“Our findings support the idea that large-diameter fuel build-up is a strong contributor to fire severity,” said Audrey Odwuor, a Ph.D. candidate in the UCI Department of Earth System Science and the lead author of the new study.

Researchers have known for decades that an increasing number of trees and an increasing abundance of dead plant matter on forest floors are the things making California wildfires more severe — but until now it was unclear what kinds of plant debris contribute most to a fire.

To tackle the question, Odwuor and two of the study’s co-authors — James Randerson, professor of Earth system science at UCI, and Alondra Moreno from the California Air Resources Board — drove a mobile lab owned and operated by the lab of study co-author and UCI alumna Francesca Hopkins at UC Riverside, to the southern Sierra Nevada mountains during 2021’s KNP Complex Fire.

The KNP Complex Fire burned almost 90,000 acres in California’s Sequoia and Kings Canyon National Parks. In the fire’s smoke, the team took samples of particulate matter-laden air and analyzed the samples for their radiocarbon content at UCI’s W.M. Keck Accelerator Mass Spectrometer facility with co-author and UCI Earth system science professor Claudia Czimczik.

Different fuel types, explained Czimczik, have different radiocarbon signatures, such that when they analyzed the smoke they discovered radiocarbon values associated with large fuel sources like fallen tree logs.

“What we did was pretty distinctive, as we were able to identify fuel sources by measuring the wildfire smoke,” said Czimczik. “Our approach provides what we think of as an integrated picture of the fire because we’re sampling smoke produced over the course of the fire that has been transported downwind.”

The team also saw elevated levels of particulate matter that is 2.5 microns in diameter or less, which includes particles that, if inhaled, are small enough to absorb into the bloodstream.

The preponderance of large-diameter fuels is new in western forests. “We’re really in a situation that’s a consequence of both management strategies and climate warming since European-American settlement began in California,” Odwuor said. “These fuels are building up on the forest floor over periods of decades, which is not typically how these forests were maintained.”

It’s information that, according to Odwuor, could help California better manage its wildfires.

“The knowledge that large-diameter fuels drive fires and fire emissions — at least in the KNP Complex Fire — can be useful for knowing which fuels to target with fuel treatments and what might end up in the smoke from both wildfires and prescribed fire,” said Odwuor. “The idea is that because we can’t control the climate, we can only do our best to manage the fuels, which will theoretically have an impact on fire severity and the composition of the smoke.”

But the solution isn’t as straightforward as removing trees from forest floors, because, among other things, they provide habitat for wildlife. That, and “once you get them out, where do you send them? There are only so many mills in California that can handle all the wood,” Odwuor said.

Where the new knowledge could be helpful is with prescribed burns, wherein teams burn tracks of forest in a planned fashion with the aim of reducing the amount of fuel available for future wildfires.

“We’re hoping to build some urgency for these management strategies,” said Odwuor.

Not only does the new research shed light on the nature of TRAPPIST-1 b, the exoplanet orbiting closest to the system’s star, it has also shown the importance of parent stars when studying exoplanets.

Published in Astrophysical Journal Letters, the findings by astronomers at UdeM’s Trottier Institute for Research on Exoplanets (iREx) and colleagues in Canada, the U.K. and U.S. shed light on the complex interplay between stellar activity and exoplanet characteristics.

Captured the attention

TRAPPIST-1, a star much smaller and cooler than our sun located approximately 40 light-years away from Earth, has captured the attention of scientists and space enthusiasts alike since the discovery of its seven Earth-sized exoplanets seven years ago. These worlds, tightly packed around their star with three of them within its habitable zone, have fueled hopes of finding potentially habitable environments beyond our solar system.

Led by iREx doctoral student Olivia Lim, the researchers employed the powerful James Webb Space Telescope (JWST) to observe TRAPPIST-1 b. Their observations were collected as part of the largest Canadian-led General Observers (GO) program during the JWST’s first year of operations. (This program also included observations of three other planets in the system, TRAPPIST-1 c, g and h.) TRAPPIST-1 b was observed during two transits — the moment when the planet passes in front of its star — using the Canadian-made NIRISS instrument aboard the JWST.

“These are the very first spectroscopic observations of any TRAPPIST-1 planet obtained by the JWST, and we’ve been waiting for them for years” said Lim, the GO program’s principal Investigator.

She and her colleagues used the technique of transmission spectroscopy to peer deeper into the distant world. By analysing the central star’s light after it has passed through the exoplanet’s atmosphere during a transit, astronomers can see the unique fingerprint left behind by the molecules and atoms found within that atmosphere.

‘Just a small subset’

“This is just a small subset of many more observations of this unique planetary system yet to come and to be analysed,” adds René Doyon, Principal Investigator of the NIRISS instrument and co-author on the study. “These first observations highlight the power of NIRISS and the JWST in general to probe the thin atmospheres around rocky planets.”

The astronomers’ key finding was just how significant stellar activity and contamination are when trying to determine the nature of an exoplanet. Stellar contamination refers to the influence of the star’s own features, such as dark spots and bright faculae, on the measurements of the exoplanet’s atmosphere.

The team found compelling evidence that stellar contamination plays a crucial role in shaping the transmission spectra of TRAPPIST-1 b and, likely, the other planets in the system. The central star’s activity can create “ghost signals” that may fool the observer into thinking they have detected a particular molecule in the exoplanet’s atmosphere.

This result underscores the importance of considering stellar contamination when planning future observations of all exoplanetary systems, the sceintists say. This is especially true for systems like TRAPPIST-1, since the system is centred around a red dwarf star which can be particularly active with starspots and frequent flare events.

“In addition to the contamination from stellar spots and faculae, we saw a stellar flare, an unpredictable event during which the star looks brighter for several minutes or hours,” said Lim. “This flare affected our measurement of the amount of light blocked by the planet. Such signatures of stellar activity are difficult to model but we need to account for them to ensure that we interpret the data correctly.”

A range of models explored

Based on their collected JWST observations, Lim and her team explored a range of atmospheric models for TRAPPIST-1 b, examining various possible compositions and scenarios.

They found they could confidently rule out the existence of cloud-free, hydrogen-rich atmospheres — in other words, there appears to be no clear, extended atmosphere around TRAPPIST-1 b. However, the data could not confidently exclude thinner atmospheres, such as those composed of pure water, carbon dioxide, or methane, nor an atmosphere similar to that of Titan, a moon of Saturn and the only moon in the Solar System with its own atmosphere.

These results are generally consistent with previous (photometric, and not spectroscopic) JWST observations of TRAPPIST-1 b with the MIRI instrument. The new study also proves that Canada’s NIRISS instrument is a highly performing, sensitive tool able to probe for atmospheres on Earth-sized exoplanets at impressive levels.

On his first voyage to the Caribbean in 1492, Christopher Columbus noted that flocks of parrots were so abundant they “obscured the sun.” Today, more than half of parrot species in the Caribbean have gone extinct, from large particolored macaws to a parrotlet the size of a sparrow.

Biologists attempting to conserve the remaining parrot species are stymied by how little is known of their former distributions. This is due, primarily, to their complicated history with humans.

“People have always been obsessed with parrots,” said lead author Jessica Oswald, a senior biologist with the U.S. Fish and Wildlife Service Forensics Lab. “Indigenous peoples have moved parrots across continents and between islands for thousands of years. Later, European colonists continued that practice, and we’re still moving them around today.”

Centuries of exchange and trade have made it difficult to know how parrots wound up where they are now. Half of the 24 parrot species that currently live in the Caribbean were introduced from other areas, and it’s unclear whether native parrots evolved on the islands they inhabit or were similarly transported there.

Fortunately, their popularity with humans means parrots are occasionally found in archaeological sites as well. Their bones have been recovered from refuse piles — called middens — alongside shells, fish bones and other scraps from previous meals.

“There are records of parrots being kept in homes, where they were valued for their feathers and, in some cases, potentially as a source of food,” said senior author Michelle LeFebvre, curator of South Florida archaeology and ethnography at the Florida Museum of Natural History.

Parrots also have an uncharacteristically good fossil record in the Caribbean, compared with other tropical regions. However, specimens are rarely found intact. More often, their bones are broken or isolated, and it’s not always possible to determine which species they belonged to.

DNA can provide unequivocal answers where physical comparisons fall short, and co-author David Steadman was eager to see if they could extract any residual genetic material preserved in bone tissue. Oswald — who worked as a graduate student and postdoctoral associate at the Florida Museum — had recently completed a proof of concept, in which she successfully sequenced the first DNA from an extinct Caribbean bird that had been preserved in a blue hole for 2,500 years. Using the same methods, she later discovered that an extinct flightless bird from the Caribbean was most closely related to similarly bygone, ground-dwelling birds from Africa and New Zealand.

“For me, the single most satisfying thing about this project is we can use fossils in ways that weren’t even imaginable when they came out of the ground,” said Steadman, a retired curator of ornithology at the Florida Museum.

The authors pieced together the long history of parrots in the genus Amazona, focusing on two species — the Cuban (A. leucocephala) and Hispaniolan (A. ventralis) parrots — for which they could obtain ancient DNA samples.

Of the two, Cuban parrots are currently the most widespread, with isolated populations in Cuba and on a few islands in the Bahamas and Turks and Caicos. They’re one of the only native parrots in the region not in imminent danger of extinction.

The Hispaniolan parrot has had a harder time adapting to human-wrought changes. It’s listed as vulnerable to extinction on the International Union for Conservation of Nature’s Red List and is entirely endemic to its eponymous island.

Most of the fragmentary fossils collected outside of Hispaniola and Puerto Rico were consequently identified as belonging to the more common Cuban parrots. But when the DNA results came back, they told a different story. The fossils from the Bahamian paleontological sites were actually from Hispaniolan parrots, indicating that this species formerly had a range that extended up through the Bahamas before human arrival to the islands.

Similarly, the results indicate that Cuban parrots once inhabited the largest island in the Turks and Caicos, from which they are now absent.

“One of the striking things about this study is the discovery of what could be considered dark extinctions,” LeFebvre said. “We’re learning about diversity we didn’t even know existed until we took a closer look at museum specimens.”

Bones from archaeological sites in the Turks and Caicos and from Montserrat — an island far to the south in the Lesser Antilles — were also determined to be from Hispaniolan parrots. These had likely been transported there by humans, and the species is no longer present on the islands.

According to Oswald, knowing where species once thrived — both naturally by their own devices and artificially with the aid of humans — is the first step to conserving what’s left of their diversity.

“We have to think about what we consider to be natural,” she said. “People have been altering the natural world for thousands of years, and species that we think are endemic to certain areas might be the product of recent range loss due to humans. It takes paleontologists, archaeologists, evolutionary biologists and museum scientists all working together to really understand the long-term role of humans on diversity change.”

The authors published their study in the journal PNAS. Brian Smith of the American Museum of Natural History, Julie Allen of Virginia Tech and Robert Guralnick of the Florida Museum of Natural History are also co-authors on the study.

Genetic risk-scoring algorithms are considered a promising method to identify high-risk groups of individuals who could benefit from preventive interventions for various diseases and conditions, such as cancers and heart diseases. These risk-scoring algorithms are based on large-scale genetic studies that link certain DNA variants to higher or lower disease risks.

The vast majority of subjects in these genetic studies have been people of European ancestry. The resulting risk-scoring algorithms have not always performed well in other populations, due to genetic differences across populations.

The new method, described in a paper that appears online today in Nature Genetics, has been applied to data from genetic studies from 23andMe Inc. and other sources involving more than 5 million individuals across diverse populations to generate genetic scores for 13 traits, including health conditions like coronary artery diseases and depression, in five different ancestry categories: European, African, Latino, East Asian, and South Asian. The researchers also tested the new method in large-scale simulation studies.

“We showed that our method can help close the risk-scoring performance gap for non-European-ancestry populations,” says study senior author Nilanjan Chatterjee, PhD, Bloomberg Distinguished Professor in the Bloomberg School’s Department of Biostatistics. “At the same time, we also concluded that we can’t fully close the gap with new methods alone — we also need larger datasets on these populations.”

Many risk-scoring models derived from genetic studies in non-European-ancestry populations often fall short because those studies typically are relatively small in scale. This results in a performance gap in risk-scoring between European-ancestry and other-ancestry populations, which may contribute to health care disparities.

The new method — which the researchers call CT-SLEB — used a combination of AI techniques including machine learning and Bayesian statistical modeling. In addition to the 23andMe database, the researchers “trained” CT-SLEB on data from the Global Lipids Genetics Consortium, the National Institutes of Health’s All of Us research program, and UK Biobank.

The research team’s benchmarking analyses showed that these new ancestry-specific risk-scoring models for the non-European populations generally outperformed standard polygenic risk score models that are based on mostly European-ancestry datasets, or are based on smaller non-European-ancestry datasets.

The researchers also compared CT-SLEB to a number of alternative methods. They found the proposed method is particularly helpful to improve genetic risk scores in African ancestry populations where scoring accuracy is generally the lowest. The team also found that CT-SLEB is computationally much fastercompared to its closest competitors, and thus could be amenable to analyzing much larger numbers of DNA variants and more populations.

The team is now working with more advanced methods that are even better performing but are still computationally fast, Chatterjee says.

He also emphasizes that, as the team’s calculations in the study showed, having polygenic risk score models that work equally well in non-European-ancestry and European-ancestry populations will require more genome-wide association studies in non-European-ancestry populations.

“A lot of people think machine-learning and AI can do magic but without large, well-designed studies, algorithms will not be as useful,” Chatterjee says.

The paper’s lead author is Haoyu Zhang, PhD, who was a doctoral student at the Bloomberg School at the time the study began and is currently an investigator at the National Cancer Institute. Researchers from 23andMe contributed to development of the new method and the analysis of the data. The CT-SLEB code is publicly available via GitHub. The code availability section in the paper includes a link to GitHub which includes the CT-SLEB code.

Funding was provided by the National Institutes of Health (K99 CA256513-01, R00 HG012223, 5T32HL007604-37, R35-CA197449, U19-CA203654, R01-HL163560, U01-HG009088, U01-HG012064, R01 HG010480-01 and U01HG011724).