The inclusion of quadratic mean diameter (QMD) and relative spacing index (RSI) substantially improved the predictive capacity of height–diameter at breast height (d.b.h.) and crown ratio models (CR), respectively. Data were obtained from 208 permanent plots established in western Arkansas and eastern Oklahoma during 1985–1987 and remeasured for the sixth time (2012–2014). Existing height–d.b.h. and CR estimation models for naturally occurring shortleaf pine forests (Pinus echinata Mill.) were updated and modified for improved performance. Additionally, eight height–d.b.h. relationship models that use only d.b.h. (fundamental local models) were modified using covariates. The model performance was evaluated using fit statistics [root mean square error (RMSE), Fit index and Akaike information criteria (AIC)]. The results showed that the best model form which was an extended non-linear model with autoregressive first order AR (1) structure and power variance function performed better than extended mixed-effects models and predicted well as an ordinary least squares non-linear model. The autocorrelation within individual trees was larger for the height–d.b.h. relationship than for CR estimation. The addition of QMD to mean dominant height (H_D) greatly improved height–d.b.h. relationship with a reduction of 8 per cent in RMSE, compared with the use of basal area per hectare. Similarly, multiplying a fundamental local model by using QMD raised to a parametric power reduced RMSE by 16 per cent, improved Fit index by 12 per cent and decreased the AIC value by 7 per cent. D.b.h., H_D and RSI best explained the crown ratio relationship with an improved Fit index by 6.7 per cent compared with alternative non-linear models without RSI. The logistic model for CR also provided prediction accuracy similar to that of a commonly used non-linear model. A non-linear model with an application of remedial measures to enhance adherence to modelling assumptions can provide better parameter estimates than mixed-effects modelling approach.

Musk deerMoschusspp. are endemic to the high mountain forests of central Asia. The taxonomic status of musk deer in the central and western Himalayas is poorly understood. We investigated the phylogenetic relationship of musk deer from the central and western Himalayas based on mitochondrial genomic data of Cytochrome b (380 bps) and D-loop (1000 bps). Our results distinguished two divergent lineages using higher bootstrap support (bs) values from the Maximum likelihood and Bayesian posterior probabilities (bpp). Both the Manang and Kaski lineages from central Nepal are confirmed as Himalayan musk deerMoschus leucogasterand represent a species complex widespread throughout the central and eastern Himalayan region. The musk deer Mustang lineage was confirmed as Kashmir musk deerMoschus cupreusand has wide distribution in the western Himalayas (from central Nepal to Afghanistan). Our analysis validates that Kashmir musk deer is a genetically distinct species and it clarifies that Himalayan musk deer and Kashmir musk deer are confirmed instead of Alpine musk deerMoschus chrysogasterwhich has been previously described from the southern parts of Himalayas of Nepal, India and Pakistan.

Musk deer is threatened by poaching for musk pod and habitat fragmentation. The species is endangered within its distribution range including Nepal, Bhutan, India, Pakistan, Mongolia, China, Korea and Russia. In Nepal, the deer is confined to protected areas throughout the Himalaya. It is a solitary and shy forest dweller with a crepuscular activity pattern. It prefers and repeatedly uses one or more specific defecation sites. Such behaviour is believed to function in chemical communication among individuals. This study was conducted in Neshyang valley in the Annapurna Conservation Area, and aims to evaluate how musk deer selects the latrine site in its habitat taking into account both biotic and abiotic factors. In order to achieve this aim, we laid out a total of 23 transects of 500 m length throughout the study area. Within each transect, 20 m × 20 m nested structured quadrats were located at an interval of 100 m. We found that musk deer preferred to use latrine sites in fir forest while they avoid both blue pine forest and open areas. Furthermore, distance from water sources and mixed forests have a crucial role in selecting latrine sites by musk deer.

Himalayan musk deer (Moschus leucogaster; hereafter musk deer) are endangered as a result of poaching and habitat loss. The species is nocturnal, crepuscular, and elusive, making direct observation of habitat use and behavior difficult. However, musk deer establish and repeatedly use the same latrines for defecation. To quantify musk deer habitat correlates, we used observational spatial data based on presence–absence of musk deer latrines, as well as a range of fine spatial‐scale ecological covariates. To determine presence–absence of musk deer, we exhaustively searched randomly selected forest trails using a 20‐m belt transect in different study sites within the Neshyang Valley in the Annapurna Conservation Area. In a subsequent way, study sites were classified as habitat or nonhabitat for musk deer. A total of 252 plots, 20 × 20 m, were systematically established every 100 m along 51 transects (each ~0.5 km long) laid out at different elevations to record a range of ecological habitat variables. We used mixed‐effect models and principal component analysis to characterize relationships between deer presence–absence data and habitat variables. We confirmed musk deer use latrines in forests located at higher elevations (3,200–4,200 m) throughout multiple seasons and years. Himalayan birch (Betula utilis) dominated forest, mixed Himalayan fir (Abies spectabilis), and birch forest were preferred over pure Himalayan fir and blue pine (Pinus wallichiana) forest. Greater crown cover and shrub diversity were associated with the presence of musk deer whereas tree height, diameter, and diversity were weakly correlated. Topographical attributes including aspect, elevation, distance to water source, and slope were also discriminated by musk deer. Over‐ and understory forest management can be used to protect forests likely to have musk deer as predicted by the models to ensure long‐term conservation of this rare deer.

Forests play a significant role in sequestering carbon and regulating the global carbon and energy cycles. Accurately estimating forest biomass is crucial for understanding carbon stock and sequestration, forest degradation, and climate change mitigation. This study was conducted to estimate above-ground biomass (AGB) and compare the accuracy of the AGB estimating models using LiDAR (light detection and ranging) data and forest inventory data in the central Terai region of Nepal. Airborne LiDAR data were collected in 2021 and made available by Nepal Ban Nigam Limited, Government of Nepal. Thirty-two metrics derived from the laser-scanned LiDAR point cloud data were used as predictor variables (independent variables), while the AGB calculated from field data at the plot level served as the response variable (dependent variable). The predictor variables in this study were LiDAR-based height and canopy metrics. Two statistical methods, the stepwise linear regression (LR) and the random forest (RF) models, were used to estimate forest AGB. The output was an accurate map of AGB for each model. The RF method demonstrated better precision compared to the stepwise LR model, as the R² metric increased from 0.65 to 0.85, while the RMSE values decreased correspondingly from 105.88 to 60.9 ton/ha. The estimated AGB density varies from 0 to 446 ton/ha among the sample plots. This study revealed that the height-based LiDAR metrics, such as height percentile or maximum height, can accurately and precisely predict AGB quantities in tropical forests. Consequently, we confidently assert that substantial potential exists to monitor AGB levels in forests effectively by employing airborne LiDAR technology in combination with field inventory data.