Where on Earth did life arise, and where else could it occur? A comprehensive answer to this question is most likely still a long way off. But it may depend on how many suitable sites for abiogenesis there are on different worlds.
We have only one data point for life: good old Earth. Studying abiogenesis, the natural process by which life arises from nonliving matter, can't be done by observing other places where it has occurred. Instead, scientists use models to get to the bottom of the big question.
Manasvi Lingam is an astrobiologist at Florida Tech University. In a new study, Lingam and his colleagues examine the probability of life arising in different places on Earth. The study is titled “A Bayesian analysis of the probability of life arising in places conducive to abiogenesis.” It was published in the journal Astrobiology and the other authors are Ruth Nichols and Amedeo Balbi.
“We can't look into the past. Sometimes you can get answers just by cleverly using limited data… but there's a part you'll never know.”
Manasvi Lingam, astrobiologist, Florida Tech University
A Bayesian analysis uses existing knowledge – in this case, the appearance of life on Earth – to estimate how likely it is that the same thing will happen somewhere else. If we ignore panspermia, we know that life has arisen on Earth at least once. Scientists can use it to determine how likely it is that life has arisen elsewhere.
There are many obstacles on our path to understanding the spontaneous origin of life. “One of the greatest of these current limitations is our lack of conclusive knowledge of the minimal conditions required for the emergence of abiogenesis, as well as the lack of clear data indicating the likely location(s) where this process occurred,” the authors write.
But the fact that it occurred on Earth at least once, and possibly in several places, is an informative fact. However, the information does not indicate its existence. Scientists have to find out first. “Nevertheless, the occurrence of abiogenesis on Earth still has considerable informational value,” the authors explain.
An image of Earth taken by the Galileo spacecraft in 1990. Even though we don't know how life began, scientists can use the fact that life exists to study the probability. Image credit: NASA/JPL
In a new study, Lingam and his colleagues developed a model based on arable sites, places where life could emerge. The results were surprising and counterintuitive.
Arable sites are environments where we believe life can arise. These include hydrothermal vents, impact sites, lakes and ponds, and natural nuclear reactors such as the one that existed in Gabon two billion years ago.
In this paper, the researchers compiled a list of possible locations, and each type has a corresponding level of conduciveness to the emergence of life. They designed their models based on two questions: how many locations could have given rise to life on Earth, and what is the probability that life arose at each of those locations.
It is important to understand that this work cannot tell us how and where life arose. Rather, it was about understanding how to interpret the results of the models.
In their simulations, the researchers looked at three different scenarios, each with a different number of reclaimed sites. One had only 10 reclaimed sites, one had 1016 reclaimed sites, and one had 1031 reclaimed sites. They also worked with optimistic, pessimistic, and uninformative scenarios. The optimistic scenario had a higher probability of life emerging per reclaimed site, the pessimistic had a lower probability, and uninformative scenarios meant the results were just that.
Warm ponds are one type of exploitable site. This artist's impression shows the early Earth, where the continental crust was below sea level and the only exposed land was volcanic islands. On these islands, bombarded by lightning, gas from volcanoes could have formed increasingly complex molecules in ponds. Eventually, a molecule could have formed that could store information, reproduce it, and mutate randomly. As these islands eroded, these molecules could have entered the ocean. Image credit: NASA
The researchers assumed that a larger number of accessible sites would mean a higher probability of life emerging. But to their surprise, the opposite was true. More sites meant a lower probability of life emerging, and fewer sites meant a higher probability.
“These are the two situations we have here. One where there are many locations, but the probability is very low, [of life] per location. And secondly, there are very few locations, but the probability per location is very high,” Lingam said in a press release.
“Usually, the attitude for many things in life is 'the more the better,'” says Lingam. “But more is not always better. If it's less, but the right kind of less, then that can actually be better.”
This means that in their model, where there are the fewest accessible sites on Earth, the probability of life emerging at a single site is higher. Where there are many sites, the probability of life emerging at any one of them is lower.
This black smoker hydrothermal vent was discovered in the Atlantic Ocean in 1979. It is fed by magma deep below the surface, which superheats the water. The plume carries minerals and other materials into the sea. Vents like this are a type of exploitable site. Image credit: USGS.
Although these results seem counterintuitive, they are still valuable, Lingam says. There is no consensus on the usable location where life arose, so different researchers can use them in their experiments to understand their own preferred environments in experiments. “Then they can do lab experiments and try to get a sense of how many attempts it might take to actually get to something resembling life,” Lingam says.
Although we still don't know much about the origin of life and these models can't tell us how life arose, Lingam's work can still help other researchers make progress.
“We can't look into the past,” says Lingam. “Sometimes you can get answers just by cleverly using limited data… but there's a part you'll never know.”
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