Selective harvesting at rational intervals promotes carbon sequestration in temperate coniferous and broad-leaved mixed forests in China

Evidence-based selective cutting at prescribed intervals as part of good forest management can enhance the carbon sequestration capacity of the forest.The effect of forest management on carbon sequestration has,however,not been quantified.Thus,carbon content of various organs was measured for 323 tree species,247 shrub species,and233 herb species in seven temperate coniferous and broadleaved mixed forests that were subjected to selective cutting with restoration durations of 100,55,45,36,25,14,and6 years to explore dynamic changes in carbon storage.The results showed that biomass carbon allocation in different organs followed a pattern:trunk>root>branch>leaf for all forests.With longer restoration durations,more carbon accumulated in different organs and in soils.Interestingly,when the restoration duration exceeded 50 years,carbon storage in ecosystem was larger than that in primary forests with 100-year cutting intervals,suggesting that a reasonable selective cutting interval can increase forest carbon sequestration.Mean diameter at breast height(DBH)and forest carbon storage were significantly positively correlated,and carbon storage of selectively cut forests exceeded that of primary forests when the stand mean DBH exceeded 15.66 cm.Therefore,mean DBH of forests can be an indicator for combining sustainable forest management and forest carbon sequestration.Additionally,the classic coefficients of 0.45 and 0.50 used to estimate carbon sequestration underestimated values by 2.65%and overestimated by 8.16%,respectively,in comparison with the measured carbon content from different plant organs.

Influence of individual tree characteristics,spatial structure and logging history on tree-related microhabitat occurrence in North American hardwood forests

Background:Tree-related microhabitats(hereafter,"TreMs")are key components of forest biodiversity but they are still poorly known in North American hardwood forests.The spatial patterns of living trees bearing TreMs(hereafter,"TreM-trees")also remain to be determined.As logging practices can lead to a loss of TreM-trees and of their associated biodiversity,it is essential to identify the factors explaining TreM occurrence to better integrate them into forest management.We therefore inventoried TreMs in 40.5-ha survey strips in northern hardwood forests in Quebec,Canada,while recording the spatial location of each tree.Two strips were located in unmanaged oldgrowth forests,and 2 were in forests managed under selection cutting.All 4 stands were dominated by sugar maple(Acer saccharum Marsh.)and American beech(Fagus grandifolia Ehrn.).Beech bark disease,an exotic pathology,was observed in all the strips.Results:Large diameter at breast height and low tree vigor were the main characteristics explaining the presence of TreMs at the tree scale.TreM-trees presented slight spatial aggregation patterns.These aggregates,however,were not well-defined and were generally constituted by a large number of trees bearing few different types of TreMs.Two TreM classes(broken branch or top and woodpecker lodge)also presented a spatial aggregation.Logging practices had no significant effect on TreM occurrence.Beech bark disease increased the frequency of senescent beeches.The impact of this pathology on TreMs was however mitigated by the small size of infected trees and probably by the short time elapsed since its appearance.Conclusion:The factors explaining the presence and abundance of TreMs on trees has so far been little studied in North American hardwood forests.Our results highlight that TreM-tree characteristics in the surveyed forests are consistent with those of previous studies conducted in other forest types and regions(e.g.,Europe or Northwestern America).To our knowledge,this study is also the first to identify a spatial aggregation of TreM-trees and of specific TreM classes.It will be nevertheless necessary to determine whether the small impact of logging activities we observed results from current or past management practices.

Artificial Logging or Natural Growth

Global climate change makes forestry carbon sequestration a hot issue. In order to improve the comprehensive benefits of forest management, this paper studies the carbon accounting problem, and uses the forest stock conversion factor method to create a carbon sequestration accounting model based on the reserve transformation method. Then, the HWP carbon sequestration accounting algorithm is obtained after the improvement of the reserve change method and the atmospheric flow method with the HWP half-life as a bridge. Based on the ecological and economic benefits, a multi-objective and multi-attribute decision-making model for forest management plan is constructed, and the optimal strategy of stand structure based on selective cutting is proposed. Finally, the entropy weight TOPSIS method is used to quantitatively analyze the comprehensive benefit value and provide suggestions for forestry departments. To verify the model, we chose the Greater Khingan Mountains forest region as the research site. Through successive iterations of CSAM, we calculate that the forest will absorb 534 million tons of live forest and forest products in 100 years. From the stand structure of the forest area, when the selected cutting intensity is 20% and the selected cutting cycle is 10.7 years, the comprehensive benefit value of the Greater Khingan Mountains is the highest.

Comparison of Social Benefits of Forest under Different Management Models: A Case Study of Close-to-Nature Forest Management in Harbin, China

Different forest management modes definitely create different results. A study of social benefits nurtured by scientific forest management had been conducted in 1998-2008 with Danqinghe Experiment Forest Farm, Zhuanshan Experiment Forest Farm and Shanhe Experiment Forest Farmtogether as the project area. The method that was centered on quantification and supplemented byqualification was employed to compare the social benefits of forests separately under the scientificmanagement model and the traditional management model. The study illustrated the huge social benefits produced by scientific forest management with detailed data.

Enhancing cut selection through reinforcement learning

With the rapid development of artificial intelligence in recent years,applying various learning techniques to solve mixed-integer linear programming(MILP)problems has emerged as a burgeoning research domain.Apart from constructing end-to-end models directly,integrating learning approaches with some modules in the traditional methods for solving MILPs is also a promising direction.The cutting plane method is one of the fundamental algorithms used in modern MILP solvers,and the selection of appropriate cuts from the candidate cuts subset is crucial for enhancing efficiency.Due to the reliance on expert knowledge and problem-specific heuristics,classical cut selection methods are not always transferable and often limit the scalability and generalizability of the cutting plane method.To provide a more efficient and generalizable strategy,we propose a reinforcement learning(RL)framework to enhance cut selection in the solving process of MILPs.Firstly,we design feature vectors to incorporate the inherent properties of MILP and computational information from the solver and represent MILP instances as bipartite graphs.Secondly,we choose the weighted metrics to approximate the proximity of feasible solutions to the convex hull and utilize the learning method to determine the weights assigned to each metric.Thirdly,a graph convolutional neural network is adopted with a self-attention mechanism to predict the value of weighting factors.Finally,we transform the cut selection process into a Markov decision process and utilize RL method to train the model.Extensive experiments are conducted based on a leading open-source MILP solver SCIP.Results on both general and specific datasets validate the effectiveness and efficiency of our proposed approach.