pangolin_habitat {ecoteach} | R Documentation |
Habitat Occupancy of the Critically Endangered Chinese Pangolin
Description
A dataset containing habitat occupancy observations of the Critically Endangered Chinese pangolin (Manis pentadactyla) in the urban landscape of Dharan Sub-metropolitan City, Nepal. The data were collected to analyze spatial distribution, habitat use patterns, and anthropogenic impacts on habitat occupancy of Chinese pangolins. The study used a single-season occupancy modeling approach, investigating factors influencing detection probability and habitat occupancy across 134 grid cells of 600m × 600m each.
Usage
pangolin_habitat
Format
A data frame with 152 rows and 18 variables:
- object_id
Unique identifier for each grid cell
- replicate_1
Detection (1) or non-detection (0) in first survey replicate
- replicate_2
Detection (1) or non-detection (0) in second survey replicate
- replicate_3
Detection (1) or non-detection (0) in third survey replicate
- replicate_4
Detection (1) or non-detection (0) in fourth survey replicate
- replicate_5
Detection (1) or non-detection (0) in fifth survey replicate
- replicate_6
Detection (1) or non-detection (0) in sixth survey replicate
- distance_to_water
Distance to nearest water body in meters
- terrain_ruggedness
Terrain Ruggedness Index (TRI), a measure of topographic heterogeneity
- mean_ndvi
Mean Normalized Difference Vegetation Index, a measure of vegetation density
- habitat_type
Type of habitat: "Sal Forest", "Mixed Forest", "Human Settlement", or "Agricultural Land"
- habitat_structure
Topographic structure: "Terrace" or "Cliff"
- human_disturbance_index
Index of human disturbance, ranging from 0 (low) to 1 (high)
- termite_mounds
Number of termite mounds in the grid cell
- detection_sum
Total number of detections across all six replicates
- detected
Binary indicator of whether pangolin was detected (1) or not (0) in any replicate
- disturbance_level
Categorized human disturbance: "Low", "Medium-Low", "Medium-High", or "High"
Details
The dataset is particularly valuable for teaching concepts in wildlife conservation, occupancy modeling, and human-wildlife interactions in urban environments. It demonstrates how ecological and anthropogenic factors affect endangered species in human-dominated landscapes.
Source
Subba, Asmit and Tamang, Ganesh and Lama, Sony and Basnet, Nabin and Kyes, Randall C. and Khanal, Laxman (2024). Habitat occupancy of the critically endangered Chinese pangolin (Manis pentadactyla) under human disturbance in an urban environment: Implications for conservation. Dryad Digital Repository. doi:10.5061/DRYAD.73N5TB34T
Examples
# Load the dataset
data(pangolin_habitat)
# Basic exploration
head(pangolin_habitat)
summary(pangolin_habitat)
# Examine detection rates across habitat types
table(pangolin_habitat$habitat_type, pangolin_habitat$detected)
# Visualize the relationship between termite mounds and pangolin detection
boxplot(termite_mounds ~ detected, data = pangolin_habitat,
main = "Termite Mounds and Pangolin Detection",
xlab = "Pangolin Detected", ylab = "Number of Termite Mounds",
names = c("Not Detected", "Detected"))
# Examine the effect of human disturbance on pangolin detection
boxplot(human_disturbance_index ~ detected, data = pangolin_habitat,
main = "Human Disturbance and Pangolin Detection",
xlab = "Pangolin Detected", ylab = "Human Disturbance Index",
names = c("Not Detected", "Detected"))
# Visualize detection across disturbance levels
barplot(prop.table(table(pangolin_habitat$disturbance_level,
pangolin_habitat$detected), 1)[,2],
main = "Pangolin Detection Rate by Disturbance Level",
xlab = "Disturbance Level", ylab = "Detection Rate")