The article focuses on a recent paper from Richard Lenski's group at Michigan State University (Lenski et al., 2015). Lenski's group asked a different question. They wanted to know whether there was a limit to the increase in fitness in their evolving E. coli populations in the Long-Term Evolution Experiment (LTEE). It's a different question than whether evolution can select for a "perfect" organism because Lenski and his collaborators understand modern evolutionary theory. They know that mutations causing small fitness increases are beyond the reach of natural selection in their evolving populations and they know that deleterious mutations can be fixed by random genetic drift.
They know that real evolving populations can never reach the summit of an adaptive peak or, if they do, they can never stay there.
But, given these theoretical limitations, can their E. coli cultures reach a limit beyond which further adaptation is not possible because of the limitations of natural selection under their growth conditions? You would think that the answer is "yes." Most of us would imagine a hyperbolic curve of decreasing fitness over time where the cultures approach but do not reach a "perfect" level of adaptation. In other words, we imagine that the cultures will climb slowly toward the top of an adaptive peak but get stuck somewhere below the summit where they reach a steady-state condition of survival at optimum—but not perfect—adaptation.
This paper extends the results to 60,000 generations but it's just confirming the earlier results of Wiser et al. (2013). Here's how they describe the result ...
Wiser et al.  challenged the presumption that there must be an upper bound to organismal fitness. They measured the fitness trajectories over 50 000 generations for Escherichia coli populations in the long-term evolution experiment (LTEE). They compared the fit of two simple models—a hyperbolic model and a power-law model—that both predict a decelerating fitness trajectory (i.e. a declining rate of fitness improvement), but only the former has an upper limit, or asymptote. The power-law model, by contrast, predicts that the logarithm of fitness will increase with the logarithm of time, a relationship that has no asymptote. Both models fit the observed fitness trajectories well, but the power-law model fit much better. Moreover, if truncated datasets (e.g. from only the first 20 000 generations) were used to predict the subsequent trajectories, the hyperbolic model consistently underestimated the extent of future improvement, whereas the power-law model accurately predicted the changes seen in later generations. Despite having no upper limit, but owing to its logarithmic dependence on time, the power law did not lead to absurd predictions that would seem to violate physical constraints. Indeed, when the power-law model was extrapolated millions of generations into the future, the predicted fitness levels correspond to growth rates that are within the range that some bacterial species can achieve under optimal conditions.What's going on? These populations are evolving in a constant environment so why isn't there an optimal limit to adaptation?
Evolution is complicated. One of the remarkable things to come out of the LTEE is that the trajectories of evolution are different for each of the twelve populations. The one that evolved the ability to use citrate isn't included in this study and two others can't be included because they don't grow on the plates used to assay for fitness.
Of the remaining nine populations, you can see that they all differ in relative fitness. That's because they have all followed different trajectories over the past 60,000 generations. This is contingency in action. It doesn't explain the slope of the curve but it does suggest that there is no absolute fitness landscape that applies to all of the cultures.
One of the cultures, Ara+1, has the lowest overall fitness of any population. It also showed the lowest increase in fitness—a result that seems counter-intuitive because it has the most to gain. This population has an unusually active transposon (IS150) that creates an unusual number of mutations by insertion. Possibly because of this active transposon, the authors note that, "... this population has, in some sense, gotten stuck in a genotypic region of the fitness landscape that constrains its evolvability."
But the real question is whether the fitness landscape is rugged, with large peaks and low valleys, or smooth, with gently rolling hills. Maybe the whole idea of climbing Mount Improbable is flawed from the get-go and real evolution wanders around a changing landscape of gentle slopes and shallow valleys trying lots of adaptive hill climbing but never getting stuck at the top of a steep hill. As the populations diverge by fixing different (mostly neutral) mutations the adaptive landscape also changes so there's never a "perfect" adaptive goal but only transient hilltops that are forever changing.
The Discovery article quotes Michael Wiser who says,
“The reality is that what would be perfection is going to depend on lots and lots of circumstances. So as the populations adapt and you get different mutations arising and sweeping through the population…you’re going to have different sets of things that are beneficial and deleterious at different times along the evolutionary trajectory.”It's important to recognize that the trajectory, or path, that a population takes over many generations is a feature that's often overlooked. That path is determined by many chance events, including the chance occurrence of particular mutations at a particular point in time (contingency). This is one reason why mutationism deserves more attention and one more reason in support of evolution by accident.
"Palouse hills northeast of Walla Walla" by Lynn Suckow from Walla Walla, WA, USA - Hills, grain elevator, and little yellow plane (really)Uploaded by X-Weinzar. Licensed under CC BY-SA 2.0 via Commons - https://commons.wikimedia.org/wiki/File:Palouse_hills_northeast_of_Walla_Walla.jpg#/media/File:Palouse_hills_northeast_of_Walla_Walla.jpg
Lenski, R.E., Wiser, M.J., Ribeck, N., Blount, Z.D., Nahum, J.R., Morris, J.J., Zaman, L., Turner, C.B., Wade, B.D., Maddamsetti, R., Burmeister, A.R., Baird, E.J., Bundy, J., Grant, N.A., Card, K.J., Rowles, M., Weatherspoon, K., Papoulis, S.E., Sullivan, R., Clark, C., Mulka, J.S., and Hajela, N. (2015) Sustained fitness gains and variability in fitness trajectories in the long-term evolution experiment with Escherichia coli. Proceedings of the Royal Society of London B: Biological Sciences, 282(1821). [doi: 10.1098/rspb.2015.2292]
Wiser, M.J., Ribeck, N., Lenski, R.E. (2013) Long-term dynamics of adaptation in asexual populations. Science 342:1364–1367. [doi: 10.1126/science.1243357]