The decomposition of coarse woody debris (CWD) affects the energy flow and nutrient cycling in forest ecosystems. Previous studies on CWD have focused on the input, decomposition, reserve dynamics, and CWD functions, but coarse woody debris decomposition is complex and the results from different regions vary considerably. It is not clear which factors affect decay rate (k), especially at different decomposition stages. In this study, a single-exponential decay model was used to analyze the characteristics of CWD decomposition in Larix gmelinii forests over the 33 years following a fire in the Greater Khingan Mountains. The results show that the decay rate of coarse woody debris was positively correlated to decay class. The average decomposition rate was 0.019, and 41 years and 176 years are needed for a 50% and 95% mass loss, respectively. CWD nutrient content, density, and water content could explain the variance in the decay rate (~ 42%) of the decay factors such as amount of leaching, degree of fragmentation, respiration of the debris, and biotransformation, and varied significantly between different decay classes. Using the space–time substitution method, this study arranged the coarse woody debris of different mortality times to form a 33 year chronosequence which revealed the decomposition process. It was concluded that the decay rate was mainly explained by structural component of the debris and its nitrogen and water contents. This paper quantifies the indicators affecting CWD decay to explain the decomposition process.

The Weibull function, a continuous probability distribution, is widely used for diameter distribution modelling, in which parameter estimation performance is affected by stand attributes and fitting methods. The Weibull cumulative distribution function is nonlinear, and classical fitting methods may provide a not optimal solution. Invoking the use of artificial intelligence by metaheuristics is reasonable for this optimisation task. Therefore, aimed and compared (1) the metaheuristics genetic algorithm and simulated annealing performance over the moment and percentile methods; (2) the hybrid strategy merging the metaheuristics tested and the percentile method and, (3) the metaheuristics fitness functions under basal area and density errors. A long-term experiment in a Pinus taeda stand subjected to crown thinning was used. According to our findings, all methods have a similar performance, independent of the thinning regimes and age. The pattern of the estimated parameters tends to be acceptable, as b increases over time and c increases after thinning. Overall, our findings suggest that methods based on metaheuristics have a higher precision than classical methods for estimating Weibull parameters. According to the classification test, the methods that involved simulated annealing stood out. The hybrid method involving this metaheuristic also stood out, with accurate estimates. Classical methods showed the poorest performance, and we therefore suggest the use of simulated annealing due to its faster processing time and high-quality solution.

Forest fires are one of the major environmental issues globally. In Nepal, substantial amounts of forest biomass and carbon are lost due to fire. Nepal’s high value lowland forests are particularly vulnerable to fire. However, there are limited studies on the estimation of biomass loss and carbon emissions due to fire. Thus, this research addresses the information gap in the tropical mixed broad-leaved forests of Nawalparasi District. The forests were divided into three strata: Lower Tropical Sal Mixed Broad-leaved Forest, Hill Sal Forest and Riverine Forest, and from these four community-managed forests were selected for estimating above ground biomass. Ninety-two sample plots were set out for above ground biomass estimation in burnt and non-burnt areas. Forest fire incidences from 2001 to 2017 were acquired from the MODIS fire data. Forest biomass and carbon emissions were estimated using standard allometric equations. The fuel fraction consumed during the fire was estimated through field surveys during the forest fire season. The results show that every year, over 3158 ha of forests are burnt, resulting in some 1108 tons of carbon emissions, equivalent to approximately 4066 t CO₂, 2581 t CO and 1474 t CH₄. Among the forests, the Hill Sal Forest was more vulnerable to fire. Forest management strategies, therefore, should include construction of fire lines and conservation ponds along with capacity building and raising awareness among local communities and stakeholders.

This study investigates the spatial variability of soil organic matter (SOM), soil organic carbon (SOC) and pH in the upper 20-cm layer and 20–40 cm layer in Moso bamboo (Phyllostachys pubescens Pradelle) forests using a geostatistics model. Interpolation maps of SOM, SOC, and pH were developed using ordinary kriging (OK) and inverse distance weighted (IDW) methods. The pH, SOC, and SOM of the two soil layers ranged from 4.6 to 4.7, from 1.5 to 2.7 g kg⁻¹ and from 20.3 to 22.4 g kg⁻¹, respectively. The coefficient of variation for SOM and SOC was 29.9–43.3% while a weak variability was found for pH. Gaussian and exponential models performed well in describing the spatial variability of SOC contents with R² varying from 0.95 to 0.90. The nugget/sill values of pH are less than 25%, which indicates a strong spatial correlation, while the nugget/sill values of SOC and SOM fall under moderate spatial correlation. Interpolation using ordinary kriging and inverse distance weighted methods revealed that the spatial distribution of SOM, SOC, and pH was inconsistent due to external and internal factors across the plots. Regarding the cross-validation results, the ordinary kriging method performed better than inverse distance weighted method for selected soil properties. This study suggests that the spatial variability of soil chemical properties revealed by geostatistics modeling will help decision-makers improve the management of soil properties.

Although petroleum is an important source of energy and an economic driver of growth, it is also a major soil pollutant that has destroyed large swathes of vegetation and forest cover. Therefore, it is vital to develop affordable and efficient methods for the bioremediation of petroleum-contaminated forest soils to restore vegetation and improve tree survival rates. In this study, bioremediation experiments were performed in an electrically heated thermostatic reactor to test the effects of organic matter additives, surfactants, and oxygen providers of nine hydrocarbon-degrading fungal strains on crude oil removal rates. In the three soil temperatures tested (20 °C, 25 °C, and 30 °C), the highest average crude oil removal rate was at 25 °C (74.8%) and the lowest at 30 °C (49.4%). At each temperature, variations in the addition of organic matter and oxygen providers had significant effects on crude oil removal rate. Variations in surfactant addition was significant at 20 °C and 25 °C but insignificant at 30 °C. Given the same surfactant treatment, variations in temperature, organic additives, and oxygen providers was significant for crude oil removal rate. Treatments without surfactants and treatments with Tween80 exhibited their highest crude oil removal rates at 25 °C. However, treatments that included the SDS surfactant exhibited their highest crude oil removal rates at 30 °C. Amongst the treatments without surfactants, treatments with corn cob addition had the highest crude oil removal rates, and with surfactants, treatments that included the organic fertilizer exhibited the highest crude oil removal rates. Given the same organic fertilizer treatment, the highest crude oil removal rate was at 25 °C. At each level of oxygen availability, the maximum crude oil removal rate always occurred at 25 °C, and the treatments that included organic fertilizer exhibited the highest crude oil removal rates. Amongst the treatments without oxygen providers, treatments without surfactants had the highest crude oil removal rates, and with an oxygen provider, treatments with SDS addition exhibited the highest crude oil removal rates. Based on the crude oil removal rates of the treatments, we determined that S₁W₁O₁ (addition of Tween80, organic fertilizers, and H₂O₂) was optimum for remediating petroleum-contaminated forest soils in cold, high-altitude regions. This study is helpful to vegetation restoration and reforestation on petroleum contaminated forestlands.

The Original article has been corrected.