Can Radical Transparency Overcome Resistance to COVID-19 Vaccines?
When historians look back on the COVID-19 pandemic, they may mark November 9, 2020 as the day the tide began to turn. That's when the New York-based pharmaceutical giant Pfizer announced that clinical trials showed its experimental vaccine, developed with the German firm BioNTech, to be 90 percent effective in preventing the disease.
A week later, Massachusetts biotech startup Moderna declared its vaccine to be 95 percent effective. By early December, Great Britain had begun mass inoculations, followed—once the Food and Drug Administration gave the thumbs-up—by the United States. In this scenario, the worst global health crisis in a century was on the cusp of resolution.
Yet future chroniclers may instead peg November 9 as the day false hope dawned. That could happen if serious safety issues, undetected so far, arise after millions of doses are administered. Experts consider it unlikely, however, that such problems alone (as opposed to the panic they might spark) would affect enough people to thwart a victory over the coronavirus. A more immediate obstacle is vaccine hesitancy—the prospect that much of the populace will refuse to roll up their sleeves.
To achieve "herd immunity" for COVID-19 (the point at which a vaccine reduces transmission rates enough to protect those who can't or won't take it, or for whom it doesn't work), epidemiologists estimate that up to 85 percent of the population will have to be vaccinated. Alarmingly, polls suggest that 40 to 50 percent of Americans intend to decline, judging the risks to be more worrisome than those posed by the coronavirus itself.
COVID vaccine skeptics occupy various positions on a spectrum of doubt. Some are committed anti-vaxxers, or devotees of conspiracy theories that view the pandemic as a hoax. Others belong to minority groups that have historically been used as guinea pigs in unethical medical research (for horrific examples, Google "Tuskegee syphilis experiment" or "Henrietta Lacks"). Still others simply mistrust Big Pharma and/or Big Government. A common fear is that the scramble to find a vaccine—intensified by partisan and profit motives—has led to corner-cutting in the testing and approval process. "They really rushed," an Iowa trucker told The Washington Post. "I'll probably wait a couple of months after they start to see how everyone else is handling it."
The COVID crisis has spurred calls for secretive Data Safety and Monitoring Boards to come out of the shadows.
The consensus among scientists, by contrast, is that the process has been rigorous enough, given the exigency of the situation, that the public can feel reasonably confident in any vaccine that has earned the imprimatur of the FDA. For those of us who share that assessment, finding ways to reassure the hesitant-but-persuadable is an urgent matter.
Vax-positive public health messaging is one obvious tactic, but a growing number of experts say it's not enough. They prescribe a regimen of radical transparency throughout the system that regulates research—in particular, regarding the secretive panels that oversee vaccine trials.
The Crucial Role of the Little-Known Panels
Like other large clinical trials involving potentially high-demand or controversial products, studies of COVID-19 vaccines in most countries are supervised by groups of independent observers. Known in the United States as data safety and monitoring boards (DSMBs), and elsewhere as data monitoring committees, these panels consist of scientists, clinicians, statisticians, and other authorities with no ties to the sponsor of the study.
The six trials funded by the federal program known as Operation Warp Speed (including those of newly approved Moderna and frontrunner AstraZeneca) share a DSMB, whose members are selected by the National Institutes of Health; other companies (including Pfizer) appoint their own. The panel's job is to monitor the safety and efficacy of a treatment while the trial is ongoing, and to ensure that data is being collected and analyzed correctly.
Vaccine studies are "double-blinded," which means neither the participants nor the doctors running the trial know who's getting the real thing and who's getting a placebo. But the DSMB can access that information if a study volunteer has what might be a serious side effect—and if the participant was in the vaccine group, the board can ask that the trial be paused for further investigation.
The DSMB also checks for efficacy at pre-determined intervals. If it finds that the vaccine group and the placebo group are getting sick at similar rates, the panel can recommend stopping the trial due to "futility." And if the results look overwhelmingly positive, the DSMB can recommend that the study sponsor apply for FDA approval before the scheduled end of the trial, in order to hurry the product to market.
With this kind of inside dope and high-level influence, DSMBs could easily become targets for outside pressure. That's why, since the 1980s, their membership has typically been kept secret.
During the early days of the AIDS crisis, researchers working on HIV drugs feared for the safety of the experts on their boards. "They didn't want them to be besieged and harassed by members of the community," explains Susan Ellenberg, a professor of biostatistics, medical ethics and health policy at the University of Pennsylvania, and co-author of Data Monitoring Committees in Clinical Trials, the DSMB bible. "You can understand why people would very much want to know how things were looking in a given trial. They wanted to save their own lives; they wanted to save their friends' lives." Ellenberg, who was founding director of the biostatistics branch of the AIDS division at the National Institute of Allergy and Infectious Diseases (NIAID), helped shape a range of policies designed to ensure that DSMBs made decisions based on data and nothing else.
Confidentiality also shields DSMB members from badgering by patient advocacy groups, who might urge that a drug be presented for approval before trial results are conclusive, or by profit-hungry investors. "It prevents people from trying to pry out information to get an edge in the stock market," says Art Caplan, a bioethicist at New York University.
Yet the COVID crisis has spurred calls for DSMBs to come out of the shadows. One triggering event came in March 2020, when the FDA approved hydroxychloroquine for COVID-19—a therapy that President Donald J. Trump touted, despite scant evidence for its efficacy. (Approval was rescinded in June.) If the agency could bow to political pressure on these medications, critics warned, it might do so with vaccines as well. In the end, that didn't happen; the Pfizer approval was issued well after Election Day, despite Trump's goading, and most experts agree that it was based on solid science. Still, public suspicion lingers.
Another shock came in September, after British-based AstraZeneca announced it was pausing its vaccine trial globally due to a "suspected adverse rection" in a volunteer. The company shared no details with the press. Instead, AstraZeneca's CEO divulged them in a private call with J.P. Morgan investors the next day, confirming that the volunteer was suffering from transverse myelitis, a rare and serious spinal inflammation—and that the study had also been halted in July, when another volunteer displayed neurological symptoms. STAT News broke the story after talking to tipsters.
Although both illnesses were found to be unrelated to the vaccine, and the trial was restarted, the incident had a paradoxical effect: while it confirmed for experts that the oversight system was working, AstraZeneca's initial lack of candor added to many laypeople's sense that it wasn't. "If you were seeking to undermine trust, that's kind of how you would go about doing it," says Charles Weijer, a bioethicist at Western University in Ontario, who has helped develop clinical trial guidelines for the World Health Organization.
Both Caplan and Weijer have served on many DSMBs; they believe the boards are generally trustworthy, and that those overseeing COVID vaccine trials are performing their jobs well. But the secrecy surrounding these groups, they and others argue, has become counterproductive. Shining a light on the statistical sausage-makers would help dispel doubts about the finished product.
"I'm not suggesting that any of these companies are doing things unethically," Weijer explains. "But the circumstances of a global pandemic are sufficiently challenging that perhaps they ought to be doing some things differently. I believe it would be trust-producing for data monitoring committees to be more forthcoming than usual."
Building Trust: More Transparency
Just how forthcoming is a matter of debate. Caplan suggests that each COVID vaccine DSMB reveal the name of its chair; that would enable the scientific community, as well as the media and the general public, to get a sense of the integrity and qualifications of the board as a whole while preserving the anonymity of the other members.
Indeed, when Operation Warp Speed's DSMB chair, Richard Whitley, was outed through a website slip-up, many observers applauded his selection for the role; a professor of pediatrics, microbiology, medicine and neurosurgery at the University of Alabama at Birmingham, he is "an exceptionally experienced and qualified individual," Weijer says. (Reporters with ProPublica later identified two other members: Susan Ellenberg and immunologist William Makgoba, known for his work on the South African AIDS Vaccine Initiative.)
Caplan would also like to see more details of the protocols DSMBs are using to make decisions, such as the statistical threshold for efficacy that would lead them to seek approval from the FDA. And he wishes the NIH would spell out specific responsibilities for these monitoring boards. "They don't really have clear, government-mandated charters," he notes. For example, there's no requirement that DSMBs include an ethicist or patient advocate—both of which Caplan considers essential for vaccine trials. "Rough guidelines," he says, "would be useful."
Weijer, for his part, thinks DSMBs should disclose all their members. "When you only disclose the chair, you leave questions unanswered," he says. "What expertise do [the others] bring to the table? Are they similarly free of relevant conflicts of interest? And it doesn't answer the question that will be foremost on many people's minds: are these people in the pocket of pharma?"
Weijer and Caplan both want to see greater transparency around the trial results themselves. Because the FDA approved the Pfizer and Moderna vaccines with emergency use authorizations rather than full licensure, which requires more extensive safety testing, these products reached the market without the usual paper trail of peer-reviewed publications. The same will likely be true of any future COVID vaccines that the agency greenlights. To add another level of scrutiny, both ethicists suggest, each company should publicly release its data at the end of a trial. "That offers the potential for academic groups to go in and do an analysis," Weijer explains, "to verify the claims about the safety and efficacy of the vaccine." The point, he says, is not only to ensure that the approval was justified, but to provide evidence to counter skeptics' qualms.
Caplan may differ on some of the details, but he endorses the premise. "It's all a matter of trust," he says. "You're always watching that, because a vaccine is only as good as the number of people who take it."
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.”