DOI: 10.6084/m9.figshare.17118803
title: Nesting distances, elevations, disturbance events and monthly temperature and precipitation for modeling loggerhead sea turtle clutch failure in the southeastern United States

description: Nesting data: These data, obtained from National Park Service were used to constructing a hierarchical Bayesian model of nest success for loggerhead sea turtles. Nest locations were recorded by trained volunteers and National Park Service biologists through daily, early morning surveys of all beaches. We made use of all nest locality data for the years in which Seashores used handheld GPS units for recording precise latitude and longitude (Canaveral National Seashore (CANA): 2013–2018, Cumberland Island National Seashore (CUIS): 1999–2017, Cape Lookout National Seashore (CALO): 2001–2018, Cape Hatteras National Seashore (CAHA): 2005–2018, Everglades National Park (ENP): 2014-2017). We categorized nests as having clutch failure ("Fail") if they were excavated after the incubation and there was no evidence of successful hatching or if the nests could not be located for excavation. Nests where there was evidence that at least one hatchling emerged were considered successful. Park biologists and volunteers recorded nesting date ("Date") and whether a nest was flooded with seawater ("Inundation") or depredated ("Depredation") during incubation period, when available the identity of the predator was also included. We also looked at whether a nest had an anti-predator treatment (PredTrt).Using the latitude and longitude of the nesting location we calculated other spatial attributes. "Elevation" was we calculated using Digital Elevation Model rasters from the NOAA coastal viewer (Office of Coastal Management 2020). DEMs for the year closest to nesting were used for elevation data; if a LiDAR flight was performed after a major storm, the prior year’s DEM was used for all nests incubating before the storm event. All distances were calculated as the shortest distance between each nest site and land feature using Near tool in ArcGIS (version 10.4). We measured distance to high water line ("MHW") using the mean high water line vector, retrieved from NOAA or USGS, for the year closest to the time when each nest was recorded (see Table 2 in Lyons et al 2020). Based on the angle between nest point and shoreline, we were able to determine which nests were below the high water line and recorded these as negative values. Distances to development (Dev), forests (Forest), and wetlands (95 and 90) were calculated using the 2016 National Landcover Dataset (Jin et al. 2019) and classifications 21-24 for development, 41-43 for forest, 90 for forested wetlands, and 95 for emergent wetlands. Lyons, M. P., B. Von Holle, M. A. Caffrey, and J. F. Weishampel. 2020. Quantifying the impacts of future sea level rise on nesting sea turtles in the southeastern United States. Ecological Applications 30:457–15. Jin, S., C. G. Homer, L. Yang, P. Danielson, J. Dewitz, C. Li, Z. Zhu, G. Xian, and D. Howard. 2019. Overall methodology design for the United States National Land Cover Database 2016 products. Remote Sensing, 11:2971<br>Climate data:Monthly air temperature and precipitation values from NOAA weather stations downloaded using R version 3.4.2 and the R package rnoaa version 0.8.4 (Chamberlain 2019). We selected the closest weather stations to each Park that were in proximity to the beach and avoided inland weather stations (Appendix S1: Table S3 in Lyons et al. 2022). If two weather stations were equidistant from the study site, we used the average value from the stations for months where both stations recorded values.<br>Chamberlain, S. 2019. rnoaa: ‘NOAA’ weather data from R. R package version 0.8.4. https://CRAN.R-project.org/package=rnoaa