The Only Hydroxychloroquine Story You Need to Read
Dr. Adalja is focused on emerging infectious disease, pandemic preparedness, and biosecurity. He has served on US government panels tasked with developing guidelines for the treatment of plague, botulism, and anthrax in mass casualty settings and the system of care for infectious disease emergencies, and as an external advisor to the New York City Health and Hospital Emergency Management Highly Infectious Disease training program, as well as on a FEMA working group on nuclear disaster recovery. Dr. Adalja is an Associate Editor of the journal Health Security. He was a coeditor of the volume Global Catastrophic Biological Risks, a contributing author for the Handbook of Bioterrorism and Disaster Medicine, the Emergency Medicine CorePendium, Clinical Microbiology Made Ridiculously Simple, UpToDate's section on biological terrorism, and a NATO volume on bioterrorism. He has also published in such journals as the New England Journal of Medicine, the Journal of Infectious Diseases, Clinical Infectious Diseases, Emerging Infectious Diseases, and the Annals of Emergency Medicine. He is a board-certified physician in internal medicine, emergency medicine, infectious diseases, and critical care medicine. Follow him on Twitter: @AmeshAA
In the early days of a pandemic caused by a virus with no existing treatments, many different compounds are often considered and tried in an attempt to help patients.
It all relates back to a profound question: How do we know what we know?
Many of these treatments fall by the wayside as evidence accumulates regarding actual efficacy. At that point, other treatments become standard of care once their benefit is proven in rigorously designed trials.
However, about seven months into the pandemic, we're still seeing political resurrection of a treatment that has been systematically studied and demonstrated in well-designed randomized controlled trials to not have benefit.
The hydroxychloroquine (and by extension chloroquine) story is a complicated one that was difficult to follow even before it became infused with politics. It is a simple fact that these drugs, long approved by the Food and Drug Administration (FDA), work in Petri dishes against various viruses including coronaviruses. This set of facts provided biological plausibility to support formally studying their use in the clinical treatment and prevention of COVID-19. As evidence from these studies accumulates, it is a cognitive requirement to integrate that knowledge and not to evade it. This also means evaluating the rigor of the studies.
In recent days we have seen groups yet again promoting the use of hydroxychloroquine in, what is to me, a baffling disregard of the multiple recent studies that have shown no benefit. Indeed, though FDA-approved for other indications like autoimmune conditions and preventing malaria, the emergency use authorization for COVID-19 has been rescinded (which means the government cannot stockpile it). Still, however, many patients continue to ask for the drug, compelled by political commentary, viral videos, and anecdotal data. Yet most doctors (like myself) are refusing to write the prescriptions outside of a clinical trial – a position endorsed by professional medical organizations such as the American College of Physicians and the Infectious Diseases Society of America. Why this disconnect?
It all relates back to a profound question: How do we know what we know? In science, we use the scientific method – the process of observing reality, coming up with a hypothesis about what might be true, and testing that hypothesis as thoroughly as possible until we discover the objective truth.
The confusion we're seeing now stems from an inability to distinguish between anecdotes reported by physicians (observational data) and an actual evidence base. This is understandable among the general public but when done by a healthcare professional, it reveals a disdain for reason, logic, and the scientific method.
The Difference Between Observational Data and Randomized Controlled Trials
The power of informal observation is crucial. It is part of the scientific method but primarily as a basis for generating hypotheses that we can test. How do we conduct medical tests? The gold standard is the double-blind, randomized, placebo-controlled trial. This means that neither the researchers nor the volunteers know who is getting a drug and who is getting a sugar pill. Then both groups of the trial, called arms, can be compared to determine whether the people who got the drug fared better. This study design prevents biases and the placebo effect from confounding the data and undermining the veracity of the results.
For example, a seemingly beneficial effect might be seen in an observational study with no blinding and no control group. In such a case, all patients are openly given the drug and their doctors observe how they do. A prime example is the 36-patient single-arm study from France that generated a tremendous amount of interest after President Trump tweeted about it. But this kind of a study by its nature cannot answer the critical question: Was the positive effect because of hydroxychloroquine or just the natural course of the illness? In other words, would someone have recovered in a similar fashion regardless of the drug? What is the role of the placebo effect?
These are reasons why it is crucial to give a placebo to a control group that is as similar in every respect as possible to those receiving the intervention. Then we attempt to find out by comparing the two groups: What is the side effect profile of the drug? Are the groups large enough to detect a relatively rare safety concern? How long were the patients followed for? Was something else responsible for making the patients get better, such as the use of steroids (as likely was the case in the Henry Ford study)?
Looking at the two major hydroxychloroquine trials, it is apparent that, when studied using the best tools of clinical trials, no benefit is likely to occur.
All of these considerations amount to just a fraction of the questions that can be answered more definitively in a well-designed large randomized controlled trial than in observational studies. Indeed, an observational study from New York failed to show any benefit in hospitalized patients, showing how unclear and disparate the results can be with these types of studies. A New York retrospective study (which examined patient outcomes after they were already treated) had similar results and included the use of azithromycin.
When evaluating a study, it is also important to note whether conflicts of interest exist, as well as the quality of the peer review and the data itself. In the case of the French study, for example, the paper was published in a journal in which one of the authors was editor-in-chief, and it was accepted for publication after 24 hours. Patients who fared poorly on hydroxychloroquine were also left out of the study altogether, skewing the results.
What Randomized Controlled Trials Have Shown
Looking at the two major hydroxychloroquine trials, it is apparent that, when studied using the best tools of clinical trials, no benefit is likely to occur. The most important of these studies to announce results was part of the Recovery trial, which was designed to test multiple interventions in the treatment of COVID-19. This trial, which has yet to be formally published, was a randomized controlled trial that involved over 1500 hospitalized patients being administered hydroxychloroquine compared to over 3000 who did not receive the medication. Clinical testing requires large numbers of patients to have the power to demonstrate statistical significance -- the threshold at which any apparent benefit is more than you would expect by random chance alone.
In this study, hydroxychloroquine provided no mortality benefit or even a benefit in hospital length of stay. In fact, the opposite occurred. Hydroxychloroquine patients were more likely to stay in the hospital longer and were more likely to require mechanical ventilation. Additionally, smaller randomized trials conducted in China have not shown benefit either.
Another major study involved the use of hydroxychloroquine to prevent illness in people who were exposed to COVID-19. These results, published in The New England Journal of Medicine, included over 800 patients who were studied in a randomized double-blind controlled trial and also failed to show any benefit.
But what about adding the antibiotic azithromycin in conjunction with hydroxychloroquine? A three-arm randomized controlled study involving over 500 patients hospitalized with mild to moderate COVID-19 was conducted. Its results, also published in The New England Journal of Medicine, failed to show any benefit – with or without azithromycin – and demonstrated evidence of harm. Those who received these treatments had elevations of their liver function tests and heart rhythm abnormalities. These findings hold despite the retraction of an observational study showing similar results.
Additionally, when used in combination with remdesivir – an experimental antiviral – hydroxychloroquine has been shown to be associated with worse outcomes and more side effects.
But what about in mildly ill patients not requiring hospitalization? There was no benefit found in a randomized double-blind placebo-controlled trial of 400 patients, the majority of whom were given the drug within one day of symptoms.
Some randomized controlled studies have yet to report their findings on hydroxychloroquine in non-hospitalized patients, with the use of zinc (which has some evidence in the treatment of the common cold, another ailment that can be caused by coronaviruses). And studies have yet to come out regarding whether hydroxychloroquine can prevent people from getting sick before they are even exposed. But the preponderance of the evidence from studies designed specifically to find benefit for treating COVID-19 does not support its use outside of a research setting.
Today – even with some studies (including those with zinc) still ongoing – if a patient asked me to prescribe them hydroxychloroquine for any severity or stage of illness, with or without zinc, with or without azithromycin, I would refrain. I would explain that, based on the evidence from clinical trials that has been amassed, there is no reason to believe that it will alter the course of illness for the better.
Failing to recognize the reality of the situation runs the risk of crowding out other more promising treatments and creating animosity where none should exist.
What has been occurring is a continual shifting of goalposts with each negative hydroxychloroquine study. Those in favor of the drug protest that a trial did not include azithromycin or zinc or wasn't given at the right time to the right patients. While there may be biological plausibility to treating illness early or combining treatments with zinc, it can only be definitively shown in a randomized, controlled prospective study.
The bottom line: A study that only looks at past outcomes in one group of patients – even when well conducted – is at most hypothesis generating and cannot be used as the sole basis for a new treatment paradigm.
Some may argue that there is no time to wait for definitive studies, but no treatment is benign. The risk/benefit ratio is not the same for every possible use of the drug. For example, hydroxychloroquine has a long record of use in rheumatoid arthritis and systemic lupus (whose patients are facing shortages because of COVID-19 related demand). But the risk of side effects for many of these patients is worth taking because of the substantial benefit the drug provides in treating those conditions.
In COVID-19, however, the disease apparently causes cardiac abnormalities in a great deal of many mild cases, a situation that should prompt caution when using any drugs that have known effects on the cardiac system -- drugs like hydroxychloroquine and azithromycin.
My Own Experience
It is not the case that every physician was biased against this drug from the start. Indeed, most of us wanted it to be shown to be beneficial, as it was a generic drug that was widely available and very familiar. In fact, early in the pandemic I prescribed it to hospitalized patients on two occasions per a hospital protocol. However, it is impossible for me as a sole clinician to know whether it worked, was neutral, or was harmful. In recent days, however, I have found the hydroxychloroquine talk to have polluted the atmosphere. One recent patient was initially refusing remdesivir, a drug proven in large randomized trials to have effectiveness, because he had confused it with hydroxychloroquine.
Moving On to Other COVID Treatments: What a Treatment Should Do
The story of hydroxychloroquine illustrates a fruitless search for what we are actually looking for in a COVID-19 treatment. In short, we are looking for a medication that can decrease symptoms, decrease complications, hasten recovery, decrease hospitalizations, decrease contagiousness, decrease deaths, and prevent infection. While it is unlikely to find a single antiviral that can accomplish all of these, fulfilling even just one is important.
For example, remdesivir hastens recovery and dexamethasone decreases mortality. Definitive results of the use of convalescent plasma and immunomodulating drugs such as siltuxamab, baricitinib, and anakinra (for use in the cytokine storms characteristic of severe disease) are still pending, as are the trials with monoclonal antibodies.
While it was crucial that the medical and scientific community definitively answer the questions surrounding the use of chloroquine and hydroxychloroquine in the treatment of COVID-19, it is time to face the facts and accept that its use for the treatment of this disease is not likely to be beneficial. Failing to recognize the reality of the situation runs the risk of crowding out other more promising treatments and creating animosity where none should exist.
Dr. Adalja is focused on emerging infectious disease, pandemic preparedness, and biosecurity. He has served on US government panels tasked with developing guidelines for the treatment of plague, botulism, and anthrax in mass casualty settings and the system of care for infectious disease emergencies, and as an external advisor to the New York City Health and Hospital Emergency Management Highly Infectious Disease training program, as well as on a FEMA working group on nuclear disaster recovery. Dr. Adalja is an Associate Editor of the journal Health Security. He was a coeditor of the volume Global Catastrophic Biological Risks, a contributing author for the Handbook of Bioterrorism and Disaster Medicine, the Emergency Medicine CorePendium, Clinical Microbiology Made Ridiculously Simple, UpToDate's section on biological terrorism, and a NATO volume on bioterrorism. He has also published in such journals as the New England Journal of Medicine, the Journal of Infectious Diseases, Clinical Infectious Diseases, Emerging Infectious Diseases, and the Annals of Emergency Medicine. He is a board-certified physician in internal medicine, emergency medicine, infectious diseases, and critical care medicine. Follow him on Twitter: @AmeshAA
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.”