He is modelling ecological systems

The theory of evolution implies that species evolve to have the best chance of survival in any given environment. Then why are there so many species, and why does that pose a problem for theoreticians? György (Gyuri) Barabas’s research is concerned with the theory of coexistence between species. He is a theoretical population biologist whose daily work consists of translating species’ traits and relationships into equations and mathematical models.

György Barabas, Assistant professor, IFM Olov Planthaber

"There are a lot of species out there, they seem to coexist just fine as a default modus operandi", says Gyuri, "so why does that pose a problem?" He illustrates it by explaining the process of natural selection in humans. "Selective pressures favoured those who were able to think and reflect better, and those genes took over and here we are today. We take it for granted that if we have two very similar types, they are not going to coexist: the one that is better suited for the environment is the one that is going to persist. In a way we have a strong confidence in species not coexisting. In the face of the premise that only one type best suited for the environment persists, how come we have so many different species? And more importantly, how is it that similar species can coexist with one another?"

As species in many cases don’t interact much with one another and hence don’t stand in each other’s way, it is easy to at least intuitively comprehend why they can coexist. But what about those that do interact? Gyuri gives us an example of birds, where one could expect that they will adapt their beaks so that they can eat the most nutritious and most commonly available seeds. But if all the birds developed in this way, they would all be competing with one another, which would cause them a bigger hindrance to their population growth than having a beak that is not all that optimal, but it avoids competition on the same foods. If they were the only bird, the optimal beak size would be the one that gives optimal feeding. This means that they evolve away from the optimal beak size to avoid competition on the same foods, while still having a reasonable amount of food available.

 

In this graph, Gyuri illustrates the above mentioned beak size optimisation process.

The x-axis can be read as a measure of the beak size of birds. Each beak size is optimally fit to eat the seeds of a corresponding size. The rate at which seeds of a given size are produced is symbolized by the red dashed curve: the taller it is, the more seeds are produced. For example, the seeds that can optimally be eaten with a beak of trait value 0 are the ones being produced the fastest, whereas those that can be eaten best with a beak of trait value 0.5 or larger are not available at all in the environment.

The solid curves (green and purple) show the distribution of beak sizes within two bird species. Since individuals, even within a species are not identical, these distributions are somewhat spread out.

The y-axis measures the heights of these curves, whose meaning is the density of individuals of a given trait value.

Initially, both species have an average trait value centred at the optimum - the trait value that would be optimal in the absence of competitors. As time progresses, we see that the distributions start shifting away from one another: the green species shifts towards lower trait values, and the purple species towards higher ones. What is happening is that individuals born with a trait value near zero have a higher chance of dying. This is despite the fact that under ideal conditions, a trait value of zero would be optimal; conditions are not ideal because of the presence of too many competitors. Thus, individuals that happen to be born with trait values farther from zero have the edge. Although they have fewer seeds to feast on, they also avoid intense competition in the middle. This process continues for a while, but eventually stabilises when the species reach a point where evolving even more extreme traits would go to the detriment of their feeding. Thus, the final evolutionarily stable state is a compromise between being sufficiently different from competitors to avoid competition while still having trait values close enough to zero to have a steady supply of seeds to eat.

Sensitivity to environmental change

A related aspect of his coexistence research investigates sensitivity of communities to perturbations in the environment, such as climate change. We have species that coexist, and a perturbation in their environment happens: it might get warmer, or the variability in temperature might increase, etc. Gyuri develops mathematical techniques to be able to answer what happens to a community in response to such changes.  Apart from the practical benefits of predicting the possible future fate of an ecological community, this question turns out to hold a surprisingly strong connection with the question posed above: how can similar species coexist. “It turns out that the coexistence of similar species is not impossible, just highly unlikely under most circumstances – precisely because communities of similar species become oversensitive to changes in the environment”, says Gyuri. This weaker, sensitivity-based interpretation of species coexistence holds the key to understanding how many species can arise and coexist, even coming from a single ancestor.

Theoretical studies with applied collaborations

Gyuri Barabas’s work is for the most part basic research, with some elements of applied research conducted in collaboration with other scientists. One of his collaborators is Anna Eklöf, seated across the corridor from him, with whom he has a project where they work on the Baltic sea and the ecosystem services there, and how various threats are expected to affect them. He also has ongoing collaborations with a group of biologists in Hungary interested in how eukaryotic cells (those with a nucleus, found in all domains of life except for bacteria and archaea) arose approximately 2 billion years ago, and with the research group headed by Frédérik De Laender at the University of Namur in Belgium. With this latter group, Gyuri works on questions of ecological trait diversity. In terms of the bird example given above: instead of focusing on how many bird species there are, one can instead look at how many different beak sizes and shapes are present in a community. Since even within a single species there can be a large variety of beak morphologies, looking at communities through this lens requires different methods than the more traditional species-based approach.

As a theoretician, Gyuri’s research does not require expensive equipment or elaborate measuring devices. All he needs is a whiteboard, a computer, and an intact brain – with occasional support from the high-performance computing clusters of the National Supercomputer Centre (NSC). Starting from empirical data and a burning research question, he will usually try to translate his scientific ideas into a set of model equations. Once created, they can either be solved by hand, or more commonly, be implemented on a computer. Once the models yield their answers to the original questions, one can compare them with observed data to see if they match, or if the models must be modified and updated to give better descriptions of reality. This feedback between theoretical modelling and data is what forms the theoretical work into science, and is an integral part of Gyuri’s daily work routine.

From physics to ecology

Ecology wasn’t the most obvious choice for Gyuri, as he started his research path in physics doing a masters' degree at Eötvös University in Budapest, Hungary. While looking for interesting thesis projects to engage with, Gyuri talked to various people and one project really caught his attention, even though the topic was on ecological dynamics. He was intrigued, but also uncertain whether this was a field for a physicist. He continued talking to Géza Meszéna, the scientist in charge of the project, and they found a common ground and became a good team. The thesis work sparked an interest in the field of theoretical ecology and evolutionary biology, so much so that he started looking for PhD prospects in this field.

The search took him to Ann Arbor and University of Michigan, where he did his PhD training under the supervision of Annette Ostling. He describes his time in Ann Arbor between 2009 and 2013 as having been fantastic – both academically and more generally, thanks to the culturally vibrant milieu of the University. After graduating, he spent a period of three years at the University of Chicago as a postdoc. There he worked with Stefano Allesina, a supervisor who also became his role model for his impeccable work ethic, but also for his ability to make hard work and intense scientific inquiry an enjoyable, inclusive, and intellectually rewarding enterprise for everyone around him. He also made a point of being collaborative and helpful to everyone, from beginning graduate students to professors emeriti. No matter who would approach him in his office, the door of which was always wide open, he would immediately stop what he was doing and give his full attention to the visitor’s question. And he would do so regardless of how pressed he himself was for time, or how urgently he needed to finish something. This remarkable collaborativeness is something Gyuri responded to with admiration, and he tries his very best to emulate it in his own daily routine as well.

As it happened, Gyuri’s mentor Stefano Allesina was years earlier also the postdoctoral advisor of LiU researcher Anna Eklöf. As he knew that Anna now was looking for a postdoc for her own research group, he connected the two of them, which resulted in Gyuri coming to LiU for a second postdoctoral appointment at the end of 2016. During his first year in Linköping, Gyuri received a VR starting grant at LiU, which then enabled him to get a position as an assistant professor here. Since then, he has been appointed docent, and is currently applying for promotion to associate professor.

Academia and research funding systems

His studies and work in different countries have given him experience from and perspectives on the world of academia in several countries, all of which have their own unique advantages as well as challenges. As an example, Gyuri talks about the tenure system in the USA and the grant-chasing system in Sweden. Both are a source of stress, as not getting a tenure or not getting external grants mean that one cannot continue one’s research. He describes the grant seeking system as being overly competitive. "Your research may be excellent, perhaps evidenced by having published several articles in top journals; your research grant proposal could be well thought-through and well-written – yet none of this is a warrant for getting funding for your research. This is a critical weakness in a system where even one’s own salary must be procured through external funding. Such "soft-money" systems work well if good work and a solid research proposal predictably lead to funding for the next grant cycle. Instead, the Swedish system combines the worst of two worlds, being based on soft money, which at the same time is very difficult to come by", he says.György Barabas, Assistant professor, IFMGyörgy Barabas, Assistant professor, IFM

However, he is cautiously optimistic that the new change in the system of distribution of internal funding at LiU will have a positive effect on the funding situation. Otherwise, he feels that the extreme difficulties of maintaining a scientific career foreshadow a dystopian future for science down the road. In this future, talented researchers may simply be unwilling to put up with the high stress, long working hours, and financial uncertainty of academia, seeking jobs in industry instead. In the long run, this could lead to counterselection. The best minds would be soaked up by industry, leaving only the not-so-talented in academic positions – which in turn would weaken the role and influence of academia. “It is in the long-term interest of both academia and the public to provide a working environment to scientists in which one can focus on doing the best possible science. Otherwise, the aforementioned positive feedback loop might gradually erode academia to the extent that it will no longer be able to take the role of intellectual leadership in the world.”

Digital evolution systems

Next for Gyuri in his research is to try to understand the complexity of ecological communities from a slightly different angle. Ecology is a challenging subject for two main reasons. First, many of the experiments one would like to do in community ecology either cannot be performed, take too much time, or are unethical. Gyuri gives an example: as an ecologist, one would love to know how a forest would respond if one were to cut down half the trees of one given species. The problem is that even if this could be done, one would have to wait several hundred years for the forest to recover, all the while dutifully watching and recording the precise process of recovery – at which point the experiment would have to be repeated, now with a different tree species...

Clearly, such an empirical research program is neither feasible nor environmentally justifiable. The second reason why ecology is a challenging subject is that biological organisms are the most complicated machines in the known universe. As such, their interactions have been honed by millions of years of natural selection, which means they have a tendency to resist description in simple terms. Traditional mathematical models, the kind that make up Gyuri’s main research, can therefore only go so far in capturing these interactions. In this new research program, Gyuri is hoping to resolve both limitations at once, by using digital evolution to model complex communities.

Digital evolution systems are software which emulate a special kind of computer. In this virtual computer, whenever an instruction is copied from one memory location to another, there is some chance that the copying happens with an error, and a different instruction gets copied instead. If one initiates such a system with a single self-replicating program, akin to a computer virus, but without any harmful side effects, it will keep reproducing itself, but imperfectly so due to the copying errors. This means that several "mutant" programs arise – and if for some reason these mutants have better properties than the parent organisms, they will spread faster. Thus, Darwinian natural selection can and will start to operate in these systems, which will then give rise to whole ecosystems of self-replicating programs that interact with one another in myriad different ways. Even better, the kinds of strategies that arise in the digital world often have striking parallels with those found in nature. For example, parasitic interactions or specializing to eat just one resource but do so effectively have all been observed in digital evolution systems.

Gyuri argues that such observations reinforce the intuition that digital ecosystems provide the best existing metaphor for natural ones, such as lakes or rainforests. He is therefore planning to use them to explore ecological and evolutionary dynamics. The key to doing this is that, unlike with natural systems, one may perform experiments on digital ecosystems at will – the worst that can happen, if the experiment were to fail, is that one "rewinds" the system to a previously saved state. In this way, he hopes to gain insight into the inner workings of complex adaptive systems that would not be available via other methods.

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