In The Fake News Era, Are We Too Gullible? No, Says Cognitive Scientist
One of the oddest political hoaxes of recent times was Pizzagate, in which conspiracy theorists claimed that Hillary Clinton and her 2016 campaign chief ran a child sex ring from the basement of a Washington, DC, pizzeria.
To fight disinformation more effectively, he suggests, humans need to stop believing in one thing above all: our own gullibility.
Millions of believers spread the rumor on social media, abetted by Russian bots; one outraged netizen stormed the restaurant with an assault rifle and shot open what he took to be the dungeon door. (It actually led to a computer closet.) Pundits cited the imbroglio as evidence that Americans had lost the ability to tell fake news from the real thing, putting our democracy in peril.
Such fears, however, are nothing new. "For most of history, the concept of widespread credulity has been fundamental to our understanding of society," observes Hugo Mercier in Not Born Yesterday: The Science of Who We Trust and What We Believe (Princeton University Press, 2020). In the fourth century BCE, he points out, the historian Thucydides blamed Athens' defeat by Sparta on a demagogue who hoodwinked the public into supporting idiotic military strategies; Plato extended that argument to condemn democracy itself. Today, atheists and fundamentalists decry one another's gullibility, as do climate-change accepters and deniers. Leftists bemoan the masses' blind acceptance of the "dominant ideology," while conservatives accuse those who do revolt of being duped by cunning agitators.
What's changed, all sides agree, is the speed at which bamboozlement can propagate. In the digital age, it seems, a sucker is born every nanosecond.
The Case Against Credulity
Yet Mercier, a cognitive scientist at the Jean Nicod Institute in Paris, thinks we've got the problem backward. To fight disinformation more effectively, he suggests, humans need to stop believing in one thing above all: our own gullibility. "We don't credulously accept whatever we're told—even when those views are supported by the majority of the population, or by prestigious, charismatic individuals," he writes. "On the contrary, we are skilled at figuring out who to trust and what to believe, and, if anything, we're too hard rather than too easy to influence."
He bases those contentions on a growing body of research in neuropsychiatry, evolutionary psychology, and other fields. Humans, Mercier argues, are hardwired to balance openness with vigilance when assessing communicated information. To gauge a statement's accuracy, we instinctively test it from many angles, including: Does it jibe with what I already believe? Does the speaker share my interests? Has she demonstrated competence in this area? What's her reputation for trustworthiness? And, with more complex assertions: Does the argument make sense?
This process, Mercier says, enables us to learn much more from one another than do other animals, and to communicate in a far more complex way—key to our unparalleled adaptability. But it doesn't always save us from trusting liars or embracing demonstrably false beliefs. To better understand why, leapsmag spoke with the author.
How did you come to write Not Born Yesterday?
In 2010, I collaborated with the cognitive scientist Dan Sperber and some other colleagues on a paper called "Epistemic Vigilance," which laid out the argument that evolutionarily, it would make no sense for humans to be gullible. If you can be easily manipulated and influenced, you're going to be in major trouble. But as I talked to people, I kept encountering resistance. They'd tell me, "No, no, people are influenced by advertising, by political campaigns, by religious leaders." I started doing more research to see if I was wrong, and eventually I had enough to write a book.
With all the talk about "fake news" these days, the topic has gotten a lot more timely.
Yes. But on the whole, I'm skeptical that fake news matters very much. And all the energy we spend fighting it is energy not spent on other pursuits that may be better ways of improving our informational environment. The real challenge, I think, is not how to shut up people who say stupid things on the internet, but how to make it easier for people who say correct things to convince people.
"History shows that the audience's state of mind and material conditions matter more than the leader's powers of persuasion."
You start the book with an anecdote about your encounter with a con artist several years ago, who scammed you out of 20 euros. Why did you choose that anecdote?
Although I'm arguing that people aren't generally gullible, I'm not saying we're completely impervious to attempts at tricking us. It's just that we're much better than we think at resisting manipulation. And while there's a risk of trusting someone who doesn't deserve to be trusted, there's also a risk of not trusting someone who could have been trusted. You miss out on someone who could help you, or from whom you might have learned something—including figuring out who to trust.
You argue that in humans, vigilance and open-mindedness evolved hand-in-hand, leading to a set of cognitive mechanisms you call "open vigilance."
There's a common view that people start from a state of being gullible and easy to influence, and get better at rejecting information as they become smarter and more sophisticated. But that's not what really happens. It's much harder to get apes than humans to do anything they don't want to do, for example. And research suggests that over evolutionary time, the better our species became at telling what we should and shouldn't listen to, the more open to influence we became. Even small children have ways to evaluate what people tell them.
The most basic is what I call "plausibility checking": if you tell them you're 200 years old, they're going to find that highly suspicious. Kids pay attention to competence; if someone is an expert in the relevant field, they'll trust her more. They're likelier to trust someone who's nice to them. My colleagues and I have found that by age 2 ½, children can distinguish between very strong and very weak arguments. Obviously, these skills keep developing throughout your life.
But you've found that even the most forceful leaders—and their propaganda machines—have a hard time changing people's minds.
Throughout history, there's been this fear of demagogues leading whole countries into terrible decisions. In reality, these leaders are mostly good at feeling the crowd and figuring out what people want to hear. They're not really influencing [the masses]; they're surfing on pre-existing public opinion. We know from a recent study, for instance, that if you match cities in which Hitler gave campaign speeches in the late '20s through early '30s with similar cities in which he didn't give campaign speeches, there was no difference in vote share for the Nazis. Nazi propaganda managed to make Germans who were already anti-Semitic more likely to express their anti-Semitism or act on it. But Germans who were not already anti-Semitic were completely inured to the propaganda.
So why, in totalitarian regimes, do people seem so devoted to the ruler?
It's not a very complex psychology. In these regimes, the slightest show of discontent can be punished by death, or by you and your whole family being sent to a labor camp. That doesn't mean propaganda has no effect, but you can explain people's obedience without it.
What about cult leaders and religious extremists? Their followers seem willing to believe anything.
Prophets and preachers can inspire the kind of fervor that leads people to suicidal acts or doomed crusades. But history shows that the audience's state of mind and material conditions matter more than the leader's powers of persuasion. Only when people are ready for extreme actions can a charismatic figure provide the spark that lights the fire.
Once a religion becomes ubiquitous, the limits of its persuasive powers become clear. Every anthropologist knows that in societies that are nominally dominated by orthodox belief systems—whether Christian or Muslim or anything else—most people share a view of God, or the spirit, that's closer to what you find in societies that lack such religions. In the Middle Ages, for instance, you have records of priests complaining of how unruly the people are—how they spend the whole Mass chatting or gossiping, or go on pilgrimages mostly because of all the prostitutes and wine-drinking. They continue pagan practices. They resist attempts to make them pay tithes. It's very far from our image of how much people really bought the dominant religion.
"The mainstream media is extremely reliable. The scientific consensus is extremely reliable."
And what about all those wild rumors and conspiracy theories on social media? Don't those demonstrate widespread gullibility?
I think not, for two reasons. One is that most of these false beliefs tend to be held in a way that's not very deep. People may say Pizzagate is true, yet that belief doesn't really interact with the rest of their cognition or their behavior. If you really believe that children are being abused, then trying to free them is the moral and rational thing to do. But the only person who did that was the guy who took his assault weapon to the pizzeria. Most people just left one-star reviews of the restaurant.
The other reason is that most of these beliefs actually play some useful role for people. Before any ethnic massacre, for example, rumors circulate about atrocities having been committed by the targeted minority. But those beliefs aren't what's really driving the phenomenon. In the horrendous pogrom of Kishinev, Moldova, 100 years ago, you had these stories of blood libel—a child disappeared, typical stuff. And then what did the Christian inhabitants do? They raped the [Jewish] women, they pillaged the wine stores, they stole everything they could. They clearly wanted to get that stuff, and they made up something to justify it.
Where do skeptics like climate-change deniers and anti-vaxxers fit into the picture?
Most people in most countries accept that vaccination is good and that climate change is real and man-made. These ideas are deeply counter-intuitive, so the fact that scientists were able to get them across is quite fascinating. But the environment in which we live is vastly different from the one in which we evolved. There's a lot more information, which makes it harder to figure out who we can trust. The main effect is that we don't trust enough; we don't accept enough information. We also rely on shortcuts and heuristics—coarse cues of trustworthiness. There are people who abuse these cues. They may have a PhD or an MD, and they use those credentials to help them spread messages that are not true and not good. Mostly, they're affirming what people want to believe, but they may also be changing minds at the margins.
How can we improve people's ability to resist that kind of exploitation?
I wish I could tell you! That's literally my next project. Generally speaking, though, my advice is very vanilla. The mainstream media is extremely reliable. The scientific consensus is extremely reliable. If you trust those sources, you'll go wrong in a very few cases, but on the whole, they'll probably give you good results. Yet a lot of the problems that we attribute to people being stupid and irrational are not entirely their fault. If governments were less corrupt, if the pharmaceutical companies were irreproachable, these problems might not go away—but they would certainly be minimized.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.
DNA gathered from animal poop helps protect wildlife
On the savannah near the Botswana-Zimbabwe border, elephants grazed contentedly. Nearby, postdoctoral researcher Alida de Flamingh watched and waited. As the herd moved away, she went into action, collecting samples of elephant dung that she and other wildlife conservationists would study in the months to come. She pulled on gloves, took a swab, and ran it all over the still-warm, round blob of elephant poop.
Sequencing DNA from fecal matter is a safe, non-invasive way to track and ultimately help protect over 42,000 species currently threatened by extinction. Scientists are using this DNA to gain insights into wildlife health, genetic diversity and even the broader environment. Applied to elephants, chimpanzees, toucans and other species, it helps scientists determine the genetic diversity of groups and linkages with other groups. Such analysis can show changes in rates of inbreeding. Populations with greater genetic diversity adapt better to changes and environmental stressors than those with less diversity, thus reducing their risks of extinction, explains de Flamingh, a postdoctoral researcher at the University of Illinois Urbana-Champaign.
Analyzing fecal DNA also reveals information about an animal’s diet and health, and even nearby flora that is eaten. That information gives scientists broader insights into the ecosystem, and the findings are informing conservation initiatives. Examples include restoring or maintaining genetic connections among groups, ensuring access to certain foraging areas or increasing diversity in captive breeding programs.
Approximately 27 percent of mammals and 28 percent of all assessed species are close to dying out. The IUCN Red List of threatened species, simply called the Red List, is the world’s most comprehensive record of animals’ risk of extinction status. The more information scientists gather, the better their chances of reducing those risks. In Africa, populations of vertebrates declined 69 percent between 1970 and 2022, according to the World Wildlife Fund (WWF).
“We put on sterile gloves and use a sterile swab to collect wet mucus and materials from the outside of the dung ball,” says Alida de Flamingh, a postdoctoral researcher at the University of Illinois Urbana-Champaign.
“When people talk about species, they often talk about ecosystems, but they often overlook genetic diversity,” says Christina Hvilsom, senior geneticist at the Copenhagen Zoo. “It’s easy to count (individuals) to assess whether the population size is increasing or decreasing, but diversity isn’t something we can see with our bare eyes. Yet, it’s actually the foundation for the species and populations.” DNA analysis can provide this critical information.
Assessing elephants’ health
“Africa’s elephant populations are facing unprecedented threats,” says de Flamingh, the postdoc, who has studied them since 2009. Challenges include ivory poaching, habitat destruction and smaller, more fragmented habitats that result in smaller mating pools with less genetic diversity. Additionally, de Flamingh studies the microbial communities living on and in elephants – their microbiomes – looking for parasites or dangerous microbes.
Approximately 415,000 elephants inhabit Africa today, but de Flamingh says the number would be four times higher without these challenges. The IUCN Red List reports African savannah elephants are endangered and African forest elephants are critically endangered. Elephants support ecosystem biodiversity by clearing paths that help other species travel. Their very footprints create small puddles that can host smaller organisms such as tadpoles. Elephants are often described as ecosystems’ engineers, so if they disappear, the rest of the ecosystem will suffer too.
There’s a process to collecting elephant feces. “We put on sterile gloves (which we change for each sample) and use a sterile swab to collect wet mucus and materials from the outside of the dung ball,” says de Flamingh. They rub a sample about the size of a U.S. quarter onto a paper card embedded with DNA preservation technology. Each card is air dried and stored in a packet of desiccant to prevent mold growth. This way, samples can be stored at room temperature indefinitely without the DNA degrading.
Earlier methods required collecting dung in bags, which needed either refrigeration or the addition of preservatives, or the riskier alternative of tranquilizing the animals before approaching them to draw blood samples. The ability to collect and sequence the DNA made things much easier and safer.
“Our research provides a way to assess elephant health without having to physically interact with elephants,” de Flamingh emphasizes. “We also keep track of the GPS coordinates of each sample so that we can create a map of the sampling locations,” she adds. That helps researchers correlate elephants’ health with geographic areas and their conditions.
Although de Flamingh works with elephants in the wild, the contributions of zoos in the United States and collaborations in South Africa (notably the late Professor Rudi van Aarde and the Conservation Ecology Research Unit at the University of Pretoria) were key in studying this method to ensure it worked, she points out.
Protecting chimpanzees
Genetic work with chimpanzees began about a decade ago. Hvilsom and her group at the Copenhagen Zoo analyzed DNA from nearly 1,000 fecal samples collected between 2003 and 2018 by a team of international researchers. The goal was to assess the status of the West African subspecies, which is critically endangered after rapid population declines. Of the four subspecies of chimpanzees, the West African subspecies is considered the most at-risk.
In total, the WWF estimates the numbers of chimpanzees inhabiting Africa’s forests and savannah woodlands at between 173,000 and 300,000. Poaching, disease and human-caused changes to their lands are their major risks.
By analyzing genetics obtained from fecal samples, Hvilsom estimated the chimpanzees’ population, ascertained their family relationships and mapped their migration routes.
“One of the threats is mining near the Nimba Mountains in Guinea,” a stronghold for the West African subspecies, Hvilsom says. The Nimba Mountains are a UNESCO World Heritage Site, but they are rich in iron ore, which is used to make the steel that is vital to the Asian construction boom. As she and colleagues wrote in a recent paper, “Many extractive industries are currently developing projects in chimpanzee habitat.”
Analyzing DNA allows researchers to identify individual chimpanzees more accurately than simply observing them, she says. Normally, field researchers would install cameras and manually inspect each picture to determine how many chimpanzees were in an area. But, Hvilsom says, “That’s very tricky. Chimpanzees move a lot and are fast, so it’s difficult to get clear pictures. Often, they find and destroy the cameras. Also, they live in large areas, so you need a lot of cameras.”
By analyzing genetics obtained from fecal samples, Hvilsom estimated the chimpanzees’ population, ascertained their family relationships and mapped their migration routes based upon DNA comparisons with other chimpanzee groups. The mining companies and builders are using this information to locate future roads where they won’t disrupt migration – a more effective solution than trying to build artificial corridors for wildlife.
“The current route cuts off communities of chimpanzees,” Hvilsom elaborates. That effectively prevents young adult chimps from joining other groups when the time comes, eventually reducing the currently-high levels of genetic diversity.
“The mining company helped pay for the genetics work,” Hvilsom says, “as part of its obligation to assess and monitor biodiversity and the effect of the mining in the area.”
Of 50 toucan subspecies, 11 are threatened or near-threatened with extinction because of deforestation and poaching.
Identifying toucan families
Feces aren't the only substance researchers draw DNA samples from. Jeffrey Coleman, a Ph.D. candidate at the University of Texas at Austin relies on blood tests for studying the genetic diversity of toucans---birds species native to Central America and nearby regions. They live in the jungles, where they hop among branches, snip fruit from trees, toss it in the air and catch it with their large beaks. “Toucans are beautiful, charismatic birds that are really important to the ecosystem,” says Coleman.
Of their 50 subspecies, 11 are threatened or near-threatened with extinction because of deforestation and poaching. “When people see these aesthetically pleasing birds, they’re motivated to care about conservation practices,” he points out.
Coleman works with the Dallas World Aquarium and its partner zoos to analyze DNA from blood draws, using it to identify which toucans are related and how closely. His goal is to use science to improve the genetic diversity among toucan offspring.
Specifically, he’s looking at sections of the genome of captive birds in which the nucleotides repeat multiple times, such as AGATAGATAGAT. Called microsatellites, these consecutively-repeating sections can be passed from parents to children, helping scientists identify parent-child and sibling-sibling relationships. “That allows you to make strategic decisions about how to pair (captive) individuals for mating...to avoid inbreeding,” Coleman says.
Jeffrey Coleman is studying the microsatellites inside the toucan genomes.
Courtesy Jeffrey Coleman
The alternative is to use a type of analysis that looks for a single DNA building block – a nucleotide – that differs in a given sequence. Called single nucleotide polymorphisms (SNPs, pronounced “snips”), they are very common and very accurate. Coleman says they are better than microsatellites for some uses. But scientists have already developed a large body of microsatellite data from multiple species, so microsatellites can shed more insights on relations.
Regardless of whether conservation programs use SNPs or microsatellites to guide captive breeding efforts, the goal is to help them build genetically diverse populations that eventually may supplement endangered populations in the wild. “The hope is that the ecosystem will be stable enough and that the populations (once reintroduced into the wild) will be able to survive and thrive,” says Coleman. History knows some good examples of captive breeding success.
The California condor, which had a total population of 27 in 1987, when the last wild birds were captured, is one of them. A captive breeding program boosted their numbers to 561 by the end of 2022. Of those, 347 of those are in the wild, according to the National Park Service.
Conservationists hope that their work on animals’ genetic diversity will help preserve and restore endangered species in captivity and the wild. DNA analysis is crucial to both types of efforts. The ability to apply genome sequencing to wildlife conservation brings a new level of accuracy that helps protect species and gives fresh insights that observation alone can’t provide.
“A lot of species are threatened,” Coleman says. “I hope this research will be a resource people can use to get more information on longer-term genealogies and different populations.”