During winter months, a few days after the full moon, thousands of fish make their way to the warm tropical waters off the west coast of Little Cayman, Cayman Island. Nassau Grouper are typically territorial and don’t interact often, but once per year, they gather in the same spot where they all spawn to carry on the tradition of releasing gametes, in the hopes that some of them will develop to adulthood and carry on the population.
Our guest this week is Janelle Layton, a Masters (and soon to be PhD) student in Dr. Scott Heppel’s lab in the Department of Fisheries, Wildlife, and Conservation Sciences. Janelle’s research focuses on this grouper, which is listed as near threatened under the Endangered Species Act. Overfishing has been the largest threat to Nassau Grouper populations, but another threat looms: warming waters due to climate change. This threat is what Janelle is interested in studying – how does the warming water temperature affect the growth and development of grouper larvae?
Each winter Janelle travels to this aggregation site in the Cayman Islands, where these large groups of grouper (grouper groups?) aggregate for a few days to reproduce. During this time, she collects thousands of fertilized Nassau Grouper eggs to take back to the lab and study. These eggs will develop in varying water temperatures for 6 days, where each day a subset of samples are preserved for future analysis.
So far, Janelle is finding that the larvae raised in higher temperatures tend to demonstrate not only an increase in mortality, but an increase in variability in mortality. What does this mean? Basically, eggs from some females are able to survive and develop under these stressful conditions better than eggs from other females – so is there a genetic component to being able to survive these temperature increases?
The answer may lie in proteins
Aside from development and mortality, Janelle is investigating this theory by measuring the expression of heat shock proteins in the fertilized eggs and larvae. Heat shock proteins are expressed in response to environmental stressors such as increased temperatures, and can be measured through RNA sequencing. The expression of these proteins might hold the key to understanding why some grouper are more likely to survive than others. Janelle’s work is a collaborative effort between Oregon State University, Scripps Institute of Oceanography, Reef Environmental Education Foundation and the Cayman Islands Department of Environment.
To learn more about Nassau Grouper, heat shock proteins, and what it’s like being a Black woman in marine science, tune into Janelle’s episode this upcoming Sunday, March 12th at 7 PM! Be sure to listen live on KBVR 88.7FM, or download the podcast if you missed it. You can also catch Janelle on TikTok or at her website.
This week we have a Fisheries and Wildlife Master’s student and ODFW employee, Gabriella Brill, joining us to discuss her research investigating the impact of dams on the movement and reproduction habits of the White Sturgeon here in Oregon. Much like humans, these fish can live up to 100 years and can take 25 years to fully mature. But the similarities stop there, as they can also grow up to 10 ft long, haven’t evolved much in 200 million years, and can lay millions of eggs at a time (makes the Duggar family’s 19 Kids and Counting not seem so bad).
Despite being able to lay millions of eggs at a time, the White Sturgeon will only do so if the conditions are right. This fish Goldilocks’ its way through the river systems, looking for a river bed that’s just right. If it doesn’t like what it sees, the fish can just choose not to lay the eggs and will wait for another year. When the fish don’t find places they want to lay their eggs, it can cause drastic changes to the overall population size. This can be a problem for people whose lives are intertwined with these fish: such as fishermen and local Tribal Nations (and graduate students).
The white sturgeon was once a prolific fish in the Columbia River and holds ceremonial significance to local Tribal Nations, however, post-colonialization a fishery was established in 1888 that collapsed the population just four years later in 1892. Due to the long lifespan of these fish, the effects of that fishery are something today’s populations have still not fully recovered from.
Can you hear me now
Gabriella uses sound transmitters to track the white sturgeon’s movements. Essentially, the fish get a small sound-emitting implant that is picked up by a series of receivers – as long the receivers don’t get washed away by a strong current. By monitoring the fish’s journey through the river systems, she can then determine if the man-made dams are impacting their ability to find a desirable place to lay eggs.
Journey to researching a sturgeon’s journey
Gabriella always gravitated towards ecology due to the ways it blends many different sciences and ideas – and Fish are a great system for studying ecology. She started with studying Salmon in undergrad which eventually led to a position with the ODFW. Working with the ODFW inspired her to get a Master’s degree so that she could gain the necessary experience and credentials to be a more effective advocate for changes in conservation efforts that are being made. One way to get clout in the fish world: study a highly picky fish with a long life cycle. Challenge accepted.
To hear more about these finicky fish be sure to listen live on Sunday February 26th at 7PM on 88.7FM, or download the podcast.
This week we have a robotics PhD student, Everardo Gonzalez, joining us to discuss his research on coordinating robots with artificial intelligence (AI). That doesn’t mean he dresses them up in matching bow ties (sadly), but instead he works on how to get a large collective of robots, also called a swarm, to work collectively towards a shared goal.
Why should we care about swarming robots?
Aside from the potential for an apocalyptic robot world domination, there are actually many applications for this technology. Some are just as terrifying. It could be applied to fully automated warfare – reducing accountability when no one is to blame for pulling the trigger (literally).
However, it could also be used to coordinate robots used in healthcare and with organizing fleets of autonomous vehicles, potentially making our lives, and our streets, safer. In the case of the fish-inspired Blue Bots, this kind of coordinated robot system can also help us gather information about our oceans as we try to resolve climate change.
Having a group of intelligent robots behaving intelligently sounds like it’s a problem of quantity, however, it’s not that simple. These bots can also suffer from there being “too many cooks in the kitchen”, and, if all bots in the swarm are intelligent, they can start to hinder each other’s progress. Instead, the swarm needs both a few leader bots, that are intelligent and capable of learning and trying new things, along with follower bots, which can learn from their leader. Essentially, the bots play a game of “Follow the Leaders”.
All robots receive feedback with respect to a shared objective, which is typical of AI training and allow the bots to infer which behaviors are effective. In this case, the leaders will get additional feedback on how well they are influencing their followers.
Unlike social media, one influencer with too many followers is a bad thing – and the bots can become ineffective. There’s a famous social experiment in which actors in a busy New York City street stopped to stare at a window to determine if strangers would do the same. If there are not enough actors staring at the window, strangers are unlikely to respond. But as the number of actors increases, the likeness of a stranger stopping to look will also increase. The bot swarms also have an optimal number of leaders required to have the largest influence on their followers. Perhaps we’re much more like robots than the Turing test would have us believe.
Dot to dot
We’re a long way from intelligent robot swarms, though, as Everardo is using simplified 2D particle simulations to begin to tackle this problem. In this case the particles replace the robots, and are essentially just dots (rodots?) in a shared environment that only has two dimensions. The objectives or points of interest for these dot bots are more dots! Despite these simplifications, translating system feedback into a performance review for the leaders is still a challenging problem to solve computationally. Everardo starts by asking the question “what if the leader had not been there”, but then you have to ask “what if the followers that followed that leader did something else?” and then you’ve opened a can of worms reminiscent of Smash Mouth where the “what if”’s start coming and they don’t stop coming.
What if you wanted to know more about swarming robots? Be sure to listen live on Sunday February 26th at 7PM on 88.7FM, or download the podcast if you missed it. To learn a bit more about Everardo’s work with swarms and all things robotics, check out his portfolio at everardog.github.io.
This week we have a MS (but soon to be PhD) student from the department of Fisheries and Wildlife, Charles Nye, joining us to discuss their work examining the dietary and environmental DNA of whales. So that begs the question – how exactly does an environment, or a diet, have DNA? Essentially, the DNA of many organisms can be isolated from samples of ocean water near the whales, or in the case of dietary DNA, can be taken from the whales’ fecal matter – that’s right, there’s a lot more you can get from poop than just an unpleasant smell.
Why should we care about what whales eat?
As the climate changes, so too does the composition of creatures and plants in the oceans. Examining environmental DNA gives Charles information on the nearby ecological community – which in turn gives information about what is available for the whale to eat plus what other creatures they may be in resource competition with. He is working to identify the various environmental DNA present to assist with conservation efforts for the right whale near Cape Cod – a whale that they hold as dear to their hearts on the East Coast as the folks of Depoe Bay hold the grey whale to theirs.
By digging into the whale poop to extract dietary DNA, Charles can look into how the whales’ diets shift over seasonal and yearly intervals – and he is doing precisely that with the West Coast grey whales. These dietary shifts may be important for conservation purposes, and may also be applied to studying behavior. For example, by looking at whether or not there are sex differences in diet and asking the ever-important question: do whales also experience bizarre pregnancy cravings?
How does someone even get to study whales?
Like many careers, it starts with an identity crisis. Charles originally thought they’d go into scientific illustration, but quickly realized that they didn’t want to turn a hobby he enjoyed into a job with deadlines and dread. A fortunate conversation with his ecology professor during undergrad inspired him to join a research lab studying intertidal species’ genetics – and eventually become a technician at the Monterey Bay Aquarium Research Institute.
After a while, simply doing the experiments was not enough and they wanted to be able to ask his own questions like “does all the algae found in a gray whale’s stomach indicate they may actually be omnivores, unlike their carnivorous whale peers?” (mmm, shrimp).
Turns out, in order to study whales all you have to do is start small – tiny turban snail small.
Excited for more whale tales? Us too. Be sure to listen live on Sunday, February 5th at 7PM on 88.7FM, or download the podcast if you missed it. Want to stay up to date with the world of whales and art? Follow Charles @thepaintpaddock on Twitter/Instagram for his art or @cnyescienceguy on Twitter for his marine biology musings.
You probably already know that skim milk and buttermilk are byproducts of cheese-making. But did you know that whey is another major byproduct of the cheese-making process? Maybe you did. Well, did you know that for each 1 kg of cheese obtained, there are about 9 kg of whey produced as a byproduct?! What in the world is done with all of that whey? And what even is whey? In this week’s episode, Food Science Master’s student Alyssa Thibodeau tells us all about it!
Whey is the liquid that remains after milk has been curdled and strained to produce cheese (both soft and hard cheeses) and yoghurt. Whey is mainly water but it also has lots of proteins and fats, as well as some vitamins, minerals, and a little bit of lactose. There are two types of whey: acid-whey (byproduct of yoghurt and soft cheese production) and sweet-whey (byproduct of hard cheese production). Most people are probably familiar with whey protein, which is isolated from whey. The whey protein isolates are only a small component of the liquid though and unfortunately the process of isolating the proteins is very energy inefficient. So, it is not the most efficient or effective way of using the huge quantities of whey produced. This is where Alyssa comes in. Alyssa’s research at OSU is focused on trying to develop a whey-beverage. Because of the small amounts of lactose that are in whey, yeast can be used to ferment the lactose, creating ethanol. This ethanol can then be converted by bacteria to acetic acid. Does this process sound a little familiar? It is! A similar process is involved when making kombucha and the end-product in Alyssa’s mind isn’t too far off of kombucha. She envisions creating an organic, acid-based or vinegar-type beverage from whey.
How does one get into creating the potentially next-level kombucha? Alyssa’s route to graduate school has been backwards, one that most students don’t get to experience. While the majority of students get a degree, get a job and then start a family, Alyssa started a family, got a job, and then went to graduate school. On top of being a single mother in graduate school, she is also a first-gen student and Hispanic. To quote Alyssa: “It makes me proud every day that I am able to go back to school as a single mom. In the past, this would have maybe been too hard to do or wouldn’t have been possible for older generations but our generations are progressing and people are making decisions for themselves.”.
Intrigued by Alyssa’s research and personal journey? You can hear all about it on Sunday, January 29th at 7 pm on https://kbvrfm.orangemedianetwork.com/. Missed the live show? You can listen to the recorded episode on your preferred podcast platform!
Correlation does not equal causation. This phrase gets mentioned a lot in science. In part, because many scientists can fall into the trap of assuming that correlation equals causation. Proof that this phrase is true can be found in ice cream and sharks. Monthly ice cream sales and shark attacks are highly correlated in the United States each year. Does that mean eating lots of ice cream causes sharks to attack more people? No. The likely reason for this correlation is that more people eat ice cream and get in the ocean during the summer months when it’s warmer outside, which explain why the two are correlated. But, one does not cause the other. Correlation does not equal causation.
To date, much of the research that has been conducted on LGBTQ+ health has been correlational. Our guest this week, Kalina Fahey, hopes that her dissertation project will play a part in changing this paradigm as she is trying to get more at causation. Kalina is a 5th year PhD candidate in the School of Psychological Science working with her advisors Drs. Anita Cservenka and Sarah Dermody. Her research broadly investigates LGBTQ+ health disparities and how stress impacts health in LGBTQ+ groups. She is also interested in understanding ways in which spiritual and/or religious identities can influence stress, and thereby, health. To do this, Kalina is employing a number of methods, including undertaking a systematic review to synthesize the existing research on substance use in transgender youth, analyzing large-scale publicly available datasets to look at how religious and spiritual identity relates to health outcomes, and finally developing a safe experiment to look at how specific forms of stress impact substance use-related behaviors in real time.
Most of Kalina’s time at the moment is being spent on the experimental portion of her research as part of her dissertation. For this study, Kalina is adapting the personalized guided induction stress paradigm, with the aim of safely eliciting minor stress responses in a laboratory setting. The experiment involves one virtual study visit and two in-person sessions. During the first visit, participants are asked to describe a minority-induced stressful event that occurred recently, as well as a description of a moment or situation that is soothing or calming. After this session, Kalina and her team develop two meditative scripts – one each to recreate the two events or moments described by the participant. When the participant comes back for their in-person sessions, they listen to one of two different meditative scripts and are asked a series of questions regarding their stress levels. Kalina and her team also are collecting saliva and heart rate readings to look at physiological stress levels. This project is still looking for participants. If you are a sexual-minority woman who drinks alcohol, consider checking out the following website to learn more about the study: https://oregonstate.qualtrics.com/jfe/form/SV_8e443Lq10lgyX66?fbclid=IwAR3XOdECIOvCbx1xn3QA5rrCtHfSezZrR5Ppkpnd9sx1SsicZRQnfYHAqb8. Kalina hopes to continue experiment-based research on LGBTQ+ health disparities in the future as she sees the lack of experimental studies to be a major gap in better understanding, and thereby supporting, the LGBTQ+ community.
Interested in learning more about Kalina’s research, the results, and her background? Listen live on Sunday, January 15, 2023 at 7 PM on 88.7 KBVR FM. Missed the live show? You can download the episode on our Podcast Pages! Also, check out her other work here or finder her on Twitter @faheypsych
Puffy snout syndrome: though it has a cute-sounding name, this debilitating condition causes masses on the face of Scombridae fish (a group of fish that includes mackerel and tuna.) Fish afflicted with puffy snout syndrome (PSS) develop excessive collagenous tumor-like growths around the eyes, snout, and mouth. This ultimately leads to visual impairment, difficulty feeding, and eventual death. PSS is surprisingly confined to just fish raised in captivity – those in aquaculture farms or aquariums, for example. Unfortunately, when PSS is identified in aquaculture, the only option is to cull the entire tank — no treatments or cures currently exist.
PSS was first identified in the 1950s, in a fish research center in Honolulu, Hawaii. Since then, there have only been 9 publications in the scientific literature documenting the condition and possible causes, although the fish community has come to the conclusion that PSS is likely a transmittable condition with an infectious agent as the cause. But despite this conclusion, there’s been no success so far in identifying such a cause – tests for parasites, bacterial growth, and viruses have come up empty-handed. That was until a 2021 paper, using high-resolution electron microscopy, found evidence of viral particles in facial tissues taken from Pacific mackerel. Suddenly, there was a lead: could PSS be caused by a virus that we just don’t have a test for yet?
Putting Together the Pieces
To investigate this hypothesis, this week’s guest Savanah Leidholt (a co-author of the 2021 microscopy study) is using an approach for viral detection known as metatranscriptomics. Leidholt, a fourth year PhD candidate in the Microbiology department, sees this complex approach as a sort of puzzle: “Your sample of RNA has, say, 10 giant jigsaw puzzles in it. But the individual puzzles might not be complete, and the pieces might fit into multiple places, so your job is to reassemble the pieces into the puzzles in a way that gives you a better picture of your story.”
RNA, or ribonucleic acid, is a nucleic acid similar to DNA found in all living organisms, But where DNA is like a blueprint – providing the code that makes you, you; RNA is more like the assembly manual. When a gene is expressed (meaning the corresponding protein is manufactured), the double-stranded DNA is unwound and the information is transcribed into a molecule called messenger RNA. This single-stranded mRNA is now a copy of the gene that can be translated into protein. The process of writing an mRNA copy of the DNA blueprint is called transcription, and these mRNA molecules are the target of this metatranscriptomics approach, with the prefix “meta” meaning all of the RNA in a sample (both the fish RNA and the potential viral RNA, in this case) and the suffix “omics” just referring to the fact that this approach happens on a large scale (ALL of the RNA, not just a single gene, is sequenced here!) When mRNA is sequenced in this manner, the researchers can then conclude that the gene it corresponds to was being expressed in the fish at the time the sample was collected.
So far, Leidholt has identified some specific genes in fish that tend to be much more abundant in fish from captive settings versus those found in the wild. Could these genes be related to why PSS is only seen in fish in captivity? It’s likely – the genes identified are immune markers, and the upregulation of immune markers is well-known to be associated with chronic stress. Think about a college student during finals week – stress is high after a long semester, maybe they’ve been studying until late in the night and not eating or sleeping well, consuming more alcohol than is recommended. And then suddenly, on the day of the test, they’re stuck in bed with the flu or a cold. The same thing can happen to fish (well, maybe not the part where they take a test!,) especially in captivity – Pacific mackerel, tuna, and other scombrid species susceptible to PSS are fairly large, sometimes swimming hundreds of miles in a single day in the ocean. But in captivity, they are often in very small tanks, constantly swimming in constrained circles. They’re not exposed to the same diversity of other fish, plankton, prey, and landscape as they would be in the wild. “Captivity is a great place to be if you’re a pathogen, but not great if you’re a fish”, says Leidholt.
The results of Leidholt’s study are an exciting step forward in the field of PSS research, as one of the biggest challenges currently facing aquaculture farms and aquariums is that there is no way to screen for PSS in healthy fish before symptoms begin to show. Finding these marker genes that appear in fish that could later on develop PSS means that in the future a test could be developed. If vulnerable fish could be identified and removed from the population before they begin to show symptoms and spread the condition, then it would mean fish farmers no longer have to cull the entire tank when PSS is noticed.
The elusive virus
One of the challenges that remains is going beyond the identification of genes in the fish and beginning to identify viruses in the samples. Viruses, which are small entities made up of a DNA or RNA core and a protective protein coating, are thought to be the most abundant biological entities on the planet Earth – and the smallest in terms of size. They usually get a bit of a bad reputation due to their association with diseases in humans and other animals, but there are also viruses that play important positive roles in their ecosystems – bacteriophages, for example, are viruses that infect bacteria. In humans, bacteriophages can attack and invade pathogenic or antibiotic-resistance bacteria like E. coli or S. aureus (for more information on phages and how they are actually studied as a potential therapy for infections, check out this November 2021 interview with Miriam Lipton!) Across the entire planet there are estimated to be between 10^7 to 10^9 distinct viral species – that’s between 10 million and 10 billion different species. And fish are thought to host more viruses than any other vertebrate species. Because of technological advancements, these viral species have only really been identified very recently, and identification still poses a significant challenge.
As a group, viruses are very diverse, so one of the challenges is finding a reliable way to identify them in a given sample. For bacteria, researchers can use a marker gene called the 16S rRNA gene – this gene is found in every single bacterial cell, making it universal, but it also has a region of variability. This region of variability allows for identification of different strains of bacteria. “Nothing like 16S exists for viruses,” Leidholt says. “Intense sequencing methods have to be used to capture them in a given sample.” The metatranscriptomic methods that Leidholt is using should allow her to capture elusive viruses by taking a scorched earth approach – targeting and sequencing any little bit of RNA in the sample at all, and trying to match up that RNA to a virus.
To learn more about Savanah’s research on puffy snout syndrome, her journey to Oregon State, and the amazing outreach she’s doing with high school students in the Microbiology Department, tune in to Inspiration Dissemination on Sunday, November 20th at 7 PM Pacific!
Machines take me by surprise with great frequency. – Alan Turing
This week we have a PhD student from the College of Engineering and advised by Dr. Maude David in Microbiology, Nima Azbijari, to discuss how he uses machine learning to better understand biology. Before we dig in to the research, let’s dig into what exactly machine learning is, and how it differs from artificial intelligence (AI). Both AI and machine learning learn patterns from data they are fed, but the difference is that AI is typically developed to be interacted with and make decisions in real time. If you’ve ever lost a game of chess to a computer, that was AI playing against you. But don’t worry, even the world’s champion at an even more complex game, Go, was beaten by AI. AI utilizes machine learning, but not all machine learning is AI. Kind of like how a square is a rectangle, but not all rectangles are squares. The goal of machine learning is to use data to improve at tasks using data it is fed.
So how exactly does a machine, one of the least biological things on this planet, help us understand biology?
Ten years ago it was big news that a computer was able to recognize images of cats, but now photo recognition is quite common. Similarly, Nima uses machine learning with large sets of genomic (genes/DNA), proteomic (proteins), and even gut microbiomic data (symbiotic microbes in the digestive track) to then see if the computer can predict varying patient outcomes. By using computational power, larger data sets and the relationships between the varying kinds of data can be analyzed more quickly. This is great for both understanding the biological world in which we live, and also for the potential future of patient care.
How exactly do you teach an old machine a new trick?
First, it’s important to note that he’s using a machine, not magic, and it can be massively time consuming (even for a computer) to do any kind of analysis on every element of a massive set. Potentially millions of computations, or even more. So to isolate only the data that matters, Nima uses graph neural networks to extrapolate the important pieces. Imagine if you had a data set about your home, and you counted both the number of windows and the number of blinds and found that they were the same. Then you might conclude that you only need to count windows, and that counting blinds doesn’t tell you anything new. The same idea works with reducing data into only the components that add meaning.
The phrase ‘neural network’ can invoke imagery of a massive computer-brain made of wires, but what does this neural network look like, exactly? The 1999 movie The Matrix borrowed its name from a mathematical object which contains columns and rows of data, much like the iconic green columns of data from the movie posters. These matrices are useful for storing and computing data sets since they can be arranged much like an excel sheet, with columns for each patient and rows for each type of recorded data. He (or the computer?) can then work with that matrix to develop this neural network graph. Then, the neural network determines which data is relevant and can also illustrate connections between the different pieces of data. Much like how you might be connected to friends, coworkers, and family on a social network, except in this case, each profile is a compound or molecule and the connections can be any kind of relationship, such as a common reaction between the pair. However, unlike a social network, no one cares how many degrees from Kevin Bacon they are. The goal here isn’t to connect one molecule to another but to instead identify unknown relationships. Perhaps that makes it more like 23 and Me than Facebook.
Nima is using machine learning to discover previously unknown relationships between various kinds of human biological data such as genes and the gut microbiome. Now, that’s a machine you don’t need to rage against.
Excited to learn more about machine learning? Us too. Be sure to listen live on Sunday November 13th at 7PM on 88.7FM, or download the podcast if you missed it. And if you want to stay up to date on Nima’s research, you can follow them on Twitter.
Coral reef ecosystems offer a multitude of benefits, ranging from coastline protection from storms and erosion to a source of food through fishing or harvest. In fact, it is estimated that over half a billion people depend on reefs for food, income, and/or protection. However, coral reefs face many threats in our rapidly changing world. Climate change and nutrient input due to run-off from land are two stressors that can affect coral health. How exactly do these stressors impact corals? This week’s guest Alex Vompe is trying to figure that out!
Alex is a 4th year PhD candidate in the Department of Microbiology at OSU, where he is co-advised by Dr. Becky Vega-Thurber and Dr. Tom Sharpton. The goal of Alex’s research is to understand how coral microbe communities change over time and across various sources of stress. While the microbial communities of different coral species can differ, typically under normal, non-stressed conditions, they look quite similar. However, once exposed to a stressor, changes start to arise in the microbial community between different coral species, which can have different outcomes for the coral host. This pattern has been coined the ‘Anna Karenina principle’ whereby all happy corals are alike, however as soon as things start to go wrong, corals suffer differently.
Alex is testing how this Anna Karenina principle plays out for three different coral species (Acropora retusa, Pocillopora verrucosa [also known as cauliflower coral], Porites lobata [also known as lobe coral]) in the tropical Pacific Ocean. The stressors that Alex is investigating are reduction in herbivory and introduction of fertilizer. A big source of stress for reefs is when fish populations are low, which results in a lack of grazing by fish on macroalgae. In extreme situations, macroalgae can overgrow a coral reef completely and outcompete it for light and resources. Fertilizers contain a whole host of nutrients with the intent of increasing plant growth and production on land. However, these fertilizers run-off from land into aquatic ecosystems which can often be problematic for aquatic flora and fauna.
How is Alex testing the effects of these stressors on the corals? He is achieving this both in-situ and in the lab. Alex and his lab conduct field work on coral reefs off the island of Moorea in French Polynesia. Here, they have set up experimental apparatus in the ocean on coral reefs (via scuba diving!) to simulate the effects of reduced herbivory and fertilizer introduction. This field work is conducted three times a year. When not under the water surface, Alex sets up aquaria experiments on land in Moorea using coral fragments, which he has been able to grow in order to investigate the microbial communities more closely. These samples then get processed in the lab at OSU for genomic analysis and Alex uses bioinformatics to investigate the coral microbiome dynamics.
Curious to know more about Alex’s research? Listen live on Sunday, October 23, 2022 at 7 PM on KBVR 88.7FM. Missed the live show? You can download the episode on our Podcast Pages! Also, feel free to follow Alex on Twitter (@AVompe) and Instagram (@vompedomp) to learn more about him and his research.
Around 80,000 years ago, the Earth was in the middle of the late Pleistocene era, and much of Canada and the northern part of the United States was blanketed in ice. The massive Laurentide Ice Sheet covered millions of square miles, and in some places, up to 2 miles thick. Over vast timescales this ice sheet advanced its way across the continent slowly, gouging out what we now know as the Great Lakes, carving the valleys, depositing glacial tills, and transforming the surface geology of much of the southern part of Canada and northern US. Further west, the Cordilleran ice sheet stretched across what is now Alaska, British Columbia, and the northern parts of the Western US, compressing the ground under its massive weight. As these ice sheets depressed the land beneath them, the Earth’s crust bulged outwards, and as the planet warmed and the ice sheets began to melt, the pressure was released, returning the crust underneath to its previous shape. As this happened, ocean water flowed away, resulting in lower sea levels locally, but higher levels across the other side of the planet.
The effects of massive bodies of ice forming, moving, and melting are far from negligible in their impact on the overall geology of the region, the sea level throughout history, and the patterns of a changing climate. Though there are only two ice sheets on the planet today, deducing the ancient patterns and dynamics of ice sheets can help researchers fill the geological record and even make predictions about what the planet might look like in the future. Our guest on Inspiration Dissemination this week is PhD candidate and researcher Schmitty Thompson, of the Department of Geology in CEOAS. Thompson is ultimately trying to answer questions about ice distribution, sea levels, and other unknown parameters that the geologic record is missing during two different ice age warming periods. Their research is very interdisciplinary – Thompson has degrees in both math and geology, and also uses a lot of data science, computer science, and physics in their work. They are using computer modeling to figure out just what the shorelines looked like during this time period around 80,000 years ago.
“I use models because the geologic record is pretty incomplete – the further back you go, the less complete it is. So by matching my models to the existing data, we can then infer more information about what the shoreline was like,” they explain. To do this accurately, Thompson feeds the model what the ice sheets looked like over the course of around 250,000 years. They also need to incorporate other inputs to the model to get an accurate picture – variables such as the composition of the interior of the Earth, the physics of Earth’s interior, and even the ice sheets’ own gravitational pull (ice sheets are so massive they exert a gravitational pull on the water around them!)
Using math to learn about ice
The first equation to describe global changes in sea level was published in 1976, with refining throughout the 90s and early 2000s. Thompson’s model builds on these equations in two versions: one which can run in about 10 minutes on their laptop, and another which can take multiple weeks and must run on a supercomputer. The quicker version uses spherical harmonics as the basis function for the pseudospectral formulation, which is basically a complex function that does math and incorporates coefficient representations of the earth’s radius, meridional wave numbers, variation across north/south and east/west, and a few other variables. The short of it is that it can perform these calculations across a 250k time span relatively quickly, but it makes assumptions about the homogeneity of the earth’s crust and mantle viscosity. Think of it like a gumball: a giant, magma-filled gumball with a smooth outer surface and even layers. So while this method is fast, the assumptions that it makes means the output data is limited in its usefulness. When Thompson needs a more accurate picture, they turn to collaborators who are able to run the models on a supercomputer, and then they work with the model’s outputs.
While the model is useful for filling in gaps in the historical record, Thompson also points out that it has uses in predicting what the future will look like in the context of a changing climate. After testing out these models and seeing how sensitive they are, they could be used by researchers looking at much smaller time scales and more sensitive constraints for current and future predictions. “There are still lots of open questions – if we warm the planet by a few degrees, are we going to collapse a big part of Antarctica or a small part? How much ice will melt?”
To learn more about ice sheets, sea levels, and using computer models to figure out how the shoreline looked thousands of years ago, tune in to Schmitty Thompson’s episode on Inspiration Dissemination this upcoming Sunday evening at 7 PM PST. Catch the show live by streaming on https://kbvrfm.orangemedianetwork.com/, or check out the show later wherever you get your podcasts!
Thompson was also recently featured on Alie Ward’s popular podcast Ologies. You can catch up with all things geology by checking out their episode here.