Dataset for Pangolin studies (paper title: Sunda pangolins show inconsistent responses to disturbances across multiple scales)

Datasets: “Manis_javanica_records_maxent_20210516” contains occurrence data of banded civets from published studies, GBIF, and new camera trapping. “GLMM_SundaPangolin_Data_by_Survey.xlsx” contains camera trapping data from both published studies and new camera trapping. We used it for a GLMM regional-scale analysis. Each row represents a full survey with the number of individual captures across all camera traps of the survey. This dataset includes both data on common palm civets and banded civets. “RN_SundaPangolin_Records_1km_Cells.csv” contains camera trapping data from new surveys and each row represents an individual capture. This dataset includes data on both common palm civets and banded civets. “RN_ECL_Covariates_1km_Cells_20210322.csv” contains the data for covariates at each sampling location (usually 1 camera trap but averaged if more than 1) for the new surveys. We used both of these datasets for a Royle-Nichols local-scale analysis. Data collection: Data was collected both through a literature review and with new camera trapping surveys. Data-specific information: “Manis_javanica_records_maxent_20210516.csv” Species = Species of organism of occurrence data Latitude = Latitude coordinate of occurrence data Longitude = Longitude coordinate of occurrence data “GLMM_SundaPangolin_Data_by_Survey.xlsx” X.1 = Typo column (ignore) X = Typo column (ignore) TAG = Survey identification Species = Species of organisms captured with camera traps records = Number of individual capture in the survey site2 = site name year_start = Year when survey started Landscape = Landscape where survey was located survey_ids_included = IDs of surveys in the individual study region = Region where camera is located country = country where camera is located Protected_area = is the camera in a protected area? (y/n) effort = number of trap nights in survey Y_lat = latitude coordinate of sampling unit X_long = longitude coordinate of sampling unit size_km2 = size of forest where survey is located (km2) n_points = Typo column (ignore) n_cameras = number of camera traps in survey cam_spacing = Spacing between cameras (m) indent_cap_mins = minimum number of minutes before new capture is considered independent AnnualPrecipitation = AnnualPrecipitation at location of survey (mm) “covariate”10K = each column describes data on a covariate measures in a 10 km radius around the center of the survey “covariate”20K = each column describes data on a covariate measures in a 20 km radius around the center of the survey “covariate”30K = each column describes data on a covariate measures in a 30 km radius around the center of the survey forest_type = forest type at location of survey year_end = year when survey ended “RN_SundaPangolin_Records_1km_Cells.csv” cell_survey = ID of sampling unite (Cell ID + survey ID) Polygon1km = ID of polygon (Cell ID) active_cams_at_date = cameras in the same cell_survey id that were active on that day x_centroid = Average latitude coordinate of cameras in the cell y_centroid = Average longitude coordinate of cameras in the cell Date = date of capture species = species captured independent_events = Number of independent captures in the cell on the day total_indiv_records = Number of individuals on the day trap_nights_at_date = Trapping effort per day per cell Sampling_begin = minimum start date of cameras included in the cell_survey Sampling end = maximum end date of cameras included in the cell_survey survey_id = ID of survey where the capture was detected “RN_ECL_Covariates_1km_Cells_20210322.csv” cell_survey = ID of sampling unit (camera trap(s)) Cell_effort = number of trap nights at sampling unit elevation = elevation at location of the sampling unit’s location (m) dist_to_edge = distance from sampling unit to edge (m) human_pop_density_1km = Number of human individuals in 1km2 around sampling unit’s location dist_to_river = distance from sampling unit to river (m) forest_integrity = Forest Integrity Index at the sampling unit’s location human_footprint = Human Footprint Index at the sampling unit’s location forest_cover_1km = Forest cover in 1 km radius of sampling unit’s location (%) forest_cover_2km = Forest cover in 2 km radius of sampling unit’s location (%) degraded_forest_1km = Combined land cover of oil palm, lowland mosaics, lowland open ground and regrowth/plantation in 1 km radius of sampling unit’s location (%) degraded_forest_2km = Combined land cover of oil palm, lowland mosaics, lowland open ground and regrowth/plantation in 2 km radius of sampling unit’s location (%) oil_palm_1km = Oil palm in 1 km radius of sampling unit’s location (%) oil_palm_2km = Oil palm in 2 km radius of sampling unit’s location (%) forest_loss_1km = Forest loss in 1 km radius of sampling unit’s location (%) forest_loss_2km = Forest loss in 2 km radius of sampling unit’s location (%) survey_id = ID of the survey of which the sampling unit is a part of shrink_id = ID of the survey of which the sampling unit is a part of

数据集“Manis_javanica_records_maxent_20210516”收录了来自已发表研究、全球生物多样性信息设施（GBIF, Global Biodiversity Information Facility）以及新增红外相机诱捕（camera trapping）的带纹狸猫出现记录。
“GLMM_SundaPangolin_Data_by_Survey.xlsx”收录了来自已发表研究与新增红外相机诱捕的红外相机数据，本数据集被用于广义线性混合模型（GLMM, Generalized Linear Mixed Model）的区域尺度分析。每一行代表一次完整调查，记录该调查所有红外相机的个体捕获总数量，本数据集包含马来灵猫（common palm civet）与带纹狸猫两类物种的数据。
“RN_SundaPangolin_Records_1km_Cells.csv”收录了新增调查的红外相机数据，每一行代表一次个体捕获记录，本数据集包含马来灵猫与带纹狸猫两类物种的数据。
“RN_ECL_Covariates_1km_Cells_20210322.csv”收录了新增调查各采样点位（通常布设1台红外相机，若多于1台则取多台相机的均值）的协变量数据。上述后两个数据集被用于罗伊尔-尼科尔斯（Royle-Nichols）局域尺度分析。

数据收集：本数据集通过文献综述与新增红外相机监测调查两种方式获取。

数据专项说明：
1. “Manis_javanica_records_maxent_20210516.csv”
Species = 出现记录对应的物种名称
Latitude = 出现记录的纬度坐标
Longitude = 出现记录的经度坐标
2. “GLMM_SundaPangolin_Data_by_Survey.xlsx”
X.1 = 冗余列（可忽略）
X = 冗余列（可忽略）
TAG = 调查标识
Species = 红外相机捕获的物种名称
records = 该调查样地的个体捕获总数量
site2 = 样地名称
year_start = 调查开始年份
Landscape = 调查所在的景观类型
survey_ids_included = 单个研究区域内纳入的调查ID列表
region = 红外相机布设区域
country = 红外相机所在国家
Protected_area = 红外相机是否位于保护地？（是/否）
effort = 调查总捕夜数（trap nights）
Y_lat = 采样单元的纬度坐标
X_long = 采样单元的经度坐标
size_km2 = 调查所在森林的面积（单位：平方千米）
n_points = 冗余列（可忽略）
n_cameras = 调查布设的红外相机总数量
cam_spacing = 红外相机间距（单位：米）
indent_cap_mins = 判定新捕获为独立事件所需的最短间隔时长（单位：分钟）
AnnualPrecipitation = 调查点位的年降水量（单位：毫米）
“covariate”10K = 各列分别描述以调查中心为圆心、10千米半径范围内的协变量数据
“covariate”20K = 各列分别描述以调查中心为圆心、20千米半径范围内的协变量数据
“covariate”30K = 各列分别描述以调查中心为圆心、30千米半径范围内的协变量数据
forest_type = 调查点位的森林类型
year_end = 调查结束年份
3. “RN_SundaPangolin_Records_1km_Cells.csv”
cell_survey = 采样单元ID（单元格ID+调查ID）
Polygon1km = 多边形（单元格）ID
active_cams_at_date = 当日同一cell_survey下处于工作状态的红外相机数量
x_centroid = 该单元格内红外相机的平均纬度坐标
y_centroid = 该单元格内红外相机的平均经度坐标
Date = 捕获日期
species = 被捕获的物种名称
independent_events = 当日该单元格内的独立捕获事件总数量
total_indiv_records = 当日该单元格内的个体捕获总数量
trap_nights_at_date = 当日该单元格的单日捕夜数
Sampling_begin = 纳入该cell_survey的红外相机的最早启动日期
Sampling end = 纳入该cell_survey的红外相机的最晚停止日期
survey_id = 检测到捕获事件的调查ID
4. “RN_ECL_Covariates_1km_Cells_20210322.csv”
cell_survey = 采样单元（红外相机组）的ID
Cell_effort = 该采样单元的总捕夜数
elevation = 采样点位的海拔高度（单位：米）
dist_to_edge = 采样点位至生境边缘的距离（单位：米）
human_pop_density_1km = 采样点位周边1平方千米范围内的人口数量
dist_to_river = 采样点位至河流的距离（单位：米）
forest_integrity = 采样点位的森林完整性指数
human_footprint = 采样点位的人类足迹指数
forest_cover_1km = 采样点位周边1千米半径范围内的森林覆盖率（百分比）
forest_cover_2km = 采样点位周边2千米半径范围内的森林覆盖率（百分比）
degraded_forest_1km = 采样点位周边1千米半径范围内油棕、低地镶嵌地、低地开阔地及次生林/人工林的综合土地占比（百分比）
degraded_forest_2km = 采样点位周边2千米半径范围内油棕、低地镶嵌地、低地开阔地及次生林/人工林的综合土地占比（百分比）
oil_palm_1km = 采样点位周边1千米半径范围内的油棕种植园占比（百分比）
oil_palm_2km = 采样点位周边2千米半径范围内的油棕种植园占比（百分比）
forest_loss_1km = 采样点位周边1千米半径范围内的森林损失率（百分比）
forest_loss_2km = 采样点位周边2千米半径范围内的森林损失率（百分比）
survey_id = 该采样单元所属的调查ID
shrink_id = 该采样单元所属的调查ID