It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
In industry risk models, mangroves significantly reduce surge and flood damages to properties built behind forests while properties built in front of mangroves face increased risks.
SANTA CRUZ, Calif. – A new study led by the UC Santa Cruz Center for Coastal Climate Resilience (CCCR) and East Carolina University (ECU) has found that mangroves significantly reduced storm surges and property damages during Hurricanes Irma in 2017 and Ian in 2022. In collaboration with the catastrophe risk modeling firm Moody’s RMS, the team used industry models to price the mangrove benefits during these hurricanes at $725 million and $4.1 billion, respectively.
The study, published on October 14 in the journal Cell Reports Sustainability, also assessed the expected benefits of mangroves for storm surge protection at $67 million annually in southwestern Florida’s Collier County. These natural flood defenses are especially important economically in Florida, with its extensive coastline, expensive coastal properties, extreme events, and some exceptional stands of mangrove forests still remaining.
Overall, the study found that mangroves reduce flood losses for coastal homes built inland of the trees. But in some locations, especially for properties in front of mangroves, the team found that properties actually face higher damages due to mangroves.
With their unique aerial root systems, mangroves thrive in marine environments because they can filter saltwater into freshwater. In Florida, an estimated 600,000 acres of mangrove forests contribute to the overall health of the state’s southern coastal zone and beyond, according to the state’s environmental protection department.
The study—a collaboration between CCCR, ECU, Moody’s RMS, and The Nature Conservancy—is the first to value the benefit of mangroves using catastrophe risk industry models.
“In this collaboration with the risk-modeling industry, we show the value of mangrove forests in reducing property damages from storm surges every year,” said study lead author Siddharth Narayan, a recent CCCR research fellow. “Similar to how salt-marsh wetlands from New York to North Carolina reduced damages during Hurricane Sandy, coastal properties in Florida avoided anywhere between 14 to 30% in surge losses during Hurricanes Ian and Irma due to mangroves acting as natural defenses.”
Now a professor of coastal studies at ECU, Narayan hails from Chennai, a tropical coastal city in South India where he completed his bachelor’s in civil engineering. At UC Santa Cruz and UC Santa Barbara, he focused on coastal adaptation and nature-based solutions.
Nationally, storm surges from tropical cyclones and hurricanes cause billions of dollars in coastal property damages every year. However, natural ecosystems such as mangrove forests can, by their presence on these coastlines, modify storm surges and affect property damages, the study states.
There is growing awareness that mangroves are an important part of storm defenses, and the study aims to increase understanding of when, where and how properties benefit from the effects of mangroves on storm surges.
“Mangroves provide many benefits to communities, and it is particularly important that we used a risk industry model to put a price on their flood protection benefits,” said CCCR Director Michael Beck, the study’s senior author. “Like it or not, we only protect what we value, and this is doubly true if it should influence the cost of insurance.”
Further, Beck noted: “The results of these industry models show the real benefits of conserving Florida’s mangroves for property protection and the real costs of choosing to develop in front of these natural barriers.”
Other co-authors of the study include Christopher Thomas, Kechi Nzerem, and Joss Matthewman at Moody’s RMS, Christine Shephard and Laura Geselbracht from The Nature Conservancy. Funding from the Walton Family Foundation, the Herbert W. Hoover Foundation, AXA Research Fund, and the National Science Foundation supported this work.
The spatially variable effects of mangroves on flood depths and losses from storm surges in Florida
Article Publication Date
14-Oct-2025
COI Statement
Co-authors C.J.T. and K.N. are employees of Moody’s RMS, London. C.S. and L.G. are employees of The Nature Conservancy, USA. J.M. was an employee at Moody’s RMS and is currently an employee at Reask, London.
Fatal Attraction: Electric charge connects jumping worm to aerial prey
Scientists uncover new secrets of electrostatic ecology
A tiny worm that leaps high into the air — up to 25 times its body length — to attach to flying insects uses static electricity to perform this astounding feat, scientists have found. The journal PNAS published the work on the nematode Steinernema carpocapsae, a parasitic roundworm, led by researchers at Emory University and the University of California, Berkeley.
“We’ve identified the electrostatic mechanism this worm uses to hit its target, and we’ve shown the importance of this mechanism for the worm’s survival,” says co-author Justin Burton, an Emory professor of physics whose lab led the mathematical analyses of laboratory experiments. “Higher voltage, combined with a tiny breath of wind, greatly boosts the odds of a jumping worm connecting to a flying insect.”
The researchers showed how a charge of a few hundred volts, similar to that generated by an insect’s wings beating the air, initiates an opposite charge in the worm, creating an attractive force. They identified electrostatic induction as the charging mechanism driving this process.
“Using physics we learned something new and interesting about an adaptive strategy in an organism,” says Ranjiangshang Ran, co-lead author of the paper and a postdoctoral fellow in Burton’s lab. “We’re helping to pioneer the emerging field of electrostatic ecology.”
Co-authors include Saad Bhamla and Sunny Kumar, who study biomechanics across species at Georgia Institute of Technology, where preliminary experiments were performed; and Adler Dillman, a nematode biologist at the University of California, Riverside.
The shocking lives of tiny organisms
Static electricity, that tiny zap you sometimes feel when your hand touches a metal doorknob or you pull a sweater over your head, occurs when a buildup of electrons discharges quickly upon contact with a conductor.
While the phenomenon is little more than an annoying shock at the human scale, emerging evidence shows that static electricity plays a crucial role in the lives of some small organisms.
Other research has shown how electrostatics help bees to collect pollen, flower mites to hitch rides on hummingbirds and balloon spiders to drift on silk strands over large distances.
For the current paper, the researchers wanted to investigate how electrostatic forces, in combination with aerodynamics, affects the success rate of S. carpocapsae to connect with a flying insect.
S. carpocapsae is an unsegmented roundworm, or nematode, that kills insects through a symbiotic relationship with bacteria. The worm thrives in soils nearly everywhere on Earth except the Poles. It is increasingly used for biological pest control in agriculture, with researchers around the world studying how to further drive its effectiveness as a natural pesticide.
When the worm senses an insect overhead, it curls into a loop and then launches itself in the air as high as 25 times its body length. That’s the equivalent of a human being jumping higher than a 10-story building.
If the worm hits its target, it enters the insect’s body through a natural opening. It then deposits its symbiotic bacteria, which kills the insect within 48 hours. After the death of the host, the worm feeds on the multiplying bacteria, as well as on the insect tissue, and lays eggs. Several generations may occur in the insect’s cadaver until the juvenile worms emerge into the environment to infect other insects with bacteria.
Painstaking experiments
The researchers designed experiments to investigate the physics involved in the worm’s prowess at connecting with a flying insect.
He also created a tiny wind tunnel for some of the experiments, so that the physicists could analyze the role of ambient breeze in the worm’s target success rate.
Digitizing the data
Using computer software, Ran digitized the trajectories of the worms, drawing from about 60 videos of experiments. The process was time consuming in instances when a worm left the focal plane of the camera, blurring the image, in which case Ran needed to click by hand to record its position.
Ran used a computer algorithm known as Markov chain Monte Carlo (MCMC) to analyze the digitized data. (“Markov” stands for the mathematician who developed the algorithm, while “Monte Carlo” refers to the area of Monaco famous for its casinos.)
“MCMC allows you to do random explorations, using different sets of parameters, to determine a mathematical probability for an outcome,” Ran explains.
Ran identified a set of 50,000 plausible values of fitting parameters for a single worm’s trajectory — such as the voltage of the insect, the physical dimensions and the launching velocity of the worm — to test the probability of a particular charge in a worm allowing it to hit its target.
With no electrostatics, only one out of 19 worm trajectories successfully reached the target.
The model showed that a charge of a few hundred volts — a magnitude commonly found in flying insects — generates an opposite charge in a jumping worm and significantly increases the odds of it connecting to a midair insect. A charge of just 100 volts resulted in a probability for hitting the target of less than 10%, while 800 volts boosted the probability of success to 80%.
A worm expends a vast amount of energy to jump and faces risks of predation or drying out while suspended in the air.
“Our findings suggest that, without electrostatics, it would make no sense for this jumping predatory behavior to have evolved in these worms,” Ran says.
Science past and future
The researchers had theorized that electrostatic induction was the mechanism driving the interplay between the worm and its target. Sifting through research papers eventually led them to a law of induction posited by Scottish physicist James Clerk Maxwell.
“Maxwell, one of the most prolific physicists of all time, had a wild imagination, similar to Einstein,” Ran says. “It turns out that our model for the worm-charging mechanism agreed with a prediction for electrostatic induction that Maxwell made in 1870. There are many buried treasures in scientific history. Sometimes being a scientist is like being an archeologist.”
Drag force was another key part of the equation, due to the tiny size of the worm. The researchers use the comparison of a bowling ball flying through the air, which is not much affected by drag force, and a floating feather, which is highly dependent on it.
Ran drew from the experimental data to simulate the effects of electrostatic charge combined with various wind speeds. The results revealed how the faintest breeze, just 0.2 meters per second, combined with higher voltage further increased the likelihood of a worm hitting its target.
The work serves as a new framework for further investigations into the role of electrostatics in ecology.
Kushal Vyas is an electrical and computer engineering doctoral student at Rice University and first author on a paper presented at the Medical Image Computing and Computer Assisted Intervention Society, or MICCAI. (Photo by Jeff Fitlow/Rice University)
HOUSTON – (Oct. 14, 2025) – When doctors analyze a medical scan of an organ or area in the body, each part of the image has to be assigned an anatomical label. If the brain is under scrutiny for instance, its different parts have to be labeled as such, pixel by pixel: cerebral cortex, brain stem, cerebellum, etc. The process, called medical image segmentation, guides diagnosis, surgery planning and research.
In the days before artificial intelligence (AI) and machine learning (ML), clinicians performed this crucial yet painstaking and time-consuming task by hand, but over the past decade, U-nets ⎯ a type of AI architecture specifically designed for medical image segmentation ⎯ have been the go-to instead. However, U-nets require large amounts of data and resources to be trained.
“For large and/or 3D images, these demands are costly,” said Kushal Vyas, a Rice electrical and computer engineering doctoral student and first author on a paper presented at the Medical Image Computing and Computer Assisted Intervention Society, or MICCAI, the leading conference in the field. “In this study, we proposed MetaSeg, a completely new way of performing image segmentation.”
In experiments using 2D and 3D brain magnetic resonance imaging (MRI) data, MetaSeg was shown to achieve the same segmentation performance as U-Nets while needing 90% fewer parameters ⎯ the key variables AI/ML models derive from training data and use to identify patterns and make predictions.
The study, titled “Fit Pixels, Get Labels: Meta-learned Implicit Networks for Image Segmentation,” won the best paper award at MICCAI, getting recognized from a pool of over 1,000 accepted submissions.
“Instead of U-Nets, MetaSeg leverages implicit neural representations ⎯ a neural network framework that has hitherto not been thought useful or explored for image segmentation,” Vyas said.
An implicit neural representation (INR) is an AI network that interprets a medical image as a mathematical formula that accounts for the signal value (color, brightness, etc.) of each and every pixel in a 2D image or every voxel in a 3D one.
While INRs offer a very detailed yet compact way to represent information, they are also highly specific, meaning they typically only work well for the single signal/image they trained on: An INR trained on a brain MRI cannot typically generalize rules about what different parts of the brain look like, so if provided with an image of a different brain, the INR would typically falter.
“INRs have been used in the computer vision and medical imaging communities for tasks such as 3D scene reconstruction and signal compression, which only require modeling one signal at a time,” Vyas said. “However, it was not obvious before MetaSeg how to use them for tasks such as segmentation, which require learning patterns over many signals.”
To make it useful for medical image segmentation, the researchers taught INRs to predict both the signal values and the specific segmentation labels for a given image. To do so, they used meta-learning, an AI training strategy whose literal translation is “learning to learn” that helps models rapidly adapt to new information.
“We prime the INR model parameters in such a way so that they are further optimized on an unseen image at test time, which enables the model to decode the image features into accurate labels,” Vyas said.
This special training allows the INRs to not only quickly adjust themselves to match the pixels or voxels of a previously unseen medical image but to then also decode its labels, instantly predicting where the outlines for different anatomical regions should go.
“MetaSeg offers a fresh, scalable perspective to the field of medical image segmentation that has been dominated for a decade by U-Nets,” said Guha Balakrishnan, assistant professor of electrical and computer engineering at Rice and a member of the university’s Ken Kennedy Institute. “Our research results promise to make medical image segmentation far more cost-effective while delivering top performance.”
While MetaSeg can be applied to a range of imaging contexts, its demonstrated potential to enhance brain imaging illustrates the kind of research Proposition 14 ⎯ on the ballot in Texas Nov. 4 ⎯ could help expand statewide.
The research was supported by the U.S. National Institutes of Health (R01DE032051), the Advanced Research Projects Agency for Health (D24AC00296) and the National Science Foundation (2107313, 1648449). The content herein is solely the responsibility of the authors and does not necessarily represent the official views of the funding organizations and institutions.
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This news release can be found online at news.rice.edu.
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Peer-reviewed paper:
Fit Pixels, Get Labels: Meta-learned Implicit Networks for Image Segmentation | The Medical Image Computing and Computer Assisted Intervention Society - MICCAI 2025 | DOI: 10.1007/978-3-032-04947-6_19
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