Investigators used data on more than 9,000 soil samples from around the world and found that carbon storage “declines strongly” as average temperatures increase.

This is an example of a “positive feedback”, where global warming causes more carbon to be released into the atmosphere, further accelerating climate change.

Importantly, the amount of carbon that could be released depends on the soil type, with coarse-textured (low-clay) soils losing three times as much carbon as fine-textured (clay-rich) soils.

The researchers, from the University of Exeter in Exeter, Devon, South West England, United Kingdom, and Stockholm University, say their findings help to identify vulnerable carbon stocks and provide an opportunity to improve Earth System Models (ESMs) that simulate future climate change. 

“Because there is more carbon stored in soils than there is in the atmosphere and all the trees on the planet combined, releasing even a small percentage could have a significant impact on our climate,” said Professor Iain Hartley of Exeter’s College of Life and Environmental Sciences.

“Our analysis identified the carbon stores in coarse-textured soils at high-latitudes (far from the Equator) as likely to be the most vulnerable to climate change.

“Such stores, therefore, may require particular attention given the high rates of warming taking place in cooler regions.

“In contrast, we found carbon stores in fine-textured soils in tropical areas to be less vulnerable to climate warming.”

The data on the 9,300 soil profiles came from the World Soil Information database, with the study focusing on the top 50cm of soil.

By comparing carbon storage in places with different average temperatures, the researchers estimated the likely impact of global warming.

For every 10°C of increase in temperature, average carbon storage (across all soils) fell by more than 25%.

“Even bleak forecasts do not anticipate this level of warming, but we used this scale to give us confidence that the effects we observed were caused by temperature rather than other variables,” Professor Hartley said.

“Our results make it clear that, as temperatures rise, more and more carbon is released from soil.

“It’s important to note that our study did not examine the timescales involved, and further research is needed to investigate how much carbon could be released this century.”

The researchers found that their results could not be represented by an established ESM.

“This suggests that there is an opportunity to use the patterns we have observed to improve how models represent soils, and further reduce uncertainty in their projections,” Professor Hartley said.

The differences in carbon storage based on soil texture occur because finer soils provide more mineral surface area for carbon-based organic material to bond to, reducing the ability of microbes to access and decompose it.

UPDATED April 8, 2024

Insights can guide surveillance to prevent secondary spillover, new variants

Back and forth transmission of SARS-CoV-2 between people and other mammals increases the risk of new variants and threatens efforts to control COVID-19. A new study used a novel modeling approach to predict the zoonotic capacity of more than 6,495 species of mammals that have been identified by scientists, extending predictive capacity by an order of magnitude. Of the high-risk species flagged, many live near people and in COVID-19 hotspots. 

A major bottleneck to predicting high-risk mammal species is limited data on ACE2, the cell receptor that SARS-CoV-2 binds to in animals. ACE2 allows SARS-CoV-2 to enter host cells and is found in all major vertebrate groups. All vertebrates likely have ACE2 receptors, but sequences were only available for 326 species.

To overcome this obstacle, the team developed a machine learning model that combined data on the biological traits of 6,495 mammal species with available data on ACE2. The goal: to identify mammal species with high ‘zoonotic capacity’ – the ability to become infected with SARS-CoV-2 and transmit it to other animals and people. The method they developed could help extend predictive capacity for disease systems beyond COVID-19.

Co-lead scientist Ilya Fischhoff, a postdoctoral associate at Cary Institute of Ecosystem Studies in Millbrook, NY, comments, “SARS-CoV-2, the virus that causes COVID-19, originated in an animal before making the jump to people. Now, people have caused spillback infections in a variety of mammals, including those kept in farms, zoos, and even our homes. Knowing which mammals are capable of re-infecting us is vital to preventing spillback infections and dangerous new variants.”

When a virus passes from people to animals and back to people it is called secondary spillover. This phenomenon can accelerate new variants established in humans that are more virulent and less responsive to vaccines. Secondary spillover of SARS-CoV-2 has already been reported among farmed mink in Denmark and the Netherlands, where it has led to at least one new SARS-CoV-2 variant.

Senior author and Cary Institute disease ecologist, Barbara Han, says, “Secondary spillover allows SARS-CoV-2 established in new hosts to transmit potentially more infectious strains to people. Identifying mammal species that are efficient at transmitting SARS-CoV-2 is an important step in guiding surveillance and preventing the virus from continually circulating between people and other animals, making disease control even more costly and difficult.” 

Binding to ACE2 receptors is not always enough to facilitate SARS-CoV-2 viral replication, shedding, and onward transmission. The team trained their models on a conservative binding strength threshold informed by published ACE2 amino acid sequences of vertebrates, analyzed using a software tool called HADDOCK (High Ambiguity Driven protein-protein DOCKing). This software scored each species on predicted binding strength; stronger binding likely promotes successful infection and viral shedding.

Co-lead author and Cary Institute postdoctoral analyst, Adrian Castellanos, says, “The ACE2 receptor performs important functions and is common among vertebrates. It likely evolved in animals alongside other ecological and life-history traits. By comparing biological traits of species known to have the ACE2 receptor with traits of other mammal species, we can make predictions about their capacity to transmit SARS-CoV-2.”

This combined modeling approach predicted the zoonotic capacity of mammal species known to transmit with 72% accuracy and identified numerous additional mammal species with the potential to transmit SARS-CoV-2. Predictions matched observed results for white-tailed deer, mink, raccoon dogs, snow leopard, and others. The model found that the riskiest mammal species were often those that live in disturbed landscapes and close to people – including domestic animals, livestock, and animals that are traded and hunted.

The top 10% of high-risk species spanned 13 orders. Primates were predicted to have the highest zoonotic capacity and strongest viral binding among mammal groups. Water buffalo, bred for dairy and farming, had the highest risk among livestock. The model also predicted high zoonotic potential among live-traded mammals, including macaques, Asiatic black bears, jaguars, and pangolins – highlighting the risks posed by live markets and wildlife trade.

SARS-CoV-2 also presents challenges for wildlife conservation. An infection has already been confirmed in Western lowland gorillas. For high-risk charismatic species like mountain gorillas, the spillback infection could occur through ecotourism. Grizzly bears, polar bears, and wolves, all in the 90th percentile for predicted zoonotic capacity, are frequently handled by biologists for research and management.

Han explains, “Our model is the only one that has been able to make risk predictions across nearly all mammal species. Every time we hear about a new species being found SARS-CoV-2 positive, we revisit our list and find they are ranked high. Snow leopards had a risk score around the 80th percentile. We now know they are one of the wildlife species that could die from COVID-19." 

People working near high-risk mammals should take extra precautions to prevent SARS-CoV-2 spread. This includes prioritizing vaccinations among veterinarians, zookeepers, livestock handlers, and other people in regular contact with animals. Findings can also guide targeted vaccination strategies for at-risk mammals.

Han concludes, “We found that the riskiest mammal species are often the ones that live alongside us. Targeting these species for additional lab validation and field surveillance is critical. We should also explore underutilized data sources like natural history collections, to fill data gaps about animal and pathogen traits. More efficient iteration between computational predictions, lab analysis, and animal surveillance will help us better understand what enables spillover, spillback, and secondary transmission – insight that is needed to guide zoonotic pandemic response now and in the future.”