pangolin_habitat: Habitat Occupancy of the Critically Endangered Chinese...

In ecoteach: Educational Datasets for Ecology and Agriculture

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. \Sexpr[results=rd]{tools:::Rd_expr_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")

ecoteach documentation built on June 29, 2025, 5:08 p.m.