In Silico Science: Ecology Without the Nature

When dealing with complicated ecological concepts, theoretical models – though they may seem abstract – often help create bridges to fill in our understanding, writes Thomas Haaland (Image Credit: Aga Khan, CC BY-SA 4.0, Image Cropped)

It should not come as a surprise any more that most ecologists don’t spend all that much (work) time outside. Numerous posts on this blog about data management and ecological modelling draw a picture of a modern biologist spending most of their time in front of a computer rather than out in the field. However, the work is still intimately related to the natural world. Gathering the data is simply the first step on the way to scientific understanding, and between organizing data, analyzing data, interpreting results and writing them up, the computer hours just vastly outweigh the outdoor hours. But there is another, more mysterious breed of researchers that has even less to do with nature: theoretical biologists.

What do Theoretical Biologists Do?

Theoretical biology involves modelling, but not the kind you may have heard of on this website before, where one attempts to apply a statistical model to ecological data. Neither does it involve engaging in idealized depictions of humans with or without clothes, or obsessively crafted representations of railroad systems, cityscapes or spaceships. Still, models in theoretical biology more closely resemble these last examples than they do statistical models. In biology, purely theoretical models involve limited or no use of actual data, and often don’t even aim to describe a particular biological phenomenon. Rather, they teach us something about general principles in biology by using abstraction, often through mathematics or computer simulations.

Is This Even Ecology?

At this point you might say, ehm, come again? No real-world data? What is this! You’re not even describing a specific system? Well, not all scientific hypotheses are best tested in situ. If we want to learn about the natural world, it may seem like a good idea to just go out in nature and measure what’s going on. But despite its entire lack of doing just that, theoretical biology is surprisingly useful, and has in many ways laid the foundation for modern ecology and evolutionary biology. Let me give you an example.

Perhaps the most well-known theoretical model in ecology for this blog’s readership – at least one that is encountered in the earlier courses in most biology bachelor (undergraduate) studies – is the Lotka-Volterra model of predator-prey cycles. Named after two early-1900’s mathematicians, this model includes a set of differential equations that describes how the population sizes of two competing species (often a predator and its prey) change over time, depending on the population size of the other species, and the strength of competition between them. The model doesn’t need to describe a specific system – to actually measure what the “competition coefficients” are for a given species pair can be difficult. Rather, a modeler can investigate the outcomes for any combination of parameters, allowing them to identify the limits of when one or another result could be expected. Many of these combinations might never occur in nature, but if they would, we have an idea of what would happen. In short: theoretical models inform us of what is possible in nature. So yes, this is indeed ecology, and a crucial part of it at that.

If all this still sounds vague or a bit useless, consider this. Put a bit provocatively, fitting a statistical model to biological data is easy. One could, potentially, mess around with covariates, response and predictor variables, data formatting, statistical tests, and subsets of results until it looks like the data show what one was hoping for. Typically, interpreting the results comes after they have been gathered. In contrast, when a scientific study is motivated by a theoretical model, any results need to be embedded within the theoretical framework. If the results don’t match the predictions, it can point either to a flaw in the theory, or the study methods. However, embedded within the right mental narrative, we can make sense even of results that may seem counterintuitive, confusing and difficult to interpret.

Scientific Reasoning: Theory and Mental Narratives

A fascinating paper, named “Theory in Service of Narratives in Ecology and Evolution” just came out in the journal The American Naturalist. It’s by theoretical biologist Sarah Otto (of “Otto & Day: A Biologist’s Guide to Mathematical Modelling” fame; this book is frequently referred to as “The Bible” by us theoreticians) and philosopher of science Alirio Rosales. It’s a different but illuminating paper, putting words to a lot of the thoughts I’ve had myself (but haven’t been able to express) about why we do what we do. Building on developments in psychology, Otto & Rosales point out that whereas we used to believe that humans reason logically and axiomatically (e.g. if A → B, and B → C, then A → C), we now recognize that human reasoning is actually much more based on narratives. We attempt to understand the world (be it a social situation, a question, or an abstract concept) through forming mental narratives, i.e. the process of building a story that makes sense.

These stories link our starting point with our desired outcome (the right action to take in the social setting, a solution to the question, an understanding of the concept), much like a path leading through a landscape. Some paths are straight, easily visible, even signposted – whereas others are windy, dark, difficult to follow in places, and meet forks in the road. Perhaps we know a certain path well because previous experiments or careful observations have supported our mental narrative and show us the way to the answer. In other situations, conditions might be difficult to reason through, outcomes don’t seem to match what we expected, or maybe we need really detailed predictions, for example when making decisions about conservation actions, or public health recommendations. In these situations where our mental narratives need help, we can build theoretical models. Theory can help to bridge gaps, often showing how different fields (with different narratives of how something works) are connected, or aren’t. Theory can also demonstrate flaws in our mental narratives, and points out when our reasoning is wrong.

The history of biology is full of examples where seemingly conflicting results have been united by a useful theoretical framework, or where clashing narratives have been reconciled through not only new data, but also new theoretical models. Importantly, it is not the theory itself that explains how ecology works, but how the mathematical theory supports and is placed within a narrative. However, instead of delving into 20th century history of ecology, I’ll turn to the current example that is on everyone’s minds.

Theoretical biology has had a prominent role in predicting the spread of COVID-19, and in identifying the best 'exit strategies' from lockdowns... but quality control is crucial in a time when the pressure to produce rapid science is greater than ever. (Image credit: Jonathan Berlier)

Theoretical biology has had a prominent role in predicting the spread of COVID-19, and in identifying the best ‘exit strategies’ from lockdowns… but quality control is crucial in a time when the pressure to produce rapid science is greater than ever. (Image credit: Jonathan Berlier)

COVID-19: Theoretical Biology in the Spotlight, For Better or Worse

The COVID-19 pandemic has proved a striking illustration of both the necessity of theoretical models, and the risks of relying on them without an adequate understanding of how they work. The actions taken by most governments have been in response to dire and urgent warnings from scientists, and there has been urgent societal pressure for both better models, and better quality-control (hasty work is unfortunately often sloppy work). Epidemiology (the study of the spread of diseases) is a long-standing branch of ecology that has seen a productive combination of experiments, observation and theory. Mathematical models (remember all that talk about exponential growth?) have helped us understand the spread of diseases under complex ecological conditions and multispecies communities, agricultural pest control, and host-parasite coevolution. But even with all this background knowledge, there were huge parts of the narrative map that were in the dark when the COVID-19 pandemic presented new challenges. Simple predictions showed that social distancing measures, immigration bans, and even full lockdown were fully necessary to flatten the curve and save thousands of lives, but new questions quickly emerged. What would happen as people grew tired of regulations that didn’t seem to have any effect? Will SARS-CoV-2 begin to evolve as we restrict transmission possibilities? What is the best “exit strategy” from lockdown? What are the effects of the strong age structure and sex bias in infection and susceptibility? Basic theory does not yet cover these questions, and a major challenge for the scientific community has been not only to develop appropriate models quickly, but also to convey this uncertainty to the people (and policymakers).

When so much is still unknown about the virus, theoretical models can’t always give us the correct answers, but they can point at where we need to look. They can identify which parameters are the crucial ones that determine what strategy is best, which data we need to focus on collecting. And they can point out when reasoning is wrong. If aiming for acquiring herd immunity might seem like an alluring narrative as many of us enter month four of lockdowns, important modeling work has shown that this is only feasible if we rely heavily on some very uncertain quantities (such as the percentage of asymptomic cases and infection risk of asymptomatics). As better data has become available from around the world, it has become clear that a herd immunity strategy would be very unlikely to work, and would lead to a large number of avoidable deaths.

Theoretical biologists aren’t the ones out gathering the data or performing the experiments needed to answer these questions. So are the literal thousands of theory papers that have been published on COVID-19 (much of it simply on preprint servers and not in peer-reviewed journals, where the publishing process can be long and arduous, for good reason) doing any good? Well, if you’re still reading I hope I’ve convinced you of the value theory can have, and that the problem in our current situation is rather the lack of rigor and quality control. Since anyone with basic mathematical skills and coding knowledge can dip their toes into this kind of modelling, people from around the world (many of them non-biologists) are understandably attempting to contribute positively while in quarantine. Meanwhile, ecologists and epidemiologists are attempting to review the oncoming papers, filter the good from the bad, and make sure that the useful contributions can be heard where it matters. It’s an exciting time to be a scientist for sure.

Thomas Haaland is a theoretical biologist currently working as a postdoctoral researcher at the University of Zürich, Switzerland. He is interested in many topics in ecology, evolution and animal behaviour, mostly centred on understanding adaptations to variable and unpredictable environments. You can follow his scientific endeavours on Twitter and his boredom-induced lockdown endeavours on Tiktok.

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