Bat is animal that occupies aerosphere, especially fruit bats that forage on the space around the trees. The fruit bats use whether narrow space below tree canopy or in edge space on the edge of canopy. Whereas the aerosphere occupancy of fruits bats related to the specific tree species is poorly understood. Here, this paper aims to assess and model the association of fruit bat Cynopterus brachyotis aerosphere occupancy (ψ) with tree species planted in mountainous paddy fields in West Java. The studied tree species including Alianthus altissima, Acacia sp., Cocos nucifera, Mangifera indica, Pinus sp., and Swietenia macrophylla. The result shows that the tree species diversity has significantly (x2 = 27.67, P < 0.05) affected the C. brachyotis aerosphere occupancy. According to values of ψ and occupancy percentage, high occupancy of narrow space by C. brachyotis was observed in Swietenia macrophylla (ψ = 0.934, 78%), followed by Alianthus altissima (ψ = 0.803, 57%), and Mangifera indica (ψ = 0.913, 55%). While high occupancy of edge space was observed in Mangifera indica (ψ = 0.685, 41%), followed by Pinus sp. (ψ = 0.674, 38%), and Alianthus altissima sp. (ψ = 0.627, 36%). The best model for explaining C. brachyotis occupation in narrow space is the tree height with preferences on high tree (ψ~tree height, AIC = 1.574, R2 = 0.5535, Adj. R = 0.4047). While for edge space occupant, the best model is also the tree height (ψ~tree height, AIC = -26.1510, R2 = 0.7944, Adj. R = 0.7258).

Malaria remains a major public health problem mainly in particular South East Asian countries. As malaria transmission and Anopheles spp. continues to spread, control interventions should emphasize on the ability to define potential areas that can favor Anopheles spp. distribution. Then there is an urgent need to use novel approach capable to predict potential spatial patterns of Anopheles spp. and delineate malaria potential hotspots for better environmental health planning and management. Here, this study modeled Anopheles spp. potential distribution as a function of 15 bioclimatic variables using Species Distribution Modeling (SDM) in South Coast of West Java Province spans over 20 km from West to East. Findings of this study show that bioclimatic variables and SDM can be used to predict Anopheles spp. habitat suitability, suggesting the possibility of developing models for malaria early warning based on habitat suitability model. The resulting model shows that the potential distributions of Anopheles spp. encompassed areas from West to Central parts of the coasts, with Central parts were the most potential prevalence areas of Anopheles spp. considering this area has higher precipitation. The less potential prevalence areas of Anopheles spp. were observed in the East parts of the coast. The model also shows that inland areas adjacent to the settlements were more potential in comparison to the areas near coast and in the beach. Land cover conditions dominated by cropland, herbaceous wetland, and inundated land were also influencing the Anopheles spp. potential distribution.

The reason whale and dolphin stranding is not fully understood and it is not linked to a standalone variable. Theories assume intertwined factors including sickness, underwater noise, navigational error, geographical features, the presence of predators, poisoning from pollution or algal blooms, geomagnetic field, and extreme weather are responsible to whale stranding. On 19th February 2021, a pod consists of 45 pilot whales Globicephala macrorhynchus was stranded in a remote 7050 m2 Modung white beach of Indonesian coast. This paper aims to assess the environmental factors that may be can explain and link to this stranding cases. Those factors include bathymetry, plankton cell density measured using MODIS, water sediment load measured using Sentinel 2 Bands 4,3,1, vessel traffic, precipitation (inch) and thunder (CAPE index J/kg), water salinity and temperature, and geomagnetic field (nT). The results show the water near stranding sites were shallow, has sediment load, high plankton density, warmer, receiving torrential rain prior stranding, having weak geomagnetic field and high total magnetic field change/year. The combination of those environmental covariates may influence the behavior, navigation, and echolocation of the said stranded pilot whale.

Felids are mammal groups that also experiencing the effects of forest fire and deforestation rate. By using camera detection method, two felid species, Prionailurus bengalensis and Pardofelis marmorata, of tropical rainforests in SE Asia have been studied. The studied area was a rainforest in Sumatra that has experienced several forest fires with annual deforestation rates of 1.69%-2.89%. Occupancy model using Akaike Information Criterion (AIC) is in agreement that deforestation rate is the best explanatory covariate explaining the declining occupancy of those felid species. P. marmorata was known more sensitive to the both deforestation rate and forest fire frequency covariate effects since it has similar AIC values. While P. bengalensis was slightly affected by forest fires. Values of Area Under The Curve (AUC) of Receiver Operating Characteristic (ROC) were >0.5 and these indicate adequate probability of forest fire effects on felid occupancy. Cut off value of occupancy of P. bengalensis was higher than P. marmorata. For P. bengalensis, the cut off value was 1.75 leading to a sensitivity and specificity of 62%. This is the threshold value for the prediction of numbers of P. bengalensis individual occurred where both sensitivity and specificity are maximized and as an effect of forest fire, and this can be used to classify areas as occupied by P. bengalensis.

Microbes play essential roles in the ecology of various environments and structure. Diversity of soil microbial communities is an important scientific interest in tropical landscape including in South East Asia. Soil environment in SE Asia is diverse and this influences the microbial diversity. Soil microbes in this study were collected using DNA isolation protocols and the whole microbial community structure of sampled soil was analyzed through next generation sequencing. Soil microbial identification was conducted by amplifying the 16S rRNA gene. Soils were sampled from 4 different environments in Sumatra and Kalimantan ecosystems representing plantation, swamp, peatland, and coastal ecosystems. Measured soil covariates including soil organic carbon C analyzed based on the Walkley-Black method and Kjeldahl method for N. Model of microbes in soil ecosystems were based on Akaike model selection (AIC) by testing the influence of soil covariates on soil microbial phylum. The microbial community was found to be comprising of a total number of 11 phyla. In soil of coastal ecosystem, microbial diversity at phylum level were dominated by Proteobacteria (82.3%), Actinobacteria (9.41%), Acidobacteria (4.7%), and the lowest was Bacteroidetes and Chloroflexi (0.58%). In plantation soil, the microbial abundance order was Firmicutes (41.6%) > Actinobacteria (29.7%) > Acidobacteria (20.0%) > Gemmatimonadetes (5.94%). The soil microbial abundance order in swamp was Proteobacteria (42.2%) > Acidobacteria (20.04%) > Bacteroidetes > (10.46%) > Actinobacteria (9.24%). In soil of peatland ecosystem, taxonomic assignments of microbial at the phylum level were dominated by Firmicutes (66.18%), Proteobacteria(16.94%), and Actinobacteria (16.87%). According to the values of AIC, Firmicutes was a microbial phylum that has high abundance in soil ecosystems influenced by pH covariates with AIC values of -6.54. Other soil covariates show less influence on Firmicutes with AIC values of -5.06. The combination model also show that pH cofactor was the important determinants with AIC values of -0.54 for Firmicutes (pH+C) and Firmicutes (pH+N) models. While AIC value for combination model of Firmicutes (C+N) only equals to 0.14.