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Social inequities and citizen science can skew our view of the natural world

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Social inequities and citizen science can skew our view of the natural world

Maps of St. Louis squirrels reveals how citizen-gathered data can be biased by race and poverty. A group of scientists have thoughts on how to counteract that.
March 20, 2024

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Call it the mystery of the invisible squirrels.

Shortly after Elizabeth Carlen arrived in St. Louis for job as a postdoctoral researcher at Washington University, the biologist posted a message on X, the social media site formerly known as Twitter.

“Wondering about bias in community science data? Here is an example from St. Louis,” she wrote. Then Carlen posted two maps. One showed the city’s stark racial segregation, with its northern half mostly black and southern half predominantly white. The other showed where sightings of Eastern gray squirrels had been recorded on the app iNaturalist, used by phone-wielding amateur naturalists to report their observations.

Judging by the app, there were virtually no squirrels in the city’s northern neighborhoods, something that Carlen, whose research focuses on how urbanization affects squirrels, knew wasn’t true. “Squirrels are abundant in the northern part of the city,” wrote Carlen. “But there are no recorded observations.”

Her insight spawned an online conversation among scientists, and now a peer-reviewed paper, about the blind spots and biases that can skew data gathered through informal amateur networks – often known as citizen science.  

Citizen science has become an important part of biodiversity research – whether it’s volunteers collaborating directly with scientists to gather data, researchers tapping into apps like eBird or iNaturalist, or even people just posting pictures of wildlife on their Instagram feeds. In just one example, a paper published in late February documents a dramatic population crash among humpback whales in the Pacific Ocean, based on thousands of photos submitted to the whale-tracking website Happywhale.com.

But as the St. Louis squirrels show, such data can produce results that have little to do with natural history and a lot to do with human society.

 

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“We need to be very conscious about how we’re using this data and how we’re interpreting where animals are,” said Carlen, who is based at the university’s Living Earth Collaborative, which is dedicated to the study of biodiversity.

In the new paper, which Carlen co-wrote with 13 other scientists at institutions around the country, the scientists offer a cautionary road map of the ways in which bias can creep into such data gathering and the resulting picture of the natural world.

For starters, the data depends on who participates and where they are located. Observations from sites like eBird tend to be concentrated in cities and near roads, because that’s where the most people are, not because birds prefer it. Likewise, wealthier people are more likely to take part in citizen science, increasing the likelihood that data will come from where they live. Racial disparities – which can reflect social and economic inequities – have also emerged in citizen science, with white participants overrepresented relative to people of color.

Then there are the different ways people discriminate towards the organisms they are spotting or where they go looking for them. Reclusive species, such as salamanders that prefer to hide from site, get less attention than showy animals or plants. People are more likely to report wildlife sightings when they are in parks or on trails. And then there is our preference for colorful or unusual creatures.

“There’s not a lot of people photographing rats and putting them on iNaturalist — or pigeons, for that matter,” Carlen said.

The case of the St. Louis squirrels is a stark example. Carlen found that while tree cover and city parks are relatively evenly spread around the city, not so entries to eBird or iNaturalist. Those make it look like the southern half of the city is teeming with wildlife – including squirrels, while the northern half is a naturalist’s desert.

These imbalances can have implications beyond scientific papers, the authors warn. If that data is used to help inform decisions about where to focus conservation efforts such as habitat restoration or protections, it can further amplify biases and inequality.

The scientists aren’t suggesting ditching citizen-gathered data. In some cases, researchers have devised statistical workarounds to adjust for outside factors. But Carlen and her collaborators are pushing their fellow scientists to go further than that. Rather than just hoovering up databases, they call on their peers to approach citizen science in ways that build stronger connections with the communities where the research is happening. That could include things as basic as translating project information into the languages used in a place and as complex as working with community members to decide what to study.

It might take more time and effort. But the rewards can be science that helps address some of society’s problems, rather than reinforcing them.

“We can’t just show residents the app and tell them that they need to use it,” said Carlen. “That ignores the underlying problem that our society is still segregated and not everyone has the resources to participate.”

Carlen, et. al. “A framework for contextualizing social-ecological biases in contributory science data.” People and Nature. March 3, 2024.

Image: ©Anthropocene Magazine

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