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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 available for White bellied Pangolin during dry season, 338 km was highly suitable, 209 km was suitable, 126 km was moderately suitable, 65 km was less suitable and 44 km was not suitable. During the wet season 235 km was highly suitable, 225 km was suitable, 164 km was moderately suitable, 100 km was less suitable and 57 km 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.

Purpose of the Review Here, we describe a new evolving research area of focus for soundscape ecology called soundscape analytics. Soundscape analytics follows traditional, as well as state-of-the-art, data and visual analytics that chain together tools and approaches to explore and analyze massive data. The theoretical underpinning of soundscape analytics is anchored in recent advances in machine learning that have been very successful in other applications, such as business, medicine, and psychology. We present and summarize four main components – data processing, data mining, integration and interpretation - of soundscape analytics pipelines that are being used today by soundscape ecologists. Recent Findings In the last five years, the number of tools advancing our ability to analyze big acoustic data for soundscape ecology research has increased considerably, especially those leveraging generic deep learning methods. A considerable portion of this work has focused on using soundscape recordings to assess biodiversity trends across space and time. Many of these implementations are based on R and Python routines designed to be executed on supercomputers with specialized data storage arrays as well as cloud-based user interface software. Quick porting of interactive data visualizations to the web enables scientists around the world to collaborate and share information for management and with the public. Summary Big data in ecology has arrived, illustrated by the massive amounts of acoustic data being collected by soundscape ecologists. The challenge of soundscape analytics is to make the most of each available computational resource so many application problems can be solved from similar data.