The study shows that mosquito swarms show collective behavior similar to “phase transitions of the second order” a concept that, in physics, is already well consolidated
How do mosquitoes form those intriguing swarms at sunset? The research conducted by our team from the center for research, innovation and diffusion in neuromatematics (neuromat) of the University of San Paolo (USP) shows that each mosquito regulates its position based on the proximity between its neighbors. With computational simulations, we have been able to form natural shamans to natural ones using a simple model.
Other models assume that the mosquitoes are attracted by a central point, as if they were linked to an invisible spring. But this perspective can be better related to cases of clouds of insects formed around lamps, which do not apply to the phenomenon of natural agglomeration and will analyze here.
Another hypothesis also assumed that the mosquito could calculate the distance between it and the center of the cloud.
The last possibility is based on the density of the mosquitoes present in a cloud, evaluating the collective behavior of these insects. Therefore, in addition to challenging traditional hypotheses, the results expand the understanding of the biological applications of the concepts of physics.
How the model works
In the study of our team, published in the Brazilian Journal of Physics Magazine, we use the concept of the neighborhood of Moore – an approach common to computational models, which allows you to simulate the spacing between insects in a cloud.
The original test is to use the information on the position of the eight closest neighbors, on an imaginary level at nine points. For our three -dimensional approach, we calculate the equivalent of a 3x3x3 cube, that is 26 neighbors near a central mosquito.
This division of space into grids is based on a concept known as discreteization. Something that is not necessary or realistic, but sufficient to demonstrate the idea that mosquitoes do not need long distance information to self-organize shamans. Each mosquito regulates its position based on the density of the site, i.e. the number of neighbors. This simple but robust approach carefully reproduced the formation and dispersion of shamans, revealing surprising models.
Second order phase transitions
In the results of the model, two phases appear: one with very compact and rigid swarms and another phase with very scattered swarms. Only in the transition region between these two phases (critical region) the model is able to describe clouds of real mosquitoes. Therefore, from a scientific point of view, the main discovery of our study is that the shamans formed by mosquitoes show collective behavior similar to the “second -order phase transitions” so called, a well established concept in physics.
This type of second order transition is characterized by a continuous transformation of a substance caused by an external factor. A classic example of this is the very studied behavior of magnets. As the temperature increases, atoms gradually lose their magnetic organization. Until, in a critical point, the material is no longer magnetic.
Unlike this behavior, the so-called first order transitions involve sudden changes in the physical-chemical characteristics of some studio substances. And the best and oldest example of this is very simple: the passage of liquid steam water, accelerated by intense heat.
And what does this have to do with the brain?
Phase transitions and criticisms in biological systems are important research topics in current statistical physics. This topic, which at first glance might seem disconnected, arrived in Neuromat from the research line on criticality in the brain, developed by our network of collaborators.
In this line of research, we show that neuron networks can develop information more efficiently when they are fundamental on the threshold of a phase transition. At this point, the network becomes more sensitive to stimuli and can detect very weak and very strong signals at the same time. This mechanism can help to explain how the brain interprets smells and images, since similar phenomena occur in the olfactory and retina system. In addition, we propose that the electrical connection between neurons improves this ability, allowing us to perceive the world more accurately.
Our interest in this area of research (collective movements of animals, such as mosquitoes), is due to the universality of the ideas of statistical physics applied in computational biology. Remember that the winner of the Nobel Prize of Physics in 2021, Giorgio Parisi, dedicated himself intensely to the problems of transitions in neuronal networks. And recently in drunk movements. Even more recent, the Nobel prizes of 2024 have been awarded their phase transition ideas on neuronal networks.
Seeing the theme of phase transitions in these distinct phenomena, from neurons to mosquito populations, shows how much the theoretical ideas are connected in physics and biology. In addition, these standards are also observed in ecology, epidemiology and even sociology and economyBy suggesting that the collective organization on the margins of a phase transition is a central theme in complex systems.
Although the study has advanced the theoretical understanding of the collective behavior of mosquitoes, intriguing questions still remain: how do insects detect local density and coordinate their actions during the transition? These mysteries continue to fascinate and strengthen the importance of exploring daily phenomena to understand the universal laws of nature. We believe that our work contributes to expanding the scope of mathematical and biological sciences by connecting fundamental concepts of physics to biological systems on various stairs.
Osame Kinouchi receives funding from Fapesp and CNPQ
Guilherme Roncaratti Galanti receives funding from CNPQ.
Source: Terra
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