five

lynx_allData

收藏
DataONE2017-11-27 更新2024-06-26 收录
下载链接:
https://search.dataone.org/view/null
下载链接
链接失效反馈
官方服务:
资源简介:
This table contains the GPS locations of lynx associated with habitat variables and temporal variables that were used to build a logistic regression modelling the proportion of time lynx spend active. The data in this table refers to the all data model in Gehr et al. 2017 (Ecology and Evolution). The data was restricted to locations between the beginning of astronomical twilight in the morning (sun angle < 18 degrees below the horizon) and the end of astronomical twilight in the evening (sun angle > 18 degrees below the horizon). Hence, to repeat the model in Gehr et al. 2017 (Ecology and Evolution) the data with sun angle <= -18 degrees have to be removed. For the model used to create Figure S4 in the Appendix S1 the complementary dataset has to be used. The table is divided into locations assigned to an active or inactive behavioral state (column “active” is a dummy variable - 1=active/0=inactive). Swisstopo in the column headers refers to the source of the environmental variables. Cover swisstopo is a dummy variable for open/cover. Edge_dist_swisstopo refers to the distance to the closest forest edge. Slope_sq and altitude_sq are the squared slope and altitude variables. Aspect_swisstopoS is the southern exposition. Cover_edge refers to an interaction term between cover_swisstopo and edge_dist_swisstopo. House_road_dist_small refers to an interaction between house_density and road_dist_small. The 6 temporal variables are time harmonics of a Fourier transform for time of day (tsin, tcos; period of 24) and day of year (ytsin, ytcos, ytsin2, ytcos2; period of 365). All habitat variables were measured at the end of a step. All continuous variables in this table are mean centered and standardized to a SD=1. For information on the raw data or for R-code used to run the models please contact the data owner.
创建时间:
2017-11-27
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作