The New Prospective Parenthood: When Does More Info Become Too Much?
Peggy Clark was 12 weeks pregnant when she went in for a nuchal translucency (NT) scan to see whether her unborn son had Down syndrome. The sonographic scan measures how much fluid has accumulated at the back of the baby's neck: the more fluid, the higher the likelihood of an abnormality. The technician said the baby was in such an odd position, the test couldn't be done. Clark, whose name has been changed to protect her privacy, was told to come back in a week and a half to see if the baby had moved.
"With the growing sophistication of prenatal tests, it seems that the more questions are answered, the more new ones arise."
"It was like the baby was saying, 'I don't want you to know,'" she recently recalled.
When they went back, they found the baby had a thickened neck. It's just one factor in identifying Down's, but it's a strong indication. At that point, she was 13 weeks and four days pregnant. She went to the doctor the next day for a blood test. It took another two weeks for the results, which again came back positive, though there was still a .3% margin of error. Clark said she knew she wanted to terminate the pregnancy if the baby had Down's, but she didn't want the guilt of knowing there was a small chance the tests were wrong. At that point, she was too late to do a Chorionic villus sampling (CVS), when chorionic villi cells are removed from the placenta and sequenced. And she was too early to do an amniocentesis, which isn't done until between 14 and 20 weeks of the pregnancy. So she says she had to sit and wait, calling those few weeks "brutal."
By the time they did the amnio, she was already nearly 18 weeks pregnant and was getting really big. When that test also came back positive, she made the anguished decision to end the pregnancy.
Now, three years after Clark's painful experience, a newer form of prenatal testing routinely gives would-be parents more information much earlier on, especially for women who are over 35. As soon as nine weeks into their pregnancies, women can have a simple blood test to determine if there are abnormalities in the DNA of chromosomes 21, which indicates Down syndrome, as well as in chromosomes 13 and 18. Using next-generation sequencing technologies, the test separates out and examines circulating fetal cells in the mother's blood, which eliminates the risks of drawing fluid directly from the fetus or placenta.
"Finding out your baby has Down syndrome at 11 or 12 weeks is much easier for parents to make any decision they may want to make, as opposed to 16 or 17 weeks," said Dr. Leena Nathan, an obstetrician-gynecologist in UCLA's healthcare system. "People are much more willing or able to perhaps make a decision to terminate the pregnancy."
But with the growing sophistication of prenatal tests, it seems that the more questions are answered, the more new ones arise--questions that previous generations have never had to face. And as genomic sequencing improves in its predictive accuracy at the earliest stages of life, the challenges only stand to increase. Imagine, for example, learning your child's lifetime risk of breast cancer when you are ten weeks pregnant. Would you terminate if you knew she had a 70 percent risk? What about 40 percent? Lots of hard questions. Few easy answers. Once the cost of whole genome sequencing drops low enough, probably within the next five to ten years according to experts, such comprehensive testing may become the new standard of care. Welcome to the future of prospective parenthood.
"In one way, it's a blessing to have this information. On the other hand, it's very difficult to deal with."
How Did We Get Here?
Prenatal testing is not new. In 1979, amniocentesis was used to detect whether certain inherited diseases had been passed on to the fetus. Through the 1980s, parents could be tested to see if they carried disease like Tay-Sachs, Sickle cell anemia, Cystic fibrosis and Duchenne muscular dystrophy. By the early 1990s, doctors could test for even more genetic diseases and the CVS test was beginning to become available.
A few years later, a technique called preimplantation genetic diagnosis (PGD) emerged, in which embryos created in a lab with sperm and harvested eggs would be allowed to grow for several days and then cells would be removed and tested to see if any carried genetic diseases. Those that weren't affected could be transferred back to the mother. Once in vitro fertilization (IVF) took off, so did genetic testing. The labs test the embryonic cells and get them back to the IVF facilities within 24 hours so that embryo selection can occur. In the case of IVF, genetic tests are done so early, parents don't even have to decide whether to terminate a pregnancy. Embryos with issues often aren't even used.
"It was a very expensive endeavor but exciting to see our ability to avoid disorders, especially for families that don't want to terminate a pregnancy," said Sara Katsanis, an expert in genetic testing who teaches at Duke University. "In one way, it's a blessing to have this information (about genetic disorders). On the other hand, it's very difficult to deal with. To make that decision about whether to terminate a pregnancy is very hard."
Just Because We Can, Does It Mean We Should?
Parents in the future may not only find out whether their child has a genetic disease but will be able to potentially fix the problem through a highly controversial process called gene editing. But because we can, does it mean we should? So far, genes have been edited in other species, but to date, the procedure has not been used on an unborn child for reproductive purposes apart from research.
"There's a lot of bioethics debate and convening of groups to try to figure out where genetic manipulation is going to be useful and necessary, and where it is going to need some restrictions," said Katsanis. She notes that it's very useful in areas like cancer research, so one wouldn't want to over-regulate it.
There are already some criteria as to which genes can be manipulated and which should be left alone, said Evan Snyder, professor and director of the Center for Stem Cells and Regenerative Medicine at Sanford Children's Health Research Center in La Jolla, Calif. He noted that genes don't stand in isolation. That is, if you modify one that causes disease, will it disrupt others? There may be unintended consequences, he added.
"As the technical dilemmas get fixed, some of the ethical dilemmas get fixed. But others arise. It's kind of like ethical whack-a-mole."
But gene editing of embryos may take years to become an acceptable practice, if ever, so a more pressing issue concerns the rationale behind embryo selection during IVF. Prospective parents can end up with anywhere from zero to thirty embryos from the procedure and must choose only one (rarely two) to implant. Since embryos are routinely tested now for certain diseases, and selected or discarded based on that information, should it be ethical—and legal—to make selections based on particular traits, too? To date so far, parents can select for gender, but no other traits. Whether trait selection becomes routine is a matter of time and business opportunity, Katsanis said. So far, the old-fashioned way of making a baby combined with the luck of the draw seems to be the preferred method for the marketplace. But that could change.
"You can easily see a family deciding not to implant a lethal gene for Tay-Sachs or Duchene or Cystic fibrosis. It becomes more ethically challenging when you make a decision to implant girls and not any of the boys," said Snyder. "And then as we get better and better, we can start assigning genes to certain skills and this starts to become science fiction."
Once a pregnancy occurs, prospective parents of all stripes will face decisions about whether to keep the fetus based on the information that increasingly robust prenatal testing will provide. What influences their decision is the crux of another ethical knot, said Snyder. A clear-cut rationale would be if the baby is anencephalic, or it has no brain. A harder one might be, "It's a girl, and I wanted a boy," or "The child will only be 5' 2" tall in adulthood."
"Those are the extremes, but the ultimate question is: At what point is it a legitimate response to say, I don't want to keep this baby?'" he said. Of course, people's responses will vary, so the bigger conundrum for society is: Where should a line be drawn—if at all? Should a woman who is within the legal scope of termination (up to around 24 weeks, though it varies by state) be allowed to terminate her pregnancy for any reason whatsoever? Or must she have a so-called "legitimate" rationale?
"As the technical dilemmas get fixed, some of the ethical dilemmas get fixed. But others arise. It's kind of like ethical whack-a-mole," Snyder said.
One of the newer moles to emerge is, if one can fix a damaged gene, for how long should it be fixed? In one child? In the family's whole line, going forward? If the editing is done in the embryo right after the egg and sperm have united and before the cells begin dividing and becoming specialized, when, say, there are just two or four cells, it will likely affect that child's entire reproductive system and thus all of that child's progeny going forward.
"This notion of changing things forever is a major debate," Snyder said. "It literally gets into metaphysics. On the one hand, you could say, well, wouldn't it be great to get rid of Cystic fibrosis forever? What bad could come of getting rid of a mutant gene forever? But we're not smart enough to know what other things the gene might be doing, and how disrupting one thing could affect this network."
As with any tool, there are risks and benefits, said Michael Kalichman, Director of the Research Ethics Program at the University of California San Diego. While we can envision diverse benefits from a better understanding of human biology and medicine, it is clear that our species can also misuse those tools – from stigmatizing children with certain genetic traits as being "less than," aka dystopian sci-fi movies like Gattaca, to judging parents for making sure their child carries or doesn't carry a particular trait.
"The best chance to ensure that the benefits of this technology will outweigh the risks," Kalichman said, "is for all stakeholders to engage in thoughtful conversations, strive for understanding of diverse viewpoints, and then develop strategies and policies to protect against those uses that are considered to be problematic."
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.”