“Deep Fake” Video Technology Is Advancing Faster Than Our Policies Can Keep Up
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.
Alethea.ai sports a grid of faces smiling, blinking and looking about. Some are beautiful, some are oddly familiar, but all share one thing in common—they are fake.
Alethea creates "synthetic media"— including digital faces customers can license saying anything they choose with any voice they choose. Companies can hire these photorealistic avatars to appear in explainer videos, advertisements, multimedia projects or any other applications they might dream up without running auditions or paying talent agents or actor fees. Licenses begin at a mere $99. Companies may also license digital avatars of real celebrities or hire mashups created from real celebrities including "Don Exotic" (a mashup of Donald Trump and Joe Exotic) or "Baby Obama" (a large-eared toddler that looks remarkably similar to a former U.S. President).
Naturally, in the midst of the COVID pandemic, the appeal is understandable. Rather than flying to a remote location to film a beer commercial, an actor can simply license their avatar to do the work for them. The question is—where and when this tech will cross the line between legitimately licensed and authorized synthetic media to deep fakes—synthetic videos designed to deceive the public for financial and political gain.
Deep fakes are not new. From written quotes that are manipulated and taken out of context to audio quotes that are spliced together to mean something other than originally intended, misrepresentation has been around for centuries. What is new is the technology that allows this sort of seamless and sophisticated deception to be brought to the world of video.
"At one point, video content was considered more reliable, and had a higher threshold of trust," said Alethea CEO and co-founder, Arif Khan. "We think video is harder to fake and we aren't yet as sensitive to detecting those fakes. But the technology is definitely there."
"In the future, each of us will only trust about 15 people and that's it," said Phil Lelyveld, who serves as Immersive Media Program Lead at the Entertainment Technology Center at the University of Southern California. "It's already very difficult to tell true footage from fake. In the future, I expect this will only become more difficult."
How do we know what's true in a world where original videos created with avatars of celebrities and politicians can be manipulated to say virtually anything?
As the U.S. 2020 Presidential Election nears, the potential moral and ethical implications of this technology are startling. A number of cases of truth tampering have recently been widely publicized. On August 5, President Donald Trump's campaign released an ad featuring several photos of Joe Biden that were altered to make it seem like was hiding all alone in his basement. In one photo, at least ten people who had been sitting with Biden in the original shot were cut out. In other photos, Biden's image was removed from a nature preserve and praying in church to make it appear Biden was in that same basement. Recently several videos of Speaker of the House Nancy Pelosi were slowed down by 75 percent to make her sound as if her speech was slurred.
During a campaign event in Florida on September 15 of this year, former Vice President Joe Biden was introduced by Puerto Rican singer-songwriter Luis Fonsi. After he was introduced, Biden paid tribute to the singer-songwriter—he held up his cell phone and played the hit song "Despecito". Shortly afterward, a doctored version of this video appeared on self-described parody site the United Spot replacing the Despicito with N.W.A.'s "F—- Tha Police". By September 16, Donald Trump retweeted the video, twice—first with the line "What is this all about" and second with the line "China is drooling. They can't believe this!" Twitter was quick to mark the video in these tweets as manipulated media.
Twitter had previously addressed several of Donald Trump's tweets—flagging a video shared in June as manipulated media and removing altogether a video shared by Trump in July showing a group promoting the hydroxychloroquine as an effective cure for COVID-19. Many of these manipulated videos are ultimately flagged or taken down, but not before they are seen and shared by millions of online viewers.
These faked videos were exposed rather quickly, as they could be compared with the original, publicly available source material. But what happens when there is no original source material? How do we know what's true in a world where original videos created with avatars of celebrities and politicians can be manipulated to say virtually anything?
"This type of fake media is a profound threat to our democracy," said Reid Blackman, the CEO of VIRTUE--an ethics consultancy for AI leaders. "Democracy depends on well-informed citizens. When citizens can't or won't discern between real and fake news, the implications are huge."
In light of the importance of reliable information in the political system, there's a clear and present need to verify that the images and news we consume is authentic. So how can anyone ever know that the content they are viewing is real?
"This will not be a simple technological solution," said Blackman. "There is no 'truth' button to push to verify authenticity. There's plenty of blame and condemnation to go around. Purveyors of information have a responsibility to vet the reliability of their sources. And consumers also have a responsibility to vet their sources."
Yet the process of verifying sources has never been more challenging. More and more citizens are choosing to live in a "media bubble"—gathering and sharing news only from and with people who share their political leanings and opinions. At one time, United States broadcasters were bound by the Fairness Doctrine—requiring them to present controversial issues important to the public in a way that the FCC deemed honest, equitable and balanced. The repeal of this doctrine in 1987 paved the way for new forms of cable news channels such as Fox News and MSNBC that appealed to viewers with a particular point of view. The Internet has only exacerbated these tendencies. Social media algorithms are designed to keep people clicking within their comfort zones by presenting members with only the thoughts and opinions they want to hear.
"I sometimes laugh when I hear people tell me they can back a particular opinion they hold with research," said Blackman. "Having conducted a fair bit of true scientific research, I am aware that clicking on one article on the Internet hardly qualifies. But a surprising number of people believe that finding any source online that states the fact they choose to believe is the same as proving it true."
Back to the fundamental challenge: How do we as a society root out what's false online? Lelyveld suggests that it will begin by verifying things that are known to be true rather than trying to call out everything that is fake. "The EU called me in to talk about how to deal with fake news coming out of Russia," said Lelyveld. "I told them Hollywood has spent 100 years developing special effects technology to make things that are wholly fictional indistinguishable from the truth. I told them that you'll never chase down every source of fake news. You're better off focusing on what can be proved true."
Arif Khan agrees. "There are probably 100 accounts attributed to Elon Musk on Twitter, but only one has the blue checkmark," said Khan. "That means Twitter has verified that an account of public interest is real. That's what we're trying to do with our platform. Allow celebrities to verify that specific videos were licensed and authorized directly by them."
Alethea will use another key technology called blockchain to mark all authentic authorized videos with celebrity avatars. Blockchain uses a distributed ledger technology to make sure that no undetected changes have been made to the content. Think of the difference between editing a document in a traditional word processing program and editing in a distributed online editing system like Google Docs. In a traditional word processing program, you can edit and copy a document without revealing any changes. In a shared editing system like Google Docs, every person who shares the document can see a record of every edit, addition and copy made of any portion of the document. In a similar way, blockchain helps Alethea ensure that approved videos have not been copied or altered inappropriately.
While AI companies like Alethea are moving to ensure that avatars based on real individuals aren't wrongly identified, the situation becomes a bit murkier when it comes to the question of representing groups, races, creeds, and other forms of identity. Alethea is rightly proud that the completely artificial avatars visually represent a variety of ages, races and sexes. However, companies could conceivably license an avatar to represent a marginalized group without actually hiring a person within that group to decide what the avatar will do or say.
"I don't know if I would call this tokenism, as that is difficult to identify without understanding the hiring company's intent," said Blackman. "Where this becomes deeply troubling is when avatars are used to represent a marginalized group without clearly pointing out the actor is an avatar. It's one thing for an African American woman avatar to say, 'I like ice cream.' It's entirely different thing for an African American woman avatar to say she supports a particular political candidate. In the second case, the avatar is being used as social proof that real people of a certain type back a certain political idea. And there the deception is far more problematic."
"It always comes down to unintended consequences of technology," said Lelyveld. "Technology is neutral—it's only the implementation that has the power to be good or bad. Without a thoughtful approach to the cultural, moral and political implications of technology, it often drifts towards the bad. We need to make a conscious decision as we release new technology to ensure it moves towards the good."
When presented with the idea that his avatars might be used to misrepresent marginalized groups, Khan was thoughtful. "Yes, I can see that is an unintended consequence of our technology. We would like to encourage people to license the avatars of real people, who would have final approval over what their avatars say or do. As to what people do with our completely artificial avatars, we will have to consider that moving forward."
Lelyveld frankly sees the ability for advertisers to create avatars that are our assistants or even our friends as a greater moral concern. "Once our digital assistant or avatar becomes an integral part of our life—even a friend as it were, what's to stop marketers from having those digital friends make suggestions about what drink we buy, which shirt we wear or even which candidate we elect? The possibilities for bad actors to reach us through our digital circle is mind-boggling."
Ultimately, Blackman suggests, we as a society will need to make decisions about what matters to us. "We will need to build policies and write laws—tackling the biggest problems like political deep fakes first. And then we have to figure out how to make the penalties stiff enough to matter. Fining a multibillion-dollar company a few million for a major offense isn't likely to move the needle. The punishment will need to fit the crime."
Until then, media consumers will need to do their own due diligence—to do the difficult work of uncovering the often messy and deeply uncomfortable news that's the truth.
[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.”