Habitat occupancy of the critically endangered Chinese pangolin (Manis pentadactyla) under human disturbance in an urban environment: Implications for conservation
Data files
Sep 11, 2024 version files 10.18 KB
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Chinese_pangolin_data_Dharan.csv
8.15 KB
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README.md
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Abstract
Globally, urban expansion has led to habitat fragmentation and altered resource availability thus posing significant challenges for wildlife. This study analyzed spatial distribution, habitat use patterns, and anthropogenic impacts on habitat occupancy of the Critically Endangered Chinese pangolin (Manis pentadactyla) in the urban landscape of Dharan Sub-metropolitan City, Nepal. Using a single-season occupancy modelling approach, we investigated the factors influencing the detection probability and habitat occupancy of Chinese pangolins in 134 grids of each 600 m × 600 m. Our study identified the role of termite mounds in influencing detection probability, emphasizing the species' myrmecophilous behavior. Additionally, the Human Disturbance Index (HDI) emerged as a significant factor negatively affecting habitat occupancy of Chinese pangolins. We observed a medium level of anthropogenic disturbances in the grids where pangolin presence was detected. The findings emphasize the need for targeted conservation efforts, considering the fine-scale ecological and anthropogenic factors impacting Chinese pangolins in urban and peri-urban areas. The results underscore the urgency of implementing effective conservation measures to ensure the long-term survival of Critically Endangered Chinese pangolins in urban environments, not only in Dharan but also in similar lowlands areas and the Churia range of Nepal.
README: Habitat Occupancy of the Critically Endangered Chinese Pangolin (Manis pentadactyla) under Human Disturbance in an Urban Environment: Implications for Conservation
https://doi.org/10.5061/dryad.73n5tb34t
Description of the data and file structure
This *.csv data file contains the field data collected during the Chinese pangolin surveys in the selected 152 study grids. It contains 14 columns and 153 rows.
On each grid, there were six replicates (Replicate 1 - Replicate 6). The values in these columns indicate pangolin sign presence as '1' and absence as '0'.
Distance to the nearest water body was calculated as the distance from the centroid of the study grid to the nearest water sources using Euclidean distance tool in ArcGIS 10.5. All the distances were measured in meter.
The terrain Ruggedness Index (TRI) was calculated from SRTM-DEM data.
NDVI was derived from 30 m resolution Landsat 8 imagery captured during November 2020.
Habitat type was categorized based on the habitat types including dominant Shorea robusta forests (SF), mixed forests (MF), riverine forests (RF), human settlement areas (HS), and agricultural land (AG).
Habitat structure were categorized as Terai (T) and Churia/Siwalik (C) based on the area where the grids fell.
Human Disturbance Index was calculated as HDI = (VD×0.2) + (F×0.2) + (L×0.2) + (H×0.2) + (K×0.2), where, VD= vegetation destruction; F = Forest fire; L = livestock grazing and related indicators; H= human presence, foot and vehicle trails; and K= signs of pangolin poaching. The HDI values from six replicates were then averaged for each study grid.
Termite mounds indicate the total number of termite mounds within each grid.
Sharing/Access information
Data was derived from the following sources:
- Field surveys and remote sensing data
Code/Software
We utilized the Unmark package (version 1.4.1; Kellner et al., 2023) in R software v.4.4.1 (R Core Team, 2023).
Methods
Field surveys in Dharan Sub-metropolitan City dividing the area into grids of each 600m * 600m. A subset of grids (n-152) were randomly selected and surveyed using the line transect of 600m length. Within every 600 m of each transect, Chinese pangolin detection/non-detection data along with field-based covariates (described below) were collected in every 100 m segment. For each 100 m alternate segment within 600 m transects, we gathered quantified data on environmental variables and threats, including vegetation destruction (VD), forest fires (F), livestock grazing and related signs (L), human presence, foot and vehicular trails (H), and evidence of pangolin killing (K).
We utilized the Unmark package (Kellner et al., 2023) in R software to conduct a single-season occupancy hierarchical model.