Seabirds are often used as markers of ecosystem health due to their heavy dependence on the base of the food chain. The Alcidae family consists of small, diving birds who feed exclusively within the water column and rely on the sea year-round. Because of this, many view alcids as the only “true” seabirds. A research apprenticeship at UW’s Friday Harbor Laboratories on the pelagic ecosystem, Pelagic Ecosystem Function, has observed all seabirds since 2004, with consistent data since 2013. Alcids have been largely ignored in previous Pelagic Ecosystem Function studies, as the Common Murre (Uria aalge) artificially inflates the alcid family data due to their high abundance within the San Juan Channel. Upon removal of this species, it is found that non-Common Murre alcids are declining at a higher rate than any other seabird family within the channel, with a near-linear decline since 2013. In order to investigate the leading drivers of population decline, variables regarding food availability and habitat were collected in the form of chlorophyll, photosynthetically active radiation, and sea surface temperature. Compelling correlations were found between non-Common Murre alcid density and photosynthetically active radiation, as well as between chlorophyll and sea surface temperature. The data presented here is important not only for the mitigation of local ecosystem degradation, but also due to the consistency with global trends of seabird populations.

Blood flow in microcirculation is a significant physiological parameter that reflects the adaptive response of organs to disease, trauma, and cancer. Although ultrasound Doppler imaging was previously unable to assess blood flow in the microvasculature (< 0.5 cm/sec), the introduction of microbubble contrast agents has removed this limitation. However, blood flow of the entire vascular tree is mixed together during imaging. We present a method that segments and visualizes the entire vascular tree, including capillary blood flow, larger sub-spatially resolved vasculature and larger vasculature (>50 µm). In this work, we present an approach that decomposes nonlinear Doppler acquisitions into different groups of velocity projections. We demonstrated the ability to segment these different levels of vasculature in a rat spinal cord injury model where the varying rates of low velocity microbubble decorrelations captured by our high frame rate acquisitions enable us to quantify microvascular blood flow. This approach overcomes limitations encountered in conventional imaging methods by removing tissue signal before Doppler processing by combining high-frame rate plane wave imaging, microbubble nonlinear pulse sequences, and Doppler segmentation of blood flow. Singular value decomposition was used to segment the nonlinear Doppler signal. Our results successfully illustrate the segmentation of lower velocity sub-resolution microvascular flow and higher velocity flow in larger vessels in a rat spinal cord injury model. We isolated low and mid-velocity flow in sub-resolution vasculature (<20 µm). We observed different spatial distribution and bolus kinetics between low- mid- and higher velocity Doppler projections. We are assessing the utility of these different blood flow features for the management of spinal cord injury and other applications (e.g. oncological).

Sea ice plays a significant role in the global climate, but the details of summer melt processes remain poorly resolved. Sustained low-wind events over the Arctic Ocean have been observed to correspond to high salinity and temperature stratification in sea-ice leads, consequently increasing lateral melt rates during summer. Such an effect is currently unaccounted for in leading climate models, and appropriate parameterization could potentially reduce error in projections. We used a wind reanalysis dataset produced by the National Center for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) to study wind patterns in the Arctic. We focused on daily variation in oceanic regions at latitudes above 65 degrees during Summer (May 1 - September 30) over 2010-2019. An index of low-wind events was extracted from the reanalysis data and these events were classified according to length of event and geographical extent. Our results suggest that low wind events are frequent throughout the summer, but are not equally distributed spatially across the basin. Low-wind events were compared to other climate data, including sea-ice extent, on a spatial and temporal basis. Our results were compared to climate model output and differences were examined. We propose a basic parameterization of the effect of low-wind events on sea ice melt, and suggest further collection of observational data to improve physical representation in climate models.

Monitoring marine mammals can help researchers observe the adverse effects of pollution and climate change in the ocean. Passive acoustic monitoring is one of the many ways researchers observe mammals, as most aquatic mammals communicate with sound. I utilize new object detection methods and vision-based neural networks to automatedly detect and observe marine life. First, I use annotated fin-whale acoustic data to produce spectrograms needed to train and test the neural network. The neural network is composed of two main parts: the feature extractor CNN and the object detector. A pretrained Convolutional Neural Network (CNN) is a neural network trained on over a billion images from Image.net, thus it “understands” how to look for features. Here, I use a pretrained CNN Resnet18 to extract important visual features from the spectrograms. I then change the last layers of the pretrained neural network to include You Only Look Once v2 (YOLO) model, which is an object detection model that classifies parts of an image into different categories. The resulting network should be able to take a spectrogram as input and identify which part of the image contains the fin-whale call (if any). The findings from this study offer a new way to detect fin-whale calls using underwater acoustic data.