Habitat Suitability Modelling of White-Bellied Pangolin (Phataginus tricuspis) in Oluwa Forest Reserve, Ondo State, Nigeria

栖息地 自然保护区 地理 穿山甲 森林保护区 生态学 林业 农林复合经营 生物
作者
Adebola Esther Adeniji,B.N. Ejidike,Oluwatobi Emmanuel Olaniyi
出处
期刊:Journal of Landscape Ecology [De Gruyter Open]
卷期号:17 (2): 130-146
标识
DOI:10.2478/jlecol-2024-0013
摘要

Abstract Most endangered species face a significant threat from habitat loss. The destruction and degradation of natural tropical forest across West Africa has likely been the biggest threat to White-bellied Pangolin and has contributed to their decline as they depend on the habitat for different resources like food, water, and shelter. The current study investigated the habitat suitability of white-bellied pangolins in Oluwa Forest Reserve. The presence data of White-bellied pangolin was collected by taking the Global Positioning System (GPS) coordinates of the indirect signs observed. These data, along with the 19 bioclimatic variables, slopes, soil PH, soil texture, distance to rivers, distance to roads, and Normalized Difference Vegetation Index (NDVI), were used to generate habitat suitability maps using MaxEnt software. The MaxEnt analysis showed that out of 781 km 2 available for White bellied Pangolin during dry season, 338 km 2 was highly suitable, 209 km 2 was suitable, 126 km 2 was moderately suitable, 65 km 2 was less suitable and 44 km 2 was not suitable. During the wet season 235 km 2 was highly suitable, 225 km 2 was suitable, 164 km 2 was moderately suitable, 100 km 2 was less suitable and 57 km 2 was not suitable habitat. The jackknife test of variable contribution revealed that during the dry season, NDVI was the most important predictor variable as measured by the gain produced by a one-variable model, followed by aspects such as distance to the river, slope, distance to the road, and temperature seasonality. During the wet season, the jackknife-cross-validation test showed the highest gain when NDVI was used in isolation. Aspects were found to be the second most important predictor variable as measured by the gain produced by a one-variable model, followed by distance to the road, slope, elevation, and the mean temperature of the wettest quarter.

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