I’m a Black, Genderqueer Medical Student: Here’s My Hard-Won Wisdom for Students and Educational Institutions
This article is part of the magazine, "The Future of Science In America: The Election Issue," co-published by LeapsMag, the Aspen Institute Science & Society Program, and GOOD.
In the last 12 years, I have earned degrees from Harvard College and Duke University and trained in an M.D.-Ph.D. program at the University of Pennsylvania. Through this process, I have assembled much educational privilege and can now speak with the authority that is conferred in these ivory towers. Along the way, as a Black, genderqueer, first-generation, low-income trainee, the systems of oppression that permeate American society—racism, transphobia, and classism, among others—coalesced in the microcosm of academia into a unique set of challenges that I had to navigate. I would like to share some of the lessons I have learned over the years in the format of advice for both Black, Indigenous, and other People of Color (BIPOC) and LGBTQ+ trainees as well as members of the education institutions that seek to serve them.
To BIPOC and LGBTQ+ Trainees: Who you are is an asset, not an obstacle. Throughout my undergraduate years, I viewed my background as something to overcome. I had to overcome the instances of implicit bias and overt discrimination I experienced in my classes and among my peers. I had to overcome the preconceived, racialized, limitations on my abilities that academic advisors projected onto me as they characterized my course load as too ambitious or declared me unfit for medical school. I had to overcome the lack of social capital that comes with being from a low-resourced rural community and learn all the idiosyncrasies of academia from how to write professional emails to how and when to solicit feedback. I viewed my Blackness, queerness, and transness as inconveniences of identity that made my life harder.
It was only as I went on to graduate and medical school that I saw how much strength comes from who I am. My perspective allows me to conduct insightful, high-impact, and creative research that speaks to uplifting my various intersecting communities. My work on health equity for transgender people of color (TPOC) and BIPOC trainees in medicine is my form of advocacy. My publications are love letters to my communities, telling them that I see them and that I am with them. They are also indictments of the systems that oppress them and evidence that supports policy innovations and help move our society toward a more equitable future.
To Educators and Institutions: Allyship is active and uncomfortable. In the last 20 years, institutions have professed interest in diversifying their members and supporting marginalized groups. However, despite these proclamations, most have fallen short of truly allying themselves to communities in need of support. People often assume that allyship is defined by intent; that they are allies to Black people in the #BLM era because they, too, believe that Black lives have value. This is decency, not allyship. In the wake of the tragic killings of Breonna Taylor and George Floyd, and the ongoing racial inequity of the COVID-19 pandemic, every person of color that I know in academia has been invited to a townhall on racism. These meetings risk re-traumatizing Black people if they feel coerced into sharing their experiences with racism in front of their white colleagues. This is exploitation, not allyship. These discussions must be carefully designed to prioritize Black voices but not depend on them. They must rely on shared responsibility for strategizing systemic change that centers the needs of Black and marginalized voices while diffusing the requisite labor across the entire institution.
Allyship requires a commitment to actions, not ideas. In education this is fostering safe environments for BIPOC and LGBTQ+ students. It is changing the culture of your institution such that anti-racism is a shared value and that work to establish anti-racist practices is distributed across all groups rather than just an additional tax on minority students and faculty. It is providing dedicated spaces for BIPOC and LGBTQ+ students where they can build community amongst themselves away from the gaze of majority white, heterosexual, and cisgender groups that dominate other spaces. It is also building the infrastructure to educate all members of your institution on issues facing BIPOC and LGBTQ+ students rather than relying on members of those communities to educate others through divulging their personal experiences.
Among well-intentioned ally hopefuls, anxiety can be a major barrier to action. Anxiety around the possibility of making a mistake, saying the wrong thing, hurting or offending someone, and having uncomfortable conversations. I'm here to alleviate any uncertainty around that: You will likely make mistakes, you may receive backlash, you will undoubtedly have uncomfortable conversations, and you may have to apologize. Steel yourself to that possibility and view it as an asset. People give their most unfiltered feedback when they have been hurt, so take that as an opportunity to guide change within your organizations and your own practices. How you respond to criticism will determine your allyship status. People are more likely to forgive when a commitment to change is quickly and repeatedly demonstrated.
The first step to moving forward in an anti-racist framework is to compensate the students for their labor in making the institution more inclusive.
To BIPOC and LGBTQ+ Trainees: Your labor is worth compensation and recognition. It is difficult to see your institution failing to adequately support members of your community without feeling compelled to act. As a Black person in medicine I have served on nearly every committee related to diversity recruitment and admissions. As a queer person I have sat on many taskforces dedicated to improving trans education in medical curricula. I have spent countless hours improving the institutions at which I have been educated and will likely spend countless more. However, over the past few years, I have realized that those hours do not generally advance my academic and professional goals. My peers who do not share in my marginalized identities do not have the external pressure to sequester large parts of their time for institutional change. While I was drafting emails to administrators or preparing journal clubs to educate students on trans health, my peers were studying.
There were periods in my education where there were appreciable declines in my grades and research productivity because of the time I spent on institutional reform. Without care, this phenomenon can translate to a perceived achievement gap. It is not that BIPOC and LGBTQ+ achieve less; in fact, in many ways we achieve more. However, we expend much of our effort on activities that are not traditionally valued as accomplishments for career advancement. The only way to change this norm is to start demanding compensation for your labor and respectfully declining if it is not provided. Compensation can be monetary, but it can also be opportunities for professional identity formation. For uncompensated work that I feel particularly compelled to do, I strategize how it can benefit me before starting the project. Can I write it up for publication in a peer-reviewed scientific journal? Can I find an advisor to support the task force and write a letter of reference on my behalf? Can I use the project to apply for external research funding or scholarships? These are all ways of translating the work that matters to you into the currency that the medical establishment values as productivity.
To Educators and Institutions: Compensate marginalized members of your organizations for making it better. Racism is the oldest institution in America. It is built into the foundation of the country and rests in the very top office in our nation's capital. Analogues of racism, specifically gender-based discrimination, transphobia, and classism, have similarly seeped into the fabric of our country and education system. Given their ubiquity, how can we expect to combat these issues cheaply? Today, anti-racism work is in vogue in academia, and institutions have looked to their Black and otherwise marginalized students to provide ways that the institution can improve. We, as students, regularly respond with well-researched, scholarly, actionable lists of specific interventions that are the result of dozens (sometimes hundreds) of hours of unpaid labor. Then, administrators dissect these interventions and scale them back citing budgetary concerns or hiring limitations.
It gives the impression that they view racism as an easy issue to fix, that can be slotted in under an existing line item, rather than the severe problem requiring radical reform that it actually is. The first step to moving forward in an anti-racist framework is to compensate the students for their labor in making the institution more inclusive. Inclusion and equity improve the educational environment for all students, so in the same way one would pay a consultant for an audit that identifies weaknesses in your institution, you should pay your students who are investing countless hours in strategic planning. While financial compensation is usually preferable, institutions can endow specific equity-related student awards, fellowships, and research programs that allow the work that students are already doing to help further their careers. Next, it is important to invest. Add anti-racism and equity interventions as specific items in departmental and institutional budgets so that there is annual reserved capital dedicated to these improvements, part of which can include the aforementioned student compensation.
To BIPOC and LGBTQ+ Trainees: Seek and be mentors. Early in my training, I often lamented the lack of mentors who shared important identities with myself. I initially sought a Black, queer mentor in medicine who could open doors and guide me from undergrad pre-med to university professor. Unfortunately, given the composition of the U.S. academy, this was not a realistic goal. While our white, cisgender, heterosexual colleagues can identify mentors they reflect, we have to operate on a different mentorship model. In my experience, it is more effective to assemble a mentorship network: a group of allies who facilitate your professional and personal development across one or more arenas. For me, as a physician-scholar-advocate, I need professional mentors who support my specific research interests, help me develop as a policy innovator and advocate, and who can guide my overall career trajectory on the short- and long- term time scales.
Rather than expecting one mentor to fulfill all those roles, as well as be Black and queer, I instead seek a set of mentors that can share in these roles, all of whom are informed or educable on the unique needs of Black and queer trainees. When assembling your own mentorship network, remember personal mentors who can help you develop self-care strategies and achieve work-life balance. Also, there is much value in peer mentorship. Some of my best mentors are my contemporaries. Your experiences have allowed you to accumulate knowledge—share that knowledge with each other.
To Educators and Institutions: Hire better mentors. Be better mentors. Poor mentorship is a challenge throughout academia that is amplified for BIPOC and LGBTQ+ trainees. Part of this challenge is due to priorities established in the hiring process. Institutions need to update hiring practices to explicitly evaluate faculty and staff candidates for their ability to be good mentors, particularly to students from marginalized communities. This can be achieved by including diverse groups of students on hiring committees and allowing them to interview candidates and assess how the candidate will support student needs. Also, continually evaluate current faculty and staff based on standardized feedback from students that will allow you to identify and intervene on deficits and continually improve the quality of mentorship at your institution.
The suggestions I provided are about navigating medical education, as it exists now. I hope that incorporating these practices will allow institutions to better serve the BIPOC and LGBTQ+ trainees that help make their communities vibrant. I also hope that my fellow BIPOC and LGBTQ+ trainees can see themselves in this conversation and feel affirmed and equipped in navigating medicine based on the tools I provide here. However, my words are only a tempering measure. True justice in medical education and health will only happen when we overhaul our institutions and dismantle systems of oppression in our society.
[Editor's Note: To read other articles in this special magazine issue, visit the beautifully designed e-reader version.]
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.”