Pennsylvania. Although it does not refer to them by name, there’s little doubt that Wilf and Ewens intended their work to respond to the arguments put forward by intelligent-design proponents, since it declares in its first paragraph:

…One of the main objections that have been raised holds that there has not been enough time for all of the species complexity that we see to have evolved by random mutations. Our purpose here is to analyze this process, and our conclusion is that when one takes account of the role of natural selection in a reasonable way, there has been ample time for the evolution that we observe to have taken place.

Evolutionary biologist Professor Jerry Coyne praised the paper, saying that it provides “one step towards dispelling the idea that Darwinian evolution works too slowly to account for the diversity of life on Earth today.” Famous last words.

A 2012 paper, Time and Information in Evolution, by Winston Ewert, Ann Gauger, William Dembski and Robert Marks II, contains a crushing refutation of Wilf and Ewens’ claim that there’s plenty of time for evolution to occur. The authors of the new paper offer a long list of reasons why Wilf and Ewens’ model of evolution isn’t biologically realistic:

Wilf and Ewens argue in a recent paper that there is plenty of time for evolution to occur. They base this claim on a mathematical model in which beneficial mutations accumulate simultaneously and independently, thus allowing changes that require a large number of mutations to evolve over comparatively short time periods. Because changes evolve independently and in parallel rather than sequentially, their model scales logarithmically rather than exponentially. This approach does not accurately reflect biological evolution, however, for two main reasons. First, within their model are implicit information sources, including the equivalent of a highly informed oracle that prophesies when a mutation is “correct,” thus accelerating the search by the evolutionary process. Natural selection, in contrast, does not have access to information about future benefits of a particular mutation, or where in the global fitness landscape a particular mutation is relative to a particular target. It can only assess mutations based on their current effect on fitness in the local fitness landscape. Thus the presence of this oracle makes their model radically different from a real biological search through fitness space. Wilf and Ewens also makeunrealistic biological assumptions that, in effect, simplify the search. They assume no epistasis between beneficial mutations, no linkage between loci, and an unrealistic population size and base mutation rate, thus increasing the pool of beneficial mutations to be searched. They neglect the effects of genetic drift on the probability of fixation and the negative effects of simultaneously accumulating deleterious mutations. Finally, in their model they represent each genetic locus as a single letter. By doing so, they ignore the enormous sequence complexity of actual genetic loci (typically hundreds or thousands of nucleotides long), and vastly oversimplify the search for functional variants. In similar fashion, they assume that each evolutionary “advance” requires a change to just one locus, despite the clear evidence that most biological functions are the product of multiple gene products working together. Ignoring these biological realities infuses considerable active information into their model and eases the model’s evolutionary process.

After reading this devastating refutation of Wilf and Ewens’ 2012 paper, I think it would be fair to conclude that we don’t currently have an adequate mathematical model explaining how macroevolution can occur at all, let alone one showing that it can take place within the time available. Four billion years might sound like a long time, but if your model requires not billions, but quintillions of years for it to work, then obviously, your model of macroevolution isn’t mathematically up to scratch.

Debunking another popular myth: “The eye could have evolved in a relatively short period.”

Parts of the eye: 1. vitreous body 2. ora serrata 3. ciliary muscle 4. ciliary zonules 5. canal of Schlemm 6. pupil 7. anterior chamber 8. cornea 9. iris 10. lens cortex 11. lens nucleus 12. ciliary process 13. conjunctiva 14. inferior oblique muscle 15. inferior rectus muscle 16. medial rectus muscle 17. retinal arteries and veins 18. optic disc 19. dura mater 20. central retinal artery 21. central retinal vein 22. optic nerve 23. vorticose vein 24. bulbar sheath 25. macula 26. fovea 27. sclera 28. choroid 29. superior rectus muscle 30. retina. Image courtesy of Chabacano and Wikipedia.

In 1994, Dan-Erik Nilsson and Susanne Pelger of Lund University in Sweden wrote a paper entitled, A Pessimistic Estimate of the Time Required for an Eye to Evolve(Proceedings: Biological Sciences, Vol. 256, No. 1345, April 22 1994, pp. 53-58) in which they cautiously estimated the time required for a fully-developed lens eye to develop from a light-sensitive spot to be no more than 360,000 years or so.

In 2003, the mathematician David Berlinski wrote an incisive critique of this outlandish claim. (See here for Nilsson’s response.) Some of Berlinski’s contentions turned out to be based on a misunderstanding of Nilsson and Pelger’s data, but Berlinski scored significantly when he pointed out that Nilsson and Pelger’s paper was lacking in the mathematical details one might expect in support of their claim that the eye took only 360,000 years to evolve:

“Nilsson and Pelger’s paper contains no computer simulation, and no computer simulation has been forthcoming from them in all the years since its initial publication…

“There are two equations in Nilsson and Pelger’s paper, and neither requires a computer for its solution; and there are no others.”

Indeed, Nilsson had even admitted as much, in correspondence with Berlinski:

“You are right that my article with Pelger is not based on computer simulation of eye evolution. I do not know of anyone else who [has] successfully tried to make such a simulation either. But we are currently working on it.”

That was in 2001. As far as I am aware, no simulation has since been forthcoming from Nilsson and Pelger, although as we’ll see below, a genetic algorithm developed by an Israeli researcher in 2007 demonstrated that their model was based on wildly optimistic assumptions about evolutionary pathways.

In the meantime, Nilsson and Pelger’s 1994 paper has been gleefully cited by evolutionary biologists as proof that the origin of complex structures is mathematically modelable. Here is how Professor Jerry Coyne describes Nilsson and Pelger’s work in his book, Why Evolution Is True:

We can, starting with a simple precursor, actually model the evolution of the eye and see whether selection can turn that precursor into a more complex eye within a reasonable amount of time. Dan Nilsson and Susanne Pelger of Lund University in Sweden made such a mathematical model, starting with a patch of light-sensitive cells backed by a pigment layer (a retina). They then allowed the tissues around this structure to deform themselves randomly, limiting the amount of change to only 1% of size or thickness at each step. To mimic natural selection, the model accepted only mutations that improved the visual acuity, and rejected those that degraded it.

Within an amazingly short time, the model yielded a complex eye, going through stages similar to the real-animal series described above. The eyes folded inward to form a cup, the cup became capped with a transparent surface, and the interior of the cup gelled to form not only a lens, but a lens with dimensions that produced the best possible image.

Beginning with a flatworm-like eyespot, then, the model produced something like the complex eye of vertebrates, all through a series of tiny adaptive steps – 1,829 of them, to be exact. But Nilsson and Pelget could also calculate how long this process would take. To do this, they made some assumptions about how much genetic variation for eye shape existed in the population that began experiencing selection, and how strongly selection would favor each useful step in eye size. These assumptions were deliberately conservative, assuming that there were reasonable but not large amounts of genetic variation and that natural selection was very weak. Nevertheless, the eye evolved very quickly: the entire process from rudimentary light-patch to camera eye took fewer than 400,000 years.

– Coyne, Jerry A. Why Evolution Is True. 2009. Oxford University Press, p. 155.

I’d like to point out here that Coyne’s starry-eyed description of Nilsson and Pelger’s research overlooks a vital point raised by Professor Michael Behe in his article,Molecular Machines: Experimental Support for the Design Inference. Readers will recall that Behe declared:

“The relevant steps in biological processes occur ultimately at the molecular level, so a satisfactory explanation of a biological phenomenon such as sight, or digestion, or immunity, must include a molecular explanation. It is no longer sufficient, now that the black box of vision has been opened, for an ‘evolutionary explanation’ of that power to invoke only the anatomical structures of whole eyes, as Darwin did in the 19th century and as most popularizers of evolution continue to do today. Anatomy is, quite simply, irrelevant.”

Nilsson and Pelger’s mathematical calculations addressed the evolution of the eye’s anatomy, but they said nothing about the underlying biochemistry. Using Behe’s criteria, we can see at once that their macroevolutionary model of the evolution of the eye is a failure. Professor James Tour would dismiss it on similar grounds. He would doubtless ask, rhetorically: “Does anyone understand the chemical details behind the macroevolution of the eye?” I hope that Nick Matzke will now concede that this is a reasonable question.

A more skeptical assessment of Nilsson and Pelger’s 1994 paper can be found in an online applied physics thesis by Dov Rhodes, entitled, Approximating the Evolution Time of the Eye: A Genetic Algorithms Approach. The thesis makes for fascinating reading. I shall quote a few brief excerpts:

“A paper published in 1994 by the Swedish scientists Nilsson and Pelger [6] gained immediate worldwide fame for describing the evolution process for an eye, and approximating the time required for an eye to evolve from a simple patch that sense electromagnetic radiation. Nilsson and Pelger (NP) outlined an evolutionary path, where by minute improvements on each step a cameratype eye can evolve in approximately 360,000 years, which is extremely fast on an evolutionary time scale… (p. 1)

“The main problem with the NP model is that although the evolutionary path that it describes might be a legitimate one, it neglects consideration for divergent paths. It is easy to construct a situation in which the best temporary option for the improvement of an eye does not lead towards the development of the globally optimal solution. This idea motivates our alternative approach, the method of genetic algorithms. In this paper we use the genetic algorithm with a simplified (2-dimensional) version of NP’s setup and show the error in their approach. We argue that if their approach is mistaken in the simplified model, it is even farther from reality in the full evolutionary setting. (p. 2)

“Although the paraboloid landscape guarantees convergence, the GA is still a probabilistic algorithm and thus will not always converge quickly. As in evolution, the most efficient path is not necessarily the one taken. This fact suggests that our already conservative value of lambda = 5.41 would be even larger if compared with a real deterministic algorithm such as the NP (Nilsson-Pelger) model. Even though their computation accounts to some extent for the average probability of evolutionary development over time, it fails to consider the countless different evolutionary paths, and instead chooses just one.

“Rather than 360 thousand generations, a reasonable lower bound should be at least 5*360,000 = 1.8*10^6 generations, and if our previous speculations have merit, an order of magnitude higher would ramp up the estimate to around 18 million generations. Future experiments that would be useful for improving the accuracy of our results might involve varying the mutation parameter, and most importantly letting algorithms run for longer, allowing the lower bound for convergence to be pushed even higher.” (p. 15)

What Rhodes’ paper demonstrates is that the 1994 estimate by Nilsson and Pelger of how long it took the eye to evolve is more like a case of intelligently guided evolution than Darwinian evolution. As Rhodes puts it: “Even though their computation accounts to some extent for the average probability of evolutionary development over time, it fails to consider the countless different evolutionary paths, and instead chooses just one.”