Nesting distances, elevations, disturbance events and monthly temperature and precipitation for modeling loggerhead sea turtle clutch failure in the southeastern United States

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

筑巢数据：本数据集取自国家公园管理局（National Park Service），用于构建红海龟（loggerhead sea turtles）筑巢成功率的分层贝叶斯模型（hierarchical Bayesian model）。筑巢点位由经过培训的志愿者与国家公园管理局生物学家通过每日清晨对所有海滩的普查记录得到。我们采用了所有使用手持全球定位系统（handheld GPS）记录精确经纬度年份的筑巢点位数据：卡纳维拉尔国家海岸（CANA）：2013–2018年，坎伯兰岛国家海岸（CUIS）：1999–2017年，瞭望角国家海岸（CALO）：2001–2018年，哈特拉斯角国家海岸（CAHA）：2005–2018年，大沼泽地国家公园（ENP）：2014–2017年。我们将巢穴划分为"孵化失败（Fail）"类别，条件为：孵化期结束后对巢穴进行挖掘时未发现成功孵化的痕迹，或无法找到巢穴进行挖掘；若巢穴至少有一只幼龟破壳而出，则判定为孵化成功。公园生物学家与志愿者会记录筑巢日期（"Date"）、孵化期内巢穴是否被海水淹没（"Inundation"）或遭受捕食（"Depredation"）；若有相关信息，还会记录捕食者的种类。我们同时统计了巢穴是否采取了反捕食处理措施（PredTrt）。基于筑巢点位的经纬度，我们计算了其他空间特征。"海拔（Elevation）"数据由美国国家海洋和大气管理局（NOAA）海岸可视化平台获取的数字高程模型（Digital Elevation Model, DEM）栅格数据计算得到（海岸管理办公室，2020）。我们采用筑巢年份最接近的DEM数据；若某场强风暴后开展了激光雷达（LiDAR）航测，则针对风暴发生前处于孵化期的所有巢穴，采用风暴前一年的DEM数据。所有距离均通过ArcGIS（10.4版）的"邻近分析工具（Near tool）"计算得到，即每个筑巢点与各类地表特征间的最短直线距离。我们利用平均高潮线矢量数据计算了巢穴至平均高潮线（MHW）的距离：该矢量数据取自美国国家海洋和大气管理局或美国地质调查局（USGS），年份与对应巢穴的记录年份最为接近（详见Lyons等人2020年研究的表2）。基于筑巢点位与海岸线的夹角，我们可判定哪些巢穴位于平均高潮线以下，并将这类点位的距离记为负值。我们还基于2016年全国土地覆盖数据集（National Landcover Dataset）计算了巢穴至建成区（Dev）、森林（Forest）与湿地（95和90类）的距离：该数据集由Jin等人2019年发布，其中建成类对应土地分类代码21–24，森林类对应41–43，森林湿地对应90，新生湿地对应95。
Lyons, M. P., B. Von Holle, M. A. Caffrey, and J. F. Weishampel. 2020. 量化未来海平面上升对美国东南部筑巢海龟的影响. 生态应用, 30:457–15.
Jin, S., C. G. Homer, L. Yang, P. Danielson, J. Dewitz, C. Li, Z. Zhu, G. Xian, and D. Howard. 2019. 美国2016年全国土地覆盖数据库产品的整体方法学设计. 遥感, 11:2971.
气候数据：本部分数据采用R 3.4.2版本与R包rnoaa 0.8.4版本（Chamberlain, 2019），从美国国家海洋和大气管理局气象站点获取月均气温与降水量数据。我们选取了距离各公园海滩最近的气象站点，排除内陆站点（详见Lyons等人2022年研究的附录S1表S3）。若存在两个与研究点位距离相等的气象站点，则取两个站点在对应月份的观测平均值。
Chamberlain, S. 2019. rnoaa：基于R的NOAA气象数据获取工具. R包版本0.8.4. https://CRAN.R-project.org/package=rnoaa