How Will the New Strains of COVID-19 Affect Our Vaccination Plans?
When the world's first Covid-19 vaccine received regulatory approval in November, it appeared that the end of the pandemic might be near. As one by one, the Pfizer/BioNTech, Moderna, AstraZeneca, and Sputnik V vaccines reported successful Phase III results, the prospect of life without lockdowns and restrictions seemed a tantalizing possibility.
But for scientists with many years' worth of experience in studying how viruses adapt over time, it remained clear that the fight against the SARS-CoV-2 virus was far from over. "The more virus circulates, the more it is likely that mutations occur," said Professor Beate Kampmann, director of the Vaccine Centre at the London School of Hygiene & Tropical Medicine. "It is inevitable that new variants will emerge."
Since the start of the pandemic, dozens of new variants of SARS-CoV-2 – containing different mutations in the viral genome sequence - have appeared as it copies itself while spreading through the human population. The majority of these mutations are inconsequential, but in recent months, some mutations have emerged in the receptor binding domain of the virus's spike protein, increasing how tightly it binds to human cells. These mutations appear to make some new strains up to 70 percent more transmissible, though estimates vary and more lab experiments are needed. Such new strains include the B.1.1.7 variant - currently the dominant strain in the UK – and the 501Y.V2 variant, which was first found in South Africa.
"I'm quite optimistic that even with these mutations, immunity is not going to suddenly fail on us."
Because so many more people are becoming infected with the SARS-CoV-2 virus as a result, vaccinologists point out that these new strains will prolong the pandemic.
"It may take longer to reach vaccine-induced herd immunity," says Deborah Fuller, professor of microbiology at the University of Washington School of Medicine. "With a more transmissible variant taking over, an even larger percentage of the population will need to get vaccinated before we can shut this pandemic down."
That is, of course, as long as the vaccinations are still highly protective. The South African variant, in particular, contains a mutation called E484K that is raising alarms among scientists. Emerging evidence indicates that this mutation allows the virus to escape from some people's immune responses, and thus could potentially weaken the effectiveness of current vaccines.
What We Know So Far
Over the past few weeks, manufacturers of the approved Covid-19 vaccines have been racing to conduct experiments, assessing whether their jabs still work well against the new variants. This process involves taking blood samples from people who have already been vaccinated and assessing whether the antibodies generated by those people can neutralize the new strains in a test tube.
Pfizer has just released results from the first of these studies, declaring that their vaccine was found to still be effective at neutralizing strains of the virus containing the N501Y mutation of the spike protein, one of the mutations present within both the UK and South African variants.
However, the study did not look at the full set of mutations contained within either of these variants. Earlier this week, academics at the Fred Hutchinson Cancer Research Center in Seattle suggested that the E484K spike protein mutation could be most problematic, publishing a study which showed that the efficacy of neutralizing antibodies against this region dropped by more than ten-fold because of the mutation.
Thankfully, this development is not expected to make vaccines useless. One of the Fred Hutch researchers, Jesse Bloom, told STAT News that he did not expect this mutation to seriously reduce vaccine efficacy, and that more harmful mutations would need to accrue over time to pose a very significant threat to vaccinations.
"I'm quite optimistic that even with these mutations, immunity is not going to suddenly fail on us," Bloom told STAT. "It might be gradually eroded, but it's not going to fail on us, at least in the short term."
While further vaccine efficacy data will emerge in the coming weeks, other vaccinologists are keen to stress this same point: At most, there will be a marginal drop in efficacy against the new variants.
"Each vaccine induces what we call polyclonal antibodies targeting multiple parts of the spike protein," said Fuller. "So if one antibody target mutates, there are other antibody targets on the spike protein that could still neutralize the virus. The vaccine platforms also induce T-cell responses that could provide a second line of defense. If some virus gets past antibodies, T-cell responses can find and eliminate infected cells before the virus does too much damage."
She estimates that if vaccine efficacy decreases, for example from 95% to 85%, against one of the new variants, the main implications will be that some individuals who might otherwise have become severely ill, may still experience mild or moderate symptoms from an infection -- but crucially, they will not end up in intensive care.
"Plug and Play" Vaccine Platforms
One of the advantages of the technologies which have been pioneered to create the Covid-19 vaccines is that they are relatively straightforward to update with a new viral sequence. The mRNA technology used in the Pfizer/BioNTech and Moderna vaccines, and the adenovirus vectors used in the Astra Zeneca and Sputnik V vaccines, are known as 'plug and play' platforms, meaning that a new form of the vaccine can be rapidly generated against any emerging variant.
"With a rapid pipeline for manufacture established, these new vaccine technologies could enable production and distribution within 1-3 months of a new variant emerging."
While the technology for the seasonal influenza vaccines is relatively inefficient, requiring scientists to grow and cultivate the new strain in the lab before vaccines can be produced - a process that takes nine months - mRNA and adenovirus-based vaccines can be updated within a matter of weeks. According to BioNTech CEO Uğur Şahin, a new version of their vaccine could be produced in six weeks.
"With a rapid pipeline for manufacture established, these new vaccine technologies could enable production and distribution within 1-3 months of a new variant emerging," says Fuller.
Fuller predicts that more new variants of the virus are almost certain to emerge within the coming months and years, potentially requiring the public to receive booster shots. This means there is one key advantage the mRNA-based vaccines have over the adenovirus technologies. mRNA vaccines only express the spike protein, while the AstraZeneca and Sputnik V vaccines use adenoviruses - common viruses most of us are exposed to - as a delivery mechanism for genes from the SARS-CoV-2 virus.
"For the adenovirus vaccines, our bodies make immune responses against both SARS-CoV-2 and the adenovirus backbone of the vaccine," says Fuller. "That means if you update the adenovirus-based vaccine with the new variant and then try to boost people, they may respond less well to the new vaccine, because they already have antibodies against the adenovirus that could block the vaccine from working. This makes mRNA vaccines more amenable to repeated use."
Regulatory Unknowns
One of the key questions remains whether regulators would require new versions of the vaccine to go through clinical trials, a hurdle which would slow down the response to emerging strains, or whether the seasonal influenza paradigm will be followed, whereby a new form of the vaccine can be released without further clinical testing.
Regulators are currently remaining tight-lipped on which process they will choose to follow, until there is more information on how vaccines respond against the new variants. "Only when such information becomes available can we start the scientific evaluation of what data would be needed to support such a change and assess what regulatory procedure would be required for that," said Rebecca Harding, communications officer for the European Medicines Agency.
The Food and Drug Administration (FDA) did not respond to requests for comment before press time.
While vaccinologists feel it is unlikely that a new complete Phase III trial would be required, some believe that because these are new technologies, regulators may well demand further safety data before approving an updated version of the vaccine.
"I would hope if we ever have to update the current vaccines, regulatory authorities will treat it like influenza," said Drew Weissman, professor of medicine at the University of Pennsylvania, who was involved in developing the mRNA technology behind the Pfizer/BioNTech and Moderna vaccines. "I would guess, at worst, they may want a new Phase 1 or 1 and 2 clinical trials."
Others suggest that rather than new trials, some bridging experiments may suffice to demonstrate that the levels of neutralizing antibodies induced by the new form of the vaccine are comparable to the previous one. "Vaccines have previously been licensed by this kind of immunogenicity data only, for example meningitis vaccines," said Kampmann.
While further mutations and strains of SARS-CoV-2 are inevitable, some scientists are concerned that the vaccine rollout strategy being employed in some countries -- of distributing a first shot to as many people as possible, and potentially delaying second shots as a result -- could encourage more new variants to emerge. Just today, the Biden administration announced its intention to release nearly all vaccine doses on hand right away, without keeping a reserve for second shots. This plan risks relying on vaccine manufacturing to ramp up quickly to keep pace if people are to receive their second shots at the right intervals.
"I am not very happy about this change as it could lead to a large number of people out there with partial immunity and this could select new mutations, and escalate the potential problem of vaccine escape."
The Biden administration's shift appears to conflict with the FDA's recent position that second doses should be given on a strict schedule, without any departure from the three- and four-week intervals established in clinical trials. Two top FDA officials said in a statement that changing the dosing schedule "is premature and not rooted solidly in the available evidence. Without appropriate data supporting such changes in vaccine administration, we run a significant risk of placing public health at risk, undermining the historic vaccination efforts to protect the population from COVID-19."
"I understand the argument of trying to get at least partial protection to as many people as possible, but I am concerned about the increased interval between the doses that is now being proposed," said Kampmann. "I am not very happy about this change as it could lead to a large number of people out there with partial immunity and this could select new mutations, and escalate the potential problem of vaccine escape."
But it's worth emphasizing that the virus is unlikely for now to accumulate enough harmful mutations to render the current vaccines completely ineffective.
"It will be very hard for the virus to evolve to completely evade the antibody responses the vaccines induce," said Fuller. "The parts of the virus that are targeted by vaccine-induced antibodies are essential for the virus to infect our cells. If the virus tries to mutate these parts to evade antibodies, then it could compromise its own fitness or even abort its ability to infect. To be sure, the virus is developing these mutations, but we just don't see these variants emerge because they die out."
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.”