@article{chazal_carr_haines_leight_nelson_2024, title={Assessing the utility of shellfish sanitation monitoring data for long-term estuarine water quality analysis}, volume={203}, ISSN={["1879-3363"]}, DOI={10.1016/j.marpolbul.2024.116465}, abstractNote={Regular testing of coastal waters for fecal coliform bacteria by shellfish sanitation programs could provide data to fill large gaps in existing coastal water quality monitoring, but research is needed to understand the opportunities and limitations of using these data for inference of long-term trends. In this study, we analyzed spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, and assessed the feasibility of using these monitoring data to infer long-term water quality dynamics. We evaluated trends in fecal coliform concentrations for a 20-year period (1999-2021) using data collected from spatially fixed sampling sites (n = 466) in North Carolina (USA). Findings indicated that shellfish sanitation data can be used for long-term water quality inference under relatively stationary management conditions, and that salinity trends can be used to investigate management-driven bias in fecal coliform observations collected in a particular area.}, journal={MARINE POLLUTION BULLETIN}, author={Chazal, Natalie and Carr, Megan and Haines, Andrew and Leight, Andrew K. and Nelson, Natalie G.}, year={2024}, month={Jun} }
 @article{chazal_carr_leight_saia_nelson_2024, title={Short-term forecasting of fecal coliforms in shellfish growing waters}, volume={200}, ISSN={["1879-3363"]}, DOI={10.1016/j.marpolbul.2024.116053}, abstractNote={This study sought to develop models for predicting near-term (1-3 day) fecal contamination events in coastal shellfish growing waters. Using Random Forest regression, we (1) developed fecal coliform (FC) concentration models for shellfish growing areas using watershed characteristics and antecedent hydrologic and meteorologic observations as predictors, (2) tested the change in model performance associated when forecasted, as opposed to measured, rainfall variables were used as predictors, and (3) evaluated model predictor importance in relation to shellfish sanitation management criteria. Models were trained to 10 years of coastal FC measurements (n = 1285) for 5 major shellfish management areas along the Florida (USA) coast. Model performance varied between the 5 management areas with R2 ranging from 0.36 to 0.72. Antecedent precipitation variables were among the most important predictors in the day-of forecast models in all management areas. When forecasted rainfall was included in the models, wind components became increasingly important.}, journal={MARINE POLLUTION BULLETIN}, author={Chazal, Natalie and Carr, Megan and Leight, Andrew K. and Saia, Sheila M. and Nelson, Natalie G.}, year={2024}, month={Mar} }
 @article{hayes_gray_harris_sedgwick_crawford_chazal_crofts_johnston_2021, title={Drones and deep learning produce accurate and efficient monitoring of large-scale seabird colonies}, volume={123}, ISSN={["2732-4621"]}, DOI={10.1093/ornithapp/duab022}, abstractNote={Abstract}, number={3}, journal={ORNITHOLOGICAL APPLICATIONS}, author={Hayes, Madeline C. and Gray, Patrick C. and Harris, Guillermo and Sedgwick, Wade C. and Crawford, Vivon D. and Chazal, Natalie and Crofts, Sarah and Johnston, David W.}, year={2021}, month={Aug} }