{ "134616-21874600": { "datetime_modified":"20250410T101050", "datetime_start":"20250425T120000", "datetime_end":"20250425T140000", "has_end_time":1, "date_start":"2025-04-25", "date_end":"2025-04-25", "time_start":"12:00:00", "time_end":"14:00:00", "time_zone":"America\/Detroit", "event_title":"EEB Student Dissertation Defense - How fish and fisher behavior affect production in small-scale reef fisheries", "occurrence_title":"", "combined_title":"EEB Student Dissertation Defense - How fish and fisher behavior affect production in small-scale reef fisheries: Katrina Sky Munsterman", "event_subtitle":"Katrina Sky Munsterman", "event_type":"Workshop \/ Seminar", "event_type_id":"21", "description":"Title: How fish and fisher behavior affect production in small-scale reef fisheries\r\n\r\nSummary: Reef fisheries, which provide food and livelihoods for over one billion people, are in decline due to growing population demands and climate-driven habitat loss. As the challenges facing these social-ecological systems intensify, it is critical to understand the relationships between fishing communities and reef ecosystems. My dissertation draws on ecological and social science theory, quantitative modeling, field experiments, and ethnographic research to provide an insight into the role of fish and fishers\u2019 behavior in shaping small-scale reef fisheries. In my first study, I documented behavioral responses of fishers in The Bahamas to a Category 5 hurricane and the COVID-19 pandemic, highlighting key factors that influence resilience in the face of external shocks. In two subsequent studies, I investigated the use of artificial reefs to augment fisheries production in the Caribbean. I used an individual-based model to demonstrate how fish behavior and size structure alter production dynamics on artificial reefs. Next, I combined a decade of timeseries data on artificial reefs, empirical data on fish population dynamics, and production models to provide mechanistic evidence that artificial reefs enhance fisheries production. Together, my research provides scientific insight into community resilience and fisheries management strategies that can be used to help support small-scale fisheries and the communities who depend on them.", "occurrence_notes":null, "guid":"134616-21874600@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/134616", "building_id":"1000172", "building_name":"Rackham Graduate School (Horace H.)", "campus_maps_id":"71", "room":"East Conference Room", "location_name":"Rackham Graduate School (Horace H.)", "has_livestream":0, "cost":"", "tags":["Ecology & Biology","Biology","Biosciences","Bsbsigns","department of ecology and evolutionary biology","developmental biology","Discussion","Dissertation","ecology","Ecology And Evolutionary Biology","eeb","evolution","Free","Graduate School","Graduate Students","Museum - Herbarium","Museum - Zoology","biological science"], "website":"", "sponsors":[ { "group_name":"Ecology and Evolutionary Biology", "group_id":"1190", "website":"" }, { "group_name":"EEB Defenses", "group_id":"3821", "website":"" } ], "image_url":"https:\/\/events.umich.edu\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "styled_images":{ "event_thumb":"https:\/\/events.umich.edu\/media\/cache\/event_thumb\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "event_large":"https:\/\/events.umich.edu\/media\/cache\/event_large\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "event_large_2x":"https:\/\/events.umich.edu\/media\/cache\/event_large_2x\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "event_large_lightbox":"https:\/\/events.umich.edu\/media\/cache\/event_large_lightbox\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "group_thumb":"https:\/\/events.umich.edu\/media\/cache\/group_thumb\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "group_thumb_square":"https:\/\/events.umich.edu\/media\/cache\/group_thumb_square\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "group_large":"https:\/\/events.umich.edu\/media\/cache\/group_large\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "group_large_lightbox":"https:\/\/events.umich.edu\/media\/cache\/group_large_lightbox\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "event_large_crop":"https:\/\/events.umich.edu\/media\/cache\/event_large_crop\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "event_list":"https:\/\/events.umich.edu\/media\/cache\/event_list\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "event_list_2x":"https:\/\/events.umich.edu\/media\/cache\/event_list_2x\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "event_grid":"https:\/\/events.umich.edu\/media\/cache\/event_grid\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "event_grid_2x":"https:\/\/events.umich.edu\/media\/cache\/event_grid_2x\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "event_feature_large":"https:\/\/events.umich.edu\/media\/cache\/event_feature_large\/media\/attachments\/2025\/04\/event_134616_original-1.jpg", "event_feature_thumb":"https:\/\/events.umich.edu\/media\/cache\/event_feature_thumb\/media\/attachments\/2025\/04\/event_134616_original-1.jpg" }, "occurrence_count":1, "first_occurrence":21874600 } , "135130-21876338": { "datetime_modified":"20250424T183253", "datetime_start":"20250425T120000", "datetime_end":"20250425T130000", "has_end_time":1, "date_start":"2025-04-25", "date_end":"2025-04-25", "time_start":"12:00:00", "time_end":"13:00:00", "time_zone":"America\/Detroit", "event_title":"Frontiers in Scientific Machine Learning Seminar 13: Statistical learning for Summary Statistics of Physics-based Model Outputs and their Correction and Probabilistic Outputs from Neural Networks applied to Super-resolution", "occurrence_title":"", "combined_title":"Frontiers in Scientific Machine Learning Seminar 13: Statistical learning for Summary Statistics of Physics-based Model Outputs and their Correction and Probabilistic Outputs from Neural Networks applied to Super-resolution: Julie Bessac (National Renewable Energy Laboratory)", "event_subtitle":"Julie Bessac (National Renewable Energy Laboratory)", "event_type":"Workshop \/ Seminar", "event_type_id":"21", "description":"This is a hybrid seminar. Attendees can join online or at 2636 GGB (Breakfast and coffee will be provided)\r\nLink to join via Zoom: https:\/\/umich.zoom.us\/j\/97823527756?pwd=H01BbvtuG5q02Wzb8LJvhUnvijlAIe.1\r\n\r\nAbstract:\r\nIn the first part of this talk, we will discuss summary statistics of physics-based model outputs and their correction with observational data.\r\nPhysics-based models capture broad-scale dynamics across various spatial and temporal scales, they often face challenges such as modeling biases, high computational costs, along with large outputs that are challenging to manipulate. On the other hand, observations capture localized variability but are typically sparse. This talk presents an innovative approach to address these challenges by utilizing summary statistics from physics-based model outputs and enhancing them with observational information via neural networks.\r\n\r\nIn the second part of the talk, we will present neural networks with closed-form probabilistic loss that applied to super-resolution of surface wind speed. We will illustrate that the use of a closed-form probabilistic loss provides the neural network with a sampling capability and a spatial covariance for super-resolved wind fields.\r\n\r\nThese are joint work with Atlanta Chakraborty (NREL), Harrison Goldwyn (NREL), Daniel Getter (USC), Johann Rudi (Virginia Tech) and Mitchell Krock (University of Missouri)\r\n\r\nBio: Julie Bessac received her Ph.D. degree in 2014 in Applied Mathematics from the University of Rennes 1, France. Between 2014 and 2023, she was a post-doctoral appointee and a research scientist in the Mathematics and Computer Science Division at Argonne National Laboratory. She joined National Renewable Energy Laboratory in 2023 as a computational statistician. She is an adjunct professor at the Department of Statistics at Virginia Tech. Her research focuses on statistical and machine learning methods for modeling, forecasting and uncertainty quantification for diverse applications: geophysical processes and their applications to energy systems, computer science and nuclear physics.", "occurrence_notes":"This is a hybrid event. 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