Sturdivant Emily

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  • Article
    Predicted sea-level rise-driven biogeomorphological changes on Fire Island, New York: implications for people and plovers
    (American Geophysical Union, 2022-03-29) Zeigler, Sara L. ; Gutierrez, Benjamin T. ; Lentz, Erika E. ; Plant, Nathaniel G. ; Sturdivant, Emily ; Doran, Kara S.
    Forecasting biogeomorphological conditions for barrier islands is critical for informing sea-level rise (SLR) planning, including management of coastal development and ecosystems. We combined five probabilistic models to predict SLR-driven changes and their implications on Fire Island, New York, by 2050. We predicted barrier island biogeomorphological conditions, dynamic landcover response, piping plover (Charadrius melodus) habitat availability, and probability of storm overwash under three scenarios of shoreline change (SLC) and compared results to observed 2014/2015 conditions. Scenarios assumed increasing rates of mean SLC from 0 to 4.71 m erosion per year. We observed uncertainty in several morphological predictions (e.g., beach width, dune height), suggesting decreasing confidence that Fire Island will evolve in response to SLR as it has in the past. Where most likely conditions could be determined, models predicted that Fire Island would become flatter, narrower, and more overwash-prone with increasing rates of SLC. Beach ecosystems were predicted to respond dynamically to SLR and migrate with the shoreline, while marshes lost the most area of any landcover type compared to 2014/2015 conditions. Such morphological changes may lead to increased flooding or breaching with coastal storms. However—although modest declines in piping plover habitat were observed with SLC—the dynamic response of beaches, flatter topography, and increased likelihood of overwash suggest storms could promote suitable conditions for nesting piping plovers above what our geomorphology models predict. Therefore, Fire Island may offer a conservation opportunity for coastal species that rely on early successional beach environments if natural overwash processes are encouraged.
  • Article
    UAS-SfM for coastal research : geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery
    (MDPI AG, 2017-10-03) Sturdivant, Emily ; Lentz, Erika E. ; Thieler, E. Robert ; Farris, Amy S. ; Weber, Kathryn M. ; Remsen, David P. ; Miner, Simon ; Henderson, Rachel E.
    The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.
  • Article
    Using a Bayesian network to understand the importance of coastal storms and undeveloped landscapes for the creation and maintenance of early successional habitat
    (Public Library of Science, 2019-07-25) Zeigler, Sara L. ; Gutierrez, Benjamin T. ; Sturdivant, Emily ; Catlin, Daniel H. ; Fraser, James D. ; Hecht, Anne ; Karpanty, Sarah M. ; Plant, Nathaniel G. ; Thieler, E. Robert
    Coastal storms have consequences for human lives and infrastructure but also create important early successional habitats for myriad species. For example, storm-induced overwash creates nesting habitat for shorebirds like piping plovers (Charadrius melodus). We examined how piping plover habitat extent and location changed on barrier islands in New York, New Jersey, and Virginia after Hurricane Sandy made landfall following the 2012 breeding season. We modeled nesting habitat using a nest presence/absence dataset that included characterizations of coastal morphology and vegetation. Using a Bayesian network, we predicted nesting habitat for each study site for the years 2010/2011, 2012, and 2014/2015 based on remotely sensed spatial datasets (e.g., lidar, orthophotos). We found that Hurricane Sandy increased piping plover habitat by 9 to 300% at 4 of 5 study sites but that one site saw a decrease in habitat by 27%. The amount, location, and longevity of new habitat appeared to be influenced by the level of human development at each site. At three of the five sites, the amount of habitat created and the time new habitat persisted were inversely related to the amount of development. Furthermore, the proportion of new habitat created in high-quality overwash was inversely related to the level of development on study areas, from 17% of all new habitat in overwash at one of the most densely developed sites to 80% of all new habitat at an undeveloped site. We also show that piping plovers exploited new habitat after the storm, with 14–57% of all nests located in newly created habitat in the 2013 breeding season. Our results quantify the importance of storms in creating and maintaining coastal habitats for beach-nesting species like piping plovers, and these results suggest a negative correlation between human development and beneficial ecological impacts of these natural disturbances.
  • Article
    Piping plovers demonstrate regional differences in nesting habitat selection patterns along the U. S. Atlantic coast
    (Ecological Society of America, 2021-03-11) Zeigler, Sara L. ; Gutierrez, Benjamin T. ; Hecht, Anne ; Plant, Nathaniel G. ; Sturdivant, Emily
    Habitat studies that encompass a large portion of a species’ geographic distribution can explain characteristics that are either consistent or variable, further informing inference from more localized studies and improving management successes throughout the range. We identified landscape characteristics at Piping Plover nests at 21 sites distributed from Massachusetts to North Carolina and compared habitat selection patterns among the three designated U.S. recovery units (New England, New York–New Jersey, and Southern). Geomorphic setting, substrate type, and vegetation type and density were determined in situ at 928 Piping Plover nests (hereafter, used resource units) and 641 random points (available resource units). Elevation, beach width, Euclidean distance to ocean shoreline, and least-cost path distance to low-energy shorelines with moist substrates (commonly used as foraging habitat) were associated with used and available resource units using remotely sensed spatial data. We evaluated multivariate differences in habitat selection patterns by comparing recovery unit-specific Bayesian networks. We then further explored individual variables that drove disparities among Bayesian networks using resource selection ratios for categorical variables and Welch’s unequal variances t-tests for continuous variables. We found that relationships among variables and their connections to habitat selection were similar among recovery units, as seen in commonalities in Bayesian network structures. Furthermore, nesting Piping Plovers consistently selected mixed sand and shell, gravel, or cobble substrates as well as areas with sparse or no vegetation, irrespective of recovery unit. However, we observed significant differences among recovery units in the elevations, distances to ocean, and distances to low-energy shorelines of used resource units. Birds also exhibited increased selectivity for overwash habitats and for areas with access to low-energy shorelines along a latitudinal gradient from north to south. These results have important implications for conservation and management, including assessment of shoreline stabilization and habitat restoration planning as well as forecasting effects of climate change.
  • Article
    Integrating Bayesian Networks to forecast sea‐level rise impacts on Barrier Island characteristics and habitat availability
    (American Geophysical Union, 2022-10-14) Gutierrez, Benjamin T. ; Zeigler, Sara L. ; Lentz, Erika ; Sturdivant, Emily J. ; Plant, Nathaniel G.
    Evaluation of sea‐level rise (SLR) impacts on coastal landforms and habitats is a persistent need for informing coastal planning and management, including policy decisions, particularly those that balance human interests and habitat protection throughout the coastal zone. Bayesian networks (BNs) are used to model barrier island change under different SLR scenarios that are relevant to management and policy decisions. BNs utilized here include a shoreline change model and two models of barrier island biogeomorphological evolution at different scales (50 and 5 m). These BNs were then linked to another BN to predict habitat availability for piping plovers (Charadrius melodus), a threatened shorebird reliant on beach habitats. We evaluated the performance of the two linked geomorphology BNs and further examined error rates by generating hindcasts of barrier island geomorphology and habitat availability for 2014 conditions. Geomorphology hindcasts revealed that model error declined with a greater number of known inputs, with error rates reaching 55% when multiple outputs were hindcast simultaneously. We also found that, although error in predictions of piping plover nest presence/absence increased when outputs from the geomorphology BNs were used as inputs in the piping plover habitat BN, the maximum error rate for piping plover habitat suitability in the fully‐linked BNs was only 30%. Our findings suggest this approach may be useful for guiding scenario‐based evaluations where known inputs can be used to constrain variables that produce higher uncertainty for morphological predictions. Overall, the approach demonstrates a way to assimilate data and model structures with uncertainty to produce forecasts to inform coastal planning and management.