What’s the Right Way to Regulate Gene-Edited Crops?
In the next few decades, humanity faces its biggest food crisis since the invention of the plow. The planet's population, currently 7.6 billion, is expected to reach 10 billion by 2050; to avoid mass famine, according to the World Resource Institute, we'll need to produce 70 percent more calories than we do today.
Imagine that a cheap, easy-to-use, and rapidly deployable technology could make crops more fertile and strengthen their resistance to threats.
Meanwhile, climate change will bring intensifying assaults by heat, drought, storms, pests, and weeds, depressing farm yields around the globe. Epidemics of plant disease—already laying waste to wheat, citrus, bananas, coffee, and cacao in many regions—will spread ever further through the vectors of modern trade and transportation.
So here's a thought experiment: Imagine that a cheap, easy-to-use, and rapidly deployable technology could make crops more fertile and strengthen their resistance to these looming threats. Imagine that it could also render them more nutritious and tastier, with longer shelf lives and less vulnerability to damage in shipping—adding enhancements to human health and enjoyment, as well as reduced food waste, to the possible benefits.
Finally, imagine that crops bred with the aid of this tool might carry dangers. Some could contain unsuspected allergens or toxins. Others might disrupt ecosystems, affecting the behavior or very survival of other species, or infecting wild relatives with their altered DNA.
Now ask yourself: If such a technology existed, should policymakers encourage its adoption, or ban it due to the risks? And if you chose the former alternative, how should crops developed by this method be regulated?
In fact, this technology does exist, though its use remains mostly experimental. It's called gene editing, and in the past five years it has emerged as a potentially revolutionary force in many areas—among them, treating cancer and genetic disorders; growing transplantable human organs in pigs; controlling malaria-spreading mosquitoes; and, yes, transforming agriculture. Several versions are currently available, the newest and nimblest of which goes by the acronym CRISPR.
Gene editing is far simpler and more efficient than older methods used to produce genetically modified organisms (GMOs). Unlike those methods, moreover, it can be used in ways that leave no foreign genes in the target organism—an advantage that proponents argue should comfort anyone leery of consuming so-called "Frankenfoods." But debate persists over what precautions must be taken before these crops come to market.
Recently, two of the world's most powerful regulatory bodies offered very different answers to that question. The United States Department of Agriculture (USDA) declared in March 2018 that it "does not currently regulate, or have any plans to regulate" plants that are developed through most existing methods of gene editing. The Court of Justice of the European Union (ECJ), by contrast, ruled in July that such crops should be governed by the same stringent regulations as conventional GMOs.
Some experts suggest that the broadly permissive American approach and the broadly restrictive EU policy are equally flawed.
Each announcement drew protests, for opposite reasons. Anti-GMO activists assailed the USDA's statement, arguing that all gene-edited crops should be tested and approved before marketing. "You don't know what those mutations or rearrangements might do in a plant," warned Michael Hansen, a senior scientist with the advocacy group Consumers Union. Biotech boosters griped that the ECJ's decision would stifle innovation and investment. "By any sensible standard, this judgment is illogical and absurd," wrote the British newspaper The Observer.
Yet some experts suggest that the broadly permissive American approach and the broadly restrictive EU policy are equally flawed. "What's behind these regulatory decisions is not science," says Jennifer Kuzma, co-director of the Genetic Engineering and Society Center at North Carolina State University, a former advisor to the World Economic Forum, who has researched and written extensively on governance issues in biotechnology. "It's politics, economics, and culture."
The U.S. Welcomes Gene-Edited Food
Humans have been modifying the genomes of plants and animals for 10,000 years, using selective breeding—a hit-or-miss method that can take decades or more to deliver rewards. In the mid-20th century, we learned to speed up the process by exposing organisms to radiation or mutagenic chemicals. But it wasn't until the 1980s that scientists began modifying plants by altering specific stretches of their DNA.
Today, about 90 percent of the corn, cotton and soybeans planted in the U.S. are GMOs; such crops cover nearly 4 million square miles (10 million square kilometers) of land in 29 countries. Most of these plants are transgenic, meaning they contain genes from an unrelated species—often as biologically alien as a virus or a fish. Their modifications are designed primarily to boost profit margins for mechanized agribusiness: allowing crops to withstand herbicides so that weeds can be controlled by mass spraying, for example, or to produce their own pesticides to lessen the need for chemical inputs.
In the early days, the majority of GM crops were created by extracting the gene for a desired trait from a donor organism, multiplying it, and attaching it to other snippets of DNA—usually from a microbe called an agrobacterium—that could help it infiltrate the cells of the target plant. Biotechnologists injected these particles into the target, hoping at least one would land in a place where it would perform its intended function; if not, they kept trying. The process was quicker than conventional breeding, but still complex, scattershot, and costly.
Because agrobacteria can cause plant tumors, Kuzma explains, policymakers in the U.S. decided to regulate GMO crops under an existing law, the Plant Pest Act of 1957, which addressed dangers like imported trees infested with invasive bugs. Every GMO containing the DNA of agrobacterium or another plant pest had to be tested to see whether it behaved like a pest, and undergo a lengthy approval process. By 2010, however, new methods had been developed for creating GMOs without agrobacteria; such plants could typically be marketed without pre-approval.
Soon after that, the first gene-edited crops began appearing. If old-school genetic engineering was a shotgun, techniques like TALEN and CRISPR were a scalpel—or the search-and-replace function on a computer program. With CRISPR/Cas9, for example, an enzyme that bacteria use to recognize and chop up hostile viruses is reprogrammed to find and snip out a desired bit of a plant or other organism's DNA. The enzyme can also be used to insert a substitute gene. If a DNA sequence is simply removed, or the new gene comes from a similar species, the changes in the target plant's genotype and phenotype (its general characteristics) may be no different from those that could be produced through selective breeding. If a foreign gene is added, the plant becomes a transgenic GMO.
Companies are already teeing up gene-edited products for the U.S. market, like a cooking oil and waxy corn.
This development, along with the emergence of non-agrobacterium GMOs, eventually prompted the USDA to propose a tiered regulatory system for all genetically engineered crops, beginning with an initial screening for potentially hazardous metaboloids or ecological impacts. (The screening was intended, in part, to guard against the "off-target effects"—stray mutations—that occasionally appear in gene-edited organisms.) If no red flags appeared, the crop would be approved; otherwise, it would be subject to further review, and possible regulation.
The plan was unveiled in January 2017, during the last week of the Obama presidency. Then, under the Trump administration, it was shelved. Although the USDA continues to promise a new set of regulations, the only hint of what they might contain has been Secretary of Agriculture Sonny Perdue's statement last March that gene-edited plants would remain unregulated if they "could otherwise have been developed through traditional breeding techniques, as long as they are not plant pests or developed using plant pests."
Because transgenic plants could not be "developed through traditional breeding techniques," this statement could be taken to mean that gene editing in which foreign DNA is introduced might actually be regulated. But because the USDA regulates conventional transgenic GMOs only if they trigger the plant-pest stipulation, experts assume gene-edited crops will face similarly limited oversight.
Meanwhile, companies are already teeing up gene-edited products for the U.S. market. An herbicide-resistant oilseed rape, developed using a proprietary technique, has been available since 2016. A cooking oil made from TALEN-tweaked soybeans, designed to have a healthier fatty-acid profile, is slated for release within the next few months. A CRISPR-edited "waxy" corn, designed with a starch profile ideal for processed foods, should be ready by 2021.
In all likelihood, none of these products will have to be tested for safety.
In the E.U., Stricter Rules Apply
Now let's look at the European Union. Since the late 1990s, explains Gregory Jaffe, director of the Project on Biotechnology at the Center for Science in the Public Interest, the EU has had a "process-based trigger" for genetically engineered products: "If you use recombinant DNA, you are going to be regulated." All foods and animal feeds must be approved and labeled if they consist of or contain more than 0.9 percent GM ingredients. (In the U.S., "disclosure" of GM ingredients is mandatory, if someone asks, but labeling is not required.) The only GM crop that can be commercially grown in EU member nations is a type of insect-resistant corn, though some countries allow imports.
European scientists helped develop gene editing, and they—along with the continent's biotech entrepreneurs—have been busy developing applications for crops. But European farmers seem more divided over the technology than their American counterparts. The main French agricultural trades union, for example, supports research into non-transgenic gene editing and its exemption from GMO regulation. But it was the country's small-farmers' union, the Confédération Paysanne, along with several allied groups, that in 2015 submitted a complaint to the ECJ, asking that all plants produced via mutagenesis—including gene-editing—be regulated as GMOs.
At this point, it should be mentioned that in the past 30 years, large population studies have found no sign that consuming GM foods is harmful to human health. GMO critics can, however, point to evidence that herbicide-resistant crops have encouraged overuse of herbicides, giving rise to poison-proof "superweeds," polluting the environment with suspected carcinogens, and inadvertently killing beneficial plants. Those allegations were key to the French plaintiffs' argument that gene-edited crops might similarly do unexpected harm. (Disclosure: Leapsmag's parent company, Bayer, recently acquired Monsanto, a maker of herbicides and herbicide-resistant seeds. Also, Leaps by Bayer, an innovation initiative of Bayer and Leapsmag's direct founder, has funded a biotech startup called JoynBio that aims to reduce the amount of nitrogen fertilizer required to grow crops.)
The ruling was "scientifically nonsensical. It's because of things like this that I'll never go back to Europe."
In the end, the EU court found in the Confédération's favor on gene editing—though the court maintained the regulatory exemption for mutagenesis induced by chemicals or radiation, citing the 'long safety record' of those methods.
The ruling was "scientifically nonsensical," fumes Rodolphe Barrangou, a French food scientist who pioneered CRISPR while working for DuPont in Wisconsin and is now a professor at NC State. "It's because of things like this that I'll never go back to Europe."
Nonetheless, the decision was consistent with longstanding EU policy on crops made with recombinant DNA. Given the difficulty and expense of getting such products through the continent's regulatory system, many other European researchers may wind up following Barrangou to America.
Getting to the Root of the Cultural Divide
What explains the divergence between the American and European approaches to GMOs—and, by extension, gene-edited crops? In part, Jennifer Kuzma speculates, it's that Europeans have a different attitude toward eating. "They're generally more tied to where their food comes from, where it's produced," she notes. They may also share a mistrust of government assurances on food safety, borne of the region's Mad Cow scandals of the 1980s and '90s. In Catholic countries, consumers may have misgivings about tinkering with the machinery of life.
But the principal factor, Kuzma argues, is that European and American agriculture are structured differently. "GM's benefits have mostly been designed for large-scale industrial farming and commodity crops," she says. That kind of farming is dominant in the U.S., but not in Europe, leading to a different balance of political power. In the EU, there was less pressure on decisionmakers to approve GMOs or exempt gene-edited crops from regulation—and more pressure to adopt a GM-resistant stance.
Such dynamics may be operating in other regions as well. In China, for example, the government has long encouraged research in GMOs; a state-owned company recently acquired Syngenta, a Swiss-based multinational corporation that is a leading developer of GM and gene-edited crops. GM animal feed and cooking oil can be freely imported. Yet commercial cultivation of most GM plants remains forbidden, out of deference to popular suspicions of genetically altered food. "As a new item, society has debates and doubts on GMO techniques, which is normal," President Xi Jinping remarked in 2014. "We must be bold in studying it, [but] be cautious promoting it."
The proper balance between boldness and caution is still being worked out all over the world. Europe's process-based approach may prevent researchers from developing crops that, with a single DNA snip, could rescue millions from starvation. EU regulations will also make it harder for small entrepreneurs to challenge Big Ag with a technology that, as Barrangou puts it, "can be used affordably, quickly, scalably, by anyone, without even a graduate degree in genetics." America's product-based approach, conversely, may let crops with hidden genetic dangers escape detection. And by refusing to investigate such risks, regulators may wind up exacerbating consumers' doubts about GM and gene-edited products, rather than allaying them.
"Science...can't tell you what to regulate. That's a values-based decision."
Perhaps the solution lies in combining both approaches, and adding some flexibility and nuance to the mix. "I don't believe in regulation by the product or the process," says CSPI's Jaffe. "I think you need both." Deleting a DNA base pair to silence a gene, for example, might be less risky than inserting a foreign gene into a plant—unless the deletion enables the production of an allergen, and the transgene comes from spinach.
Kuzma calls for the creation of "cooperative governance networks" to oversee crop genome editing, similar to bodies that already help develop and enforce industry standards in fisheries, electronics, industrial cleaning products, and (not incidentally) organic agriculture. Such a network could include farmers, scientists, advocacy groups, private companies, and governmental agencies. "Safety isn't an all-or-nothing concept," Kuzma says. "Science can tell you what some of the issues are in terms of risk and benefit, but it can't tell you what to regulate. That's a values-based decision."
By drawing together a wide range of stakeholders to make such decisions, she adds, "we're more likely to anticipate future consequences, and to develop a robust approach—one that not only seems more legitimate to people, but is actually just plain old better."
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.”