Q&A with Holden Thorp: Finding Better Ways to Communicate Science
This month, Leaps.org had a chance to speak with Holden Thorp, Editor-in-Chief of the Science family of journals. We talked about the best ways to communicate science to the public, mistakes by public health officials during the pandemic, the lab leak theory, and bipartisanship for funding science research.
Before becoming editor of the Science journals, Thorp spent six years as provost of Washington University in St. Louis, where he is Rita Levi-Montalcini Distinguished University Professor and holds appointments in both chemistry and medicine. He joined Washington University after spending three decades at the University of North Carolina at Chapel Hill, where he served as the UNC's 10th chancellor from 2008 through 2013.
A North Carolina native, Thorp earned a doctorate in chemistry in 1989 at the California Institute of Technology and completed postdoctoral work at Yale University. He is a fellow of the National Academy of Inventors and the American Association for the Advancement of Science.
Read his full bio here.
This conversation was lightly edited by Leaps.org for style and format.
Matt Fuchs: You're a musician. It seems like many scientists are also musicians. Is there a link between the scientist brain and the musician brain?
Holden Thorp: I think [the overlap is] relatively common. I'm still a gigging bass player. I play in the pits for lots of college musicals. I think that it takes a certain discipline and requires you to learn a lot of rules about how music works, and then you try to be creative within that. That's similar to scientific research. So it makes sense. Music is something I've been able to sustain my whole life. I wouldn't be the same person if I let it go. When you're playing, especially for a musical, where the music is challenging, you can't let your mind wander. It’s like meditation.
MF: I bet it helps to do something totally different from your editing responsibilities. Maybe lets the subconscious take care of tough problems at work.
HT: Right.
MF: There's probably never been a greater need for clear and persuasive science communicators. Do we need more cross specialty training? For example, journalism schools prioritizing science training, and science programs that require more time learning how to communicate effectively?
HT: I think we need both. One of the challenges we've had with COVID has been, especially at the beginning, a lot of reporters who didn’t normally cover scientific topics got put on COVID—and ended up creating things that had to be cleaned up later. This isn't the last science-oriented crisis we're going to have. We've already got climate change, and we'll have another health crisis for sure. So it’d be good for journalism to be a little better prepared next time.
"Scientists are human beings who have ego and bravado and every other human weakness."
But on the other side, maybe it's even more important that scientists learn how to communicate and how likely it is that their findings will be politicized, twisted and miscommunicated. Because one thing that surprised me is how shocked a lot of scientists have been. Every scientific issue that reaches into public policy becomes politicized: climate change, evolution, stem cells.
Once one side decided to be cautious about the pandemic, you could be certain the other side was going to decide not to do that. That's not the fault of science. That’s just life in a political world. That, I think, caught people off guard. They weren't prepared to shape and process their messages in a way that accounted for that—and for the way that social media has intensified all of this.
MF: Early in the pandemic, there was a lack of clarity about public health recommendations, as you’d expect with a virus we hadn’t seen before. Should public officials and scientists have more humility in similar situations in the future? Public officials need to be authoritative for their guidance to be followed, so how do they lead a crisis response while displaying humility about what we don't know?
HS: I think scientists are people who like to have the answer. It's very tempting and common for scientists to kind of oversell what we know right now, while not doing as much as we should to remind people that science is a self-correcting process. And when we fail to do that – after we’ve collected more data and need to change how we're interpreting it – the people who want to undermine us have a perfect weapon to use against us. It's challenging. But I agree that scientists are human beings who have ego and bravado and every other human weakness.
For example, we wanted to tell everybody that we thought the vaccines would provide sterilizing immunity against infection. Well, we don't have too many other respiratory viruses where that's the case. And so it was more likely that we were going to have what we ended up with, which is that the vaccines were excellent in preventing severe disease and death. It would have been great if they provided sterilizing immunity and abruptly ended the pandemic a year ago. But it was overly optimistic to think that was going to be the case in retrospect.
MF: Both in terms of how science is communicated and received by the public, do we need to reform institutions or start new ones to instill the truth-seeking values that are so important to appreciating science?
HS: There are a whole bunch of different factors. I think the biggest one is that the social media algorithms reward their owners financially when they figure out how to keep people in their silos. Users are more likely to click on things that they agree with—and that promote conflict with people that they disagree with. That has caused an acceleration in hostilities that attend some of these disagreements.
But I think the other problem is that we haven’t found a way to explain things to people when it’s not a crisis. So, for example, a strong indicator of whether someone who might otherwise be vaccine hesitant decided to get their vaccine is if they understood how vaccines worked before the pandemic started. Because if you're trying to tell somebody that they're wrong if they don't get a vaccine, at the same time you're trying to explain how it works, that's a lot of explaining to do in a short period of time.
Lack of open-mindedness is a problem, but another issue is that we need more understanding of these issues baked into the culture already. That's partly due the fact that there hasn't been more reform in K through 12 and college teaching. And that scientists are very comfortable talking to each other, and not very comfortable talking to people who don't know all of our jargon and have to be persuaded to spend time listening to and thinking about what we're trying to tell them.
"We're almost to the point where clinging to the lab leak idea is close to being a fringe idea that almost doesn't need to be included in stories."
MF: You mentioned silos. There have been some interesting attempts in recent years to do “both sides journalism,” where websites like AllSides put different views on high profile issues side-by-side. Some people believe that's how the news should be reported. Should we let people see and decide for themselves which side is the most convincing?
HS: It depends if we're talking about science. On scientific issues, when they start, there's legitimate disagreement about among scientists. But eventually, things go back and forth, and people compete with each other and work their way to the answer. At some point, we reach more of a consensus.
For example, on climate change, I think it's gotten to the point now where it's irresponsible, if you're writing a story about climate change, to run a quote from somebody somewhere who's still—probably because of their political views—clinging to the idea that anthropogenic global warming is somehow not damaging the planet.
On things that aren't decided yet, that makes sense to run both. It's more a question of judgment of the journalists. I don't think the solution to it is put stark versions of each side, side-by-side and let people choose. The whole point of journalism is to inform people. If there's a consensus on something, that's part of what you're supposed to be informing them about.
MF: What about reporting on perspectives about the lab leak theory at various times during the pandemic?
HS: We’re the outlet that ran the letter that really restarted the whole debate. A bunch of well-known scientists said we should consider the lab leak theory more carefully. And in the aftermath of that, a bunch of those scientists who signed that letter concluded that the lab leak was very, very unlikely. Interestingly, publishing that letter actually drove us to more of a consensus. I would say now, we're almost to the point where clinging to the lab leak idea is close to being a fringe idea that almost doesn't need to be included in stories. But I would say there's been a lot of evolution on that over the last year since we ran that letter.
MF: Let's talk about bipartisanship in Congress. Research funding for the National Institutes of Health was championed for years by influential Republicans who supported science to advance health breakthroughs. Is that changing? Maybe especially with Sen. Roy Blunt retiring? Has bipartisanship on science funding been eroded by political battles during COVID?
HS: I'm optimistic that that won't be the case. Republican Congresses have usually been good for science funding. And that's because (former Sen.) Arlen Specter and Roy Blunt are two of the political figures who have pushed for science funding over the last couple decades. With Blunt retiring, we don't know who's going to step in for him. That's an interesting question. I hope there will be Republican champions for science funding.
MF: Is there too much conservatism baked into how we research new therapies and bring them to people who are sick, bench-to-bedside? I'm thinking of the criticisms that NIH or the FDA are overly bureaucratic. Are you hopeful about ARPA-H, President Biden’s proposed new agency for health innovation?
HS: I think the challenge hasn't been cracked by the federal government. Maybe DARPA has done this outside of health science, but within health science, the federal government has had limited success at funding things that can be applied quickly, while having overwhelming success at funding basic research that eventually becomes important in applications. Can they do it the other way around? They’ll need people running ARPA-H who are application first. It’s ambitious. The way it was done in Operation Warp Speed is all the money was just given to the companies. If the hypothesis on ARPA-H is for the federal government to actually do what Moderna and BioNTech did for the vaccine, themselves, that's a radical idea. It's going to require thinking very differently than the way they think about dispersing grants for basic research.
MF: You’ve written a number of bold op-eds as editor of the Science journals. Are there any op-eds you're especially proud of as voicing a view that was important but not necessarily popular?
HS: I was one of the first people to come out hard against President Trump['s handling of] the pandemic. Lots of my brothers and sisters came along afterwards. To the extent that I was able to catalyze that, I'm proud of doing it. In the last few weeks, I published a paper objecting to the splitting of the OSTP director from the science advisor and, especially, not awarding the top part of the job to Alondra Nelson, who is a distinguished scientist at black female. And instead, giving part of it to Francis Collins. He’s certainly the most important science policy figure of my lifetime, but somebody who’s been doing this now for decades. I just think we have to push as hard as we can to get a cadre of young people leading us in Washington who represent the future of the country. I think the Biden administration leaned on a lot of figures from the past. I’m pushing them hard to try to stop it.
MF: I want to circle back to the erosion of the public’s trust in experts. Most experts are specialists, and specialists operate in silos that don’t capture the complexity of scientific knowledge. Are some pushbacks to experts and concerns about the perils of specialization valid?
HS: You're on the right track there. What we need is more respect for the generalist. We can't help the fact that you have to be very specialized to do a lot of stuff. But what we need is more partnership between specialists and people who can cross fields, especially into communication and social sciences. That handoff is just not really there right now. It's hard to get a hardcore scientist to respect people who are interested in science, education and science communication, and to treat them as equals. The last two years showed that they're at least as important, if not more so.
MF: I’m grateful that you’re leading the way in this area, Holden. Thank you for sharing your thoughts and your work.
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.”