ecoteach (version 0.1.0)

pangolin_habitat: 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.

Examples

# \donttest{
# 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")
# }