Virtual Clinical Trials Are Letting More People of Color Participate in Research
Herman Taylor, director of the cardiovascular research institute at Morehouse college, got in touch with UnitedHealth Group early in the pandemic.
The very people who most require solutions to COVID are those who are least likely to be involved in the search to find them.
A colleague he worked with at Grady Hospital in Atlanta was the guy when it came to studying sickle cell disease, a recessive genetic disorder that causes red blood cells to harden into half-moon shapes, causing cardiovascular problems. Sickle cell disease is more common in African Americans than it is in Caucasians, in part because having just one gene for the disease, called sickle cell trait, is protective against malaria, which is endemic to much of Africa. Roughly one in 12 African Americans carry sickle cell trait, and Taylor's colleague wondered if this could be one factor affecting differential outcomes for COVID-19.
UnitedHealth Group granted Taylor and his colleague the money to study sickle cell trait in COVID, and then, as they continued working together, they began to ask Taylor his opinion on other topics. As an insurance company, United had realized early in the pandemic that it was sitting on a goldmine of patient data—some 120 million patients' worth—that it could sift through to look for potential COVID treatments.
Their researchers thought they had found one: In a small subset of 14,000 people who'd contracted COVID, there was a group whose bills were paid by Medicare (which the researchers took as a proxy for older age). The people in this group who were taking ACE inhibitors, blood vessel dilators often used to treat high blood pressure, were 40 percent less likely to be hospitalized than those who were not taking the drug.
The connection between ACE inhibitors and COVID hospitalizations was a correlation, a statistical association. To determine whether the drugs had any real effect on COVID outcomes, United would have to perform another, more rigorous study. They would have to assign some people to receive small doses of ACE inhibitors, and others to receive placebos, and measure the outcomes under each condition. They planned to do this virtually, allowing study participants to sign up and be screened online, and sending drugs, thermometers, and tests through the mail. There were two reasons to do it this way: First, the U.S. Food and Drug Administration had been advising medical researchers to embrace new strategies in clinical trials as a way to protect participants during the pandemic.
The second reason was why they asked Herman Taylor to co-supervise it: Clinical trials have long had a diversity problem. And going virtual is a potential solution.
Since the beginning of the pandemic, COVID-19 has infected people of color at a rate of three times that of Caucasians (killing Black people at a rate 2.5 times as high, and Hispanic and American Indian or Alaska Native people at a rate 1.3 times as high). A number of explanations have been put forth to explain this disproportionate toll. Among them: higher levels of poverty, essential jobs that increase exposure, and lower quality or inadequate access to medical care.
Unfortunately, these same factors also affect who participates in research. People of color may be less likely to have doctors recommend studies to them. They may not have the time or the resources to hang out in a waiting room for hours. They may not live near large research institutions that conduct trials. The result is that new treatments, even for diseases that affect Latin, Native American, or African American populations in greater proportions, are studied mostly in white volunteers. The very people who most require solutions to COVID are those who are least likely to be involved in the search to find them.
Virtual trials can alleviate a number of these problems. Not only can people find and request to participate in these types of trials through their phones or computers, virtual trials also cover more costs, include a larger geographical range, and have inherently flexible hours.
"[In a traditional study] you have to go to a doctor's office to enroll and drive a couple of hours and pay $20 for parking and pay $15 for a sandwich in the hospital cafeteria and arrange for daycare for your kids and take time off of work," says Dr. Jonathan Cotliar, chief medical officer of Science37, a platform that investigators can hire to host and organize their trials virtually. "That's a lot just for one visit, much less over the course of a six to 12-month study."
Cotliar's data suggests that virtual trials' enhanced access seriously affects the racial makeup of a given study's participant pool. Sixty percent of patients enrolled in Science37 trials are non-Caucasian, which is, Cotliar says, "staggering compared to what you find in traditional site-based research."
But access is not the only barrier to including more people of color in clinical trials. There is also trust. When agreeing to sign up for research, undocumented immigrants may worry about finding themselves in legal trouble or without any medical support should something go wrong. In a country with a history of experimenting on African Americans without their consent, black people may not trust institutions not to use them as guinea pigs.
"A lot of people report being somewhat disregarded or disrespected once entering the healthcare system," Taylor says. "You take it all together, then people wonder, well, okay, this is how the system tends to regard me, yet now here come these people doing research, and they're all about getting me into their studies." Not so surprising that a lot of people may respond with a resounding "No thanks."
United's ACE inhibitor trial was notable for addressing both of these challenges. In addition to covering costs and allowing study subjects to participate from their own homes, it was being co-sponsored by a professor at Morehouse, one of the country's historic black colleges and universities (often abbreviated HBCUs). United was recruiting heavily in Atlanta, whose population is 52 percent African American. The study promised a thoughtful introduction to a more egalitarian future of medical research.
There's just one problem: It isn't going to happen.
This month, in preparation for the study, United reanalyzed their ACE inhibitor data with all the new patients who'd contracted COVID in the months since their first analysis. Their original data set had been concentrated in the Northeast, mostly New York City, where the earliest outbreak took place. In the 12 weeks it had taken them to set up the virtual followup study, epicenters had shifted. United's second, more geographically comprehensive sample had ten times the number of people in it. And in that sample, the signal simply disappeared.
"I was shocked, but that's the reality," says Deneen Vojta, executive vice president of enterprise research and development for UnitedHealth Group. "You make decisions based on the data, but when you get more data, more information, you might make a different decision. The answer is the answer."
There was no point in running a virtual ACE inhibitor study if a larger, more representative sample of people indicated the drug was unlikely to help anyone. Still, the model United had established to run the trial remains viable. Even as she scrapped the ACE inhibitor study, Vojta hoped not just to continue United's relationship with Dr. Taylor and Morehouse, but to formalize it. Virtual platforms are still an important part of their forthcoming trials.
If people don't believe a vaccine has been created with them in mind, then they won't take it, and it won't matter whether it exists or not.
United is not alone in this approach. As phase three trials for vaccines against SARS CoV-2 get underway, big pharma companies have been publicly articulating their own strategies for including more people of color in clinical trials, and many of these include virtual elements. Janelle Sabo, global head of clinical innovation, systems and clinical supply chain at Eli Lilly, told me that the company is employing home health and telemedicine, direct-to-patient shipping and delivery, and recruitment using social media and geolocation to expand access to more diverse populations.
Dr. Macaya Douoguih, Head of Clinical Development and Medical Affairs for Janssen Vaccines under Johnson & Johnson, spoke to Congress about this issue during a July hearing before the House Energy and Commerce Oversight and Investigations Subcommittee. She said that the company planned to institute a "focused digital and community outreach plan to provide resources and opportunities to encourage participation in our clinical trials," and had partnered with Johns Hopkins Bloomberg School of Public Health "to understand how the COVID-19 crisis is affecting different communities in the United States."
But while some of these plans are well thought-out, others are concerningly nebulous, featuring big pronouncements but fewer tangible strategies. In that same July hearing, Massachusetts representative Joe Kennedy III (D) sounded like a frustrated teacher when admonishing four of the five leads of the present pharma companies (AstraZeneca, Johnson & Johnson, Merck, Moderna, and Pfizer) for not explaining exactly how they'd ensure diversity both in the study of their vaccines, and in their eventual distribution.
This matters: The uptake of the flu vaccine is 10 percentage points lower in both the African American and Hispanic communities than it is in Caucasians. A Pew research study conducted early in the pandemic found that just 54 percent of Black adults said they "would definitely or probably get a coronavirus vaccine," compared to 74 percent of Whites and Hispanics.
"As a good friend of mine, Dr. [James] Hildreth, president at Meharry, another HBC medical school, likes to say: 'A vaccine is great, but it is the vaccination that saves people,'" Taylor says. If people don't believe a vaccine has been created with them in mind, then they won't take it, and it won't matter whether it exists or not.
In this respect, virtual platforms remain an important first step, at least in expanding admittance. In June, United Health opened up a trial to their entire workforce for a computer game that could treat ADHD. In less than two months, 1,743 people had signed up for it, from all different socioeconomic groups, from all over the country. It was inching closer to the kind of number you need for a phase three vaccine trial, which can require tens of thousands of people. Back when they'd been planning the ACE inhibitor study, United had wanted 9,600 people to agree to participate.
Now, with the help of virtual enrollment, they hope they can pull off similarly high numbers for the COVID vaccine trial they will be running for an as-yet-unnamed pharmaceutical partner. It stands to open in September.
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.”