In a recent study, experts tracked evolution across 105 changing environments and found that repeated environmental change can yield very different outcomes.

The researchers found that studying a single population can be misleading, so claims about an entire species require data from multiple populations and environments.

The study was led by Csenge Petak at the University of Vermont (UVM). Petak studies evolutionary dynamics, the pace and direction of change, in models where conditions switch and then return at UVM.

This work grew from a core problem, because repeated environmental change can reshape which traits consistently pay off.

Real populations face change, and the analysis focused on environments that swing back and forth rather than drifting.

Temperature cycles and rainfall cycles can push the same species toward different solutions, since selection favors traits that match each pattern.

Using a computer simulation, the researchers followed evolution for 10,000 generations. Each digital organism carried a gene regulatory network, and mutations changed network weights across generations.

Targets switched every 300 generations, and cellular automata, simple rules creating complex patterns, generated each target.

In the study, success was evaluated using a fitness landscape – a map linking an organism’s traits to its reproductive success.

Wide peaks can be easier to reach and stay on, while narrow peaks can trap only a few top-performers.

A local optimum, a peak better than nearby options, can block discovery of higher peaks when conditions change before exploration finishes.

Sometimes switching conditions paid off, because the researchers showed that change pushed populations away from narrow peaks.

“In some cases, changing the environment helped populations find higher fitness peaks; in others, it hindered them,” wrote Petak.

Some switching sequences left populations stuck on plateaus, because adaptation to one target placed groups far from options in the other.

Across many trials, the analysis showed that average success rose in variable environments.

Average fitness is key for a population’s survival, since it’s the success of most individuals – not just the top performer – that ensures persistence.

Higher average fitness can reduce the risk of sudden crashes after a switch, since the whole population sits on wider peaks.

Tracking long-term change, the team followed shifts in evolvability, the ability to generate useful heritable variation.

Each organism carries a genotype-to-phenotype map, how gene changes become trait changes, and the map can evolve along with traits.

Past selection can bias new variation, so later mutations more often produce forms that worked before rather than forms never tested.

Beyond fitness, the analysis measured mutational robustness, mutations causing little change in traits.

Robust systems can absorb many mutations without breaking function, and a previous study described how neutrality and innovation can still connect.

Higher robustness often appears when a population settles on a broad solution, because many nearby genetic changes stay workable.

Starting conditions proved influential, because the study showed early steps positioned populations on different landscape regions.

Biologists call this path dependence – later outcomes shaped by earlier choices – and the pattern can make repeats look inconsistent.

One well-studied population may sit on an unusual peak, so conclusions from that group can miss what other groups do.

Digital organisms differ from cells, yet the analysis offers clues about how real populations might respond.

Computational work can scan many environment pairs quickly, then guide laboratory tests toward the few cases where results split sharply.

Better evidence comes from comparing many populations across comparable changes, rather than treating one line of descent as the final word.

Climate change raises similar questions, because rapid stress can drive extinction unless evolutionary rescue, adaptation that restores population growth, occurs.

Evolutionary rescue depends on existing genetic variation and on population size, because selection needs survivors to pass on helpful variants.

Different populations often have different past exposures, so climate swings can favor rapid adaptation in one region and failure in another.

Antimicrobial resistance – bacteria that survive drugs and keep spreading – contributes to 2.8 million infections per year in the United States.

Antibiotic pressure turns on and off across days and hospitals, and bacteria can trade short-term survival for long-term costs.

Public health tests from a single site can misread the broader threat, because different strains face different drug histories.

Artificial intelligence (AI) faces a similar test when tasks change, because neural networks can lose old skills while learning new skills.

Online continual learning tries to keep earlier skills stable in AI by keeping old knowledge usable.

Catastrophic forgetting, rapid loss of earlier learning, can leave AI re-solving old tasks after each update instead of keeping stable skills.

Future work should test whether similar patterns appear in microbes and insects, where environments can be controlled across many lines.

Wider sampling across places and seasons can also sharpen conservation forecasts, because the most studied population may not be typical.

The study is published in Proceedings of the National Academy of Sciences.

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