Study reveals changing patterns in globally important algae
Algae key to the ocean carbon cycle are scarce in equatorial waters
Tiny algae called coccolithophores play a major role in the global carbon cycle.
August 21, 2019
Globally important ocean algae called coccolithophores are mysteriously scarce in one of the most productive regions of the Atlantic Ocean, according to a new paper in Deep Sea Research I. A massive dataset has revealed patterns in the regions where coccolithophores live, illuminating the inner workings of the ocean carbon cycle.
“Understanding large-scale patterns helps us understand ocean productivity in the entire Atlantic basin,” said William Balch, a scientist at the Bigelow Laboratory for Ocean Sciences and the paper’s lead author. “Collecting this dataset has been a superhuman effort that has taken hundreds of days at sea and years of analysis.”
The researchers found that coccolithophores both struggle and thrive in unexpected places throughout the Atlantic Ocean. They are most abundant in subpolar and temperate waters and surprisingly scarce around the equator, where an abundance of nutrients and sunlight create one of the most biologically productive regions of the global ocean.
The team also discovered that some coccolithophore species thrive deep below the surface near the farthest reaches of sunlight — within or just above an ocean layer called “Sub-Antarctic mode water.” This distinct feature flows north from the Southern Ocean and provides nutrients to much of the global ocean, including the Northern Hemisphere. Balch suspects that booming coccolithophore populations in the Southern Ocean are depleting the water layer’s nutrient supply and altering its chemistry — potentially making the water inhospitable to coccolithophores by the time it reaches the equator.
“Sub-Antarctic mode water exerts a staggering level of control on much of the global ocean,” Balch said. “If coccolithophores are changing its essential properties, they could be influencing which species grow in food webs as far away as the equator or even in the Northern Hemisphere.”
Facial recognition technique could improve hail forecasts
Scientists use machine learning to recognize potentially damaging storms
Scientists are developing a new machine-learning technique that may improve hail forecasts.
August 20, 2019
The same artificial intelligence technique used in facial recognition systems could help improve prediction of hailstorms and their severity, according to a new study by scientists at the National Center for Atmospheric Research.
Researchers trained a deep learning model called a convolutional neural network to recognize features of individual storms that affect whether hail will form and how large the hailstones will be, which are difficult to predict.
The results, published in the American Meteorological Society’s Monthly Weather Review, highlight the importance of taking into account a storm’s entire structure, something that’s been challenging to do with existing hail-forecasting techniques.
“We know that the structure of a storm affects whether the storm can produce hail,” said NCAR scientist David John Gagne, who led the research team. “A supercell is more likely to produce hail than a squall line, for example. But most hail forecasting methods just look at a small slice of the storm and can’t distinguish the broader form and structure.”
“Hail – particularly large hail – can have significant economic impacts on agriculture and property,” said Nick Anderson, a program officer in NSF’s Division of Atmospheric and Geospace Sciences, which funded the research. “Using these deep learning tools in unique ways will provide additional insight into the conditions that favor large hail, improving model predictions. This is a creative, and very useful, convergence of scientific disciplines.”
Next steps for the new machine learning model include testing it using storm observations and radar-estimated hail, with the goal of transitioning the model into operational use.
Smartphone apps may connect to vulnerable backend cloud servers
Most users likely unaware of vulnerabilities in smartphone apps
A portion of the process used by SkyWalker to vet backend systems that support mobile apps.
August 15, 2019
Cybersecurity researchers have discovered vulnerabilities in the backend systems that feed content and advertising to smartphone applications, potentially exposing users’ personal information.
In results reported at this week’s USENIX Security Symposium, researchers from the Georgia Institute of Technology and The Ohio State University identified more than 1,600 vulnerabilities in the support ecosystem behind the top 5,000 free apps available in the Google Play Store. The vulnerabilities, affecting multiple app categories, could allow hackers to break into databases that include personal information — and perhaps into users’ mobile devices.
To help developers improve the security of their mobile apps, the researchers have created an automated system called SkyWalker to vet cloud servers and software library systems. SkyWalker can examine the security of the servers supporting mobile applications, which are often operated by cloud hosting services rather than individual app developers.
“A lot of people might be surprised to learn that their phone apps are communicating with not just one, but likely tens or even hundreds of servers in the cloud,” said Brendan Saltaformaggio of Georgia Tech’s School of Electrical and Computer Engineering. “Users don’t know they are communicating with these servers because only the apps interact with them and they do so in the background. Until now, that has been a blind spot where nobody was looking for vulnerabilities.”
The researchers discovered 983 instances of known vulnerabilities. They are still investigating whether attackers could get into individual mobile devices connected to vulnerable servers.
The sense of smell has been understudied in birds, scientists say.
August 15, 2019
Chickadees are interested in scents. That’s the news from a study out of Lehigh University, the first to document naturally hybridizing songbirds’ preference for the smell of their own species.
Amber Rice, an evolutionary biologist at Lehigh, studies hybridization — when separate species come into contact and mate — to better understand how species originate and how the existing, parent species are maintained. Rice researches the black-capped chickadee and its relative, the Carolina chickadee.
She and scientist Alex Van Huynh set out to test the potential for scent to act as a mate choice cue in the chickadees. They wondered whether smell might contribute to the reproductive isolation of black-capped and Carolina chickadees in a zone in Pennsylvania where birds are hybridizing.
Huynh and Rice found that black-capped and Carolina chickadees produce chemically distinct oils used to maintain their feathers; the oils also contain scent-producing compounds. The researchers discovered that both chickadee species prefer the smell of their own species over the smell of the other species. The results are published in a paper in the journal Ecology and Evolution.
“The sense of smell has been understudied in birds, particularly songbirds, because they frequently have such impressive plumage and song variation,” says Rice. “Other work has documented that some songbird species can smell, and prefer their own species’ odors, but this is the first example in currently hybridizing species that we know of.”
Jodie Jawor, a program director in NSF’s Division of Integrative Organismal Biology, which funded the research, says, “There’s still much to be learned about how animals communicate with one another. This work adds information to a unique and understudied communication dimension in birds.”