Last spring, in Ruth Shaw’s greenhouse at the University of Minnesota, Shaw and her students were cross-pollinating partridge peas. These knee-high plants with their nectar-filled yellow flowers do pretty well for themselves, thriving in all sorts of conditions on the North American continent, up into Canada, down into Mexico and across the Eastern Seaboard. They’re survivors. Shaw, an evolutionary geneticist, is interested in measuring exactly how strong her survivors will be. This coming summer, she’ll send the seeds from those genetic crosses into the field, where she and her students will monitor them closely. They’ll measure the plants’ germination and growth, as well as the genetic variation that determines their fitness, to find out how quickly the plants can adapt to their environment.

In the face of climate change, scientists like Shaw have begun to measure how effective evolution might be as a survival strategy. Since the early 1990s, scientists have understood in theory how a population could evolve fast enough to outrace extinction. Then, about five years ago, a group at McGill University saw it happen in the lab: Evolution saved populations of yeast from deadly concentrations of salt. The conditions under which this type of “evolutionary rescue” succeeds are narrow, but that hasn’t stopped scientists from modeling and collecting data to see just when and how it works. If we think of evolution as survival of the fittest, in a tough environment what matters is how quickly a population can get fit in order to survive.

“Natural selection is a hugely powerful process. But even Darwin thought — and a lot of the thinking since Darwin has been — that it’s excruciatingly slow,” Shaw said. But in the past 50 years, studies of evolution have shown that adaptation can happen much faster than anyone imagined. The question now, Shaw said, is “how fast can these changes happen?”

One of the best ideas that anyone currently has for saving the world’s species from climate change is to move them from their current homes to other, potentially more suitable places. If experiments like Shaw’s could predict how likely a particular population was to survive on its own, we would have another set of tools to help deal with the mess we’ve made. The better we can predict which species are likely to adapt to climate change, the more we’ll be able to focus on moving the ones that otherwise have no chance.

It’s difficult, but not impossible, to watch evolution unfold. Even when evolution moves fast, the changes that make species suited to their surroundings can take 50 or 100 generations to develop. That’s why, in evolutionary biology, theory usually precedes experimental work.

One of the first models describing evolutionary rescue’s basic principles came from Richard Gomulkiewicz, a professor at Washington State University’s School of Biological Sciences, and the theoretical ecologist Robert Holt. Holt had made a fascinating observation about the biological world: Even when there’s nothing obvious preventing their evolution, species don’t necessarily expand beyond their comfortable niches. European and American populations of beech trees, for instance, have been separated for millions of years, and it seems reasonable to imagine that one population might have evolved to occupy a different range of environments than the other. But all kinds of beech species, on both continents, live more or less within the same set of temperatures. At some point, they hit the same evolutionary wall.

Holt was interested in what could be holding species back, and the model he and Gomulkiewicz built began to outline what, exactly, it takes for evolution to beat truly challenging conditions.

They began with the problem of a population in decline. It’s a simple idea: When there’s fewer than one offspring for every current individual, that population eventually goes extinct. On the flip side, evolution can eventually increase a species’ rate of population growth if the evolved offspring are more likely to survive. So eventually, evolution can push the rate of population growth over one — it’s just a question of whether that happens before the population becomes so small that the randomness of life and genetics has a chance to snuff it out. Lingering below that survival threshold — Nc in the graph below — puts a population at high risk for extinction.

(Image from “When Does Evolution by Natural Selection Prevent Extinction?” Gomulkiewicz & Holt, Evolution, 1995.)

“Even though adaptive evolution is occurring, and the population is getting better, it’s still dropping in numbers,” said Gomulkiewicz. “What we pointed out is that they can drop to numbers that are so few, they can go extinct just by chance.” The faster a population can evolve, the more likely it is to avoid that outcome.

Overall, Gomulkiewicz and Holt’s models indicated that a population’s chance of facing down a life-threatening challenge depends on its size, its speed of decline, its genetic variation and how much it needs to change to survive in its new environment — variables that, for the most part, can be measured empirically and make it possible to start applying the models to real-world populations.

These models, however, described a sudden but one-time change to the environment. Imagine a person running on a treadmill, when, suddenly, the incline increases to a difficult new tilt; she needs to improve her level of fitness to run with the same ease at this new gradient. But few environmental challenges are so static. Imagine now that the incline of that treadmill keeps rising, getting steeper and steeper. That’s the sort of threat climate change poses.

Around the same time that Gomulkiewicz and Holt were doing their work, Michael Lynch, a biologist interested in genetics and evolution, was working on this problem of dynamic threats.

Lynch and his co-author Reinhard Burger’s big insight was that no amount of genetic variation could keep a population perfectly on track with a moving target. Genes determine how the expression of a heritable trait (or phenotype) is distributed across a population. Often this distribution is modeled as a bell curve: For a trait like body type, some individuals might be fatter than the ideal, others might be skinnier, but most fall somewhere towards the middle. The key to survival, Lynch and Burger found, was that the average expression of the trait couldn’t lag too far behind the optimal expression in the new environment. If it remained close, the population could hang on.

But evolution can only speed up so much, and Lynch found that for a population to avoid extinction the environment would have to change relatively slowly. For a species to survive, it would need leeway from the environment — in scientific terms, “on the order of 1 percent or fewer of a phenotypic standard deviation per generation,” as Lynch and Burger wrote.

Together, these models outline the conditions under which evolution can come to the rescue, and the limits of that power. To beat extinction, according to Gomulkiewicz and Holt’s models, a population should start out large, not decline too quickly, not need to transform too dramatically and have ample genetic variation to make that transformation. In addition, Lynch’s models show that the environment can’t be changing too fast.

Investigations like Shaw’s are only now beginning to collect data on how this process might play out in the field. This is the stage where scientists do watch evolution unfold — and on the scale of human perception, even “fast” evolution can feel excruciatingly slow. Shaw and her students are planning, quite literally, to watch plants grow for the next three years. The seeds they gather as part of this experiment will be stored for decades so that in 10, 20, or 30 years they can be planted and compared to specimens that have been evolving out in the world.

“There’s much more capacity for adaptation than has been appreciated, even in the scientific community,” Shaw said. The hope is that it’s enough to make a difference for species that are racing to beat challenges like climate change. “But even so, the rapidity of environmental change may still outstrip, if we’re not careful, the capacity of populations to adapt,” she said.

As the predictive power of evolutionary rescue models increases, though, scientists could be able to boost that capacity. Understanding how evolutionary rescue works can help us “take steps to make sure those conditions are in place for species that we’re concerned about,” Shaw said.

Combined with field data, these models could identify the populations that aren’t big enough or genetically varied enough to stage their own rescue, but could get there with a little help. In that case, the models could guide human interventions that are less dramatic than airlifting a population to a more suitable climate. Maybe a small influx of new individuals from nearby could raise a population’s numbers and its gene pool over the tipping point. Or they could simply tell us when we can leave a population be and trust it will survive on its own.