new study It reflects the uneven distribution of biodiversity data, its association with social inequalities, and its implications for contemporary environmental policy. If you look at a world map of all biodiversity records, geographical disparities are striking. Some regions of the word contain far more records than others. While the United States is fully covered in data points, Brazil and China clearly have much lower coverage, even though they host some of the world’s data points. important biodiversity areas. This difference highlights the geographic bias of environmental research, which, contrary to what its name suggests, is not limited to geographic location. For example, some species groups are more frequently recorded than others. To understand what this research gap means, we first need to examine the current science on the distribution of environmental monitoring.
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Research gap: Why does it matter?
The research gap in environmental science is a serious problem, and it has become even more serious today. Today, nature can be turned into an economic asset, the carbon stored by plants can be quantified and sold as carbon credits, and similarly biodiversity can be monitored and turned into biodiversity credits. Now it looks like this. In this way, rising carbon and biodiversity markets facilitate the flow of funds to regions that lack economic support for conservation. But determining a region’s natural assets requires data. And this is where inequality comes into play. Areas with high biodiversity records find it easier to justify environmental protection and access funding.
The same goes for policy.
Policies and regulations for environmental protection often target areas of high ecological priority. Regional priorities will again be determined by available data, including biodiversity rates, presence of endangered species, forest density, deforestation risk, etc.
As a result, uneven distribution of environmental data can have important implications for environmental governance and policy, such as prioritizing some areas and marginalizing others. But where do these biases come from, and what are the drivers of environmental monitoring?
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Existing inequalities in environmental research
Geographic location is where the greatest differences in the distribution of environmental data are observed. This is due to multiple factors, including the accessibility of different locations, research interests and priorities, available technology, and, of course, the wealth of the country.
In fact, biodiversity monitoring is often proportional to a country’s GDP. His 2024 study on the uneven distribution of current biodiversity data found that high-income countries have seven times more observations per hectare than upper-middle, lower-middle, and low-income countries. It turns out.
A look at the Global Biodiversity Information Facility (GBIF), a data repository of observations of billions of species around the world, reveals disparities in environmental research. In fact, 79% of the available data comes from just 10 countries, and an astonishing 37% comes from the United States.
However, the uneven distribution of environmental data goes beyond countries and their financial resources.
For example, accessibility is another important driver of environmental monitoring, as researchers are likely to monitor data in locations that are easily accessible. More than 80% of biodiversity data around the world has been recorded within 2.5 kilometers (1.6 miles) of roads. While it is understandable to some extent to prefer more accessible locations than more remote locations, this necessarily discriminates against low-income countries that lack the financial resources to build adequate road infrastructure and transport links. It will also be.
Data recorded through citizen science can also contribute to data disparities. Citizen science refers to public participation in recording scientific observations. Although this method of observation is very valuable for scientific research, it can be highly biased. In fact, citizen observations are more likely to represent a particular group of species. This is because people are more likely to record animals that are easy to spot or that interest them, such as birds or rabbits.
Another strong bias in environmental data is within the research themes of environmental studies. For example, some species are monitored extensively, while others are considered less relevant. A favorite of wildlife enthusiasts, birds are the most abundant species, accounting for 87% of all GBIF data. This also applies to biomes. In general, urbanized regions and terrestrial ecosystems are best represented in studies, while tropical regions are less represented despite being the world’s most biodiverse biome.
These disparities are explained by many factors and often reflect sociodemographic realities. Disparities in environmental research can be related to historical legacies such as armed conflict, political systems, corruption, democracy, and economic development.
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Impact of data disparity
Inequalities in data availability can have strong social, political, and environmental implications.
For example, in areas with extensive monitoring coverage, environmental threats such as invasive species may be observed earlier. Therefore, actions and investments towards environmental management are more likely to take place in these research hotspots.
The aforementioned study shows that this is more important than, for example, indigenous lands, which may not be tracked and monitored by scientific publications, despite housing significant (and in some cases larger) numbers. This suggests that it could be advantageous to government-managed parks where more monitoring and databases are more important for animal and plant species. Indigenous communities therefore risk marginalization.
The future of environmental data
In recent years, an increasing number of technologies have become available for accessing environmental data, including remote sensing, satellite imagery, and artificial intelligence. Ecological modeling can also be a powerful tool. Biodiversity can be modeled using other environmental factors such as climate and altitude range, giving an accurate idea of the global distribution of plants and animals.
Despite existing solutions, to date, the coverage of environmental data remains uneven and research efforts remain lacking in places where urgent conservation action is needed. Therefore, research gaps should be carefully considered to avoid marginalizing specific regions and communities in environmental finance and policy.
