Self-fertilization can improve resilience to extinction

It may sound like a contradiction, but scientists have discovered that in some situations a shift to self-fertilizing reproduction does not spell doom for the long-term survival of plant species.

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Computing resources provided through the biomed virtual organisation, DIRAC portal provided by France Grilles.

Some plants, for example tomatoes, wheat and other domesticated species with outcrossing wild ancestors, have evolved to a reproductive system based on self-fertilization where individuals develop the ability to fertilize their own ovules.

Although there are advantages in self-sufficiency, self-fertilization is generally thought to be an evolutionary dead end because it decreases the genetic diversity of a population. And low genetic diversity increases the vulnerability to change, diseases and, ultimately, to extinction.

The advantage of using EGI was being able to have several thousand simulations running at the same time, which was a huge time saver.

But is this true in all cases?

Diala Abu Awad and Sylvain Billiard, from the University of Lille in France, investigated the problem to find out if populations that are at low risk of extinction suddenly become more vulnerable just by adopting self-fertilization, or if other variables come into play.

They focused on the concept of deleterious mutations – genetic alterations that increase the susceptibility to certain diseases or metabolic malfunctions. When a plant species switches to self-fertilization, there is a risk that deleterious mutations can accumulate too quickly, with drastic consequences to the entire population.

To investigate the problem, the team designed a mathematical model to simulate the transition to self-fertilization. The model spans 150,000 generations (which translates to about 150,000 years in real life) and considers the initial size of the population, the evolution of deleterious mutations in tandem with self-fertilization, and how these mutations are introduced.

Abu Awad and Billiard explored over 3000 parameter sets and ran about 1000 simulations for each to achieve a reliable mean and confidence interval. Considering that each simulation required one core running for up to 48 hours (with an average of 12), it’s plain to see that the computational challenge was beyond the use of a normal computer.

The team ran the 3,000,000 simulations as High-Throughput Compute jobs, submitted to the EGI infrastructure via the DIRAC portal provided by France Grilles (the French national e-Infrastructure), and used resources made available by the data centres of the biomed virtual organisation.

“The advantage of using EGI was being able to have several thousand simulations running at the same time, which was a huge time saver,” says Abu Awad. “Other than that, thanks to the DIRAC platform, it was very easy and practical to use.”

The findings show that if a population has deleterious mutations with a strong effect (e.g. able to trigger a deadly genetic condition), then a switch to self-fertilization will cause a sudden high-mortality rate. This happens because self-fertilization will make it more likely for individuals to carry serious genetic diseases. But in the long-term, if the species survives this purge, the surviving individuals will have less deleterious mutations and the end result is a more robust and resilient population.

The results, published in the journal Evolution, also show the importance of taking all aspects of populations into account when making predictive models of evolution. “In our field,” Abu Awad says, “we often make the simplifying assumption that population size is a constant for practical reasons. So we may be missing out on some key processes because of this.”

Credit: Manjithkaini / Wikimedia Commons

More information

Diala Abu Awad’s research pages

Sylvain Billiard’s profile (University of Lille)

HTC usage

The team submitted 3,000,000 computing jobs using resources made available by the providers of the biomed virtual organisation.


D Abu Awad and S Billiard. 2017. Evolution. doi:10.1111/evo.13222 (full text)