“Biodiversity data trace not only the distributions species but also cities and roads, the rise of surveillance technology, shadows of colonial histories, and echoes of contemporary racial and economic inequity,” Millie Chapman, an ecologist and postdoctoral researcher at the National Center for Ecological Analysis and Synthesis, at the University of California, Santa Barbara, wrote in an email.
The stakes are high.
The 2022 adoption of a new global biodiversity agreement, known as the Kunming-Montreal Global Biodiversity Framework, included a pledge to increase financing for biodiversity work to $30 billion per year by 2030. Scientific data about species can influence where that money gets spent.
The Global Biodiversity
Information Facility is a prime example of the ways ecological data collide with social history. The government-funded international database compiles more than 2.6 billion observations of species spanning the globe. But even a glance at the facility’s data map shows that it doesn’t match with biodiversity hotspots. While the United States and phone number library Europe are replete with observations, the rainforests of central Africa—places far richer in species – are relatively blank.
This problem
Is well known among tips for selling on telegram ecologists and can be partly corrected by statistical models. But Chapman and her collaborators warn that the challenges run much deeper.“Without directly addressing and correcting for social and political disparities in data, the conservation community will likely fall into the consumer data same traps that other domains do—entrenching the inequities of the past and present in future decision-making through data,” they write.
One possibility is increasing
The number of observations with new tools, including programs that recruit non-scientists to help gather data, new sensors that can gather environmental data with less effort, and environmental DNA (eDNA), which detects species from bits of DNA floating in the air or water. But these tools can also be pitfalls. While they hold the promise of filling data gaps, there is evidence that new data sources are echoing the imbalances of the past, the authors warned.
More nuanced modeling
Could also help. But again, the researchers caution that it will be hard to account for so many social variables. While it’s one thing to control for factors like how close an area is to roads or towns, it’s far more difficult to trace the effects of decisions such as who gets scientific funding.