Old eggs, daphnia, and evolution

When predation is high, crustaceans and other water loving egg lay-ers are not hatched much. What often happens is that they will remain dormant until later in the year when the predators are much less active. This offers a great research opportunity into evolution.

By hatching these eggs, Hairston and others can compare time-suspended hatchlings with their more contemporary counterparts to better understand how a species may have evolved…

What happens is that some of these eggs can remain unhatched for years and years, not just seasons. This is the case with daphnia. These are normally seasonal crustaceans, but researchers have specimens which are upwards of 40 years old. They use these to compare the change which has happened to this species over time. Daphnia_DGC

In the 1960’s, the lake from which these daphnia were taken had non-toxic levels of algae. But in the 1970’s, pollution had caused the algae to raise to a deadly imbalance. Currently, daphnia still reside in the lake, but researchers have found they are markedly different from the eggs they hatched. The older version of the species was unable to survive in the lake, poisoned by the overwhelming cyanobacteria. Clearly, the newer species had adapted to their new environment throughout the 70’s and subsequent decades.

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9 comments on “Old eggs, daphnia, and evolution

  1. Naturally.

    The important point here (which I seem to have glossed over a bit) is that this gives scientists a new way to investigate in detail the evolution of this species by comparing its ancestors to its contemporaries.

  2. I did not say non-random. I said that there is a way to test whether this observation is the result of random factors.

    Do you believe there is a random element involved in the example you’ve provided?

  3. Research is still being done on what changes happened in the daphnia population, but assuming even one mutation occurred (and it’s sure a great many more than that actually did), then yes, there is plenty of randomness involved here.

  4. Set up a number of populations of an original population and then introduce the populations to a new environment.

    Record how long it takes for each population to make the expected change.

    The likelihood of a random factor being responsible can be tested by a simple statistical analysis.

    I predict that in this case the change would always happen within a predictable timeframe thus eliminating the possibility that any random effect is at play.

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