The mixed Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), Masson’s pine (Pinus massoniana Lamb.), and hardwood forest is a major forest type in China and of national and international importance in terms of its provision of both timber and ecosystem services. However, over-harvesting has threatened its long-term productivity and sustainability. We examined the impacts of timber harvesting intensity on soil physical and chemical properties 10 and 15 years after cutting using the research plots established with a randomized block design. We considered five treatments, including clear cutting and low (13.0% removal of growing stock volume), medium (29.1%), high (45.8%), and extra-high (67.1) intensities of selective cutting with non-cutting as the control. The impact on overall soil properties derived from principal component analysis showed increasing with a rise in cutting intensity, and the most critical impact was on soil nutrients, P and K in particular. Soil nutrient loss associated with timber harvesting even at a low cutting intensity could lead to nutrient deficits in this forest although most of the soil physical properties could be recovered under the low and medium intensities of cutting. These results indicate that clear cutting and the selective cutting of extra-high and high intensities should be avoided in this type of forest in the region.

This study examines the statistical association of wildfire risk with climatic conditions and non-climate variables in 48 continental US states. Because the response variable “wildfire risk” is a fractional variable bounded between zero and one, we use a non-linear panel data model to recognize the bounded nature of the response variable. We estimate the non-linear panel data model (fractional probit) using the Generalized Estimating Equation (GEE) approach to ensure that the parameter estimation is efficient. The statistical model, coupled with the future climates projected by Global Climate Models (GCMs), is then employed to assess the impact of global climate change on wildfire risk. Our regression results show that wildfire risk is positively related to spring, summer, and winter temperatures and human population density whereas it is negatively associated with precipitation. The simulation results based on GCMs and the regression model indicate that climate change will intensify wildfire risk throughout the entire US, especially in the South Central region, posing an increasing wildfire threat and thus calling for more effective wildfire management strategies.

The net primary productivity (NPP) of a forest is an important indicator of its potential for the provision of ecosystem services such as timber, carbon, and biodiversity. However, accurately and consistently quantifying global forest NPP remains a challenge in practice. We converted carbon stock changes using the Global Forest Resources Assessment (FRA) data and carbon losses associated with disturbances and timber removals into an NPP equivalent measurement (FRA NPP*) and compared it with the NPP derived from the MODIS satellite data (MOD17 NPP) for the world’s forests. We found statistically significant differences between the two NPP estimates, with the FRA NPP* being lower than the MOD17 NPP; the differences were correlated with forest cover, normalized difference vegetation index (NDVI), and GDP per capita in countries, and may also stem from the NPP estimation methods and scopes. While the former explicitly accounts for carbon losses associated with timber removals and disturbances, the latter better reflects the principles of photosynthesis. The discrepancies between the two NPP estimates increase in countries with a low income or low forest cover, calling for enhancing their forest resource assessment capacity. By identifying the discrepancies and underlying factors, we also provide new insights into the relationships between the MOD17 NPP and global forest carbon stock estimates, motivating and guiding future research to improve the robustness of quantifying global forest NPP and carbon sequestration potential.

The mixed Cunninghamia lanceolata (Lamb.) Hook., Pinus massoniana Lamb., and hardwood forest in southeastern China is a major assemblage in natural secondary forests, and of national and international importance in terms of both timber and ecosystem services. However, over-harvesting has threatened its long-term sustainability, and there is a knowledge gap relating to the effect of harvesting on the ecosystem. After conifer species were selected for harvesting, the mixed Chinese fir, pine, and hardwood forest was changed into mixed evergreen broadleaf forest. In this context, we observed the restoration dynamics of plant communities over a period of 15 years (1996 to 2011) with different levels of harvesting intensity, including selective harvesting at low (13.0% removal of growing stock volume), medium (29.1%), high (45.8%), and extra-high (67.1%) intensities, as well as clear-cut harvesting (100.0%), with non-harvesting as the control, based on permanent sample plots established in a randomized block design in these forests in southeastern China. The impact on the richness, diversity, and evenness of plant species derived from descriptive statistical analyses was shown to initially increase, and then decrease, with an increase in harvesting intensity. The most critical impacts were on the richness, diversity, and evenness of shrub and herb species. Richness, diversity, and evenness of plant species recovered and increased under selective harvesting at low and medium intensities, while these parameters had not recovered and significantly decreased under selective harvesting at high and extra-high intensities, as well as with clear-cut harvesting. The impact on the plant community stability was derived from the stability test method of the improved Godron M. The plant community stability was closest to the point of stability (20/80) under selective harvesting at medium intensity, followed by selective harvesting at low intensity. The plant community stability was far from the point of stability (20/80) under selective harvesting at high and extra-high intensities, as well as with clear-cut harvesting. Of these treatments, clear-cut harvesting had the greatest effect with regard to reducing stability. Therefore, these results indicate that the selective harvesting at low and medium intensities is conducive to preserve or increase the species diversity and community stability. In order to prioritize promoting plant species diversity, clear-cut harvesting and selective harvesting at high and extra-high intensities should be avoided with regard to this type of forest in this region. This study sheds light on the practice of forest operation in the study region and subtropical forests with the same environment.

This work considers a two-stage stochastic integer programming (SIP) approach for optimizing fuel treatment planning under uncertainty in weather and fire occurrence for rural forests. Given a set of areas for potentially performing fuel treatment, the problem is to decide the best treatment option for each area under uncertainty in future weather and fire occurrence. A two-stage SIP model is devised whose objective is to minimize the here-and-now cost of fuel treatment in the first-stage, plus the expected future costs due to uncertain impact from potential fires in the second-stage calculated as ecosystem services losses. The model considers four fuel treatment options: no treatment, mechanical thinning, prescribed fire, and grazing. Several constraints such as budgetary and labor constraints are included in the model and a standard fire behavior model is used to estimate some of the parameters of the model such as fuel levels at the beginning of the fire season. The SIP model was applied to data for a study area in East Texas with 15 treatment areas under different weather scenarios. The results of the study show, for example, that unless the expected ecosystem services values for an area outweigh fuel treatment costs, no treatment is the best choice for the area. Thus the valuation of the area together with the probability of fire occurrence and behavior strongly drive fuel treatment choices.