Use of Bayesian, Lasso Binary Quantile Regression to Identify Suitable Habitat for Tiger Prey Species in Thap Lan National Park, Eastern Thailand

Abstract

A Bayesian approach was used to develop binary quantile regression models featuring the lasso penalty. The models afford the advantages of all quantile regression models, such as robustness and detailed insights into covariate effects; they also handle issues associated with overfitting well. Thus, this model was used to investigate habitat suitability for the management of tiger prey species. Field data were collected from 150 sampling sites (2,416 sub-plots) in Thap Lan National Park of the Dong Phayayen-Khao Yai Forest Complex (DPKY) from August 2019 to March 2021. We focused on sambar deer (Rusa unicolor) and gaur (Bos gaurus) because they are the principal prey species of tigers. Vegetation was sampled for biomass and nutrient content to identify suitable habitat. The “bayesQR” package of R was used to identify habitats appropriate for these species. The correlation between forage crop biomass and the normalized difference vegetation index (NDVI) was significantly associated with tiger prey species presence. The habitat can be improved by increasing grass and forb biomasses as the prey species prefer open habitats, such as grassland and open areas of dry evergreen forest. Habitat management has ensured that the grass biomass of open forest is significantly higher than that of dense forest. In addition, the hemicellulose content of open forest was significantly greater than that of dense forest. We found that spatial modeling combined with Bayesian, lasso binary quantile regression could aid wildlife habitat management in a Thai National Park.

Seedling forage nutrition and spatial patterns of large prey mammals of tigers in Dong Phayayen-Khao Yai Forest Complex, Thailand

“…The current results were consistent with those of Paansri et al (2022) and confirmed that the dry evergreen forest at Mun Sam Ngam is a suitable habitat for the large prey mammals of tigers. These results will inform future wildlife In future plans to restore the populations of large prey mammal species of tiger, research is increasingly identifying prey availability as a significant limitation to tiger recovery across its range in Thailand; tiger prey populations augmentation is seen as an important catalyst that may accelerate recovery (Jornburom et al, 2020;Phoonjampa et al, 2021;Phumanee et al, 2021).…”

“…The presence of tigers depends on the presence of medium-to-large ungulates, such as gaur and sambar deer, whose populations are indicators of tiger population dynamics (Petdee, 2000). The presence of tiger prey species in Thap Lan National Park was recorded at 150 sampling sites (60.68%), composed of 740 points with sambar deer (30.63%), and 726 points with gaur (30.05%) (Paansri et al, 2022). The hypothesis of the current study was that the seedling forage of gaur and sambar deer has a high nutrition value and consists of many species.…”

“…The DPKY was declared a UNESCO World Heritage Site in 2005; however, since then tigers in the DPKY have been found only in Thap Lan National Park and not in Khao Yai National Park (Department of National Parks, Wildlife and Plant Conservation, 2016). Both of these areas contain suitable habitat for gaur and sambar deer (Paansri et al, 2022).…”

“…The value of each factor was normalized from 0 to 100. Then, the study area was divided into 10 clusters according to the scores of each factor using K-means clustering, following Paansri et al (2021) and Paansri et al (2022).…”

Spatial and Temporal Habitat Use by the Main Prey Species of Tigers in Two Protected Areas of Thailand’s Southern Western Forest Complex

“…In addition to this, McShea et al (2011) reported that gaur in Thailand required grassland and open areas of moist evergreen, dry evergreen, semi-evergreen, and mixed deciduous forests. Paansri et al (2022) however found that gaur was detected in both open and closed forests.…”