Sometimes it can be hard to see progress in biology the way we hear about physics discovering new particles or proposing Grand Unified Theories that explain the entire universe. It is tempting to believe biology is just too diverse, variable, and multitudinous to be tractable, and that we should content ourselves with Nature-special documentary anecdotes.
But recent research has uncovered at least three major advances toward predicting evolution, social altruism, and a universal explanation of biodiversity. We may soon be able to predict short-term (9-12 month) evolution of the flu virus, rigorously describe conditions necessary for social altruism, and extrapolate biodiversity estimates using insights from thermodynamics.
The complexity, idiosyncracy, and exceptions-to-the-rule in biology are still important, but so too are these simplifying general explanations. The stories of new "universal laws" linked below are perhaps best thought of as null-theories; jumping-off places rather than destinations in themselves:
1) Predicting Evolution ... Testable Fitness Values (link to article by Carl Zimmer)
Simple selective pressures yield relatively simple predictions: fitness increases linearly at first, but in the long run, weird mutations may diverge populations along novel and unpredictable trajectories. The tractable problem, then, is short-term evolution, which can still be incredibly important when it is applied to, say, the next 12 months of evolution in the flu virus. The breakthrough came with the ability to quantify fitness to predict evolvability.
One of the biggest problems of evolutionary theory has been a lack of predictive power, because fitness could only be defined tautologically, post hoc based on survival and reproduction. If biologists are able to assign fitness ranking with any skill (link) then we may finally be able to understand evolutionary ecology -- the rise and fall of species in their environment. Will most threatened and endangered species prove to be genetic weaklings, as suggested by this correlational study?
2) Predicting Social Altruism ... What Makes A Good Theory?
This is an insightful philosophy paper that deconstructs a long-standing debate about whether altruism is predicted by fundamental evolutionary pressures. The important step forward here is a robust definition of key terms and a searching analysis of what we should expect from abstract mathematical theories.
3) Predicting Biodiversity .... Metabolic Scaling Laws to the Rescue!
The tractable problem is to estimate the number of species in a given area when ecologists can only count species in relatively small plots. The breakthrough came by realizing that only two additional variables (population density and total number of species) are necessary to "collapse" idiosyncratic species-area curves into a single universal curve.
The discoverer, Dr. John Harte, explains:
"If you look at all the known species-area (S-A) curves in the world, of everyplace where somebody’s gathered species-area data, and you plot them all on one big piece of graph paper- log species vs. log area, you will find that the data points fill the graph almost completely. You get every possible behavior when you just do a plot of log S vs log A. There’s no regularity. I didn’t really think that had to be the case. What I learned from developing the theory of macroecology based on the maximum-information entropy principle, is that the theory makes a very startling testable prediction about the shape of the species-area relationship. It says that if you take any species-area curve and you plot the local slope of the log-log plot, what we call ‘z’, at any scale against a certain scaling variable that the theory identifies, namely, the number of individuals at that scale divided by the number of species at that scale, all species-area curves should collapse onto a single universal curve. And it turns out that they do"
(Quoted here.)
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