BackgroundBats are natural reservoirs for several highly pathogenic and novel viruses including coronaviruses (CoVs) (mainly Alphacoronavirus and Betacoronavirus). Lyle’s flying fox (Pteropus lylei)‘s roosts and foraging sites are usually in the proximity to humans and animals. Knowledge about age-specific pattern of CoV infection in P. lylei, prevalence, and viral shedding at roosts and foraging sites may have an impact on infection-age-structure model to control CoV outbreak.MethodsP. lylei bats were captured monthly during January–December 2012 for detection of CoV at three areas in Chonburi province; two human dwellings, S1 and S2, where few fruit trees were located with an open pig farm, 0.6 km and 5.5 km away from the bat roost, S3. Nested RT-PCR of RNA-dependent RNA polymerase (RdRp) gene from rectal swabs was used for CoV detection. The strain of CoV was confirmed by sequencing and phylogenetic analysis.ResultsCoV infection was found in both juveniles and adult bats between May and October (January, in adults only and April, in juveniles only). Of total rectal swab positives (68/367, 18.5%), ratio was higher in bats captured at S1 (11/44, 25.0%) and S2 (35/99, 35.4%) foraging sites than at roost (S3) (22/224, 9.8%). Juveniles (forearm length ≤ 136 mm) were found with more CoV infection than adults at all three sites; S1 (9/24, 37.5% vs 2/20, 10%), S2 (22/49, 44.9% vs 13/50, 26.0%), and S3 (10/30, 33.3% vs 12/194, 6.2%). The average BCI of CoV infected bats was significantly lower than uninfected bats. No gender difference related to infection was found at the sites. Phylogenetic analysis of conserved RdRp gene revealed that the detected CoVs belonged to group D betacoronavirus (n = 64) and alphacoronavirus (n = 4).ConclusionsThe fact that CoV infection and shedding was found in more juvenile than adult bats may suggest transmission from mother during peripartum period. Whether viral reactivation during parturition period or stress is responsible in maintaining transmission in the bat colony needs to be explored.

The domestication of wild animals represents a major milestone for human civilization. Chicken is the largest domesticated livestock species and used for both eggs and meat. Chicken originate from the red junglefowl (Gallus gallus). Its adaptability to diverse environments and ease of selective breeding provides a unique genetic resource to address the challenges of food security in a world impacted by climatic change and human population growth. Habitat loss has caused population declines of red junglefowl in Thailand. However, genetic diversity is likely to remain in captive stocks. We determine the genetic diversity using microsatellite genotyping and the mitochondrial D-loop sequencing of wild red junglefowl. We identified potential distribution areas in Thailand using maximum entropy models. Protected areas in the central and upper southern regions of Thailand are highly suitable habitats. The Bayesian clustering analysis of the microsatellite markers revealed high genetic diversity in red junglefowl populations in Thailand. Our model predicted that forest ranges are a highly suitable habitat that has enabled the persistence of a large gene pool with a nationwide natural distribution. Understanding the red junglefowl allows us to implement improved resource management, species reintroduction, and sustainable development to support food security objectives for local people.

No abstract

The gaur (Bos gaurus) is found throughout mainland South and Southeast Asia but is listed as an endangered species in Thailand with a decreasing population size and a reduction in suitable habitat. While gaur have shown a population recovery from 35 to 300 individuals within 30 years in the Khao Phaeng Ma (KPM) Non-Hunting Area, this has caused conflict with villagers along the border of the protected area. At the same time, the ecotourism potential of watching gaurs has boosted the local economy. In this study, 13 mitochondrial displacement-loop sequence samples taken from gaur with GPS collars were analyzed. Three haplotypes identified in the population were defined by only two parsimony informative sites (from 9 mutational steps of nucleotide difference). One haplotype was shared among eleven individuals located in different subpopulations/herds, suggesting very low genetic diversity with few maternal lineages in the founder population. Based on the current small number of sequences, neutrality and demographic expansion test results also showed that the population was likely to contract in the near future. These findings provide insight into the genetic diversity and demography of the wild gaur population in the KPM protected area that can inform long-term sustainable management action plans.

No abstract

Spatial modeling is an analytical procedure that simulates real-world conditions using remote sensing and geographic information systems. The field data in this study were collected from 318 survey plots in the area surrounding highway 304 in the Dong Phayayen-Khao Yai Forest Complex (DPKY-FC) during the 2019 rainy season. Forage-crop biomass was collected from all plots. We focused on sambar deer (Rusa unicolor) and gaur (Bos gaurus), which are the main prey for tigers in this area. We created spatial models using generalized linear models with stepwise regression. The results indicated that the normalized difference vegetation index (NDVI) varied directly with grass biomass but inversely with shrub biomass (p<0.05). Elevation varied directly with forb biomass but inversely with shrub biomass (p<0.05). The probability of occurrence of sambar deer varied directly with distance from disturbance variables, distance from the stream, and grass biomass (p<0.001), but inversely with NDVI (p<0.05). The occurrence of gaur varied directly with NDVI (p=0.08), but varied inversely with slope, distance from the road, and distance from the stream (p<0.05). Our results demonstrate that spatial modeling can be an effective tool for wildlife habitat management in the area surrounding highway 304.

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.

Knowledge of the genetic characteristics, origin, and local adaptation of chickens is essential to identify the traits required for chicken breeding programs. Chee Fah and Fah Luang are black-boned chicken breeds reared in Chiang Rai, Thailand. Chickens are an important part of the local economy and socio-culture; however, the genetic diversity, characteristics, and origins of these two breeds have been poorly studied. Here, we investigated the genetic diversity, gene pool, and origin of the Chee Fah and Fah Luang chickens using mitochondrial DNA D-loop (mtDNA D-loop) sequencing and microsatellite genotyping, as well as habitat suitability analysis using maximum entropy modeling. The MtDNA D-loop sequencing and microsatellite genotype analyses indicated that the Chee Fah and Fah Luang chickens shared haplogroups A, B, and CD with Chinese black-boned chickens. Gene pool analysis revealed that the Chee Fah and Fah Luang chickens have distinct genetic patterns compared to Thai domestic chickens and red junglefowl. Some gene pools of red junglefowl and other Thai domestic chickens were observed within the Chee Fah and Fah Luang chicken gene pool structures, suggesting genetic exchange. The data indicate that the Chee Fah and Fah Luang chickens originated from Chinese indigenous black-boned chicken breeds and experienced crossbreeding/hybridization and introgression with red junglefowl and other domestic breeds during domestication. Interestingly, the Chee Fah and Fah Luang chickens from Chiang Rai shared the same allelic gene pool, which was not shared with the Chee Fah and Fah Luang chickens from Mae Hong Son, suggesting at least two gene pool origins in the Chee Fah and Fah Luang chicken populations. Alternatively, different gene pools in the Chee Fah and Fah Luang chickens from different localities might be caused by differences in environmental factors, especially elevation.