Bangley et al. Cownose Ray Migrations in Chesapeake Bay and Cape Canaveral

Data set 1: Counts of acoustically-tagged Cownose Rays and recorded environmental data within data buoy regions in the Chesapeake Bay. Data set 2: Counts of acoustically-tagged Cownose Rays and recorded environmental data within data buoy regions in the vicinity of Cape Canaveral. Data Description: These data include counts of individual mature Cownose Rays detected within data buoy regions in the Chesapeake Bay, presented as all rays combined, males only, and females only, presence records for each ray group, and environmental data (sea surface temperature in °C, day of year, and photoperiod in hours of daylight) recorded on data buoys on the date of detection within the region the rays were detected within. These data were used in boosted regression tree models to identify relationships between ray presence/abundance (the response variable) and the three environmental variables (the explanatory variables). Boosted regression tree analysis was conducted using the gbm.auto function in the R package “gbm.auto” (Dedman et al. 2017).

数据集1：切萨皮克湾数据浮标区域内声学标记牛鼻鲼的个体计数及配套环境记录数据。数据集2：卡纳维拉尔角附近海域数据浮标区域内声学标记牛鼻鲼的个体计数及配套环境记录数据。数据说明：本数据集包含切萨皮克湾数据浮标区域内检测到的成熟牛鼻鲼个体计数，按全群合并、仅雄性、仅雌性三类分组呈现，并附带各分组的存在记录；此外还包含检测当日对应鲼类活动区域内数据浮标所记录的环境数据，具体包括以摄氏度（℃）为单位的海表温度、一年中的日序以及以小时为单位的日照时长。本数据集曾被用于构建提升回归树（Boosted Regression Tree）模型，以探究牛鼻鲼的存在情况与种群丰度（响应变量）和上述三类环境变量（解释变量）之间的关联。提升回归树分析通过R语言包"gbm.auto"中的gbm.auto函数完成（Dedman等，2017）。