New Tests Measure Your Body’s Biological Age, Offering a Glimpse into the Future of Health Care
What if a simple blood test revealed how fast you're aging, and this meant more to you and your insurance company than the number of candles on your birthday cake?
The question of why individuals thrive or decline has loomed large in 2020, with COVID-19 harming people of all ages, while leaving others asymptomatic. Meanwhile, scientists have produced new measures, called aging clocks, that attempt to predict mortality and may eventually affect how we perceive aging.
Take, for example, "senior" athletes who perform more like 50-year-olds. But people over 65 are lumped into one category, whether they are winning marathons or using a walker. Meanwhile, I'm entering "middle age," a label just as vague. It's frustrating to have a better grasp on the lifecycle of my phone than my own body.
That could change soon, due to clock technology. In 2013, UCLA biostatistician Steven Horvath took a new approach to an old carnival trick, guessing people's ages by looking at epigenetics: how chemical compounds in our cells turn genetic instructions on or off. Exercise, pollutants, and other aspects of lifestyle and environment can flip these switches, converting a skin cell into a hair cell, for example. Then, hair may sprout from your ears.
Horvath's epigenetic clock approximated age within just a few years; an above-average estimate suggested fast aging. This "basically changed everything," said Vadim Gladyshev, a Harvard geneticist, leading to more epigenetic clocks and, just since May, additional clocks of the heart, products of cell metabolism, and microbes in a person's mouth and gut.
Machine learning is fueling these discoveries. Scientists send algorithms hunting through jungles of health data for factors related to physical demise. "Nothing in [the aging] industry has progressed as much as biomarkers," said Alex Zhavoronkov, CEO of Deep Longevity, a pioneer in learning-based clocks.
Researchers told LeapsMag that this tech could help identify age-related vulnerabilities to diseases—including COVID-19—and protective drugs.
Clocking disease vulnerability
In July, Yale researcher Morgan Levine found people were more likely to be hospitalized and die from COVID-19 if their aging clocks were ticking ahead of their calendar years. This effect held regardless of pre-existing conditions.
The study used Levine's biological aging clock, called PhenoAge, which is more accurate than previous versions. To develop it, she looked at data on health indices over several decades, focusing on nine hallmarks of aging—such as inflammation—that correspond to when people die. Then she used AI to find which epigenetic patterns in blood samples were strongly associated with physical aging. The PhenoAge clock reads these patterns to predict biological age; mortality goes up 62 percent among the fastest agers.
The cocktail, aimed at restoring immune function, reversed age by an average of 2.5 years, according to an epigenetic clock measurement taken before and after the intervention.
Because PhenoAge links chronic inflammation to aging and vulnerability, Levine proposed treating "inflammaging" to counter COVID-19.
Gladyshev reported similar findings, and Nir Barzilai, director of the Institute of Aging Research at Albert Einstein College of Medicine, agreed that biological age deserves greater focus. PhenoAge is an important innovation, he said, but most precise when measuring average age across large populations. Until clocks—including his blood protein version—account for differences in how individuals age, "Multi-morbidity is really the major biomarker" for a given person. Barzilai thinks individuals over 65 with two or more diseases are biologically older than their chronological age—about half the population in this study.
He believes COVID-19 efforts aren't taking stock of these differences. "The scientists are living in silos," he said, with many unaware aging has a biology that can be targeted.
The missed opportunities could be profound, especially for lower-income communities with disproportionately advanced aging. Barzilai has read eight different observational studies finding decreased COVID-19 severity among people taking metformin, the diabetes drug, which is believed to slow down the major hallmarks of biological aging, such as inflammation. Once a vaccine is identified, biologically older people could supplement it with metformin, but the medical establishment requires lengthy clinical trials. "The conservatism is taking over in days of war," Barzilai said.
Drug benefits on time
Clocks, once validated, could gauge drug effectiveness against age-related diseases quicker and cheaper than trials that track health outcomes over many years, expediting FDA approval of such therapies. For this to happen, though, the FDA must see evidence that rewinding clocks or improving related biomarkers leads to clinical benefits for patients. Researchers believe that clinical applications for at least some of these clocks are five to 10 years away.
Progress was made in last year's TRIIM trial, run by immunologist Gregory Fahy at Stanford Medical Center. People in their 50s took growth hormone, metformin and another diabetes drug, dehydroepiandrosterone, for 12 months. The cocktail, aimed at restoring immune function, reversed age by an average of 2.5 years, according to an epigenetic clock measurement taken before and after the intervention. Don't quit your gym just yet; TRIIM included just nine Caucasian men. A follow-up with 85 diverse participants begins next month.
But even group averages of epigenetic measures can be questionable, explained Willard Freeman, a researcher with the Reynolds Oklahoma Center on Aging. Consider this odd finding: heroin addicts tend to have younger epigenetic ages. "With the exception of Keith Richards, I don't think heroin is a great way to live a long healthy life," Freeman said.
Such confounders reveal that scientists—and AI—are still struggling to unearth the roots of aging. Do clocks simply reflect damage, mirrors to show who's the frailest of them all? Or do they programmatically drive aging? The answer involves vast complexity, like trying to deduce the direct causes of a 17-car pileup on a potholed road in foggy conditions. Except, instead of 17 cars, it's millions of epigenetic sites and thousands of potential genes, RNA molecules and blood proteins acting on aging and each other.
Because the various measures—epigenetics, microbes, etc.—capture distinct aging dimensions, an important goal is unifying them into one "mosaic of biological ages," as Levine called it. Gladyshev said more datasets are needed. Just yesterday, though, Zhavoronkov launched Deep Longevity's groundbreaking composite of metrics to consumers – something that was previously available only to clinicians. The iPhone app allows users to upload their own samples and tracks aging on multiple levels – epigenetic, behavioral, microbiome, and more. It even includes a deep psychological clock asking if people feel as old as they are. Perhaps Twain's adage about mind over matter is evidence-backed.
Zhavoronkov appeared youthful in our Zoom interview, but admitted self-testing shows an advanced age because "I do not sleep"; indeed, he'd scheduled me at midnight Hong Kong time. Perhaps explaining his insomnia, he fears economic collapse if age-related diseases cost the global economy over $30 trillion by 2030. Rather than seeking eternal life, researchers like Zhavoronkov aim to increase health span: fully living our final decades without excess pain and hospital bills.
It's also a lucrative sales pitch to 7.8 billion aging humans.
Get your bio age
Levine, the Yale scientist, has partnered with Elysium Health to sell Index, an epigenetic measure launched in late 2019, direct to consumers, using their saliva samples. Elysium will roll out additional measures as research progresses, starting with an assessment of how fast someone is accumulating cells that no longer divide. "The more measures to capture specific processes, the more we can actually understand what's unique for an individual," Levine said.
Another company, InsideTracker, with an advisory board headlined by Harvard's David Sinclair, eschews the quirkiness of epigenetics. Its new InnerAge 2.0 test, announced this month, analyzes 18 blood biomarkers associated with longevity.
"You can imagine payers clamoring to charge people for costs with a kind of personal responsibility to them."
Because aging isn't considered a disease, consumer aging tests don't require FDA approval, and some researchers are skeptical of their use in the near future. "I'm on the fence as to whether these things are ready to be rolled out," said Freeman, the Oklahoma researcher. "We need to do our traditional experimental study design to [be] confident they're actually useful."
Then, 50-year-olds who are biologically 45 may wait five years for their first colonoscopy, Barzilai said. Despite some forerunners, clinical applications for individuals are mostly prospective, yet I was intrigued. Could these clocks reveal if I'm following the footsteps of the super-agers? Or will I rack up the hospital bills of Zhavoronkov's nightmares?
I sent my blood for testing with InsideTracker. Fearing the worst—an InnerAge accelerated by a couple of decades—I asked thought leaders where this technology is headed.
Insurance 2030
With continued advances, by 2030 you'll learn your biological age with a glance at your wristwatch. You won't be the only monitor; your insurance company may send an alert if your age goes too high, threatening lost rewards.
If this seems implausible, consider that life insurer John Hancock already tracks a VitalityAge. With Obamacare incentivizing companies to engage policyholders in improving health, many are dangling rewards for fitness. BlueCross BlueShield covers 25 percent of InsideTracker's cost, and UnitedHealthcare offers a suite of such programs, including "missions" for policyholders to lower their Rally age. "People underestimate the amount of time they're sedentary," said Michael Bess, vice president of healthcare strategies. "So having this technology to drive positive reinforcement is just another way to encourage healthy behavior."
It's unclear if these programs will close health gaps, or simply attract customers already prioritizing fitness. And insurers could raise your premium if you don't measure up. Obamacare forbids discrimination based on pre-existing conditions, but will accelerated age qualify for this protection?
Liz McFall, a sociologist at the University of Edinburgh, thinks the answer depends on whether we view aging as controllable. "You can imagine payers clamoring to charge people for costs with a kind of personal responsibility to them," she said.
That outcome troubles Mark Rothstein, director of the Institute of Bioethics at the University of Louisville. "For those living with air pollution and unsafe water, in food deserts and where you can't safely exercise, then [insurers] take the results in terms of biological stressors, now you're adding insult to injury," he said.
Government could subsidize aging clocks and interventions for older people with fewer resources for controlling their health—and the greatest room for improving their epigenetic age. Rothstein supports that policy, but said, "I don't see it happening."
Bio age working for you
2030 again. A job posting seeks a "go-getter," so you attach a doctor's note to your resume proving you're ten years younger than your chronological age.
This prospect intrigued Cathy Ventrell-Monsees, senior advisor at the Equal Employment Opportunity Commission. "Any marker other than age is a step forward," she said. "Age simply doesn't determine any kind of cognitive or physical ability."
What if the assessment isn't voluntary? Armed with AI, future employers could surveil a candidate's biological age from their head-shot. Haut.ai is already marketing an uncannily accurate PhotoAgeClock. Its CEO, Anastasia Georgievskaya, noted this tech's promise in other contexts; it could help people literally see the connection between healthier lifestyles and looking young and attractive. "The images keep people quite engaged," she told me.
Updating laws could minimize drawbacks. Employers are already prohibited from using genetic information to discriminate (think 23andMe). The ban could be extended to epigenetics. "I would imagine biomarkers for aging go a similar path as genetic nondiscrimination," said McFall, the sociologist.
Will we use aging clocks to screen candidates for the highest office? Barzilai, the Albert Einstein College of Medicine researcher, believes Trump and Biden have similar biological ages. But one of Barzilai's factors, BMI, is warped by Trump miraculously getting taller. "Usually people get shorter with age," Barzilai said. "His weight has been increasing, but his BMI stays the same."
As for my bio age? InnerAge suggested I'm four years younger—and by boosting my iron levels, the program suggests, I could be younger still.
We need standards for these tests, and customers must understand their shortcomings. With such transparency, though, the benefits could be compelling. In March, Theresa Brown, a 44-year-old from Kansas, learned her InnerAge was 57.2. She followed InsideTracker's recommendations, including regular intermittent fasting. Retested five months later, her age had dropped to 34.1. "It's not that I guaranteed another 10 or 20 years to my life. It's that it encourages me. Whether I really am or not, I just feel younger. I'll take that."
Which leads back to Zhavoronkov's psychological clock. Perhaps lowering our InnerAges can be the self-fulfilling prophesy that helps Theresa and me age like the super-athletes who thrive longer than expected. McFall noted the power of simple, sufficiently credible goals for encouraging better health. Think 10,000 steps per day, she said.
Want to be 34 again? Just do it.
Yet, many people's budgets just don't allow gym memberships, nutritious groceries, or futuristic aging clocks. Bill Gates cautioned we overestimate progress in the next two years, while underestimating the next ten. Policies should ensure that age testing and interventions are distributed fairly.
"Within the next 5 to 10 years," said Gladyshev, "there will be drugs and lifestyle changes which could actually increase lifespan or healthspan for the entire population."
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.”