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This paper integrates a spatial fire behavior model and a stochastic dynamic optimization model to determine the optimal spatial pattern of fuel management and timber harvest. Each year's fire season causes the loss of forest values and lives in the western US. This paper uses a multi-plot analysis and incorporates uncertainty about fire ignition locations and weather conditions to inform policy by examining the role of spatial endogenous risk-where management actions on one stand affect fire risk in that and adjacent stands. The results support two current strategies, but question two other strategies, for managing forests with fire risk.

The stochastic and spatial nature of fire poses challenges for the cost-efficient allocation of fuel treatment over the landscape. A model that addresses complex but important components of fuel management decisions, spatial and dynamic aspects of fire risk, and a carefully designed framework that allows us to draw general insight into the optimal spatial pattern of management are necessary to provide a basis for developing efficient fuel treatment plans. For this purpose, we combine a physical fire model and a spatial-dynamic optimization model to explore harvest and fuel treatment across a hypothetical landscape under risk of a moving fire over a range of physical and economic conditions. Our model is able to describe spatial trade-offs involved in decision process, namely trade-offs between protection of on-site values and protection against fire spread. We found that the spatial configuration of management units can lead to heterogeneity in management across seemingly homogeneous units.

Protected areas function as a lifeboat that can preserve the origins and maintenance of biodiversity. We assessed the representativeness of biodiversity in existing protected areas in Japan using a distribution dataset and phylogenetic tree for 5565 Japanese vascular plant species. We first examined the overlap of species distribution with the existing protected areas and identified the minimum set representing all plant species. Second, we evaluated the relative importance of environmental variables in explaining the spatial arrangement of protected areas using a random forest model. Finally, we clarified how potential drivers of plant diversity were sufficiently captured within the protected areas network. Although the protected areas captured the majority of species, nearly half of the minimum set areas were selected from outside the existing protected areas. The locations of existing protected areas are mainly associated with geographical and socio-economic factors rather than key biodiversity features (including evolutionary distinctiveness). Moreover, critical biodiversity drivers, which include current climate, paleoclimatic stability, and geographical isolation, were biasedly emulated within the existing protected areas. These findings demonstrate that current conservation planning fails to represent the ecological and evolutionary processes relevant to species sorting, dispersal limitation, and allopatric speciation. In particular, under-representativeness of historically stable habitats that function as evolutionary hotspots or refugia in response to climate changes may pose a threat to the long-term persistence of Japan's endemic biota. This study provides a fundamental basis for developing prioritization measures to retain species assembly processes and in situ diversification along current climatic and geohistorical gradients.

Seeking an optimal operational regime under different management environments has been one of the main concerns of forest managers. Traditionally, the main operational regime includes planting density or regeneration scheme, thinning time/intensity, and optimal time to harvest over the given time horizon. Deterministic approaches to tackle this type of optimization problem with different controls have dominated the solution techniques in forestry literature. We present in this paper an overview of the methodologies used in stand-level optimization, in which we show the strengths and weaknesses of these methodologies as well as provide comments on the effectiveness of the methodology. We then propose a new dynamic programing approach for generalizing solution specification and techniques.

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Forest stands and individual trees are often devastated by natural disasters such as typhoons and heavy snowfall in Japan, resulting in significant economic losses to the forestry sector. Our objective is to identify key risk factors that affect the degree of damage. We apply two types of statistical approach: one is, a logistic regression model to snow damage data to investigate if there is any geographical element affecting the degree of damage at the stand level, and the other is, a survival analysis on tree failure data for factors affecting the degree of damage at the individual tree level. A logistic regression analysis revealed that the risk probability of snow damage is higher on older and thin stands. The analysis also indicates taking advantage of certain geographic conditions to reduce wind burden could decrease the degree of damage. A Cox regression analysis showed that tree age, diameter at breast height, and species were key factors that influenced the degree of tree failure. Specifying risk factors throughout statistical modeling helps to provide a comprehensive, systematic, and objective method to assess risk in forest management.