Why Your Brain Falls for Misinformation – And How to Avoid It
This article is part of the magazine, "The Future of Science In America: The Election Issue," co-published by LeapsMag, the Aspen Institute Science & Society Program, and GOOD.
Whenever you hear something repeated, it feels more true. In other words, repetition makes any statement seem more accurate. So anything you hear again will resonate more each time it's said.
Do you see what I did there? Each of the three sentences above conveyed the same message. Yet each time you read the next sentence, it felt more and more true. Cognitive neuroscientists and behavioral economists like myself call this the "illusory truth effect."
Go back and recall your experience reading the first sentence. It probably felt strange and disconcerting, perhaps with a note of resistance, as in "I don't believe things more if they're repeated!"
Reading the second sentence did not inspire such a strong reaction. Your reaction to the third sentence was tame by comparison.
Why? Because of a phenomenon called "cognitive fluency," meaning how easily we process information. Much of our vulnerability to deception in all areas of life—including to fake news and misinformation—revolves around cognitive fluency in one way or another. And unfortunately, such misinformation can swing major elections.
The Lazy Brain
Our brains are lazy. The more effort it takes to process information, the more uncomfortable we feel about it and the more we dislike and distrust it.
By contrast, the more we like certain data and are comfortable with it, the more we feel that it's accurate. This intuitive feeling in our gut is what we use to judge what's true and false.
Yet no matter how often you heard that you should trust your gut and follow your intuition, that advice is wrong. You should not trust your gut when evaluating information where you don't have expert-level knowledge, at least when you don't want to screw up. Structured information gathering and decision-making processes help us avoid the numerous errors we make when we follow our intuition. And even experts can make serious errors when they don't rely on such decision aids.
These mistakes happen due to mental errors that scholars call "cognitive biases." The illusory truth effect is one of these mental blindspots; there are over 100 altogether. These mental blindspots impact all areas of our life, from health and politics to relationships and even shopping.
We pay the most attention to whatever we find most emotionally salient in our environment, as that's the information easiest for us to process.
The Maladapted Brain
Why do we have so many cognitive biases? It turns out that our intuitive judgments—our gut reactions, our instincts, whatever you call them—aren't adapted for the modern environment. They evolved from the ancestral savanna environment, when we lived in small tribes of 15–150 people and spent our time hunting and foraging.
It's not a surprise, when you think about it. Evolution works on time scales of many thousands of years; our modern informational environment has been around for only a couple of decades, with the rise of the internet and social media.
Unfortunately, that means we're using brains adapted for the primitive conditions of hunting and foraging to judge information and make decisions in a very different world. In the ancestral environment, we had to make quick snap judgments in order to survive, thrive, and reproduce; we're the descendants of those who did so most effectively.
In the modern environment, we can take our time to make much better judgments by using structured evaluation processes to protect yourself from cognitive biases. We have to train our minds to go against our intuitions if we want to figure out the truth and avoid falling for misinformation.
Yet it feels very counterintuitive to do so. Again, not a surprise: by definition, you have to go against your intuitions. It's not easy, but it's truly the only path if you don't want to be vulnerable to fake news.
The Danger of Cognitive Fluency and Illusory Truth
We already make plenty of mistakes by ourselves, without outside intervention. It's especially difficult to protect ourselves against those who know how to manipulate us. Unfortunately, the purveyors of misinformation excel at exploiting our cognitive biases to get us to buy into fake news.
Consider the illusory truth effect. Our vulnerability to it stems from how our brain processes novel stimuli. The first time we hear something new to us, it's difficult to process mentally. It has to integrate with our existing knowledge framework, and we have to build new neural pathways to make that happen. Doing so feels uncomfortable for our lazy brain, so the statement that we heard seems difficult to swallow to us.
The next time we hear that same thing, our mind doesn't have to build new pathways. It just has to go down the same ones it built earlier. Granted, those pathways are little more than trails, newly laid down and barely used. It's hard to travel down that newly established neural path, but much easier than when your brain had to lay down that trail. As a result, the statement is somewhat easier to swallow.
Each repetition widens and deepens the trail. Each time you hear the same thing, it feels more true, comfortable, and intuitive.
Does it work for information that seems very unlikely? Science says yes! Researchers found that the illusory truth effect applies strongly to implausible as well as plausible statements.
What about if you know better? Surely prior knowledge prevents this illusory truth! Unfortunately not: even if you know better, research shows you're still vulnerable to this cognitive bias, though less than those who don't have prior knowledge.
Sadly, people who are predisposed to more elaborate and sophisticated thinking—likely you, if you're reading the article—are more likely to fall for the illusory truth effect. And guess what: more sophisticated thinkers are also likelier than less sophisticated ones to fall for the cognitive bias known as the bias blind spot, where you ignore your own cognitive biases. So if you think that cognitive biases such as the illusory truth effect don't apply to you, you're likely deluding yourself.
That's why the purveyors of misinformation rely on repeating the same thing over and over and over and over again. They know that despite fact-checking, their repetition will sway people, even some of those who think they're invulnerable. In fact, believing that you're invulnerable will make you more likely to fall for this and other cognitive biases, since you won't be taking the steps necessary to address them.
Other Important Cognitive Biases
What are some other cognitive biases you need to beware? If you've heard of any cognitive biases, you've likely heard of the "confirmation bias." That refers to our tendency to look for and interpret information in ways that conform to our prior beliefs, intuitions, feelings, desires, and preferences, as opposed to the facts.
Again, cognitive fluency deserves blame. It's much easier to build neural pathways to information that we already possess, especially that around which we have strong emotions; it's much more difficult to break well-established neural pathways if we need to change our mind based on new information. Consequently, we instead look for information that's easy to accept, that which fits our prior beliefs. In turn, we ignore and even actively reject information that doesn't fit our beliefs.
Moreover, the more educated we are, the more likely we are to engage in such active rejection. After all, our smarts give us more ways of arguing against new information that counters our beliefs. That's why research demonstrates that the more educated you are, the more polarized your beliefs will be around scientific issues that have religious or political value overtones, such as stem cell research, human evolution, and climate change. Where might you be letting your smarts get in the way of the facts?
Our minds like to interpret the world through stories, meaning explanatory narratives that link cause and effect in a clear and simple manner. Such stories are a balm to our cognitive fluency, as our mind constantly looks for patterns that explain the world around us in an easy-to-process manner. That leads to the "narrative fallacy," where we fall for convincing-sounding narratives regardless of the facts, especially if the story fits our predispositions and our emotions.
You ever wonder why politicians tell so many stories? What about the advertisements you see on TV or video advertisements on websites, which tell very quick visual stories? How about salespeople or fundraisers? Sure, sometimes they cite statistics and scientific reports, but they spend much, much more time telling stories: simple, clear, compelling narratives that seem to make sense and tug at our heartstrings.
Now, here's something that's actually true: the world doesn't make sense. The world is not simple, clear, and compelling. The world is complex, confusing, and contradictory. Beware of simple stories! Look for complex, confusing, and contradictory scientific reports and high-quality statistics: they're much more likely to contain the truth than the easy-to-process stories.
Another big problem that comes from cognitive fluency: the "attentional bias." We pay the most attention to whatever we find most emotionally salient in our environment, as that's the information easiest for us to process. Most often, such stimuli are negative; we feel a lesser but real attentional bias to positive information.
That's why fear, anger, and resentment represent such powerful tools of misinformers. They know that people will focus on and feel more swayed by emotionally salient negative stimuli, so be suspicious of negative, emotionally laden data.
You should be especially wary of such information in the form of stories framed to fit your preconceptions and repeated. That's because cognitive biases build on top of each other. You need to learn about the most dangerous ones for evaluating reality clearly and making wise decisions, and watch out for them when you consume news, and in other life areas where you don't want to make poor choices.
Fixing Our Brains
Unfortunately, knowledge only weakly protects us from cognitive biases; it's important, but far from sufficient, as the study I cited earlier on the illusory truth effect reveals.
What can we do?
The easiest decision aid is a personal commitment to twelve truth-oriented behaviors called the Pro-Truth Pledge, which you can make by signing the pledge at ProTruthPledge.org. All of these behaviors stem from cognitive neuroscience and behavioral economics research in the field called debiasing, which refers to counterintuitive, uncomfortable, but effective strategies to protect yourself from cognitive biases.
What are these behaviors? The first four relate to you being truthful yourself, under the category "share truth." They're the most important for avoiding falling for cognitive biases when you share information:
Share truth
- Verify: fact-check information to confirm it is true before accepting and sharing it
- Balance: share the whole truth, even if some aspects do not support my opinion
- Cite: share my sources so that others can verify my information
- Clarify: distinguish between my opinion and the facts
The second set of four are about how you can best "honor truth" to protect yourself from cognitive biases in discussions with others:
Honor truth
- Acknowledge: when others share true information, even when we disagree otherwise
- Reevaluate: if my information is challenged, retract it if I cannot verify it
- Defend: defend others when they come under attack for sharing true information, even when we disagree otherwise
- Align: align my opinions and my actions with true information
The last four, under the category "encourage truth," promote broader patterns of truth-telling in our society by providing incentives for truth-telling and disincentives for deception:
Encourage truth
- Fix: ask people to retract information that reliable sources have disproved even if they are my allies
- Educate: compassionately inform those around me to stop using unreliable sources even if these sources support my opinion
- Defer: recognize the opinions of experts as more likely to be accurate when the facts are disputed
- Celebrate: those who retract incorrect statements and update their beliefs toward the truth
Peer-reviewed research has shown that taking the Pro-Truth Pledge is effective for changing people's behavior to be more truthful, both in their own statements and in interactions with others. I hope you choose to join the many thousands of ordinary citizens—and over 1,000 politicians and officials—who committed to this decision aid, as opposed to going with their gut.
[Adapted from: Dr. Gleb Tsipursky and Tim Ward, Pro Truth: A Practical Plan for Putting Truth Back Into Politics (Changemakers Books, 2020).]
[Editor's Note: To read other articles in this special magazine issue, visit the beautifully designed e-reader version.]
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.”