Habitat selection of terrestrial mammals using environmental data
Authors:   , Wan Joejyn Marcus 
Published: October 2024

It is important to have a good understanding of the habitat selection patterns of the fauna inhabiting a landscape, as it helps to guide wildlife management, conservation, and monitoring efforts. However, basic ecological knowledge of the distribution and micro-habitat preferences are lacking for many terrestrial mammals, despite the crucial role they play in maintaining ecosystem stability and function. Through camera-trap surveys, this study aims to investigate the habitat selection patterns of terrestrial mammals within the Central Catchment Area in Singapore by correlating their spatial distribution with the heterogenous environment they inhabit.

To assist in the processing of the camera-trap images obtained, a convolutional neural network model was built to classify each image as either empty, containing a human, or containing an animal. The convolutional neural network model was trained using camera-trap images captured at various nature areas and habitats across Singapore to ensure accuracy of the model to Singapore’s landscape. The trained model was found to be effective in classifying camera-trap images, with an F1 score of 89.6% for the images captured at Central Catchment Nature Reserve. It also enabled 85.9% of the images at Central Catchment Nature Reserve to be filtered out of the manual identification process, saving significant manpower and time required for image processing.

Single-season, single-species occupancy modelling was conducted using distance to trail, distance to edge, light detecting and ranging (LiDAR) scan point densities of the understorey vegetation, Normalised Difference Vegetation Index, and Inverted Red Edge Chlorophyll Index as environmental covariates. Occupancy modelling was conducted for the terrestrial mammals recorded on the camera traps set up within the Central Catchment Area. Both the effect direction and relative importance of each covariate was determined for each species, and model-averaged estimates of habitat use and detection probability were also calculated from the selected models.

None of the environmental factors were found to be significant for all of the terrestrial mammals tested; instead, each species had a unique combination of environmental factors influencing its habitat selection patterns. Each environmental covariate was also found to have different effect directions and relative importance across the different species, showing that habitat selection patterns are unique to each species and should not be generalised across species groups. Comparing these results to another habitat selection study also conducted within the Central Catchment Area, habitat selection patterns of the lesser mousedeer, sambar deer, and wild boar were similar, except for the effect of understorey density, whereby it was found that understorey density was actually a significant environmental factor influencing the habitat selection patterns of the three abovementioned species.

Estimated habitat use of each species across the entire Central Catchment Area suggests that the long-tailed macaque and wild boar use most of the available landscape (99.9% and 98.9%, respectively), compared to the other non-generalist species. Knowledge of such land use is important for wildlife managers, considering that these two species are conflict species in Singapore. Unfortunately, LiDAR data across the entire study site are currently unavailable, and this limits the identification of specific areas that are used for each species.