Learning Mitigate Genetic Drift – Scientific Reports

Genetic drift describes the random fluctuations in allelic diversity in a population. In this paper, we estimated that a systematic increase in organisms’ chances of survival in response to a random source of mortality, e.g., learning from experience, attenuates genetic drift (decreasing number of alleles) even when overall death rates remain the same. same. To test this hypothesis, we developed an agent-based simulation model. The results of our simulations show that even if the same number of prey die per time step in the learning and non-learning scenarios, their number of alleles decreases at quite different rates. This effect is explained by the tendency of learning to create a pool of older experienced individuals that have a very low chance of dying from predation and thus create a pool of alleles that are unlikely to be lost. . In addition to supporting our hypothesis in general, our results also demonstrate that this effect applies regardless of whether the affected organism is haploid and reproduces asexually or diploid and reproduces sexually.

Our results suggest that learning from experience may be an overlooked factor affecting effective population size. Unlike most other factors such as an unequal number of males and females4,9,10the ability to learn from experience eventually increases the effective population size.

The purpose of this article is to highlight the existence of a complex relationship between different mechanisms improving the survival capacity of organisms and the strength of genetic drift. Obviously, the results presented here cannot be expected to tell us anything about the strength of this effect in nature. With regard to experiential learning, the crucial factor in determining this would be to assess how effectively animals can learn to avoid or escape mortal danger. For the simulations in this article, we operated with an efficiency of 90% or more. Arguably, this probability may be too high in real-world settings. However, in certain circumstances, it is plausible. For example, a study of the barn owl (These albums) showed that these owls have a 90% chance of catching stationary food and only a 21% chance of catching moving objects11. Additionally, the owls were even less likely to catch an object heading their way (18%), and more interestingly, if the food moved sideways, the barn owls were not able to catch it at all. Therefore, it seems possible that prey could learn simple strategies that would allow them to escape at least some types of predators with surprisingly high probability. Nevertheless, even precise information about how well animals can learn to avoid or escape different sources of mortality may not be sufficient to predict the effect of learning on genetic drift in a real population, because the ability to learn is itself an adaptive trait. and including selection in the model would further complicate the interpretation of its results. Either way, our message remains the same; predicting the strength of genetic drift is more complex than usually assumed.

At first glance, it may appear that the learning effect presented in this article is caused simply by the fact that learning increases the generational interval (the average age of parents at the birth of their offspring), which which is known to cause an increase in effective population size. and therefore a smaller genetic driftten. However, as we have shown, while learning increases the generation interval in most simulation settings, there are situations where it decreases allele count loss without extending the generation interval. Therefore, longer generation intervals likely explain some, but not all, of the learning effect. However, this is only because the generation interval is an average measurement that cannot reflect changes resulting from changes in the age distribution that do not alter the average values. This can happen in some contexts with learning scenarios, because learning not only creates a pool of older individuals that are hard to kill, but also decreases the number of individuals surviving to a certain age (specific to the simulation) ; see Figs. 1c, d.

We illustrated our idea of ‚Äč‚Äčlearning to mitigate genetic drift by considering the effect of learning in the context of predation. However, for the same reasons, learning exerts the same influence on genetic drift caused by any source of mortality, which individuals of a given species can learn to avoid. For example, if individuals of a given species were able to learn to avoid traffic from experience, the effect on genetic drift would be identical to the learned ability to avoid or escape predators. To go even further, any improvement acquired in the ability to find a partner for mating, to find food and therefore reduce the risk of starvation, or to take care of its offspring and thus increase its chances to reach adulthood, will go some way to preserving genetic diversity despite the gross effects of drift. Accordingly, there may be other types of reactions, such as immunological memory, which could help preserve genetic diversity by mitigating genetic drift by the same principle.

It is important to note that our model assumes overlapping generations, and therefore all of our results only apply to such a situation. With non-overlapping generations, the benefits of nonhereditary learning are likely to be non-existent or much weaker because they have much less time to have an effect (provided the learning is immediate). In our scenarios with overlapping generations, individuals who were lucky and learned to evade predators effectively could breed multiple times before death. With non-overlapping generations, they could only reproduce once, regardless of their ability to evade predators, which would undoubtedly reduce the benefits of learning.

The relationship described here between learning and genetic drift also leads to testable predictions. First, species that can learn more efficiently should, on average, retain genetic diversity better than less able learner species living in populations of similar size. The second, related prediction is that species that are “better learners” should have a lower risk of extinction. This prediction is in good agreement with a recent study showing that bird species with greater behavioral plasticity do indeed have a lower risk of extinction.12, although there are likely other reasons responsible for this connection, such as the likely ability of behavioral plasticity to reduce mortality rates. The third prediction. Regarding our results, there are two types of excess mortality, one that individuals of a given species can learn to avoid, and the second that members of that species cannot learn to avoid or escape. Our results suggest that the first type of excess mortality should reduce genetic diversity to a lesser extent than the second type, even though they lead to the same increase in mortality rates. Analogous predictions can also be made about immunological memory or any process that reduces mortality by a systematic reactive mechanism.

Overall, our results show that the conventional view of genetic drift as independent of underlying species behavior is incomplete and that genetic drift can be affected by common processes such as learning or immunological memory, even whether the overall population-level death rates remain the same. Moreover, we showed that the level of protection against genetic drift varies across situations, suggesting that the loss of genetic diversity through genetic drift is a more complex problem than previously thought. We hope that these findings will make existing evolutionary models more accurate and may prove useful in a variety of topics, including the development of effective species conservation strategies, studies of the evolutionary past as well as the evolutionary future.

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