raccoondog_environment | R Documentation |
This dataset contains records of raccoon dog (Nyctereutes procyonoides) detections and other mammal species from camera traps in the Sizuolou Nature Reserve, Beijing, China, along with associated environmental variables. The data was collected from October 15, 2023, to February 29, 2024, covering both autumn and winter seasons. The study examined seasonal activity changes in raccoon dogs and their relationship to environmental and mammalian factors.
raccoondog_environment
A data frame with 144 rows and 21 variables:
Camera installation point ID
Number of Asian badger detection events
Number of wild boar detection events
Number of hog badger detection events
Number of leopard cat detection events
Number of masked palm civet detection events
Number of Père David's rock squirrel detection events
Number of raccoon dog detection events
Number of red squirrel detection events
Number of Siberian roe deer detection events
Number of Siberian weasel detection events
Number of Swinhoe's striped squirrel detection events
Number of Tolai hare detection events
Distance from camera to nearest impervious area (meters)
Distance from camera to nearest agricultural land (meters)
Distance from camera to nearest water source (meters)
Distance from camera to nearest road (meters)
Elevation of camera installation point (meters above sea level)
Topographic Position Index of the camera installation point
Date of detection (days since start of study, October 15, 2023)
Season at time of detection ("Autumn" or "Winter")
Type of vegetation at the camera installation point
Miyamoto, Keisuke and Chen, Chuan and Luan, Xiaofeng (2025). Seasonal activity changes in raccoon dogs and influences of environmental factors from autumn to winter. Dryad Digital Repository. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.5061/DRYAD.C866T1GJN")}
# Load the dataset
data(raccoondog_environment)
# Basic exploration
head(raccoondog_environment)
summary(raccoondog_environment)
# Compare raccoon dog detections by season
boxplot(raccoon_dog ~ Season, data = raccoondog_environment,
main = "Raccoon Dog Detections by Season",
ylab = "Number of Detections", xlab = "Season")
# Examine relationship between environmental factors and raccoon dog presence
# Create a binary presence variable
raccoondog_presence <- raccoondog_environment
raccoondog_presence$presence <- ifelse(raccoondog_presence$raccoon_dog > 0, 1, 0)
# Plot relationship with distance to agricultural land
plot(dist_agricultural ~ presence, data = raccoondog_presence,
main = "Raccoon Dog Presence vs. Distance to Agricultural Land",
xlab = "Presence (0=Absent, 1=Present)",
ylab = "Distance to Agricultural Land (m)")
# Examine vegetation types where raccoon dogs were detected
table(raccoondog_presence$Vegetation[raccoondog_presence$presence == 1])
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