Convergent evolution… tell me more
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Convergence and optimality
The prevalence of convergence shows that far from being a set of random outcomes evolution may be much more determinate than has generally been acknowledged. Such determinacy means that far from being a drunkard’s walk, stumbling from one uncertain destination to another, evolution over time will result in recurrent biological trends and traits. This may not surprise us, given that distantly related organisms may yet inhabit similar environments and must adapt to similar life challenges, but the fact that very similar biological solutions repeatedly arise re-opens the controversial topic of “optimality”.
Optimality in biology refers to the way that certain characteristics of an organism may be maximised (or in certain circumstances minimised) through natural selection to provide the most effective, or ‘optimised’ adaptation possible within the constraints of a given environment. Optimality analyses have successfully revealed many of the constraints acting on adaptations as diverse as leaf size, protein expression, life history strategies, the genetic code, metabolic pathways, foraging strategies and bird migration. It is certainly agreed that adaptations only evolve to be as good as they ‘need’ to be to confer peak fitness in their immediate context, although in a competitive context ‘good’ may be close to ‘best’. Also, the evidence from convergence suggests that in any particular system infinite evolutionary change in any direction is not possible, so ultimately a stable state conferring optimal fitness is likely to be achieved. (An exception to this scenario may exist with respect to convergent cognitive landscapes, because with intelligence many new possibilities open up.) These stable ‘optima’ may of course change over time as the dynamic nature of the natural world means that the forces of selection and constraint may shift, resulting in the necessity to modify adaptive solutions.
The concept of optimality allows examination of the shared selective forces driving parallel, convergent adaptations in distantly related taxa and evidently explains why we see convergence upon the same solutions again and again under similar conditions. For example, the process of refinement towards an optimal state surely explains why the convergence between the camera-eyes of octopus and vertebrates is so exact. Recognition of convergent adaptations could therefore provide a rich opportunity for evaluating the predictive power of optimality models and shaping their future development.
Evidence for selection and inheritance
Talk of directionality, non-randomness and optimality echo, of course, those proponents who refuse to accept evolution. Will the study of convergence, therefore, ultimately disprove evolution? Not at all! Principally this is because the very fact that similar solutions emerge provides powerful evidence for the key role of selection in driving adaptation (although many convergences also suggest, as indeed some biologists have already argued, that in a sense selection may move towards given forms that are “waiting to be discovered”). Moreover, the nature of the similarities seen in convergence paradoxically supplies powerful evidence for the reality of evolutionary change over time, because although similarities may be startlingly close, they are never exact due to the unique evolutionary histories of each organism in question. Indeed, inherited historical constraints such as hard-wired genetic or structural features, as well as non-selective processes such as neutral drift, may render a given biological solution theoretically ‘sub-optimal’. This needs always to be remembered, but so too does the fact that again and again organisms that from our perspective look ‘unpromising’ navigate to sophisticated solutions that just happen to be convergent. Convergences from the level of structures (e.g. camera eyes) to molecules (e.g. the enzyme carbonic anyhydrase) exemplify the role of historical constraint in shaping adaptations:
The position of the retina, a layer of photosensitive cells, has an opposite orientation in the octopus and human camera-eye. In the octopus eye light sensitive cells lie in front of the optic neurons, whereas in vertebrates light sensitive cells are located behind the neurons, resulting in a ‘sub-optimal’ blind spot (or fovea) at the optic nerve. This difference arises due to the different inherited patterns of development: in octopus development the retina is formed by the skin sinking inwards, while in the vertebrates the retina moves outwards as an extension of the brain. In each case the difference represents the footprint of evolution.
Consider again the example of the enzyme carbonic anhydrase. As already noted, if one was to find a protein with exactly the same sequence in organisms we otherwise thought were completely unrelated we would rightly be very suspicious indeed. But in carbonic anhydrase the convergence is much more precisely defined, because it involves the so-called active site, the factory floor of this enzyme. The site depends crucially on an atom of zinc surrounded by three amino acid residues. Usually, but not quite always, the latter are histidines. Zinc is obviously the preferred element, but in one example cadmium (which is chemically similar) has acted as a substitute. The five (or six) origins of carbonic anhydrase involve repeated convergence on the active site, and the rest of the molecular scaffolding that makes the entire protein is completely different, due to their separate evolutionary histories.
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