Wildfires are capable of altering landscapes, devastating forests, and communities, and are increasing in frequency and intensity. However, the hazard extends well past the burn phase as burn scars are at high risk for the generation of debris flows and flooding in the days, weeks, and years after a fire. Conconully, WA, experienced severe post-fire debris flows and flooding in 2022 following the 2021 Muckamuck fire. Witnessing the effects of both the fire and the subsequent debris flow on my community motivated me to explore why these events occur, to investigate how they initiate, and help inform future warnings or mitigation strategies to increase resilience in the face of these hazards. Whether these floods and debris flows were initiated by shallow landslides, or storm runoff remains unknown, and could alter how post-fire hazard or evacuation warnings are issued. In this study, we employ geospatial analysis to identify areas affected by the fire and correlate these with the origins of the debris flows and flooding. Additionally, we use meteorological data and historical records of similar incidents over the past century, to identify thresholds for flooding initiation both before and after fires. By focusing on this topic, we hope to shed light on the long-term consequences of fires on communities and initiate a dialogue about the ongoing risks they face.

During embryonic development, gene expression is temporally and spatially coordinated to control organogenesis and fetal growth. We previously identified a subset of 140 genes that conferred lethal and sub-viable phenotypes in mice and are likely to be haploinsufficient in humans. These genes presumptively play essential roles in fetal development, but their function is unknown. I aim to uncover the role of these genes in mouse embryonic development using Weighted Gene Co-Expression Analysis (WGCNA). Co-expression analysis will be conducted on mouse embryonic stem cell RNA sequencing data obtained at three different stages of in vitro differentiation and across two different genetic backgrounds, creating a subset of nine samples encompassing 12555 genes. Choosing three different time points allows us to see how expression of our genes of interest changes over time, and choosing two different genotypes (wild type and knock-in) allows us to investigate if expression changes due to a single point mutation. We performed dynamic clustering on this RNA sequencing data to identify co-expressed gene clusters. I will map these gene clusters to biological pathways to make inferences about which cellular processes, metabolic functions, or structural components the genes of interest are involved in. This may indicate the role of these genes in fetal development and help reveal why fetal viability is compromised. In future studies, the functional characterization of these genes will generate new ideas and hypotheses about the basis of genetic disease.

The El Niño-Southern Oscillation (ENSO) is the most significant year-to-year climate variation, affecting weather and climate systems worldwide. However, current prediction models, both dynamic and statistical, struggle with accuracy due to the complex mechanism of ENSO. This study introduces a regional temperature and salinity prediction method using a Long Short-Term Memory (LSTM) deep learning model, which is well-suited for identifying long-term patterns in sequential data. The model is applied to three specific regions using in-situ data from Argo floats: the central-eastern Pacific, the central tropical Pacific Niño 3.4 region, and the Western Pacific Warm Pool (WPWP). These regions are chosen because they play key roles in ENSO dynamics. Results show that the LSTM model performs best in the WPWP, where the average mean squared error (MSE) is low (0.03), indicating high accuracy and stability. This is likely due to lower noise in the original data. In contrast, the model performs poorly in the central-eastern Pacific, where the average MSE is much higher (7.03), suggesting instability due to high noise in original data. These findings highlight the potential of deep learning for regional climate predictions and suggest that LSTM models could improve local weather forecasting and fisheries management.

Pseudomonas aeruginosa commonly colonizes cystic fibrosis (CF) lungs, causing persistent infections even under novel CFTR modulator therapies such as elexacaftor-tezacaftor-ivacaftor (ETI). While antibiotic resistance and patient-specific factors partly explain this persistence, bacterial adaptation to post-ETI conditions likely plays a critical role. Previous findings of functional shifts in bacterial variants point to underlying genotypic changes, yet the genomic basis for P. aeruginosa’s persistence remains insufficiently defined. This work aims to identify the genetic adaptations enabling P. aeruginosa to persist in CF lungs despite the improved airway environment afforded by ETI. We developed a method combining temporal allele frequency shifts and cross-patient recurrence to identify selection. My preliminary analysis revealed algG, a gene involved in alginate biosynthesis, as a promising candidate showing multiple signatures of positive selection. First, algG mutations increased in frequency across two-thirds of sampled individuals. Second, the phylogenetic analysis demonstrated the parallel evolution of algG mutations within individual hosts. Third, statistical testing showed significant enrichment for non-synonymous mutations in algG, indicating protein-altering changes are favored. I am extending this work by developing null models to quantify the significance of observed parallel evolution both within and between hosts, and using protein structural prediction to evaluate the functional impact of identified mutations. This research provides novel insights into bacterial adaptation mechanisms during CF treatment and may guide the development of more effective therapies targeting P. aeruginosa persistence. The findings will enhance our understanding of pathogen evolution within human hosts and have implications for improving treatment outcomes for CF patients. 

Landslides are one of the main agents of erosion in wet and mountainous regions and can have a long-lasting impact on the landscape. In the Puget Lowland of Washington, landslides are prevalent, especially along steep coastal bluffs. Despite their common occurrence, their triggers are often unknown. In particular, their connection to strong shaking from seismically active faults versus precipitation events is an outstanding problem. The Southern Whidbey Island Fault (SWIF) stretches from Victoria B.C. across Puget Sound into the mainland near Woodinville. The SWIF has produced at least four earthquakes since the last ice age, with the most recent occurring less than 2,700 years ago, evidencing its capability of generating an earthquake up to M7.5. This work quantifies the area, extent, landslide type, roughness of the surface (as a proxy for age), and location distribution of landslides along the coastal bluffs of Whidbey Island. Our ultimate goal is to understand possible links between the landslide inventories in the coastal Whidbey Island area and the activity of the SWIF. Using high-resolution LIDAR elevation data (3 m) we perform a series of topographic analyses using GIS and Python tools to establish a landslide chronology. We use the Ledgewood-Bonair Landslide triggered by a rainstorm in 2013, as a spatial and temporal reference to calibrate our analysis. Our results will shed light on the dynamics of coastal landslides, the feedback between landslide preservation, wave and tidal erosion, and hillslope processes. This study advances our knowledge of cascading hazards from the SWIF and informs risk assessment for the region.

Optimizing cell culture methods for marine invertebrates has proven to be challenging, with only a few immortal cell lines available compared to the thousands that exist for vertebrates. Botryllus schlosseri, a colonial tunicate, is native to the Mediterranean Sea and found within marinas along U.S. coasts and other temperate locations worldwide. In addition to being a sister taxa to vertebrates, B. schlosseri undergoes whole-body regeneration regularly, making it a suitable candidate for cell culture development.The Gardell lab investigates the effects of media formulation on epithelial cell proliferation and longevity. Previously, our lab established a media formulation made of DMEM, FBS, Pen Strep, Gentamicin, Amphotericin B, and Sea Salt as resources for cell growth. Wild colonies of B. schlosseri were collected from local marinas followed by microdissection of their zooid and buds for seeding in vitro. Results from utilizing this formulation showed consistently low cell growth; ranging from an average of ~10 to ~50 cells per seeded tissue within a 5 day period. To promote cell proliferation, we explored modifying the media formulation using various ratios of complete media to seawater with similar total osmolality. By diluting the media with seawater, this simulates a similar environment that B. schlosseri regularly reproduces and replicates in. The results indicated that dilutions of 75% Media with 25% Seawater, and 50% Media with 50% Seawater yielded the most consistent growth and highest cell production within a 5 day period. Given this outcome, continued replication of cell culture with this media formulation is required to ensure consistency of results across B. schlosseri genotypes.  Once medium conditions are optimized I will determine a total estimated cell count, which is necessary to perform a time course experiment that aims to characterize the gene and protein regulation of cells in vitro.

Submarine channels represent the offshore continuation of onshore rivers. The shape of submarine channels captures valuable information about changes on the seafloor caused by fault movement during earthquakes. Many submarine channel systems are observed at the Cascadia subduction zone off the coast of Washington and Oregon. The Cascadia subduction zone is a tectonically dynamic system that exhibits many faults which appear to interact with these channels. These interactions are analyzed by quantifying the shape, or morphology, of the Astoria submarine channel, the offshore continuation of the Columbia River. We quantify channel morphology in ArcGIS Pro and Python in order to answer the hypotheses that 1) channels incise deeper where they cross active faults and 2) channel width is not affected by faulting. Some of these measurements include channel width, depth, width-depth ratios, bank slope, bank angle, cross swath profiles, and longitudinal profile analysis. This will offer insight into the behavior and evolution of faulting at the Cascadia subduction zone and how this affects people living along the Pacific Northwest coast who are at risk of earthquakes and tsunamis.

In the Puget Sound region, some lowland lake ecosystems have been contaminated with metals from the former ASARCO copper smelter located in Ruston, WA. Arsenic, a toxic metalloid, has accumulated in various parts of lake environments from this contamination. Chinese Mystery Snails (CMS) are a ubiquitous freshwater snail species that feed on periphyton, an environmental compartment found to hyperaccumulate arsenic (Hull et al., 2023). This feeding could be a key entry point of arsenic into our food chain. Our research has utilized CMS to test the hypothesis that trophic transfer of arsenic occurs through consuming periphyton and their gut microbiome is altered as a result. To test this hypothesis, our lab conducted a feeding-based arsenic exposure with lab acclimated reference lake CMS. These CMS were either fed algae wafers (control) or periphyton obtained from a high arsenic concentration lake. Trophic transfer of arsenic and gut microbiome alterations were not observed in the food-based arsenic exposure. This led us to hypothesize that waterborne arsenic exposure is an important route for bioaccumulation in CMS, with arsenic concentration correlating to gut microbiome changes. To test this, we conducted a comparative waterborne experiment, exposing CMS to arsenic concentrations of 0, 20ppb and 200ppb. At the end of the exposure, 16S amplicon sequencing was performed on CMS gut contents to assess how the varying arsenic concentrations affect microbiome composition. Whole-body arsenic quantification was conducted using ICP-MS to determine the degree of arsenic bioaccumulation that occurs at different concentrations. 

This study investigates the physical mechanisms driving spatiotemporal variability of

barrier layers in the Western Tropical Pacific (WTP) along 149°E, with a specific focus on the

La Niña phase of the El Niño-Southern Oscillation (ENSO). Barrier layers, which separate the

surface mixed layer from the thermocline, regulate ocean-atmosphere interactions and influence

climate dynamics. This research assesses the relative contributions of freshwater input from

precipitation, and wind stress on barrier layer formation and thickness. Data were collected

during a research cruise in January 2025 aboard the R/V Thomas G. Thompson from an

Underway Conductivity Temperature and Density (UCTD) sensor for temperature profiles, and

public-source meteorological data for atmospheric conditions (ERA5). Seven stations, spaced

two degrees apart in latitude, were sampled along a transect from 4°N to 15°N. Each station

provided data to analyze barrier layer thickness, with spatiotemporal variability determined by

comparing different formation mechanisms across stations. Spearman Correlation analyses were

used to determine dominant factors influencing barrier layer thickness and variability. We found

that barrier layer thickness in the WTP shows a general positive but statistically insignificant

relationship with freshwater (ρ 0.32 and p-value 0.48), and a general negative but statistically

insignificant relationship with wind stress (ρ 0.18 and p-value 0.70). During La Niña conditions,

these effects are expected to drive variability, with thicker layers forming in regions of high

precipitation and weak wind stress. Increased freshwater input enhances stratification, while

strong wind stress likely promotes surface and subsurface mixing, leading to barrier layer

thinning. Understanding these dynamics has implications for improving ocean-atmospheric

interaction climate models in the tropical Pacific.