Not just red in tooth and claw: biology's big debates

In the second of a series, biologist and priest, Andrew Davison, examines why it’s important to keep up with biology’s big debates.

Andrew Davison

Andrew works at the intersection of theology, science and philosophy. He is Canon and Regius Professor of Divinity at Christ Church, Oxford.

An osprey, in flight, holds a fish in its claws.

‘Wherever there’s water or air to navigate, the laws of fluid dynamics are bound to throw up wings, and bodies shaped like fish.’

Photo by Mathew Schwartz on Unsplash.

There’s hardly been a livelier time for evolutionary science than today; indeed, passions can run high. It’s not that Darwin’s vision of evolution is fundamentally in doubt: species adapt by natural selection, there’s variation between individuals, and those better adapted for their environment survive more often, passing on their genes to their children. In that, the theory of evolution stands, but many other parts of the evolutionary picture from the second half of the twentieth century are coming under criticism. That includes the following maxims:

‘the only significant form of inheritance involves genetic code’,

‘nothing that happens to an organism during its lifetime is passed on to its progeny’,

‘we agree what we mean by “species”’,

‘genes pass down the branches of the tree of life, not between them’,

and ‘evolution is fundamentally all about competition, not cooperation’.

Among the excellent crop of writers on these themes, Eva Jablonka and Marion Lamb stand out for their elegant prose, and a gift for communicating complex ideas clearly. As they recognise, the standard mid-twentieth century model of evolution might be worth criticising, but it’s also landed all sorts of important basic points. (They list ten.) The shortfall of the earlier, dominant theory was in being too narrow, with each insight too quickly eclipsing others.

Here are two examples. First, the classic twentieth century picture saw inheritance in terms of DNA and genes, passed on by ‘germline’ cells, such sperm and pollen. That’s all true, but it shouldn’t restrict our wider view of inheritance to that. Today, writers such as Jablonka and Lamb stress that organisms inherit from their parents (or parent) in all sorts of ways.

A second plank of the twentieth century picture is that evolution involves descent from a common ancestor. Again, that says something vital, even central, accepted by evolutionist old and new. The twentieth century position, however, added a restriction: that’s all that’s important on this score. The newer perspective recognises that while genes are – of course – central, and passed on from parent to child, organisms also swap genes between themselves (between branches of the tree of life, not just along those branches), even between very different species.

If we’re not careful, what’s written and taught (not least by theologians), even with the best will in the world, will be thirty or even fifty years out of date.

There’s a lot of excitement around these sorts of claims (and, remember, Jablonka and Lamb make eight more), and that can get quite noisy. Defenders of the older, narrower picture typically say that the newer themes are simply fuss over minor points. Advocates of the newer perspective disagree, saying that the twentieth century picture risks missing some important features of biology, which are now coming into better focus.

Why such debates matters

Why might this ferment among biologists matter for a site like this one, and for theologians, and discussions of religious matters? Well, for one thing, as I pointed out in my previous article, nothing quite dissolves the supposed animosity between science and religion (which is, after all, a relatively recent invention) like theologians and religious people getting excited about biology. It’s also important that any humanities scholar, the theologian among them, who’s engaging with science should keep up to date. If we’re not careful, what’s written and taught (not least by theologians), even with the best will in the world, will be thirty or even fifty years out of date.

But there’s more at stake. As we have seen, the twentieth century picture, for all it brought an admirable clarity to evolutionary thought, was reductionistic. We see that in Jablonka and Lamb’s exhortation to scientists: ‘yes, stress x, but don’t think that means you have to deny y.’ A religious vision tends to be an expansive one. It wants to recognise the reality and value of all sorts of things. Yes, there’s matter, atoms, molecules, and genes, but there’s also organisms, agents, cultures, groups, economies, hopes, loves. They’re all real. We can’t reduce one to the other: not organisms to genes, or agents to economies. A turn from reduction is welcome.

More than that, almost everything in the emerging twenty-first century view of evolution is fascinating from a theological perspective.

Take convergence, for instance. It turns out that evolution isn’t just driven by randomness, or by the demands of the surroundings. Also important are various features of physics, or mathematics – the contours of reality – that throw up elegant solutions to evolutionary problems, which are adopted by evolution time and again. Wherever you need to sturdy and space-efficient packing of cells (as in a honey comb, or a a wasp’s nest), the hexagon is ready and waiting. Wherever there’s water or air to navigate, the laws of fluid dynamics are bound to throw up wings, and bodies shaped like fish, dolphins, and penguins (which are all quite similar in shape).

How do we know this? Because evolution has converged on wings and that body shape independently, many times, as also on eyes, and everything else that Simon Conway Morris lists in the nine closely printed columns of convergences in the index to his book Life’s Solution. Evolution certainly involves randomness and need, but alongside them is something more like Plato’s forms: timeless realities, there to be discovered and put to work. Among the more theological of these eternal verities, covered in Conway Morris’s book, are perception, intelligence, community, communication, cooperation, altruism, farming, or construction

Exceeding a zero-sum game

Then there’s cooperation. Ever since Darwin’s Origin was published, and, even more, ever since Tennyson wrote about nature ‘red in tooth and claw’, theologians have been embarrassed about the place of cooperation in their vision of the world. Now, however, it turns out, competition isn’t the only force at work in biology or evolution after all. One of the features of reality that evolution discovers and puts to work again and again is cooperation, and ways to exceed a ‘zero-sum’ game. We see that in cooperation within a species, but also in cooperation between species, which is ubiquitous in nature: called mutualism, it’s found everywhere. As a rule, once two species stick around in proximity for the long run, down many generations, their relationship will turn to mutual benefit.

Ethicists are often wary of the suggestion that we can look at the way things are, and read a moral code there (getting an ‘ought’ from an ‘is’), but it’s an unusual person whose vision of right and wrong isn’t shaped, to some degree, by a sense of what the world is like. Well, it turns out that nature bears witness to the enduring worth of cooperation, and not only to competition.

In the first of these articles on biology, I pointed out the significance of ethics in thinking about biology, and about evolution in particular. For better or worse, and often for worse, thinking about evolution has been an ethical, social, political story. The evolutionary has been put to work for immoral, ends. It turns out to be wrong twice over to suppose evolution commends only competition. It’s wrong, first of all, because we are rational creatures, who can aspire to an understanding of good and evil that transcends the realm of nature. But also, as we now see, it’s wrong even to suppose the nature is only red in tooth and claw. There’s competition, but there’s also a lot of cooperation.

Suggested further reading

Archibald, John. 2014. One Plus One Equals One: Symbiosis and the Evolution of Complex Life. Oxford: Oxford University Press. An accessible introduction to biological mutualism, with an emphasis on the role of hybrid organisms (one living inside another) in major evolutionary transitions.

Bronstein, Judith L., ed. 2015. Mutualism. Oxford: Oxford University Press. The new standard treatment of biological mutualism.

Morris, Simon Conway. 2008. Life’s Solution: Inevitable Humans in a Lonely Universe. Cambridge: Cambridge University Press. A comprehensive discussion of convergence in evolution.

Day, Troy, and Russell Bonduriansky. 2018. Extended Heredity: A New Understanding of Inheritance and Evolution. Princeton, NJ: Princeton University Press. An engaging introduction to a broadened picture of inheritance.

Davison, Andrew. 2020a. Biological Mutualism: A Scientific Survey. Theology and Science 18 (2): 190–210. An accessible survey of some of the science of biological mutualism.

———. 2020b. Christian Doctrine and Biological Mutualism: Some Explorations in Systematic and Philosophical Theology. Theology and Science 18 (2): 258–78. A foray into some of the significance of mutualism for Christian theology.

Jablonka, Eva, and Marion Lamb. 2020. Inheritance Systems and the Extended Synthesis. Cambridge University Press. A short discussion of many of the more expansive aspects proposed for contemporary evolutionary thought.

Jablonka, Eva, Marion J. Lamb, and Anna Zeligowski. 2014. Evolution in Four Dimensions: Genetic, Epigenetic, Behavioral, and Symbolic Variation in the History of Life. Revised edition. Cambridge, MA: MIT Press. One of the most substantial discussions of the new perspective.

Laland, Kevin, Tobias Uller, arc Feldman, Kim Sterelny, Gerd B. Müller, Armin Moczek, Eva Jablonka, et al. 2014. Does Evolutionary Theory Need a Rethink? Nature 514 (7521): 161–64. MA short two-sided piece, asking whether a transformation in evolutionary thinking is under way.

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21 June 2024

4 min read

It's our mistakes that make us human

What we learn distinguishes us from tech.

Silvianne Aspray

Silvianne Aspray is a theologian and postdoctoral fellow at the University of Cambridge.

A man staring at a laptop grimmaces and holds his hands to his head.

Francisco De Legarreta C. on Unsplash.

The distinction between technology and human beings has become blurry: AI seems to be able to listen, answer our questions, even respond to our feelings. It becomes increasingly easy to confuse machines with humans. In this situation, it is increasingly important to ask: What makes us human, in distinction from machines? There are many answers to this question, but for now I would like to focus on just one aspect of what I think is distinctively human: As human beings, we live and learn in time.

To be human means to be intrinsically temporal. We live in time and are oriented towards a future good. We are learning animals, and our learning is bound up with the taking of time. When we learn to know or to do something, we necessarily make mistakes, and we take practice. But keeping in view something we desire – a future good – we keep going.

Let’s take the example of language. We acquire language in community over time. Toddlers make all sorts of hilarious mistakes when they first try to talk, and it takes them a long time even to get single words right, let alone to try and form sentences. But they keep trying, and they eventually learn. The same goes with love: Knowing how to love our family or our neighbours near and far is not something we are good at instantly. It is not the sort of learning where you absorb a piece of information and then you ‘get’ it. No, we learn it over time, we imitate others, we practice and even when we have learned, in the abstract, what it is to be loving, we keep getting it wrong.

This, too, is part of what it means to be human: to make mistakes. Not the sort of mistakes machines make, when they classify some information wrongly, for instance, but the very human mistake of falling short of your own ideal. Of striving towards something you desire – happiness, in the broadest of terms – and yet falling short, in your actions, of that very goal. But there’s another very human thing right here: Human beings can also change. They – we – can have a change of heart, be transformed, and at some point in time, actually start to do the right thing – even against all the odds. Statistics of past behaviours, do not always correctly predict future outcomes. Part of being human means that we can be transformed.

Transformation sometimes comes suddenly, when an overwhelming, awe-inspiring experience changes somebody’s life as by a bolt of lightning. Much more commonly, though, such transformation takes time. Through taking up small practices, we can form new habits, gradually acquire virtue, and do the right thing more often than not. This is so human: We are anything but perfect. As Christians would say: We have a tendency to entangle ourselves in the mess of sin and guilt. But we also bear the image of the Holy One who made us, and by the grace and favour of that One, we are not forever stuck in the mess. We are redeemed: are given the strength to keep trying, despite the mistakes we make, and given the grace to acquire virtue and become better people over time. All of this to say that being human means to live in time, and to learn in time.

So, this is a real difference between human beings and machines: Human beings can, and do strive toward a future good.

Now compare this to the most complex of machines. We say that AI is able to “learn”. But what does it mean to learn, for AI? Machine learning is usually categorized into supervised learning, unsupervised and self-supervised learning. Supervised learning means that a model is trained for a specific task based on correctly labelled data. For instance, if a model is to predict whether a mammogram image contains a cancerous tumour, it is given many example images which are correctly classed as ‘contains cancer’ or ‘does not contain cancer’. That way, it is “taught” to recognise cancer in unlabelled mammograms. Unsupervised learning is different. Here, the system looks for patterns in the dataset it is given. It clusters and groups data without relying on predefined labels. Self-supervised learning uses both methods: Here, the system uses parts of the data itself as a kind of label – such as, for instance, predicting the upper half of an image from its lower half, or the next word in a given text. This is the predominant paradigm for how contemporary large-scale AI models “learn”.

In each case, AI’s learning is necessarily based on data sets. Learning happens with reference to pre-given data, and in that sense with reference to the past. It may look like such models can consider the future, and have future goals, but only insofar as they have picked up patterns in past data, which they use to predict future patterns – as if the future was nothing but a repetition of the past.

So this is a real difference between human beings and machines: Human beings can, and do strive toward a future good. Machines, by contrast, are always oriented towards the past of the data that was fed to them. Human beings are intrinsically temporal beings, whereas machines are defined by temporality only in a very limited sense: it takes time to upload data, and for the data to be processed, for instance. Time, for machines, is nothing but an extension of the past, whereas for human beings, it is an invitation to and the possibility for being transformed for the sake of a future good. We, human beings, are intrinsically temporal, living in time towards a future good – which machines do not.

In the face of new technologies we need a sharpened sense for the strange and awe-inspiring species that is the human race, and cultivate a new sense of wonder about humanity itself.