Plants v. Darwin (again).

Three Ways that Plants Defy Darwin’s Mechanism
Evolution News | @DiscoveryCSC

Plants have no brains and limited mobility, yet they have mechanisms to thrive in place. One mechanism involves the prevention of inbreeding. The trick defies Darwin’s theory. Darwin had already called the origin of flowering plants (angiosperms) an “abominable mystery.” If he had known what Austrian scientists found, it likely would have brought on more of his notorious stomach aches.

 News from Austria’s Institute of Science and Technology (IST) explains how flowering plants prevent inbreeding. As we know, inbreeding limits diversification and leads to genetic decay. When you think about it, a flower produces its own gametes: male pollen and female ova. Self-fertilization, though, would create all the associated problems of inbreeding for a plant species. People know better than to marry their relatives, but how can a blind flower, with no brain or eyes, recognize “self” so as to prevent fertilizing itself? It’s a trick that both gametes have to cooperate on. A mutation in the pollen that enables it to recognize self won’t help if the ovum doesn’t get a corresponding mutation. The Austrian IST researchers were curious about this and decided to take a look.

  

Plants “Evolved” a Solution?

In “Recognizing others but not yourself: new insights into the evolution of plant mating,” they assume that plants “evolved” a solution. But is evolution really the answer?

Self-fertilization is a problem, as it leads to inbreeding. Recognition systems that prevent self-fertilization have evolved to ensure that a plant mates only with a genetically different plant and not with itself. The recognition systems underlying self-incompatibility are found all around us in nature, and can be found in at least 100 plant families and 40% of species. Until now, however, researchers have not known how the astonishing diversity in these systems evolves. A team of researchers at the Institute of Science and Technology Austria (IST Austria) has made steps towards deciphering how new mating types evolve in non-self recognition self-incompatibility systems, leading to the incredible genetic diversity seen in nature. The results are published in this month’s edition of Genetics.

The paper in Genetics, “Evolutionary Pathways for the Generation of New Self-Incompatibility Haplotypes in a Nonself-Recognition System,” is pretty abstruse and burdened with technical jargon. The problem, though, is easy to understand:

Self-incompatibility (SI) is a genetically based recognition system that functions to prevent self-fertilization and mating among related plants. An enduring puzzle in SI is how the high diversity observed in nature arises and is maintained.

Some plants use “self-recognition” (SR) systems; others use “nonself-recognition” systems (NSR). Here’s a garden example of an SR system:

In plants such as snapdragons and Petunia, when the pollen lands on the stigma, it germinates and starts growing. The stigma, however, contains a toxin (an SRNase) that stops pollen growth. Pollen in turn has a team of genes (F-box genes) that produce antidotes to all toxins except for the toxin produced by the “self” stigma. Therefore, pollen can fertlize [sic] when it lands on stigma that does not belong to the same plant, but not when it lands on the plant’s own stigma. It may seem like a harsh system, but plants can use this toxin-antidote system to ensure that they only mate with a genetically different plant. This is important as self-fertilization leads to inbreeding, which is detrimental for the offspring.

Lock and Key

Do you see a problem for neo-Darwinism? The stigma basically has a lock that the “self” pollen cannot unlock. The pollen, though, has a key that only works on other flowers’ locks. How could such lock-and-key systems arise in a single plant that will work on unrelated plants? They not only have to evolve the toxin and the antidote, but ensure that the key doesn’t work locally — only with unrelated plants. And that’s not the only conundrum. NSR systems use a different trick. The authors puzzle over how this one evolved:

In non-self recognition systems, the male (pollen) and female (stigma) genes work together as a team to determine recognition, so that a particular variation of the male- and female-genes forms a mating type. Non-self recognition systems are found all around us in nature and have an astonishing diversity of mating types, so the big question in their evolution is: how do you evolve a new mating type when doing so requires a mutation in both sides? For example, when there is a change in the female side (stigma), it produces a new toxin for which no other pollen has an antidote – so mating can’t occur. Does this means [sic] that there needs to be a change in the male side (pollen) first, so that the antidote appears and then waits for a corresponding change in the stigma (female side)? But how does this co-evolution work when evolution is a random process? Is there a particular order of mutations that is more likely to create a new mating type?

A Committee to the Rescue

To solve this Darwinian puzzle, they created an interdisciplinary group of specialists in evolutionary genetics, game theory and applied mathematics — a committee. “This project shows how collaboration between scientists with very different backgrounds can combine biological insight with mathematical analysis, to shed some light on a fascinating evolutionary puzzle,” one of them said hopefully. With enough free parameters in your model, you can always come up with possibilities. Let’s think through their proposed solution:

Through theoretical analysis and simulation, the researchers investigated how new mating types can evolve in a non-self recognition system. They found that there are different pathways by which new types can evolve. In some cases this happens through an intermediate stage of being able to self-fertilize; but in other cases it happens by staying self-incompatible. They also found that new mating types only evolved when the cost of self-fertilization (through inbreeding) was high. Being incomplete – i.e., having missing F-box genes that produce antidotes to female toxins — was found to be important for the evolution of new mating types: complete mating types (with a full set of F-Box genes) stayed around for the longest time, as they have the highest number of mating partners. New mating types evolved more readily when there was [sic] less mating types in the population. Also, the demographics in a population affect the evolution of non-self recognition systems: population size and mutation rates all influence how this system evolves.

The analytical model worked in the committee, but does it work in the real world? In a model, you can assume that beneficial mutations will arise on cue. Nature, however, doesn’t work that way. Their model didn’t compare very well with real flowers:

So although it seems like having a full team of F-box pollen genes (and therefore antidotes) is the best way for new mating types to evolve, this system is complex and can change via a number of different pathways. Interestingly, while the researchers found that new mating types could evolve, the diversity of genes in their theoretical simulations were fewer compared to what is seen in nature. For Melinda Pickup, this observation is intriguing: “We have provided some understanding of the system, but there are still many more questions and the mystery of the high diversity in nature still exists.”

It was a fun exercise, in other words, but:

Back to the Drawing Board 

A similar difficult arises when asking how plants learned to cooperate with nitrogen-fixing bacteria. In Science, László G. Nagy puzzles about why the nitrogen-fixing root nodule (NFN) “arose repeatedly during plant evolution” — an “age-old mystery.” This symbiotic relationship, so important to human agriculture, is only found in four unrelated plant groups. Nagy calls on “convergent evolution” to explain this “patchy” appearance that doesn’t follow Darwin’s branching tree pattern, offering promissory notes that someday evolutionists will figure it out.

Teasing apart the possible mechanisms behind convergently evolved traits remains a substantial challenge even in the era of genomics. It nevertheless appears that case studies and models are emerging to explain the pervasive occurrence of convergence across the tree of life.

Beating the Heat

Plants are cleverer than Darwinians. With summer upon us, RIKEN scientists investigated “how plants beat the heat.” The solution involves more than what the mutation/selection mechanism can handle:

We all know how uncomfortable it is to be stuck outside on a sweltering hot day. Now, imagine how bad it would be if you were a soybean or tomato plant without any chance of moving inside. Eventually your leaves might become bleached of color due to chloroplast membrane damage, and if you did not get any relief, you might die. Fortunately for plants, they do have a natural defense against this type of stress that involves modifying plant fats that make up chloroplast membranes. When heat causes chloroplast membranes to destabilize, polyunsaturated fatty acids are removed from the membrane lipids, which stabilizes the membranes. The team at RIKEN found the gene responsible for this process, and they did so rather quickly because of their innovative approach.

Sure, they found a candidate gene and ran controlled experiments to see whether it could help a lab plant last longer in heat — and it did. They did not speculate about how it might have evolved, at least in the news item.

A “Fundamental Failing”

But if evolutionists think neo-Darwinism could account for this beneficial trait, they need to remember what Douglas Axe says in his chapter in the new volume, Theistic Evolution. Axe again points out the “fundamental failing” with natural selection (as he did in his earlier book, Undeniable). It’s this: evolution is “clueless” about inventing things. Natural selection “shows up only after the hard work of invention has been done.”

The only inventions we know about by experience come from inventors. An invention is a “functional whole,” Axe says. The “hard work” of invention requires having a goal or plan, and then organizing components at multiple hierarchical levels to work together to fulfill that plan.

Self-recognition systems, mutual symbioses and heat stress prevention are amazing inventions. Why must we endure stories of how they “might have” evolved, when Darwinian mechanisms are already disqualified? Axe says that “the outcome of accidental causes is guaranteed to be a mess,” and so attributing the origin of functional wholes to accident is “completely out of the question.” Science should go with the cause we know is necessary and sufficient to account for inventions: intelligence.

Jehovah's folly defeats man's genius again.

Giraffe Weekend: The Recurrent Laryngeal Nerve
David Klinghoffer | @d_klinghoffer

Continuing our classic ID the Future series on the long-necked giraffe, that evolutionary icon, we confront a sort of sub-icon, a commonly cited support to arguments for dysteleology, or “poor design.” It’s the recurrent laryngeal nerve.

As Wikipedia explains:

The extreme detour of the recurrent laryngeal nerves, about 4.6 metres (15 ft) in the case of giraffes,[26]:74–75 is cited as evidence of evolution, as opposed to Intelligent Design. The nerve’s route would have been direct in the fish-like ancestors of modern tetrapods, traveling from the brain, past the heart, to the gills (as it does in modern fish). Over the course of evolution, as the neck extended and the heart became lower in the body, the laryngeal nerve was caught on the wrong side of the heart. Natural selection gradually lengthened the nerve by tiny increments to accommodate, resulting in the circuitous route now observed.[27]:360–362

Darwinists including Richard Dawkins and Jerry Coyne have called it one of “nature’s worst designs,” “obviously a ridiculous detour,” asserting that “no engineer would ever make a mistake like that.” Geneticist Wolf-Ekkehard Lönnig returns for a discussion on this point, emphasizing that it’s not a “ridiculous detour” or a “mistake” at all

 Download the podcast or listen to it here.

Occam's razor v. Darwin.

New Paper by Winston Ewert Demonstrates Superiority of Design Model
Cornelius Hunter

Did you know Mars is going backwards? For the past few weeks, and for several weeks to come, Mars is in its retrograde motion phase. If you chart its position each night against the background stars, you will see it pause, reverse direction, pause again, and then get going again in its normal direction.

And did you further know that retrograde motion helped to cause a revolution? Two millennia ago, Aristotelian physics dictated that the Earth was at the center of the universe. Aristarchus’ heliocentric model, which put the Sun at the center, fell out of favor. But what Aristotle’s geocentrism failed to explain was retrograde motion. If the planets are revolving about the Earth, then why do they sometimes pause, and reverse direction? That problem fell to Ptolemy, and the lessons learned are still important today.

Ptolemy explained anomalies such as retrograde motion with additional mechanisms, such as epicycles, while maintaining the circular motion that, as everyone knew, must be the basis of all motion in the cosmos. With less than a hundred epicycles, he was able to model, and predict accurately the motions of the cosmos. But that accuracy came at a cost — a highly complicated model.

A Better Model

In the Middle Ages, William of Occam pointed out that scientific theories ought to strive for simplicity, or parsimony. This may have been one of the factors that drove Copernicus to resurrect Aristarchus’ heliocentric model. Copernicus preserved the required circular motion, but by switching to a sun-centered model, he was able to reduce greatly the number of additional mechanisms, such as epicycles.

Both Ptolemy’s and Copernicus’ models accurately forecast celestial motion. But Copernicus was more parsimonious. A better model had been found.

Kepler proposed ellipses, and showed that the heliocentric model could become even simpler. It was not well accepted, though, because as everyone knew, celestial bodies travel in circles. How foolish to think they would travel along elliptical paths. That next step toward greater parsimony would have to wait for the likes of Newton, who showed that Kepler’s ellipses were dictated by his new, highly parsimonious, physics. Newton described a simple, universal, gravitational law. Newton’s gravitational force would produce an acceleration, which could maintain orbital motion in the cosmos.

But was there really a gravitational force? It was proportional to the mass of the object which was then cancelled out to compute the acceleration. Why not have gravity cause an acceleration straightaway?

Centuries later Einstein reported on a man in Berlin who fell out of a window. The man didn’t feel anything until he hit the ground! Einstein removed the gravitational force and made the physics even simpler yet.

Accuracy and Parsimony

The point here is that the accuracy of a scientific theory, by itself, means very little. It must be considered along with parsimony. This lesson is important today in this age of Big Data. Analysts know that a model can always be made more accurate by adding more terms. But are those additional terms meaningful, or are they merely epicycles? It looks good to drive the modeling error down to zero by adding terms, but when used to make future forecasts, such models perform worse.

There is a very real penalty for adding terms and violating Occam’s Razor, and today advanced algorithms are available for weighing the tradeoff between model accuracy and model parsimony.

This brings us to common descent, a popular theory for modeling relationships among the species. As we have discussed many times, common descent fails to model the species, and a great many additional mechanisms — biological epicycles — are required to fit the data.And just as cosmology has seen a stream of ever improving models, the biological models can also improve. This week a very important model has been proposed in a new paper, noted already by Brian Miller. It is authored by Winston Ewert, in the journal BIO-Complexity.

Three Types of Data

Inspired by computer software, Ewert’s approach models the species as sharing modules which are related by a dependency graph. This useful model in computer science also works well in modeling the species. To evaluate this hypothesis, Ewert uses three types of data, and evaluates how probable they are (accounting for parsimony as well as fit accuracy) using three models.

Ewert’s three types of data are: (i) sample computer software, (ii) simulated species data generated from evolutionary/common descent computer algorithms, and (iii) actual, real species data.

Ewert’s three models are: (i) a null model which entails no relationships between

any species, (ii) an evolutionary/common descent model, and (iii) a dependency graph model.

Ewert’s results are a Copernican Revolution moment. First, for the sample computer software data, not surprisingly the null model performed poorly. Computer software is highly organized, and there are relationships between different computer programs, and how they draw from foundational software libraries. But comparing the common descent and dependency graph models, the latter performs far better at modeling the software “species.” In other words, the design and development of computer software is far better described and modeled by a dependency graph than by a common descent tree.

Second, for the simulated species data generated with a common descent algorithm, it is not surprising that the common descent model was far superior to the dependency graph. That would be true by definition, and serves to validate Ewert’s approach. Common descent is the best model for the data generated by a common descent process.

Third, for the actual, real species data, the dependency graph model is astronomically superior compared to the common descent model.

Where It Counts

Let me repeat that in case the point did not sink in. Where it counted, common descent failed compared to the dependency graph model. The other data types served as useful checks, but for the data that mattered — the actual, real, biological species data — the results were unambiguous.

Ewert amassed a total of nine massive genetic databases. In every single one, without exception, the dependency graph model surpassed common descent.

Darwin could never have even dreamt of a test on such a massive scale. Darwin also could never have dreamt of the sheer magnitude of the failure of his theory. Because you see, Ewert’s results do not reveal two competitive models with one model edging out the other.

We are not talking about a few decimal points difference. For one of the data sets (HomoloGene), the dependency graph model was superior to common descent by a factor of 10,064. The comparison of the two models yielded a preference for the dependency graph model of greater than ten thousand.

Ten thousand is a big number. But it gets worse, much worse.

Ewert used Bayesian model selection which compares the probability of the data set given the hypothetical models. In other words, given the model (dependency graph or common descent), what is the probability of this particular data set? Bayesian model selection compares the two models by dividing these two conditional probabilities. The so-called Bayes factor is the quotient yielded by this division.

The problem is that the common descent model is so incredibly inferior to the dependency graph model that the Bayes factor cannot be typed out. In other words, the probability of the data set, given the dependency graph model, is so much greater than the probability of the data set given the common descent model, that we cannot type the quotient of their division.

Instead, Ewert reports the logarithm of the number. Remember logarithms? Remember how 2 really means 100, 3 means 1,000, and so forth?

Unbelievably, the 10,064 value is the logarithm (base value of 2) of the quotient! In other words, the probability of the data on the dependency graph model is so much greater than that given the common descent model, we need logarithms even to type it out. If you tried to type out the plain number, you would have to type a 1 followed by more than 3,000 zeros. That’s the ratio of how probable the data are on these two models!

By using a base value of 2 in the logarithm we express the Bayes factor in bits. So the conditional probability for the dependency graph model has a 10,064 advantage over that of common descent.

10,064 bits is far, far from the range in which one might actually consider the lesser model. See, for example, the Bayes factor  Wikipedia page,  which explains that a Bayes factor of 3.3 bits provides “substantial” evidence for a model, 5.0 bits provides “strong” evidence, and 6.6 bits provides “decisive” evidence.

This is ridiculous. 6.6 bits is considered to provide “decisive” evidence, and when the dependency graph model case is compared to comment descent case, we get 10,064 bits.

But It Gets Worse

The problem with all of this is that the Bayes factor of 10,064 bits for the HomoloGene data set is the very best case for common descent. For the other eight data sets, the Bayes factors range from 40,967 to 515,450.

In other words, while 6.6 bits would be considered to provide “decisive” evidence for the dependency graph model, the actual, real, biological data provide Bayes factors of 10,064 on up to 515,450.

We have known for a long time that common descent has failed hard. In Ewert’s new paper, we now have detailed, quantitative results demonstrating this. And Ewert provides a new model, with a far superior fit to the data.

It is except when it isn't?

A Tendentious Appeal for Methodological Naturalism
Paul Nelson

From “The naturalism of the sciences,” by Gregory W. Dawes and Tiddy Smith, writing in the journal Studies in History and Philosophy of Science Part A:

The sciences are characterized by what is sometimes called a “methodological naturalism,” which disregards talk of divine agency. In response to those who argue that this reflects a dogmatic materialism, a number of philosophers have offered a pragmatic defense. The naturalism of the sciences, they argue, is provisional and defeasible: it is justified by the fact that unsuccessful theistic explanations have been superseded by successful natural ones. But this defense is inconsistent with the history of the sciences. The sciences have always exhibited what we call a domain naturalism. They have never invoked divine agency, but have always focused on the causal structure of the natural world. It is not the case, therefore, that the sciences once employed theistic explanations and then abandoned them. The naturalism of the sciences is as old as science itself.

From a quick scan, this is an interesting article — but their historiography looks more than a tad tendentious. Dawes and Smith say they’re simply describing (as a “matter of fact”) the history of science. But they’ve also carefully built escape or exception clauses into their history, so that any counterexample does not count against their thesis. As they write on page 28, opening the gate so that the exceptions can wander away, leaving only the obedient sheep in the pen:

 The naturalism of the sciences is a norm of scientific inquiry and norms represent both how a community regularly behaves and how its members think one ought to behave (Pettit, 1990, p. 728). So the existence of a norm is consistent with its occasional violation. 

Well — how convenient, as the Church Lady on Saturday Night Live used to say.


I grabbed a 19th-century science textbook from my office shelves: James Dana’s Manual of Geology (1871). Dana was professor of geology at Yale and by any dispassionate description fully a “scientist.” Here is how Dana ends his discussion of the topic “The Progress of Life” (paleontological trends — a summary of the signal from the fossil record):

Geology appears to bring us directly before the Creator; and while opening to us the methods through which the forces of nature have accomplished His purpose, — while proving that there has been a plan glorious in its scheme and perfect in its system, progressing through unmeasured ages and looking ever towards Man and a spiritual end, — it leads to no other solution of the great problem of creation, whether of kinds of matter or of species of life, than this: — DEUS FECIT.  (p. 602)

Deus fecit — Latin for “God created.”

This was a widely used geology textbook: “science” by any description. But this counterexample (one of hundreds possible) won’t count, because it’s “an occasional violation” of an otherwise universal norm.  Universal generalizations sleep undisturbed when the contrary evidence isn’t allowed anywhere near the doorbell.

Moreover, the relentless late 19th-century campaign by T.H. Huxley and others against scientific explanation by divine action and for fully naturalistic or materialistic explanation should not have been necessary, if Dawes and Smith are correct in their history.

But — check the article, it’s open access — Dawes and Smith tip their hand in their concluding paragraph. Any flexing of the methodological naturalism (MN) rule will fracture science along religious lines, they say, and that’s bad. So the provisional atheism of science should continue, because that’s what science since the Greeks has always done…


…Except when it hasn’t — but we’re not counting the many exceptions.

A bit of stretch?

Giraffe Weekend: “You Cannot Simply Stretch out the Neck”
David Klinghoffer | @d_klinghoffer

For your weekend enjoyment, we’re delighted to offer the classic three-part ID the Future series on the evolutionary enigma of the long-necked giraffe. It’s an interview with geneticist Wolf-Ekkehard Lönnig on the occasion of the publication of his book  The Evolution of the Long-Necked Giraffe.

As Dr. Lönnig concludes:

You cannot, as was suggested by Richard Dawkins, simply stretch out the neck during an embryonic deviation, and then have a long-necked giraffe. You have a system of co-adaptive, coordinated parts which all must work together to allow a giraffe to survive and live in the wild. And the question is, of course, can mutations produce over millions of years these differences between a short-necked and a long-necked giraffe?

Spoiler alert: The answer is no. The giraffe is one of those all-star icons of evolution, familiar from textbook covers, that falls apart on closer inspection. Download the podcast or listen to it here.