On this special episode, we talk with David George Haskell, a writer and biologist. We discuss his most recent book, How Flowers Made our World, which explores the creative powers of flowering plants. Big Biology blogger and Biology PhD student, Clayton Glasgow, interviews David as an outreach project for the Young Voices of Science program. They discuss how flowers have played key roles in many ecosystems, the genomic innovations that underpinned the success of flowering plants, and the significant relationship between humans and flowers.

Cover art by Brianna Longo.

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We’ll be sharing these spotlights during our Big Biology episodes and on our social media.

For a chance to be featured on a future show, send us a 60-90 second audio or video recording of you talking about your work for a general audience. Feel free to record on your phone or computer. No need to use fancy tools!

In the recording, include as well:

• Your name

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Send the recording, a high quality photo of yourself, and any social media handles you want us to tag to: info@bigbiology.org.

Looking for inspiration? Check out this video that Kyle Gray made for our 2020 Student Spotlight:

In this episode, we talk with Diego Vázquez, a community ecologist at CONICET in Mendoza, Argentina, who has spent his career mapping the networks of interaction between plants and pollinators. Textbooks still teach coevolution as tight, faithful pairs, but Diego’s work tells a different story, that specialists mostly interact with generalists, and the best predictor of pollinator visits to plants turns out to be not matching traits or shared ancestry but abundance. Much of the structure ecologists prize in these networks can emerge from “neutral” models that know only who is common and when. We dig into why predicting these interactions remains hard, what his twenty-years of data on declining solitary bees is showing, and what it takes to keep long-term ecology alive in Argentina after deep cuts to science funding.

Cover art by Brianna Longo

But in recent decades, a few endocrinologists have started to measure hormones in wild animals living in their natural habitats. Work like this has become of increasing importance to conservation, and Dr. John Wingfield, Emeritus Professor of Physiology and Behavior at the University of California, Davis and a recent guest on Big Biology, has had a large hand in this success.

Wingfield pioneered the study of field endocrinology, bringing a predominantly lab-based science into the habitats where organisms actually live (similar to what previous Big Biology guest Nachum Ulanovsky is now trying to do for neuroscience).

Historically, endocrinologists studied hormones under controlled conditions in the lab. But hormones, after all, didn’t evolve in laboratories. They evolved to help organisms navigate real-world challenges like finding food, avoiding predators, competing for mates, raising offspring, and surviving environmental change.

Understanding hormones therefore requires understanding the ecological contexts in which they operate. In Wingfield’s first year studying white-crowned sparrows in the field with Don Farner at the University of Washington, Wingfield recalls: “I got the assays done, compiled the data, and we looked at the pattern, and it was completely different from anything we saw in the lab.”

And so began field endocrinology.

Over the following decades, Wingfield helped transform our understanding of hormones from simple regulators of internal physiology to mediators of how organisms interact with their environments. Much of his work focused on glucocorticoids—so-called “stress hormones.” Rather than treating these hormones as straightforward indicators of stress, Wingfield showed that their significance depends heavily on ecological context.

For example, a brief surge in corticosterone can actually be beneficial. It mobilizes glucose, sharpens attention, and helps an animal respond to immediate challenges such as predators, storms, or food shortages. But animals are not constantly responding to emergencies. They must also migrate, reproduce, molt, defend territories, and raise offspring. Through studies of birds in the Arctic, temperate regions, and deserts, Wingfield has demonstrated that endocrine systems are finely tuned to these changing ecological demands, allowing animals to shift resources among competing priorities as conditions change.

This work led to some of Wingfield’s most influential ideas, including the “emergency life history hypothesis,” which describes how animals temporarily redirect energy away from activities such as reproduction and toward immediate survival when faced with unexpected challenges. More broadly, it helped establish the view that hormones are not merely internal signals but mechanisms through which organisms integrate information about the outside world and adjust their behavior and physiology accordingly.

This insight is important not only for understanding animal biology, but also for tracking and assessing the impacts of environmental change.

In a 2005 paper titled “Field Endocrinology and Conservation Biology,” Wingfield and colleagues argued that endocrine measurements could provide conservationists with information unavailable through traditional monitoring approaches such as population surveys. Population surveys can reveal whether numbers are declining, but hormones can reveal how animals are responding to environmental change long before those declines become apparent.

For example, Wingfield and colleagues measured the glucocorticoid stress response to evaluate the effects of ecotourism on Magellanic penguins in Argentina. Although adult penguins appeared largely habituated to human visitors, chicks in areas with tourists exhibited significantly elevated glucocorticoid levels as compared to chicks in areas without tourists, revealing impacts that would have been difficult to detect through behavioral observations alone.

Hormone measurements have also been used to monitor environmental stress in imperiled amphibian populations. Amphibians are among the most threatened vertebrates on Earth, facing pressures from habitat loss, disease, pollution, invasive species, and climate change. By measuring glucocorticoids and other physiological indicators, researchers could assess how populations are responding to these challenges and identify signs of chronic stress before declines become severe. Narayan and colleagues, for example, used non-invasive hormone monitoring methods in Fiji to help show how the invasive cane toad (Rhinella marina) is reducing the reproductive success of the endemic Fijan ground frog (Platymantis vitiana).

Similar approaches have also been used to study endangered whales and assess the impacts of environmental contaminants on alligators. Across taxa and ecosystems, hormones are increasingly being used as tools for understanding how animals experience a rapidly changing world.

Importantly, changing hormone levels does not come without a cost for organisms. To understand these costs, Wingfield helped popularize the concept of allostasis, or “stability through change.” Rather than keeping internal conditions fixed, organisms maintain stability by continually adjusting their physiology to meet changing demands. Hormones are central to that process because they help regulate energy use, reproduction, immune function, and stress responses as conditions shift. But these adjustments are not free. Repeated or chronic challenges that prompt hormonal shifts can accumulate physiological costs, known as allostatic load, and eventually push organisms beyond their ability to cope, a condition termed allostatic overload.

In other words, changing hormone levels can help organisms adjust to environmental change—but only to a point. What starts as an adaptive stress response can become a physiological burden if environmental disturbances are too frequent or too extreme, preventing hormones from reverting to their regular cycles.

From a conservation perspective, this framework shifts the question from simply asking whether animals are present in a habitat to asking how much it costs them to remain there. Field endocrinology offers ideas about how to measure those costs. By identifying populations experiencing chronic stress before declines become visible in demographic data, hormones can serve as early warning signals. And by measuring hormones in wild animals, researchers gain access to something population surveys alone cannot provide: a glimpse into how environmental change is experienced from the perspective of the organisms living through it.

Hear more about this in our episode:

In this episode we talk with Matt Kaplan, a science correspondent at The Economist and author, about his most recent book I Told You So! Scientists Who Were Ridiculed, Exil…

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This knowledge was unsettling. I had paused to identify each of these species because I found them beautiful, yet it turned out that most of this beauty wasn’t native to my home. My unease only grew as I continued encountering thickets of these invasive species along the trail. What native species were they displacing? How many of the other surrounding plants were fellow invaders?

As I finished the walk, my unease slightly abated by the sighting of a native green heron and scarlet tanager. A deeper, more philosophical question came to mind: in a rapidly changing world, what constitutes an invasive species? As humans continue to move species across the globe, as invasive species become more common, and as species increasingly shift their ranges on their own in response to climate change, how should we think about invasive species?

The U.S. government offers one perspective, legally defining an invasive species under Executive Order 13112 as a species that is “non-native (or alien) to the ecosystem under consideration and…whose introduction causes or is likely to cause economic harm, environmental harm, or harm to human health.” While it is certainly true that invasive species can cause harm to the economy, environment, and human health, this definition provides a largely anthropocentric and necessarily negative view of invasive species without acknowledging the fact that human activity is the dominant driver of invasive species’ global spread.

Dr. Daniel Simberloff, our most recent guest on Big Biology and one of the founders of the field of invasion biology, uses a simpler, more ecological definition. In the textbook Conservation Biology for All, Simberloff defines an invasive species as a species “that arrives (often with human assistance) in a habitat it had not previously occupied, then establishes a population and spreads autonomously” (131). Importantly, this definition acknowledges that humans play a central role in the spread of invasive species, helping shift the responsibility for any negative consequences from the species themselves to the human activity that led them to be introduced to a new place. Additionally, from this perspective, a species becomes invasive not by any harm it causes, but rather by its ability to persist in a new habitat without human intervention. In this sense, invasive species aren’t necessarily inherently harmful—though their ability to establish and spread without assistance often increases the likelihood that they will significantly alter the ecosystems they enter.

Further complicating the simple dichotomy between ‘native’ and ‘invasive’ is that native species can themselves become invasive if they spread to previously unoccupied habitats, even if these habitats are close to their native range. Simberloff highlights two major ways in which native species can become invasive. The first is when humans introduce a new genotype of a native species from a different region and either the new genotypes or recombinants involving the new genotypes become invasive. The second is when humans modify the environment in such a way that a particular native species can rapidly spread and become dominant—for example, fire suppression and increased grazing by livestock have allowed Douglas fir and other tree species to invade grasslands in the western U.S. (Box 7.1, p. 131).

But though invasive species may not be inherently harmful, it is undeniable that many invasive species have had significant—and often devastating—ecological impacts. Species invasions are a major driver of biodiversity loss and extinction worldwide, second only to habitat loss. Invasive species can directly compete with, consume, or infect native species, but “the greatest impacts of invasive species entail modifying entire ecosystems, because such modifications are likely to affect most of the originally resident species,” writes Simberloff in Conservation Biology for All (133).

While some of these impacts are obvious, Simberloff points out that many invasive species that appear harmless may be having impacts that are difficult to observe. “The great majority of introduced species, we don’t know anything about,” he says in the Big Biology episode. “We know they’re there. We often say, ‘Oh, they’re not doing anything.’ But we really haven’t studied them. We don’t know, because many impacts are very subtle, even some very consequential ones, we don’t realize until much later that they are” doing something.

This subtlety of impact and initial benignity of many invasive species inspired the title of Simberloff’s new book, Ecological Explosions: The History of Biological Invasions and Invasion Science. Coined by Charles Elton, the term “ecological explosions” refers to the idea that “the effects of the invasion ripple beyond the very immediate, obvious impact” and can lead to “waves of explosions,” explains Simberloff.

The term also acknowledges that many invasive species fly under the radar for a period of time before “exploding” and becoming harmful. In a testimony submitted to congress in 2003, Simberloff elaborated that “introduced species sometimes remain innocuous for decades, then suddenly explode to become serious pests. Thus, some fraction of currently harmless introduced species will become plagues.”

For example, in south Florida, “fig trees were common for at least a century, restricted to residential settings because they could not reproduce without specific fig wasps,” explained Simberloff. However, once the exact species of fig wasp required for pollination was also introduced to the region, the fig trees suddenly began reproducing rapidly and are now actively spreading through natural ecosystems.

Hybridization between native and invasive species can also trigger such ecological explosions. In the mid-19th century, cordgrass from eastern North America was introduced to England where “it was a harmless, uncommon exotic there,” testified Simberloff. This introduced cordgrass would sometimes hybridize with the native Spartina maritima, only to produce sterile hybrids. But then, circa 1890, “one such hybrid individual underwent a spontaneous chromosomal mutation (doubling its number of chromosomes) to become a fertile new invasive weed, S. anglica. It has more recently invaded northern Puget Sound, where it is the target of a so far futile control effort because it is destroying the habitat of large intertidal areas.” These examples highlight the profound unpredictability of biological invasions and the difficulty of determining which introduced species may eventually become ecologically destructive.

So yes, invasive species, though perhaps not inherently bad, do pose a major threat to ecosystems and biodiversity worldwide. And this threat, given the magnitude and potential irreversibility of any consequences, must be taken seriously. In a 2001 paper, Simberloff advocates for “a shift from blacklists of prohibited species and a presumption of harmlessness to combinations of white and blacklists and a presumption that any species may be damaging.” This “presumption of harm” approach may at times seem overly cautious, but it reflects the reality that ecological consequences are extremely difficult to predict—and often even harder to reverse.

At the same time, not everyone agrees on how invasive species should be understood or managed. In her book Rambunctious Garden, environmental writer Emma Marris argues that ecosystems are not static entities frozen at some ideal historical baseline. Species distributions have always shifted, ecosystems have always changed, and humans have been reshaping landscapes for thousands of years. Similarly, Fred Pearce argues in his book The New Wild that many non-native species are capable of integrating into ecosystems and, in some cases, may even provide ecological benefits in heavily altered environments.

These perspectives complicate the simple narrative of invasive species as purely ecological villains. In many urban or disturbed ecosystems, it may no longer be feasible—or even desirable—to attempt to restore some historical baseline. Climate change further complicates matters, as many species are now shifting their ranges in response to changing temperature and precipitation patterns. If a species expands into a new region on its own and can survive given changing climate conditions, should it be considered invasive?

While these perspectives are worth contemplating, acknowledging this complexity does not require dismissing the dangers invasive species can pose. An important aim we can focus on is preserving biodiversity and maintaining healthy, functioning ecosystems—something we can all play a role in.

Apps like iNaturalist and features like Google’s search-with-a-picture function have made identifying the species around us easier and more accessible than ever before. Pausing for a few minutes to identify some of the species we encounter not only increases our knowledge of our local ecosystems, but adds observations to an immense database that researchers are using to, among other things, track the spread of invasive species. “Combined with some more serious effort in in rapid response approaches,” says Simberloff, citizen science approaches like iNaturalist, along with the use of eDNA, could “transform the entire management part…to a much better situation than we have now, but it would take resources in the response part, not just the detection part.”

For my part, I’m still not quite sure how to manage my feelings about seeing the Dame’s rocket, multiflora rose, and amur honeysuckle. Though I’m glad to have learned that they are considered invasive, I don’t quite know how to act around them: can I still enjoy their beauty? Or should I ignore them? Avert my gaze? Rip them up?

For now, I think I’ll stick to recording them on iNaturalist whenever I see them to give researchers as many data points as possible. I’ll also continue to identify the many other plant species I still don’t know by name, both to become more familiar with native species in my area and to flag any other invasive species I might come across. In a world full of changing ecosystems increasingly populated by invasive species, the first, and perhaps most important, step any of us can take is to simply learn about the species with whom we share this world.

Learn more by listening to our episode with Dan Simberloff:

In this episode, our guest is John Wingfield, Emeritus Professor of Physiology and Behavior, at the University of California, Davis. John helped develop many fundamental hypotheses in the field of endocrinology through his work on wild bird populations, in particular the white-crowned sparrow. Fran Bonier, a former PhD student of John’s and now Professor of Biology at Queen’s University in Kingston, Ontario, also joined us for the chat.

We talk with John about how he got interested in understanding wildlife endocrinology, and discuss how he helped formulate the “challenge hypothesis”, “emergency life history hypothesis”, and the concept of “allostasis”. And at the end, John gives some advice to early career researchers, based on his experience as the head of the Biology Directorate at the National Science Foundation.

Cover art by Brianna Longo.

On this episode, we bring you a live recorded conversation we had with Daniel (Dan) Simberloff at the Laurel Theater in Knoxville, Tennessee. Dan Simberloff is the Nancy Gore Hunger Chair o…

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In the oceans, however, rising sea temperatures are set to affect organisms’ movement by changing something even more innate than their behavior—their physics. “Most of the biomass in the ocean is actually really small, and it operates at a completely different scale of physics than we’re used to,” explains Dr. Brad Gemmell, our most recent guest on Big Biology. “One of the biggest things that changes the density and the physics of the water is temperature. And so if you start changing the temperature, you start changing those physics.” Such changes can impact how organisms feed, reproduce, and move through their environment, with potential ripple effects through ecosystems.

The physics involved in these movements relate to a concept known as Reynolds number, which is the ratio between the forces of inertia and viscosity. Inertia is the tendency of an object to resist changes in motion and is directly proportional to an object’s mass. Heavier objects are harder to stop when they’re moving, and they’re harder to move when they’re at rest. Viscosity describes a fluid’s resistance to flow and is commonly thought of as “stickiness”—molasses has a much higher viscosity than milk.

Humans and other larger-bodied animals have high Reynolds numbers, which is to say that “the inertial forces are hundreds of thousands or millions of times…greater than the viscous forces,” explains Gemmell. When we push off the wall of a swimming pool, for instance, we glide easily and effortlessly through the water because the viscosity of the water—water’s “stickiness”—is no match for our inertia.

Most ocean life, however, is extremely small and therefore has very low Reynolds numbers. For these tiny creatures, water becomes very sticky indeed. “To envision what it’s like to be a low Reynolds number swimmer,” Gemmell says, you can imagine jumping into “a pool filled with honey or corn syrup and trying to go underwater and push off that wall…You’re going to come to a stop pretty darn quickly.”

For us humans, few things sound more unpleasant than jumping into a pool filled with honey. But for the small organisms that make up most of the ocean’s biomass, this sticky water is the environment in which they evolved and upon which their life processes depend.

As oceans warm, ocean water becomes less viscous, meaning that the Reynolds numbers for these small marine organisms increases. This means that their environment starts to feel less sticky, which may present challenges for these creatures who are adapted to a particular fluid regime.

“If you’re something that’s evolved to be just in that viscous regime, and then you change the temperature, you’re sort of pulling that organism into a more inertial fluid regime, something that it may not be well adapted to do something,” says Gemmell. “It may take more energy to swim from point A to point B. It may not capture food as efficiently, and we don’t have a good handle on how these major taxonomic groups of organisms, you know, fish, crustaceans, cephalopods, how they’re going to be able to deal with that.”

And it won’t only be the smallest ocean creatures that are affected by this change in the ocean’s viscosity. “Most life in the ocean, even the big stuff, starts out really small, and it has to transition from this viscous dominated, physical fluid regime into an inertially dominated regime, the one that we’re familiar with,” Gemmell explains.

For example, cod, anchovies, and bluefin tuna—all commercially important fish species, the latter of which can exceed 500 pounds as adults—start life as small larvae and experience the ocean as an extremely viscous environment. But as they grow into adults, their Reynolds numbers increase dramatically, turning the ocean into a place where water feels less like syrup and more like a freely moving fluid.

Jellyfish are creatures that start out small and grow much larger, leading to a transition in their Reynolds number. Video: Gemmell lab

Changing how and when transition in their Reynolds number happens can make an already dangerous early life stage even more perilous. “Mortality is already the highest at these early life history stages” when organisms are in their larval, low Reynolds number state, points out Gemmell. “So if the balance shifts one way or another, and makes those life history stages even a little bit more vulnerable…a little bit of change or increase in mortality can have massive ripple effects throughout the population, and population growth and the amount of biomass we can sustainably extract from that population.”

As humans, our experience of the world is biased by our senses and size. For us, water is most certainly not sticky. Gemmell’s research is a reminder that to truly understand our impact on life on Earth, we must do our best to view the planet from other species’ perspectives—perspectives from which water becomes quite sticky and movement is governed as much by physics as biology. As the climate warms, those physics are changing in real time, altering the ecology of the ocean in ways we are only just beginning to understand.

Learn more by listening to the full episode:

On this episode, we bring you a live conversation from the recent University of South Florida Climate Teach-In where Marty interviewed his colleague, Associate Professor from the…

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In this episode, we talk with Kathryn Paige Harden, a Professor of Psychology at the University of Texas at Austin, where she leads the Developmental Behavior Genetics lab and…

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Such simplification and sterilization is “part of what we do in science,” acknowledges Dr. Nachum Ulanovsky, our most recent guest on Big Biology. “But, if we simplify too much,” he adds, “then this might be too far removed from behavior in the real world, which is very context dependent, very rich, very complex. And then we, as a field, may invest decades in studying the detailed mechanisms of behavior that, at the end of the day, bear very little relevance to the actual behavior that interests us.”

Dr. Ulanovsky, a Professor of Neuroscience at the Weizmann Institute of Science and author of the book Natural Neuroscience: Toward a Systems Neuroscience of Natural Behaviors, is helping push the field of neuroscience towards studying more ecologically relevant behaviors. In the book and the Big Biology episode, he discusses how studying brain activity in more naturalistic settings (for example, recording neurons in bats as they fly, navigate, and/or interact with other animals) can help us understand not just how neural circuits work, but why they evolved the way they did. “If we really want to understand how the brain operates in the real world and generates behavior and generates interactions with the environment and with other organisms,” Ulanovsky says, “We need to let go a little bit of the over controlled setups.”

Importantly, Ulanovsky makes it clear that he isn’t advocating for the abandonment of traditional lab work. Instead, he argues for a “both/and” approach where controlled experiments provide the mechanistic “how” and naturalistic studies provide the ecological and evolutionary “why”.

“I think the best way for neuroscience is to combine these controlled experiments that do allow us to look at one factor at a time, but also to acknowledge that this is, to some degree, misleading ourselves, and then do the more the full, naturalistic experiments where we allow much richer environments, much richer interactions, and then compare what the neurons are doing under the two conditions,” he proposes.

Until recently, this vision was technically impossible. Recording brain activity has typically required animals to be tethered directly to laboratory equipment, restricting their movements to small arenas. But in the past two decades, rapid advances in neural recording technology have begun to change how scientists can undertake neuroscience experiments. Miniaturized sensors, wireless recording systems, and new tracking tools now allow neuroscientists to monitor brain activity while animals move freely through the world.

Below I describe a few of the technologies and methodologies that have helped enable this shift—and some of the discoveries that Ulanovsky and his colleagues have made using them.

Miniature wireless neural loggers

One of the key technologies that is making naturalistic neuroscience experiments possible is the miniaturization of neural recording devices. Traditionally, electrophysiology experiments required animals to be connected to recording equipment by cables, limiting their movements to small enclosures. To overcome this barrier, Ulanovsky and his team, who study bats, developed tiny wireless-electrophysiology devices called neural loggers that can be mounted on a bat’s head and record the electrical activity of more than 100 neurons while the animal moves freely.

Using these neural loggers, Ulanovsky and colleagues have discovered neurons that fire when a bat is in a particular location, forming an internal map of space similar to place cells (neurons that become active when an animal is in a specific location and help form an internal map of its surrounding) first identified in other mammals. At the same time, their work has shown that bats can build spatial maps without relying on a particular pattern of brain activity that scientists once thought was essential for navigation (i.e. theta oscillations, for the neuroscientists reading), suggesting that the brain’s mapping system is more flexible than previously assumed and may operate differently across species.

More recently, Ulanovsky’s team has expanded this approach, using these neural loggers to record brain activity from multiple bats simultaneously as they interact with one another.

These experiments helped lead to the discovery of “social place cells”—neurons that respond not only to a bat’s own location, but also to the position of other bats nearby. In this way, the brain appears to map not just physical space, but social space as well, extending the idea of a cognitive map beyond navigation to include interactions between individuals. Together, these advances are allowing researchers to link neural activity not just to where an animal is, but to how it moves and interacts within a broader environment.

Large-scale flight environments

Another way Ulanovsky’s lab is pushing neuroscience toward more natural conditions is simply by building bigger experimental environments. Many classic neuroscience experiments take place in arenas only a few meters across, but animals in the wild move through spaces that are much larger and more complex.

“I was invited to give a talk at the Ecological Society of America, which is not the typical conference I go to,” Ulanovsky recounts. “And I was presenting this study that was done in bats flying in a five by six by three meter room…So I thought: ‘Oh, we’re so great. We’re recording bats in a five by six meter room. We’re amazing.’ And then I come to the ecologists and they say: ‘Well, it’s very interesting. It’s wireless, it’s natural behavior, nice, but don’t bats normally navigate kilometers outdoors? I mean a few meters.’ They were so under-impressed.”

To address this mismatch, the lab constructed large flight tunnels and arenas where bats can travel tens or even hundreds of meters. Experiments in these environments revealed that hippocampal place cells behave differently at large spatial scales. Individual neurons often become active in spaces of different sizes, suggesting that the brain represents space at multiple scales simultaneously. This finding indicates that the neural map of space is more flexible than previously thought and may adapt to the scale of the environment an animal inhabits.

Large-scale flight environments used by the Ulanovsky research group. Source: Ulanovsky Lab Website

Studying brains in the wild

In some of his most recent work, Ulanovsky and colleagues have pushed these technologies to their limits by studying bats not just in large laboratory environments, but in the wild—on a remote oceanic island. This represents a major step beyond even large flight arenas, placing neuroscience directly into a natural ecological setting.

On the island, bats were equipped with neural loggers and then released to fly freely across an open landscape. This setup allowed researchers to record brain activity while the animals navigated an environment filled with the kinds of challenges they encounter in nature, rather than the controlled conditions of a lab.

Experiments in this setting revealed that key navigation-related neurons, such as those that act like an internal compass, remain stable even in these highly complex and uncontrolled environments. In other words, the same neural systems that help guide movement in the lab also appear to operate reliably in the real world, also supporting navigation across large, natural spaces.

This work highlights the power of combining neural recordings with field-based approaches. By taking animals out of the laboratory and into the environment, Ulanovsky and colleagues are showing that effective neuroscience requires more than just precise control—it also requires context. By studying brain activity in the environments where behavior actually unfolds, researchers like Ulanovsky are beginning to reveal aspects of brain function that would otherwise remain hidden.

To learn more about Ulanovsky’s work, listen to the full episode:

In this Meet the Scientist segment, Cam references The New Creationism, an essay written by Barbara Ehrenreich …

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In this episode, we talk with Nachum Ulanovsky, Professor of Neuroscience and Head of the Zuckerman Center for Learning, Memory & Cognition at the Weizmann Institute of Science and author of the book Natural Neuroscience: Toward a Systems Neuroscien…

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Abstract: A key concept for understanding and measuring phenotypic plasticity is the reaction norm, a method that emerged in the early 20^th century and has since become an important tool for biologists. The power of the reaction norm derives from partitioning sources of phenotypic variation into genetic (G), environmental (E), and interactive (GxE) causes, which has facilitated major advances in our understanding of phenotypic complexity in ecology and evolution. The simplifying power of the reaction norm, however, could also be a weakness– we now understand that many different kinds of information beyond genes influence living systems, so decomposition of trait variation into G, E and GxE will often be insufficient. Like genomes, environments are complex, and we often do not know what about them organisms are sensing and responding to. Recent progress in systems biology underscores another weakness of the reaction norm mindset: that organisms consist of interlocking sets of processes, distributed across levels of biological organization, each with extraordinary capacities for learning and refining decisions based on experiences. Here, I propose that the concept of cognitive plasticity captures these issues well. By adopting cognition as a central concept for certain forms of plasticity, we could produce a better framework for understanding the causes and consequences of phenotypic variation within and among generations, perhaps eventually linking the smallest to the largest levels of biological organization.

Natural selection is not a consequence of how well the organism solves a set of fixed problems posed by the environment; on the contrary, the environment and the organisms actively codetermine each other. (Lewontin 1970)

An organism does not just encode a model of the world; it is a model of the world—a physical transcription of causal regularities in its eco-niche that has been sculpted by reciprocal interactions between self-organization and selection over time. (Ramstead et al. 2018)

Learning is optimization by trial and error, and so is evolution. (Vanchurin et al. 2022)

Plasticity as a core biological process

Phenotypic plasticity—the ability of living systems to alter their phenotypes in response to changing environmental conditions—is central to biology, and the main analytical framework for understanding plasticity is the reaction norm (Schlichting and Pigliucci 1998). In reaction norms, one quantitatively ascribes phenotypic variation to either genetic (G) or environmental (E) sources, or to their interaction (GxE) (Via et al. 1995). This approach has yielded significant advances, from the development of sophisticated methods for estimating these forces from increasingly complex datasets (Nussey et al. 2007, Lande 2009, Dingemanse et al. 2010, Martin et al. 2011, Smallegange 2022, Dupont et al. 2024) to inference about the trajectories of populations into the future (Jablonka et al. 2014, West-Eberhard 2003). Despite these successes, reaction norms might have sometimes misled us, at least when it comes to a very common sets of plastic traits, namely the cognitive ones. Cognitively plastic traits are not traditionally plastic traits at all; they are purposive processes (Nicholson and Dupré 2018). Whereas this statement might seem contentious because of ‘purposive’ has spooky connotations, it means that to understand some if not most biological traits, mechanistically but also evolutionarily, we would probably benefit from a different approach than the one offered by the reaction norm.

Here, like others, I define cognitive plasticity as the subset of plastic phenotypic traits arising from the dynamic interplay of information from the environment (Adami 2024) with a living system’s existing model of its world, including but not limited to its genes (Levin and Dennett 2020, Vanchurin et al. 2022, Watson and Szathmáry 2016). Cognitive plasticity thus emphasizes that many plastic traits are functional and of evolutionary consequence because they become differently adaptive through time. Ideas like this one have been proposed before (Baldwin 2018, Gottlieb 2002), but for the most part, this perspective is distinct from the dominant framework for understanding phenotypic variation and evolutionary change, the Modern Synthesis (Laland et al. 2014, Huxley 1942). The Modern Synthesis proposed that phenotypic diversity, including adaptation, is the outcome of genetic variation interacting with selection across generations. While broadly valid, this portrayal is very generic, and indeed does not capture the fundamental features of life (Walsh 2015, Laland et al. 2014, Feiner et al. 2024), that living systems are entangled sets of information-rich processes that exist far from thermodynamic equilibrium. The Modern Synthesis’ strong focus on genes has been very productive in the practical sense, but it has also led us, sometimes unintentionally, to ascribe to genes a causal power that exceeds their actual roles (Pigliucci 2007, Pigliucci 2010, Noble 2008). I think that this position is no longer defensible because as Sewall Wright said (Wright 1931) and Walsh and Sultan (2024) have recently reminded us that individual adaptability, not genes, is probably the chief object of selection.

Phenotypic plasticity and the shortcomings of reaction norms

Over the past century, many biologists have that emphasized that phenotypic plasticity is a phenomenon central to evolution (Schmalhausen 1949, Baldwin 2018, Waddington 1953). Plasticity is so compelling and so powerful because it highlights the dual role of the environment in evolution (West-Eberhard 2003): environments both induce and select phenotypic variation. These dual roles have led to excitement and conflict (Walsh 2015, Walsh and Sultan 2024, Laland et al. 2014). Some experts have suggested that plastic traits operate and evolve just like all others (Laland et al. 2014), and some have said that the existence of plasticity means that genes will more often be followers than leaders in evolution (West-Eberhard 2003, Waddington 1953). Others have claimed that plasticity (among other phenomena such as niche construction (Odling-Smee et al. 1996)) requires a complete reworking of our fundamental models of evolution (Moczek et al. 2011, Pigliucci and Müller 2010, Noble 2006, Jablonka et al. 2014, Feiner et al. 2024). Still, it is widely agreed that even simple forms of phenotypic plasticity can have major evolutionary impacts: they can shield genetic variation from selection (Price et al. 2003), release cryptic genetic variation in stressed populations (Ghalambor et al. 2007a), lead to genetic assimilation (Ledon-Rettig et al. 2010), and alter evolutionary patterns throughout whole genomes (Wade and Sultan 2023). In sum, plasticity is viewed a central to most realms of biology, but the question endures, is all plasticity understandable in the same way?

To date, most progress in the study of plasticity has benefited at least in part from the reaction norm construct, first proposed in 1909 (Woltereck 1909). Reaction norms function at two levels. First, they simply describe how traits of individuals or genotypes covary with aspects of the environment. Second, they provide a way to attribute (partition) variation in trait values to genetics (G), environments (E), their interaction (G x E), or much more complicated parsing (IxE, Nussey; GxExE, Kingsolver et al. 2006). Over the past several decades, better and better reaction norm models (e.g., random slope regression, random regression animal models, double-hierarchical GLMMs, etc. (Dingemanse et al. 2010, Dingemanse and Wolf 2013, Martin et al. 2011)) have been developed to understand many forms of plasticity (Araya-Ajoy and Dingemanse 2014), including those that parse the relative influence of genetic versus environmental effects experienced during development (Nussey et al. 2007, Westneat et al. 2011). Some reaction norm models have gone so far as to address how multiple environmental factors simultaneously affect plastic variation in one trait (Westneat et al. 2019), and others have been developed to describe plastic traits that account for some past experience (Wright et al. 2022). These methods work both for traits that vary continuously (e.g., height, metabolic rates) and traits that vary in more discrete ways (e.g., presence or absence of spots, spines, or scales), and they have been used to study plasticity at several levels of organization, from morphology and behavior (Snell-Rood 2013, Svensson et al. 2020, Martin et al. 2021, Hau et al. 2022) to physiology and even gene expression (Ghalambor et al. 2015, Rivera et al. 2021).

Despite all of this success, there are two issues that suggest our concept of plasticity to date has been too expansive. First, ‘plasticity’ has different meanings and different points of emphasis to different kinds of biologists. Semantic differences alone would not constitute a problem, but the plasticity studied by neuroscientists, immunologists, endocrinologists, and molecular biologists is not the plasticity that will yield to reaction norm style investigations. Partly, this is because whereas suborganismal biologists would often call neurogenesis, hormone regulation, and lymphocyte receptor development plastic, they rarely would parse variation in these traits into G, E and GxE (Martin et al. 2021). With exception, suborganismal biologists are either not interested in the evolutionary adaptiveness what they measure (Gluckman et al. 2009, Trotter et al. 2011), or they infer that regulated, plastic variation in neurogenesis or lymphocyte receptor diversity must be adaptive because there are so many ways such complex system could fail, how else could we explain these context-dependent, dynamic responses but via fitness (Haig 1996)? Occasionally, suborganismal researchers directly test for adaptiveness of plastic trait variation by examining individual differences in hormone levels or immune responses relative to fitness (Bonier et al. 2009, Arnold 1983), but mostly, biologists studying these forms of plasticity call their traits plastic just because they remain labile throughout the life of an individual. Who knows and who often cares how these traits fit into reaction norms?

The second issue about the expansive of the word plasticity is also highlighted by suborganismal biology. In particular, some plastic traits are dynamically plastic. Certain dynamic plasticities are simply reversible; an environment induces one phenotypic variant (i.e., a change in coat color), which then reverts to the original state prior to the environmental experience. Other plastic responses, though, never revert to the original condition; they enduringly change over the life of an individual, in many cases becoming functional because every new instantiation is adaptive and distinct from its historical form. Over the last decades, evolutionary biologists have tended to focus on comparatively simple forms of plasticity (Gilbert and Epel 2009, Feiner et al. 2024, Moczek et al. 2011, Ledon-Rettig et al. 2010). On the positive side, this approach enabled them to discern when plasticity was heritable, adaptive, non-adaptive or even maladaptive (Ghalambor et al. 2007b, Martin et al. 2021). On the downside, that success seems to have inspired an over-confidence that all plasticity is alike. In some cases, this perspective could hold. Reversible plasticities (e.g., seasonal changes in coloration or reproductive capacity) might well be decomposable with reaction norms (Piersma and Drent 2003, Williams 2008, Swanson and Merkord 2013, McWilliams and Karasov 2014), but traits that increase and decrease to different magnitudes and over very different timescales (Zimmer et al. 2022, Woods and Wilson 2014, Dantzer 2023, Dupont et al. 2024), often but not always returning to some baseline values, probably won’t. Unlike the timing of breeding, molting, migration or other reversible plasticities (Nelson et al. 2002), molecular epigenetic changes to genomes, metabolic rates, cell-cell interactions and all sorts of other suborganismal processes will never return to an original condition once elicited. Indeed, it is sometimes because they do not revert to earlier forms that they foster fitness (Sultan 2019).

These especially complex forms of plasticity probably evolved to vary among several forms throughout the life of an individual in a recurrently contingent way (Snell-Rood 2013, Snell-Rood and Ehlman 2021). This scenario also means that plastic variation in these traits will not typically be related to fitness (or even organismal function) in a simple way (Kingsolver et al. 2001). For plastic traits where the ‘solutions’ (i.e., fit of phenotype to environment) are very complex (e.g., infections, social interactions, invasion leading to new trophic interactions) or evolutionarily novel (e.g., during invasions or range expansions), cognitive plasticity will probably be necessary (Walsh and Sultan 2024, Martin and Zimmer 2022, Zimmer et al. 2022). Only through the screening, processing and use of (semiotic) information will a living system instantiate the trait variant that will solve the problem (Zimmer et al. 2022, O’Connor et al. 2019). Existing reaction norm methods simply cannot accommodate these situations, or at least they have not done so yet. Reaction norms, mostly, conceive plastic traits as static, simple things, but cognitive plasticities, traits that change or even improve system performance through time (Taleb 2014, Walsh and Sultan 2024), are deliberative processes that probably require a different paradigm (Nicholson 2019, Nicholson and Dupré 2018).

To illustrate this problem explicitly, consider a vertebrate adaptive immune response (Hedrick 2004). Hosts must mobilize a variety of defenses against each threat, then learn effectively from past experiences to protect themselves better from exposures in the future. One could, in principle, apply traditional reaction norm methods to many forms of immune data, parsing for instance how genetic, individual, and experiential factors contribute to variability in T and B cell responses (Martin et al. 2021). The equations cannot distinguish meaningful from suspect traits; any trait is potentially decomposable with a reaction norm. Even still, such studies would require immense sample sizes (Martin et al. 2011), making them typically underpowered or restricted to simpler organisms, like unicellular life. More significantly from the perspective of cognitive plasticity, describing reaction norms for arbitrarily selected immune traits would not necessarily capture the specific learning component inherent in adaptive immunity, the thing that makes it interesting to understand in the first place. Unlike other plastic traits, adaptive immune responses are antifragile (Taleb 2014); they improve in response to adversity and experience. Describing a reaction norm for antibody concentrations or some other adaptive immune trait would thus not capture the most important thing about adaptive immune plasticity we seek to know: whether and how the second response is more conducive to fitness than the first. Even sophisticated reaction norm approaches (Wright et al. 2022) will struggle with this kind of plasticity, as they have no means capture how subsequent forms of variation come to better fit a living system to a particular condition through time. Importantly, too, this situation does not apply solely to vertebrates. Invertebrates have functionally similar (i.e., cognitive) immune responses, just not the lymphocytes that produce antibodies and educated T cells (Kurtz and Armitage 2006). Most importantly, this rationale for immune responses applies to many if not most cognitively plastic traits, on any level of biological organization.

Defining and defending cognitive plasticity

Cognitive plasticity describes the regulatory capacity of an individual to mobilize a particular plastic trait variant through time. Importantly, mobilization changes the propensity of the individual to mitigate a challenge or exploit an affordance. The first step toward understanding these forms of plasticity, particularly their evolution, is to embrace that life is processual, not static as now is the prevailing fashion. As Woese wrote (Woese 2004), “it is becoming increasingly clear that to understand living systems in any deep sense, we must come to see them not materialistically, as machines, but as (stable) complex, dynamic organization.” Woese meant that living systems are alive because they are open physical systems that constantly exchange energy, matter, and information with their surroundings (Walker et al. 2016, Walker et al. 2017, Davies 2019). These traits help them maintain themselves far from thermodynamic equilibrium and thus distinguish living from other competent but non-living systems (Levin 2023, Turner 2016, Turner 2017).

Reaction norms, largely, encourage us to view living systems more like passive machines than entangled sets of informed processes (Nicholson 2019, Walker et al. 2017). Machines persist even without exchanging energy and matter with their surroundings -- think of your computer powered down and sitting in a backpack. Living systems exhibit dynamic stability (Davies 2019), meaning that you cannot power down your cat in the morning and expect to power it back up when you get home. Many things about living systems will therefore not yield to simple decomposition. Thinkers (Von Bertalanffy 1952, Waddington 2014) since Heraclitus have recognized that life is more like a stream than machine, but their ideas have yet to alter prevailing theory. As Nicholson (2019) writes,

The external form of a stream is stable only because of the constant flow of water molecules that enter into it and emerge out of it. The moment this flow is interrupted, the stream itself disappears, as its very existence depends on the steady movement of water passing through it. In the same way, the physical form of an organism is merely the visible expression of the constancy of catabolic and anabolic processes going on within it. Its persistence through time is entirely dependent on the extremely intricate balancing of these two opposing kinds of reactions.

The upshot: reaction norms imply life as decomposable and therefore static, but it is not. We therefore need to adjust our frameworks for understanding phenotypic variation, considering specifically reciprocal causality among genes, environments, their interactions (Mitchell 2023, Davies 2019, Svensson 2018) and the higher-order interactions they entail (Okasha 2006, Noble 2006).

The second step to studying cognitive plasticity effectively is to accept that many biological processes are purposive (Moczek and Sultan 2023, Walsh 2015, Turner 2017). Of course I don't mean purposive in the mystical manner that intelligent design advocates do. I mean purposive in the sense that living systems are information-rich systems with goals. Unlike simple forms of plasticity, which can be triggered by single or simple signals (Uvarov 1921), cognitive forms of plasticity emerge from the interplay of newly acquired information instantiated in comparatively transient substrates (i.e., neural, physiological, epigenetic) with older forms of information, genetic but also epigenetic and structural (Hofstadter 2007). In this light, cognitive plasticities will not involve the unspooling of genetic information into environmental space; they will be the outcomes of a set of integrated, deliberative processes, engaged to accomplish particular goals (Levin and Dennett 2020). Ironically, we have little trouble appreciating the uniqueness of cognitive plasticity in some contexts (Jablonka et al. 2014), namely when talking about actions that entail brains (Okasha 2018). Once we take more seriously that most all organisms are composed of nested, cognitive subsystems (Lyon et al. 2021, Levin 2023), a focus on cognitive plasticity becomes almost intuitive.

Here, it is critical to emphasize too, as François Jacob and Jacques Monod did long ago (Jacob and Monod 1961), that even simple forms of life are cognitive and thus make decisions. Systems need not even have brains to be cognitive. Escherichia coli, for instance, take in and metabolize glucose over other substrates when glucose is available. If glucose is absent but lactose is present, E. coli retools itself to produce the proteins needed for using lactose (Müller-Hill and Oehler 1996). How does a bacterium decide whether to switch? In the language of cognition, it understands relative concentrations of locally available sugars by making a protein (i.e., a transcription repressor) that binds reversibly to lactose. In E. coli, much like in human neurocognitive understanding, then, deciding involves an information filter (i.e., a means to distinguish signal from noise), subsequent processing, and eventual action (Wade and Sultan 2023). Bacteria thus have to discriminate meaningful (semiotic) information (i.e., the presence or absence of lactose) from the flood of other molecules in the environment (i.e., noise), and their informational filters (i.e., the lactose-binding repressor) enable them to do this (O’Connor et al. 2019). Once a resource is found, the cell decides whether it will switch its metabolism by linking the repressor’s lactose binding state to its repressing activity via use of its internal model (see below). If lactose levels in the cell rise high enough (and there is little or no glucose present), the repressor ceases to bind the operator, allowing transcription of the lac operon to proceed. The lac operon encodes three additional proteins: an intracellular enzyme that cleaves lactose into its components, a symporter that localizes to the membrane and renders it more permeable to lactose, and another enzyme that helps process monosaccharides internally. Ultimately, these phenomena sculpt each E. coli into a better model of its environment over its lifetime.

As a brief aside, cognitive plasticity appears to occur at even at the genomic level (Walsh 2015, Okasha 2018). Genomes actively undergo various modifications, such as cutting, transposing, and repairing DNA, and research since the 1930s indicates that expressed genetic variation emerges from regulated cellular processes rather than accidental mutations (Shapiro 2011).

A conceptual roadmap to studying cognitive plasticity

A major obstacle to understanding cognitive plasticity is the lack of a framework linking flows of matter, energy, and information (Hesp et al. 2019, Davies 2019). Some researchers suggest that a framework might emerge by focusing on the similarity between maximizing organismal performance within a lifetime and increasing population fitness over evolutionary time (Okasha 2018, Watson and Szathmáry 2016). Others propose frameworks built around integrated feed-forward and feedback loops (Del Giudice et al. 2018, Bernhardt et al. 2020). I agree with both perspectives and identify Karl Friston (Friston 2010) and J. Scott Turner (Turner 2016, Turner 2017) as thinkers whose proposals might significantly advance this discussion (Fig. 1).

According to Friston, cognition does not require a nervous system; entities exhibit cognition when their current or past experiences shape their future responses (Friston et al. 2023). The Free Energy Principle (FEP) posits that all non-equilibrium steady-state systems maintain viability through internal models that interpret and use environmental information received via a Markov blanket (Kirchhoff et al. 2018). In FEP, free energy quantifies uncertainty or surprise about the environment, reflecting the accuracy and confidence of a system’s predictions. Higher free energy corresponds to greater surprise. Minimizing variational free energy equates to minimizing thermodynamic entropy (Ramstead et al. 2018), enabling continuous model refinement (i.e., cognition and learning). Entities thus remain alive by predicting changes in internal and external conditions (Bayesian updating; Fig. 1A), then taking action to prevent entropic breakdown. Systems optimizing these processes achieve competitive and reproductive advantages. Although FEP is not without its skeptics (Colombo and Wright 2021), it provides a powerful way to understand cognition across diverse complex systems and levels of organization.

Turner’s approach (Turner 2016) defines free energy more traditionally—as energy available to perform work. He uses the metaphor of a standing thermodynamic wave (Fig. 1B) to illustrate interactions between life forms and their environments (Turner 2017). For Turner, cognitive plasticity embodies adaptations in system performance that are guided by both physiological (within-generation) and evolutionary (among-generation) feedbacks. In his framework, the boundaries of living systems (e.g., cell membranes) regulate exchanges of energy and materials rather than information (Turner 2009b). To Turner, living systems can access as much free energy as possible by building their own niches, moving within their environments, or modifying their phenotypes to better fit their environments (Turner 2009a).

Both Friston’s and Turner’s models reference free energy as the disparity between a system’s current state and a reference state, though Friston emphasizes informational differences while Turner focuses on chemical or energetic conditions (Fig. 1). Both envision non-equilibrium systems encased by barriers allowing bidirectional flows, with systems continually updating themselves in response to new environmental information. Crucially, both models also emphasize reciprocal causality (Svensson 2018, Mitchell 2023): that systems shape their environments, which in turn shape future system responses. Fig. 2 depicts critical aspects of such systems.

An empirical roadmap to studying cognitive plasticity

What about we empiricists? How can we contribute to a better understanding of the role of cognitive plasticity in biology? Friston’s and Turner’s conceptual models of living systems identify three aspects of cognitive plasticity that warrant attention: filters that determine what information is made available to internal models, internal models where decisions are made and responsiveness to information updated, and actions, the forms of plasticity that change the world or the system’s form within it (Fig. 2A). Many of these factors are very well-studied already (Table 1), but with some exceptions, they have not been considered as components of cognitive plasticity.

Filters, specifically semiotic ones, ensure that systems obtain and act on ecologically and evolutionarily relevant information. Many such filters are already well-characterized, such as the vertebrate Toll-like receptors (TLRs), which bind and recognize molecular motifs of bacteria, fungi, and viruses (Medzhitov and Janeway 1998). Metazoans could in principle respond to a functionally infinite variety of molecules (Deboer and Perelson 1993, Tauber 2017), yet in practice only a small subset bind TLRs and induce immune responses. TLRs are thus semiotic because they discriminate relevant environmental information from noise. They thus necessarily encode information about infection risk as shaped by the evolutionary legacies of ancestors including parents (via maternal and epigenetic effects) (Adami 2024). Analogous relationships exist between photons and photoreceptors, volatiles and olfactory receptors, toxins and cytochromes, and even metazoan hormones, cytokines, growth factors and their receptors. Rarely, though, has intraspecific variation in these factors been considered in the light of cognitive plasticity. Indeed, we know quite a bit about how variation in the architecture of TLRs and other semiotic filters affects health, but the fitness consequences of variation in natural populations are poorly known. Notable exceptions include opsins (Shichida and Matsuyama 2009, Hagen et al. 2023) and olfactory receptors (Finnerty et al. 2022). Perhaps it is time to start asking, among many things: how different are the semiotic information streams perceived by different individuals within populations (i.e., how different are their umwelts?), and how does that variation map onto fitness? Is there a relationship between rate of acquisition of semiotic information, the nature of internal model updating, and plastic responses within individuals?

Some internal models are also well-studied, especially gene and neural regulatory networks (Shen-Orr et al. 2002, Wagner et al. 2007, Newman 2010, O’Connell and Hofmann 2012). However, even the basic structure of internal models at other levels of organization are under-described (e.g., physiological regulatory networks) (Cohen et al. 2012, Martin and Cohen 2014), much less the computational processes within them (Levin 2019) and linkages among them (Okasha 2006). Understanding internal models is important because they are the processes that underpin cognitive decisions (Bongard and Levin 2023), but they will be particularly difficult to study because they are dynamic over multiple spatiotemporal scales. Fortunately, there have been recent advances to study these models (Kriegman et al. 2020, Alon 2023) including methods directed to coarse-grain lower-level variables into analogs of hyperparameters, what some have called ‘slow variables’ (Flack 2017). Approaches like these have already helped us understand synaptic plasticity and neural architecture, which control higher-level traits such as learning rate and cognition (Dellaferrera et al. 2022). Key questions remain though. Do particular model states underpin health, disease, and other complex phenotypic states related to fitness (Alon 2023)? Are there attractors in the landscape of model states (Demicheli and Coradini 2011), and how do evolutionary and ecological forces shape them (Martin et al. 2016)? When do systems update their models versus act differently in/on the world?

Examples of cognitive plasticity

To make the above abstractions as actionable as possible, I offer three concrete aspects of cognitive plasticity that could be investigated fairly easily and cost-effectively in many taxa. Critically, they are not nearly the only things that underpin cognitive plasticity (Table 1); they are simply illustrations. The first is epigenetic potential, which describes how cells use (semiotic) information and molecular epigenetic processes such as DNA methylation to match the phenotype to current and historical contexts (Kilvitis et al. 2017). In many species, CpG motifs (i.e., cytosines preceding guanines in a DNA sequence) can be methylated or unmethylated to influence gene expression (Jaenisch and Bird 2003). In vertebrates, many methyl marks are laid down over development, never to change, a process that contributes in part to cell differentiation. Other methyl marks are labile, being added or removed in response to changes in diet, hormones, methyltransferase activity, and other environmental factors (Vogt 2021).

An example of epigenetic potential with more direct connections to cognitive plasticity involves my work on one of the most broadly distributed animals on Earth, the house sparrow (Passer domesticus). In several studies, students, colleagues and I revealed epigenetic potential as important for the geographic range expansion of this species (Schrey et al. 2012, Schrey et al. 2014, Sheldon et al. 2018, Hanson et al. 2022). In one study in particular, house sparrows in newly-founded populations had more epigenetic potential (i.e., more CpG than GpC motifs) across their genomes than did birds from long-colonized sites (Hanson et al. 2022). This particular pattern seems to have arisen via natural selection, perhaps because individual sparrows with high epigenetic potential were better protected against pathogen infection than birds with low epigenetic potential, as our experimental data have shown (Sheldon et al. 2023). From a cognitive plasticity perspective, high epigenetic potential could represent a comparatively large repository of latent, cognitive plasticity for a colonizing bird (Kilvitis et al. 2017, Walsh and Sultan 2024), being adaptive because the genome of a bird first arriving in new areas would better poised to learn from and act on local information about disease risk, mitigate transposon activity (Marin et al. 2020), or both. In general, successful invaders seem to be those that are most behaviorally flexible (Sol et al. 2002). Perhaps the same holds for cognitive plasticity in immune gene expression.

Epigenetic potential relates to the ability of living systems to update their internal models, but endocrine flexibilityrepresents an example of the set of phenotypic options (i.e., actions) realizable by a cognitive system (Martin and Zimmer 2022, Zimmer et al. 2020, Zimmer et al. 2024, Zimmer et al. 2022). In other words, endocrine flexibility describes the landscape of regulated hormone variation available to an individual, with release contingent on the information the system encounters (i.e., how stressful it is). Flexibility in the regulation of many hormones warrants study, but one of the most pleiotropic steroid groups to consider is the glucocorticoids (Romero and Wingfield 2015). Glucocorticoids regulate glucose metabolism, but they also help vertebrates avoid, endure, or recover from physical and psychological stressors. Almost since their discovery, glucocorticoids have been studied by measuring their circulating concentrations and relating variation in concentrations among or within-individual animals to health or fitness (Bonier et al. 2009, Breuner et al. 2008). Whereas this approach has been productive, others (Taff and Vitousek 2016) and I think that the study of endocrine flexibility could help rectify these shortcomings because it captures the set of plastic responses latent in an individual (Baldan et al. 2021, Bonier and Martin 2016), not just generic phenotypic variation. To date, we have implicitly assumed there is one best endocrine solution to all problems, but endocrine flexibility recognizes that trait under selection might be the disposition to decide and mobilize the best hormonal response given the challenge (Zimmer et al. 2022), not mount the one best response, not one particular variant (Walsh and Sultan 2024).

A final measurable example of cognitive plasticity comes from work on insect nutrient regulation. Insect herbivores can be remarkably adept at obtaining the macronutrients they need to grow and reproduce while exploiting or avoiding plant secondary chemistry and minimizing risks from predators and parasitoids (Behmer 2009). A major step forward in understanding nutrient regulation occurred thirty years ago, with the development of the geometric framework (Raubenheimer and Simpson 1993, Simpson and Raubenheimer 1993). Using that framework, one can resolve how insects balance the intake of multiple nutrients simultaneously to meet their physiological needs. The proportions of nutrients (e.g., proteins, carbohydrates, lipids) in each diet are represented as axes in space, and insects move through that space by consuming different available foods at different rates. This approach provides a natural way to understand insect feeding goals, as most insects have well-defined intake targets and that also support high rates of growth. In terms of cognitive plasticity, one could view intake targets as internal models that insects use to provision their tissues with optimal nutrition using a broad variety of forms of information including the physiology of taste (Kvello 2024), post-ingestive regulation (Behmer 2009), and learning (Dukas and Bernays 2000). Other studies have examined how physiological and behavioral approaches to nutrient regulation change when insects are challenged with plant secondary compounds (i.e., when nutritionally superior foods also are protected by higher levels of deterrents or toxins) or predators and parasitoids (i.e., when consuming some foods carry additional risks of mortality). The geometric framework thus provides a way to visualize the state of internal models and is thus a rich experimental paradigm for analyzing how behavioral, physiological, and ecological forces dynamically alter those models. Generally, a similar framework could apply to many physiological tradeoffs, phenomena so important to the development of evolutionary theory (Stearns 1992, Garland Jr et al. 2022).

Looking forward

Reaction norms have been popular because they have worked so well for so many traits, and by work I mean effectively partition the causes of phenotypic variation into environmental or genetic sources. Here, I have questioned the reaction norm approach will work for cognitive plasticities, but I hope I’ve also identified elements important to the alternative framework necessitated by life as a dynamic process. Although reaction norms will not be appropriate for forms of cognitive plasticity themselves, it would be interesting to investigate whether they could be useful for particular facets of cognitive plasticity. For instance, one could describe reaction norms for attention, motivation, and a variety of other informational filters and internal models (Table 1) and probably learn important things about landscapes of latent plasticity. In this sense, we would not be modeling cognitive plasticity per se, but the scope of phenotypic options available to an organism should it encounter an environment (Wright 1931). In other words, maybe we can yet use the reaction norm to describe G, E, and GxE in the ability to be plastic, or plasticity in plasticity if you like. This condition seems to be what Wolterek (1909) meant reaction norms to capture anyway when he proposed them as ‘an organism’s multifaceted repertoire of contingent responses’ (Walsh and Sultan 2024).

I think that work on cognitive plasticity, generally, will give us a new appreciation about robustness, resilience and even antifragility from molecular to ecosystem levels. Resilience and robustness imply returns to baselines, and whereas for many plastic traits, this perspective is helpful and valid, for others, it does not apply. For cognitive traits, the return-to-safety mindset misses that many if not most plastic responses evolved to improve and perhaps become more adaptive through time. I expect that investigations of such biological antifragility will be of great value to conservation and medicine. Whereas homeostasis, allostasis and related concepts have been among the most powerful biological concepts ever offered, they are somewhat misleading for many processes (i.e., compensatory growth, neurogenesis, hormesis, most immune responses).

Finally, I close by noting that cognitive plasticity links naturally to the idea of agency, the capacity of a system to ‘understand,’ ‘make decisions,’ and ‘take action’. All living systems – across all branches of life and all levels – use information, are cognitive, and thus must have some level of agency (Moczek and Sultan 2023, Fábregas-Tejeda et al. 2024, Walsh 2015). Agency is presently anathema to many in biology, but it is the sole concept that unifies information with the other physical phenomena inherent to life (i.e., energy and matter) (Friston 2010, Ramstead et al. 2018, Hesp et al. 2019, Friston et al. 2023, Mitchell 2023). It is also a very old idea, tracing back to Aristotle and Epicurus, who emphasized that living things (humans, in their case) do things whereas non-living things have things done to them (Walsh 2015, Levin and Dennett 2020). In 1863, Thomas Reid went farther, arguing for “agent causation,’ that living systems have causal power not entirely reducible to the causal power of their component parts (Potter and Mitchell 2022). In modern times, almost all biologists have come to appreciate that living systems modify themselves such that non-genetic, phenotypic variation can become subject to natural selection (as in the Baldwin effect (Loison 2019, Baldwin 2018). For decades, we have also known that organisms choose when and where to move or modify their environments, both of which alters the speed and direction of evolution of their traits (as in the Bogert effect) (Huey et al. 2003, Pincebourde and Casas 2019, Muñoz 2022). I think that the main reason we have not used the word agency to describe these phenomena (besides the religious connotations of the word) is that it was not very clear that it would matter if we did so (Potter and Mitchell 2024). I do not claim here to have provided such specific guidance, but I hope that this paper inspires efforts to infuse our mathematical models of life with the key processes of life, replacing the dominant paradigm of change via accident and selection (Ågren 2021) with a paradigm that gives equal weight to how life uses information to remain far from thermodynamic equilibrium (Mitchell 2023, Walsh 2015, Davies 2019, Ramstead et al. 2018). I think that agency, like no other biological concept, explains both how life evolves and how it operates.

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Acknowledgements:

I thank Art Woods and Cameron Ghalambor for help drafting earlier versions of the paper, several careful reads of other drafts, extensive feedback thereafter, years of constructive criticism about the ideas, and the friendship that enabled oftentimes energetic debates about material in the manuscript, all of which improved its final form. I also thank Mike Levin, Karl Friston, Scott Turner, Phil Ball, Sonia Sultan, Paul Davies, Denis Walsh, Erik Svensson, Sara Walker, Kevin Mitchell, Dan Nicholson and the many other guests of the Big Biology podcast that inspired the paper, refined my thinking, challenged my ideas, and filled gaps in our knowledge.

I also thank Ruth Demree, Brad van Paridon, Clayton Glasgow, and Molly Magid for helping transform those conversations into podcast episodes, which enabled many other colleagues to offer us critical and supportive feedback, too. Finally, I thank Keating Shahmehri for the images that visually enlivened the message in this paper, and Vania Assis, Kailey McCain, Nate Dowling, Diego Alarcon, Gabi Cifarelli, Dave Westneat, Zac Cheviron, Beau Larkin, and Matt Venesky for thoughtful feedback on earlier drafts, and Aaron Schrey, Haley Hanson, and Cedric Zimmer for many conversations that improved parts of the paper. I also recognize grants 0920475, 2027040, 2110070, and 2110233 from the National Science Foundation for support while writing the manuscript and for support to collect the data that bolster the ideas in the paper.

Glossary

Affordances. Opportunities in local environments that arise by virtue of a system interacting with that environment. The idea of affordances recognizes that what is available in a particular place or time is not simply imposed by the environment but rather emerges from an interaction between a complex set of potentials that are realized by the living system itself via its filters, internal model, and actions.

Agency. The capacity of a living system to act intentionally in response to its environment. It implies the ability to acquire information, make decisions, and exert control over its actions across multiple time scales.

Antifragile. Biological systems and forms of plasticity that improve functionally after exposure to challenges.

Cognitive. Used broadly to refer to the ability of complex systems to acquire information about their environment and to process that information to decide about how to respond. Cognition does not require a brain, nor even a nervous system.

Endocrine flexibility. The collective of endocrine responses available to an individual; latent endocrine variation to be released contingent on a challenge and after cognitive processing.

Epigenetic potential. The ability of whole genomes or gene regulatory regions to use epigenetic processes to learn. One example involves the number and distribution of CpG motifs in gene regulatory regions, especially promoters, that can be methylated or unmethylated.

Filter. Part of a non-equilibrium steady-state system that constrains the kinds of information available to the system’s internal models. A key mechanism by which internal models discriminate semiotic information from noise.

Free energy. Evaluating how closely incoming information matches what is expected by an internal model by measuring ‘surprise’. In this context, the term ‘free energy’ was developed by Karl Friston by analogy with information theory and entropy.

Information. Used in two senses in this paper. Information can be syntactic, as defined by Claude Shannon and used in information theory. This use focuses on the statistical structure of messages, the number of bytes required to send them, and the sources of noise that can degrade information as it passes through different channels. Information can also be semiotic, which focuses more on its meaning; here, the difference that makes a functional difference.

Internal model. A network of interacting set of biological parts (e.g., genes, proteins, neurons, tissues, etc.) that processes incoming information, computes what that information reveals about internal and external states, compares those states to the model’s expected state, and effects downstream changes on the collective system or the world.

Markov blanket. Informational boundaries around complex systems that act as the interface between a system and its surrounding environment. This boundary enables the system to remain viable but also independent from the environment.

Niche construction. The process by which organisms alter their local environments. These alterations can affect the way organisms interact with resources and other entities as well as the nature and magnitude of selection on populations.

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