HANNAH HENRY
My Publications
2. Henry H, Wheeler I*, Luchauer G, Asay B, Bowles N, Castruccio F, Cole D, Furland T, Krumhardt K, Romashkov L, Stephens S, Manire H, Mollett S, Moulton M, Willoughby JR, Dunning K. 2025. Adaptive governance during an unprecedented marine heatwave: case study from the Florida Keys National Marine Sanctuary. Journal of Environmental Planning and Management. Advance online publication. https://doi.org/10.1080/09640568.2025.2504516
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1. Henry H*, Olivas T, Gumbleton S, Beckham N, Steury TD, Willoughby J, and Dunning K. (2025). Willingness of Recreational Anglers to Modify Hook and Bait Choices for Sea Turtle Conservation in Mobile Bay, Alabama, Gulf of Mexico. Fisheries Management and Ecology, 32(2), e12766. https://doi.org/10.1111/fme.12766
Current Research
Barrier islands are dynamic coastal landscapes shaped by the interplay of storms, sea-level rise, sediment transport, and human intervention. These systems are increasingly vulnerable to environmental change—particularly in the Mid-Atlantic U.S.—making effective, science-based management more urgent than ever. My Ph.D. research aims to understand and forecast the evolution of barrier islands through an interdisciplinary lens that combines numerical modeling, remote sensing, ecological data, and stakeholder-informed management strategies.
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Focusing on Hatteras Island, North Carolina, and the Virginia Coastal Reserve Long Term Ecological Research (LTER) site, I apply coupled models (e.g., CASCADE) and develop machine learning approaches to evaluate how geomorphic change, conservation priorities, and management decisions shape island futures. By integrating social, ecological, and physical sciences, my work supports the development of adaptive coastal strategies that balance the needs of both wildlife and human communities.
This research is conducted under the mentorship of Dr. Laura Moore at UNC–Chapel Hill and is supported by the National Oceanic and Atmospheric Administration (NOAA) and the National Science Foundation (NSF) through the LTER program. In 2024, I was honored with the Ronald F. Labisky Graduate Fellowship in Wildlife Policy in recognition of my contributions to interdisciplinary conservation science.
Simulating Human–Natural Feedbacks in Coastal Landscapes
Chapter 1: Modeling Barrier Island Evolution on Hatteras Island
In this chapter, I apply the CoAStal Community-lAnDscape Evolution (CASCADE) model to investigate the interactions between natural coastal processes and human adaptation strategies on Hatteras Island, North Carolina. CASCADE integrates physical evolution models—barrier3d and the BarrierR Inlet Environment (BRIE)—to simulate the effects of sea-level rise, storm events, and sediment transport on island morphology, including dune dynamics, overwash, and shoreface change. It also models human responses such as beach nourishment, dune construction, and road relocation to evaluate potential pathways for enhancing long-term resilience.
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A key component of this work involves preparing high-resolution spatial and temporal input datasets. I process LiDAR-derived elevation data, digitize historical shoreline change, and develop Python-compatible geospatial inputs—including island orientation, barrier width and offset, and storm time series—using GIS and NumPy workflows. These data support realistic, site-specific simulations across multiple future climate and management scenarios.
This chapter demonstrates my expertise in numerical modeling, coastal geomorphology, spatial data processing, and interdisciplinary scenario analysis. The results will contribute to science-informed decision-making for barrier island communities navigating climate-driven change.
Geomorphic Drivers of Shorebird Nesting Habitat
Chapter 2: Predicting Oystercatcher Habitat Using Machine Learning at the Virginia Coast Reserve
This chapter explores how barrier island morphology, vegetation, and human disturbance shape nesting habitat for the American Oystercatcher (Haematopus palliatus), a species of conservation concern along the Atlantic coast. I develop a machine learning model to predict suitable nesting habitat across the Virginia Coast Reserve Long Term Ecological Research (VCR LTER) site by integrating geospatial datasets, field surveys, and remotely sensed imagery.
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My approach combines spatial analysis with predictive modeling to identify how geomorphic features (e.g., elevation, slope, island width), vegetation patterns (e.g., NDVI, habitat type), and proximity to anthropogenic features influence nest site selection. I preprocess and standardize multisource data—including LiDAR-derived topography, satellite imagery, and long-term avian monitoring data—and train multiple algorithms (e.g., random forest, gradient boosting) to assess variable importance and spatial habitat suitability.
This chapter demonstrates my skills in machine learning, ecological modeling, remote sensing, and spatial data integration using Python and R. It contributes to the growing need for data-driven conservation tools that support habitat protection and adaptive management for coastal wildlife in the face of sea-level rise and landscape change.